# data[].attributes.attachment_link.readable_text
Voltar aos Detalhes do Artigo Dispositivo Intrauterino na Atenção Primária a Saúde Baixar Baixar PDF

# data[].attributes.attachment_link.keywords[]
medicina de família e comunidade; family practice; medicina familiar y comunitária;  brasil; revista brasileira; brazil; brazilian journal; atenção primária à saúde; aps; primary health care; atención primaria de salud; saúde pública; public health; salud pública; saúde coletiva

# data[].attributes.attachment_link.title
Vista do Dispositivo Intrauterino na Atenção Primária a Saúde

# data[].attributes.attachment_link.description
Revista Brasileira de Medicina de Família e Comunidade (RBMFC) publicada pela Sociedade Brasileira de Medicina da Família e Comunidade (SBMFC)

# data[].attributes.attachment_link.readable_text
Menglu Ouyang, Data curation, Formal analysis, Writing - original draft, Writing - review & editing, 1, ² Ke Peng, Formal analysis, Methodology, Validation, Writing - review & editing, 1, ² Jessica R. Botfield, Conceptualization, Methodology, Project administration, Writing - original draft, Writing - review & editing, 3,* and Kevin McGeechan, Conceptualization, Project administration, Supervision, Writing - original draft, Writing - review & editing 2, ³

Sharon Mary Brownie, Editor

Abstract

Intrauterine contraceptive devices (IUCD) are a safe and cost-effective contraceptive method for medically eligible women. Despite this, the utilisation rate for IUCDs is relatively low in many high-income countries, including Australia. Provision of education and training regarding IUCDs to healthcare providers, including nurses and midwives, is one approach to overcome some of the barriers that may prevent wider uptake of IUCDs. This study aims to explore the types and impact of IUCD insertion training for healthcare providers. A systematic review was undertaken in January 2017 to determine the effectiveness of IUCD training for healthcare providers in relation to provision of IUCDs to women. The databases MEDLINE, EMBASE, CINAHL, COCHRANE and SCOPUS were searched to identify studies from high-income countries relating to IUCD training for healthcare providers and relevant outcomes. A total of 30 studies were included in the review. IUCD training for healthcare providers contributed to increased knowledge and improved positive attitudes towards IUCDs, high rates of successful insertions, low complication rates, and increased provision of IUCDs. Successful insertions and low complication rates were similar across different healthcare provider types. No notable differences between provider types in terms of knowledge increase or insertion outcomes were observed. Different training programs for healthcare providers were found to be effective in improving knowledge and successful provision of IUCDs. Increasing the number of healthcare providers skilled in IUCD insertions in high-income countries, including nurses and midwives, will enhance access to this method of contraception and allow women greater contraceptive choice.

Introduction

Long acting reversible contraceptives (LARCs), including intrauterine contraceptive devices (IUCDs), are methods for preventing unintended pregnancies that do not require daily adherence [ 1]. The effectiveness of the IUCD as a contraceptive method is approximately 99.2% to 99.8% within the first year of use, which is higher than other shorter-term reversible contraceptive methods, such as the oral contraceptive pill, within the same timeframe of use [ 2]. Advantages of IUCDs include long-term effectiveness, easily reversible, safety for use in post-abortion patients, and use as emergency contraception [ 3].

In 2015, the proportion of women choosing IUCDs as a contraceptive method was 21% worldwide [ 4]. However, the rates of women using IUCDs in some higher income countries are relatively low. For example, the prevalence of IUCD use is 5.2% in the United States of America (USA), 10.3% in the United Kingdom (UK), 13.4% in France and 19.0% in Sweden [ 5, 6]. In Australia, the proportion of women using IUCDs is just 6.1% [ 7].

Factors related to IUCD uptake include accessibility of insertion services, users' awareness of and attitudes to this method, and healthcare providers' attitudes, knowledge and skills in insertion [ 6, 8]. Despite the long-term cost-effectiveness of IUCDs, their upfront cost can also be an obstacle for many women [ 9]. Further to this, health care providers, including primary care physicians and nurses as well as specialists, play an important role in women's access to and decision-making regarding contraceptive choice and can either restrict or enable uptake of IUCDs [ 6]. A survey undertaken in Australia to investigate perceived barriers to use of IUCDs by healthcare providers reported the following concerns: difficulties inserting IUCDs (63%), risk of pelvic inflammatory disease (53%), insertion pain (45%) and infertility (34%) [ 1]. Healthcare providers' attitudes regarding the appropriateness of IUCDs for some women, including adolescent or nulliparous women, may also limit access [ 6]. Concerns and misperceptions such as these are likely to impact on the provision and uptake of IUCDs.

One approach advocated to increase access to IUCDs is through the provision of education and training for healthcare providers to increase their knowledge, skills and confidence in discussing and providing this method of contraception [ 6, 10]. IUCD insertions are primarily undertaken by a general practitioner (GP) or gynaecologist, although nurses with appropriate training may also carry out insertions. The training requirements for nurses are similar across countries such as Australia, the USA and UK, although in Australia, relatively few nurses have been trained to carry out IUCD insertions. This may be in part due to the differences in the funding of these services between countries.

This review sought to identify the different types of education and training on IUCD insertion that have been evaluated and in particular their outcomes on provision of IUCDs. We also sought to assess whether there were any notable differences between healthcare provider type.

Method

This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines. A search of peer-reviewed literature was originally undertaken during 23 January and 31 March 2017, using the following search criteria for study selection:

1. Database: MEDLINE, EMBASE, CINAHL, COCHRANE and SCOPUS

2. Year: No limit on year of publication

3. Publication: Peer-reviewed research article

4. Countries: High-income countries, as defined by the World Bank [ 11]

5. Search terms in MEDLINE: ('exp Intrauterine Devices/' OR 'Intrauterine Devices, Copper/' OR 'IUCD.mp.' OR 'Contraceptive Devices/' OR 'IUD.mp.') AND ('exp Education/' OR 'exp Education, Continuing/' OR 'Inservice Training/' OR 'insertion training.mp.' 'training*.mp.') AND ('clinician*.mp.' OR 'physician*.mp.' OR 'Family Nurse Practitioners/' OR 'nurse*.mp.' OR 'exp General Practitioners/' OR 'Gynaecologist.mp.' OR 'Obstetrician.mp.' OR 'exp Physicians/' OR 'exp Medical Staff/')

All articles identified in the search were screened by the first author (MO) based on title and abstract for potential inclusion. Authors 1 (MO) and 2 (KP) then independently screened the full-text articles to determine their eligibility for inclusion. Disagreements were resolved by discussion or with a third review (KM). Inclusion criteria were 1) evaluated the IUCD training or clinical education programs for health care providers permitted to insert IUCDs; and 2) study participants included physicians, nurses, midwives, medical officers, general practitioners, gynaecologists and/or obstetricians. There was no limitation on the study designs except general discussions without presentation of data and results. We excluded studies that were conducted in low- or middle- income countries according to the World Bank definition [ 11] or which did not measure the outcomes of insertion/removal practices of clinicians after IUCD training. A flowchart for the selection of eligible research articles is presented in Fig 1 .

After confirming eligibility for inclusion, the following data were extracted-authors, geographical location, study design, study setting, study type and size of participants, year of publication, type/s of IUCD training and outcome details-by the first author and then independently checked by the second author. The GRADE system and risk of bias approach, as recommended by the Cochrane Collaboration, were used to evaluate the quality of evidence of each selected study [ 12]. As there was heterogeneity among the studies with respect to study design (RCT, cohort or cross-sectional study), study participants and outcomes measured (incidence, frequency or association), summary measures of the effect of IUCD training were not calculated.

Results

The combined database searches yielded 941 results. Of these, 216 were included to conduct full-text screening, following review of the title and abstract. In reviewing the full text, 30 studies were identified as eligible to include. The quality of evidence of most ranged from very low to moderate, primarily because the majority of studies were observational studies with small sample sizes ( Table 1 ).

Table 1

Affected health care providers

Author (year)

Impact of IUCD training

Level of Evidence

Doctors
Eighty-nine percent (212/238) insertions were reported to be successful in the follow up, fewer referrals in the 12 months post-training than in the three months pre-training. Two cases of malposition and 5 cases of expelled were reported. No cases of infection or perforation. Participants reported felt more confident in IUD practice after training.

Low

Goodman S et al.
(2008) [32]

The mean IUD insertions per month increased from 28 to 71 after trainings. Rate of complication is rare with 5.4% reported in post-abortal insertion and 1.8% in interval insertions.

Low

Nurses
After training, ninety-one percent (188/207) of insertions by registered nurses were successful. In the 6-month follow up, 2% reported expelled, 1% malposition confirmed and 3% removed but not replaced. No perforations are known to have occurred during insertions.

Low

Both doctors and nurses
Eighty-two percent of the respondents had no problems in providing IUD at their first year after training. Of those who had failures in inserting IUD lack of confidence was the main reason for failure

Low

Goldthwaite LM et al.
(2016) [23]

self-assessed comfortable with knowledge increased (94% at immediate post training and 86% at 6 months follow up; correctly answered knowledge significantly increased); number of HCP reported to place at least one IUD significantly increased from 60% to 81%

Low

For most OB-GYNs physicians who received training, only 74% of the provided IUD at their practices. Thirty-two percent of physician assistant and 81% of physicians applied IUCD for patients after training. There is no difference between physicians and nurses in the frequency of IUCD counselling. clinicians who received training were 60% more likely to counsel patients. Training is also significant associated with IUCD provision (OR = 1.8, 95%CI: 1.21 to 2.74).
For nurse practitioners who had training, only 43% of them provided IUD at their practices. For all participated nurses, 59% of them applied IUCD to patients after training. There is no difference between physicians and nurses in the frequency of IUCD counselling.

Low

Residents
Garcia-Rodriguez JA et al.
(2016) [26]

After training, IUD knowledge were slightly higher in Video group compare to traditional instruction group, but not significant (p>0.05). The score of IUD insertion performance was significant higher in Video group (P<0.05) compare to traditional instruction group.

Moderate

Included studies were conducted in Australia, the UK, the USA, Canada and New Zealand. The IUCD training was provided to family planning physicians (such as obstetricians and gynaecologists), general practitioners, nurses and midwives ( Table 2 ). Canada and the USA have family planning residency programs for healthcare providers; and IUCD training is compulsory for family planning and obstetrics-gynaecology residents [ 13, 14]. Physician interns, nurse practitioner students and physician assistants in the USA may also receive IUCD insertion training [ 15]. Family physicians were more likely to receive training in placement of IUCDs compared to medicine physicians (70.4% vs. 9.9%) [ 16]. Compared to nurse practitioners working in primary care, those working in women's health were more likely to receive IUCD insertion practicum training (12% vs. 66%) [ 17]. Most of the IUCD training for nurses was conducted in family planning services and clinics [ 18- 21], while for doctors and residents training was primarily in hospitals, medical centres and clinics [ 22- 25].

Table 2

Participants of training

Country

Author (year)

Type of IUCD training

Participants of training

Training program

Doctors
AU

IUCD insertion training

GPs

Approach standards training, competency-training with IUCD insertions in patients under the experienced doctors' supervision.

Nurses
AU

Competency-based training program

RNs

Competency-based training program (using pelvic model followed by supervised insertions with Copper IUCD and levonorgestrel-releasing IUCD)

Doctors and nurses
UK

IUCD provision training

NPs and GPs

Training scheme for general practitioners and practice nurses in provision of subdermal implants and IUCD

USA

General training (unclear)

Physicians, physician assistants and NPs

No specified details about this training program

Residents
CA

Garcia-Rodriguez JA et al.
(2016) [26]

Video-module instruction

Family medicine residents

Video-module instruction with necessary knowledge and skills to perform an IUCD insertion

USA

IUCD training in family medicine residency program
Maryland

FP and OBs residents

Training in family planning residency

Types of IUCD training programs

The majority (90%) of the eligible articles documented the method of training in detail, with most including either continuing education training for qualified healthcare providers or family planning residency training for residents and interns. For qualified healthcare providers, the IUCD training programs were continuing education in IUCD insertion and placement for doctors and/or nurses ( Table 2 ).

There were seventeen (57%) articles that focused on continuing education training programs, which varied by country. In Australia, programs focused on competency-based IUCD insertions, and involved utilisation of pelvic models and clinical practice under the supervision of experienced professionals [ 18, 22]. The training provided to healthcare providers in Canada involved media video modules and peer-to-peer teaching workshops [ 26, 27], while in the UK and New Zealand, programs were described as general training or sexual health training [ 28, 29, 30]. In the USA, training focussed on counselling skills as well as IUCD insertion techniques, including hands-on practice under supervision [ 13, 15, 17, 31].

The training programs also varied according to the different roles of healthcare providers. For registered nurses, the training tended to focus on insertion competency [ 18, 23, 28], whereas training for doctors further included counselling skills and knowledge of eligibility criteria for IUCD use [ 24, 27, 32].

The remaining 10 studies [ 14, 19, 26, 30, 33- 38] which detailed the method of training explored IUCD training during residency, based in Canada and the USA.

Effectiveness of IUCD training

Improved knowledge and attitudes

There were 5 (17%) studies that reported on improvements in health care providers' knowledge of and attitudes towards IUCDs after training [ 15, 17, 23, 26, 37]. For example, 94% of 84 healthcare providers who were trained in IUCD insertions in the postpartum period self-reported increased knowledge after training [ 23]. The average knowledge score for doctors and nurses in another study improved from 58% to 81% after skills-based IUCD training sessions [ 15], with increased understanding of how to determine eligibility for IUCD use [ 17] and more confidence in insertions after the IUCD training program [ 18, 22]. Doctors and nurse practitioners who received peer to peer education reported more familiarity and knowledge about IUCDs compared to those who did not receive any education about IUCDs (75% vs.59%) [ 25]. Skill-based competency training and didactic sessions also showed positive effects on confidence in IUCD insertion for both doctors and nurses [ 20, 22- 23], although one study reported didactic clinical training had no significant impact on improving obstetricians and gynaecologists' knowledge [ 13]. Another study reported increased willingness to recommend the IUCD to women with medical conditions such as diabetes, menorrhagia, dysmenorrhea or a history of ectopic pregnancy after completing the training [ 39]. For family planning residents, the residency training improved their knowledge about contraceptive management in different patient-specific situations [ 19, 26, 33, 36].

Successful IUCD insertions

Altogether, 15 (50%) of the eligible studies focussed on outcomes of IUCD insertions after training. The rate of successful insertions was reported to increase after completion of a training program for both doctors and nurses [ 18, 20, 22, 27- 28]. From a study conducted in the USA, the successful insertion rate in advanced practice healthcare providers (women's health and family practice nurse practitioners, physician assistants and certified nurse midwives) was approximately 4.8 times higher following a short training program focusing on adjunctive methods [ 20]. In Australia, of 207 insertions undertaken by registered nurses who completed a competency-based training program, 91% of their IUCD insertions were successful and required no assistance from a doctor [ 18]. For 238 IUCD insertions by doctors in Australia, 89% of the insertions were reported to be successful [ 22]. A study in the UK reported that 82% of 165 general practitioners felt they had no problems in providing IUCD insertions in the first year after completion of the training scheme [ 28]. In Canada, the number of successful insertions performed by a family physician increased to 87 insertions after training, compared to 28 successful insertions in those who had no training in the same period [ 27]. The rate of successful IUCD insertions was also high (99 out of 100) for family planning residents who received training IUCD training [ 35].

In terms of failed IUCD insertions post-training, some general practitioners reported this as due to lack of confidence [ 28], while nurse practitioners and certified nurse-midwives reported patient pain was their main cause of failure during the procedure [ 20].

Complications following IUCD insertions

Eight (27%) studies explored adverse effects and complications of IUCD insertion after training. The rates of expulsion, uterine perforation, malpositioning and infection were low in IUCDs inserted by trained doctors and nurses. In a study based in the USA, of 186 insertions performed by nurse practitioners and physician assistants, only one case of perforation and no cases of expulsion were reported [ 20]. There was no perforation or infection reported during follow up of insertions performed by trained nurses and doctors from studies conducted in Australia [ 18, 22]. The rate of expulsions and malpositioning was similar in trained nurses and doctors, with 2% of IUCDs expelled and 1% malpositioned in 207 insertions performed by registered nurse [ 18], compared to 1% expelled and 2% malpositioned of 238 IUCD insertions by doctors [ 22]. Expulsion rates following IUCD insertions similarly showed no significant difference between faculty staff and trained family planning residents in the USA [ 32]. However, in another study conducted in the USA, of IUCD insertions attempted by gynaecology and obstetrics residents, 19.3% expulsed and 11% were diagnosed with an infection [ 35]. Overall, adverse effects and complications including expulsions, perforations, malpositions and infections were rare after training for both doctors and nurses [ 18- 21, 31].

Provision of IUCD insertions

Of the included studies, 13 (43%) explored the provision and uptake of IUCDs after training of healthcare providers [ 15- 19, 21, 23, 25, 31, 33, 40- 42]. Before training, only 60% of 84 healthcare providers in one study reported inserting at least one IUCD in the postpartum period; this increased to 81% after training [ 23]. The mean IUCD insertions per month in one setting similarly increased from 28 to 71 after a training program focused at reintroducing the non-hormonal IUCD [ 31]. Compared to non-participating sites for training, the provision of IUCDs increased by 1.9 to 3.0 times in participating sites [ 15]. Training for adolescent health care providers also showed increases in provision of IUCDs for young people [ 42]. Practicum training was associated with 2.4 times provision of IUCDs in nurse practitioners' later practice compared to their pre-training practice [ 17].

Residency training also positively affected the provision of IUCD by family planning healthcare providers and residents. Obstetricians and gynaecologists who had residency training were more likely to provide IUCDs for their patients compared to those residents without training [ 33]. The provision of IUCDs also related to the timing of training: physicians who were trained in IUCD insertion after residency were two times more likely to place IUCDs than those trained during their residency [ 16].

Most of the studies did not mention the characteristics of women who received an IUCD. Of the few studies that reported this, more parous women than nulliparous women received an IUCD [ 18- 19, 21, 25]. For example, 11% of women who received an IUCD were nulliparous in an Australian study [ 18], and 22% were nulliparous in another study in the USA [ 21].

Discussion

Findings from this systematic review suggest that IUCD training for healthcare providers contributes to improved knowledge and attitudes regarding provision of IUCDs, high rates of successful insertions with low complication rates, and increased provision of IUCDs to women. These changes were similar across the different healthcare providers included in the studies.

Lower uptake of IUCDs can be attributed to low or inaccurate knowledge about this method among healthcare providers and insufficient numbers trained in IUCD insertion [ 43, 44]. Healthcare providers who are not trained to insert IUCDs may be less likely to recommend this contraceptive method, and lack of access to or inability to identify healthcare providers trained in the insertion of IUCDs also impedes uptake [ 9]. Nurses and midwives play an important role in providing preventative care and counselling in the area of women's health, especially for women at risk of an unintended pregnancy [ 45]. Nurse practitioners, registered nurses and midwives are therefore well-placed to provide IUCD insertions and removals. This is occurring in many other countries [ 16, 32, 33] but is less common in Australia [ 9].

This systematic review has found that when nurses and midwives are trained in IUCD insertion procedures their procedural outcomes are comparable to doctors [ 18, 21]. Upskilling nurses in this area would offer increased access to IUCDs and increased contraceptive options for women [ 46, 47]. Improving availability and accessibility of the full range of contraceptive options, including IUCDs, is critical to ensure that women have access to their preferred method of contraception.

This is the first systematic review to explore the effectiveness of IUCD training for healthcare providers in high income countries. Findings highlight the beneficial outcomes of IUCD trainings for healthcare providers, which will contribute to promoting provision and uptake of IUCD. However, there are some limitations in this review. We only included studies published in English language journals, which might introduce a bias if the evidence from studies in non-English speaking countries is different to that summarised here. Most of the included studies have a very low to moderate risk of bias due to low participation rates, low follow-up rates and self-reported outcomes. Furthermore, most studies involving both doctors and nurses as participants did not stratify the results to assess whether the training and subsequent insertion outcomes were different, so comparisons are unable to be made. Moreover, the sample sizes of included studies were small, which might lead to a decreased power of detecting important effects. The different country contexts, training settings and clinical scopes of practice, as well as limited sample sizes in some studies, might also affect the generalisability of findings. These issues, as well as the variety of different ways that outcomes of the studies were reported, precluded any formal meta-analysis from being carried out.

Conclusion

In summary, this review identified different types of IUCD training programs for healthcare providers and how these may contribute to IUCD insertion practices and outcomes in high income countries. Training for healthcare providers was found to be effective in increasing knowledge of IUCDs and successful provision of these. Following training, IUCD insertion outcomes appear to be similar for different healthcare providers. Increasing the number of doctors, nurses and midwives skilled in IUCD insertions will improve access to this method of contraception and allow women greater contraceptive choice.

Supporting information

S1 Checklist
PRISMA checklist.
(DOC)

Funding Statement

The authors received no specific funding for this work.

Data Availability

All relevant data are within the paper and its Supporting Information files.

References

7. Richters J., Fitzadam S., Yeung A., Caruana T., Rissel C., Simpson J. M., & de Visser R. O. (2016). Contraceptive practices among women: the second Australian study of health and relationships. [ Contraception, 94, 548-555. 10.1016/j.contraception.2016.06.016 [ PubMed] [ CrossRef] Google Scholar]

8. Black K, Lotke P, Buhling KJ, Zite NB, Intrauterine contraception for Nulliparous women: Translating Research into Action group. A review of barriers and myths preventing the more widespread use of intrauterine contraception in nulliparous women. European Journal of Contraception & Reproductive Health Care.17(5):340-50. [[ PubMed] PMC free article] [ Google Scholar]

9. Mazza D., Bateson D., Frearson M., Goldstone P., Kovacs G., & Baber R. (2017). Current barriers and potential strategies to increase the use of long-acting reversible contraception (LARC) to reduce the rate of unintended pregnancies in Australia: An expert roundtable discussion. [ Aust N Z J Obstet Gynaecol, 57, 206-212. 10.1111/ajo.12587 [ PubMed] [ CrossRef] Google Scholar]

14. Turk J, Grimm M, Fulton M, Simonson K, Landy U, Steinauer J. Impact of the ryan long-acting reversible contraception (LARC) training initiative on resident exposure to contraception provision. Contraception. 2011;84 (3):323. [ Google Scholar]

28. Heath L, Akintomide H, Adams A, Wilkinson C. Long-acting reversible contraception (LARC) training in general practice. European Journal of Contraception and Reproductive Health Care. 2016;21:101. [ Google Scholar]

31. Nippita S, Haviland MJ, Voit S, Peralta JP, Hacker MR, Paul ME. Comfort and Competency with Intrauterine Contraception Insertion: A Randomized Controlled Trial[226]. Obstetrics & Gynecology. 2015; 125 (Suppl 1): 73S-4S. [ Google Scholar]

35. Havilan M, Nippita S, Voit S, Perez-Peralta J, Hacker M, Paul M. A Randomized Controlled Trial of a High-Fidelity vs. Low-Fidelity Simulation Training on Comfort, Competence, and Skills with IUCD insertion. Contraception. 2015; 92(4): 396. [ Google Scholar]

43. Black K. I., Bateson D., & Harvey C. (2013). Australian women need increased access to long-acting reversible contraception. [ Med J Aust, 199(5), 317-318. [ PubMed] Google Scholar]

44. Weisberg E. (2014). Promoting the use of long-acting reversible contraceptives. [ Austin J Obstet Gynecol, 1(6), 1-6. Google Scholar]

46. Fleming K., Cheng Y., Botfield J., Sousa M., & Bateson D. (2018). Inclusion of intrauterine device insertion to registered nurses' scope of clinical practice. [ Collegian. Google Scholar]

Articles from PLoS ONE are provided here courtesy of Public Library of Science

# data[].attributes.attachment_link.title
Intrauterine contraceptive device training and outcomes for healthcare providers in developed countries: A systematic review

# data[].attributes.attachment_link.description
Menglu Ouyang, Data curation, Formal analysis, Writing - original draft, Writing - review & editing, 1, 2 Ke Peng, Formal analysis, Methodology, Validation, Writing - review & editing, 1, 2 Jessica R.

# data[].attributes.attachment_link.readable_text
About ScienceDirect Shopping cart Contact and supportTerms and conditionsPrivacy policy

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.

Copyright © 2020 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V.

# data[].attributes.attachment_link.title
Intrauterine Device Insertion and Removal

# data[].attributes.attachment_link.readable_text
About ScienceDirect Shopping cart Contact and supportTerms and conditionsPrivacy policy

We use cookies to help provide and enhance our service and tailor content and ads. By continuing you agree to the use of cookies.

Copyright © 2020 Elsevier B.V. or its licensors or contributors. ScienceDirect ® is a registered trademark of Elsevier B.V.

# data[].attributes.attachment_link.readable_text
Studies in Family Planning

Abstract

Contraceptive failure is a major contributor to unintended pregnancy worldwide. DHS retrospective calendars, which are the most widely used data source for estimating contraceptive failure in low‐income countries, vary in quality across countries and surveys. We identified surveys with the most reliable calendar data and analyzed 105,322 episodes of contraceptive use from 15 DHSs conducted between 1992 and 2014. We estimate contraceptive method‐specific 12‐month failure rates. We also examined how failure rates vary by age, education, socioeconomic status, contraceptive intention, residence, and marital status using multilevel piecewise exponential hazard models. Our failure rate estimates are significantly lower than results from the United States and slightly higher than previous studies that included more DHS surveys, including some with lower‐quality data. We estimate age‐specific global contraceptive failure rates and find strong, consistent age patterns with the youngest users experiencing failure rates up to ten times higher than older women for certain methods. Failure also varies by socioeconomic status, with the poorest, and youngest, women at highest risk of experiencing unintended pregnancy due to failure.

INTRODUCTION

Contraceptive failure is a major contributor to unintended pregnancy around the world, and represents a gap between women's and couples' intentions to avoid pregnancy and their ability to implement those intentions. Elimination of that gap is a goal of policies and programs worldwide (Brown et al. 2014; Galati 2015). Despite the programmatic and demographic significance of contraceptive failure, remarkably little is known about its correlates, especially outside of high‐income countries. Recent studies in the United States and France have generally found contraceptive failure rates to decrease as strength of motivation to avoid pregnancy increases, and as socioeconomic status increases. Results are inconsistent, however, and vary by contraceptive method selected (Moreau et al. 2007; Kost et al. 2008; Black et al. 2010). In low‐ and middle‐income settings, two multicountry studies modeled correlates of contraceptive failure using Demographic and Health Survey (DHS) data from the 1980s and early 1990s (Moreno 1993; Curtis and Blanc 1997). More recently, two studies estimated contraceptive failure based on data from a number of more recent DHS surveys (Ali, Cleland, and Shah 2012; Polis et al. 2016).

We recently published a report estimating failure rates from the most recent DHS survey in 43 countries (Polis et al. 2016), acknowledging a prior analysis of DHS calendar data quality (Bradley, Winfrey, and Croft 2015). We concluded that some failure rates in our earlier (Polis et al. 2016) report were likely underestimated, due to underreporting of contraceptive episodes in the calendars of some surveys. We estimated contraceptive failure rates by binary groupings of sociodemographic characteristics (e.g., age <25 and 25+; parity 0-2 and 3+; primary education or below and secondary+ education), but we did not examine finer categorizations, nor correlates of failure in a multivariate framework (Polis et al. 2016).

In the present study, rather than analyzing the widest range and most recent data possible, we focus on a smaller number of surveys that we believe most accurately represent women's reproductive experiences, trading comprehensiveness (and in some cases, survey recency) for data reliability concerns noted in Polis and colleagues ( 2016). We evaluate the reliability of calendar data in every DHS survey that collected the necessary calendar data and was made publicly available on the DHS program website as of January 2016. We pool together the 15 surveys judged to have the most reliable data, drawn from a wide range of low‐ and middle‐income countries. We also test whether limiting our analysis to the most reliable surveys conducted in the past 10 years, or limiting the calendar recall period (described below) to a single year, changes estimates of contraceptive failure.

By pooling episodes of contraceptive use for the same method across multiple surveys with reliable data, we are able to produce finely disaggregated estimates of failure, including age‐specific failure rates for implants, IUDs, injectables, pills, condoms, withdrawal, and periodic abstinence, as well as method‐specific multilevel hazard models to examine correlates of contraceptive failure in a multivariate framework.

Age and Contraceptive Failure

Age‐specific failure rates by contraceptive method can provide an important contribution to our understanding of contraceptive use dynamics. A priori, we would expect to see large variations in failure rates by age for multiple reasons. One, women's biologic fecundity, or the probability of conception per coital act, decreases with increasing age (Menken, Trussell, and Larsen 1986), as does their male partner's (Kuhnert 2004; Matorras et al. 2011). Two, coital frequency also decreases with age (Westoff 1974). Three, older contraceptive users are likely to have more experience using the method and may be less likely to experience failures due to method unfamiliarity. However, patterns of failure by age have not always followed these expectations for all methods. In France, the hazard of condom failure was higher among women aged 20-34 than among teenagers (Moreau et al. 2007). In the United States, contraceptive failure rates for all methods combined were lower for women 30+ compared to women in their twenties (Kost et al. 2008). A subsequent US analysis assessing specific methods reported similar age patterns for IUDs, pills, and other hormonal methods, but reported no differences in failure rates by age for condoms, withdrawal, and all reversible methods combined (Sundaram et al. 2017). In Polis and colleagues ( 2016), women under 25 had significantly higher failure rates than women aged 25+ for every method except implants, which had a failure rate of 0.6 pregnancies per 100 use episodes in both age groups. We found the lack of consistent age patterns in previous analyses of failure rates surprising, and investigate these patterns in more detail with this rich dataset.

Other Correlates of Contraceptive Failure

We reviewed known factors associated with contraceptive failure, as described in an analysis of DHS data from 43 countries globally (Polis et al. 2016), an analysis using the most recent nationally representative data from the United States (Sundaram et al. 2017), and a review of literature on factors associated with contraceptive failure (Black et al. 2010). All sources found some correlations between age and contraceptive failure, with the exceptions noted above. Union status may also be associated with contraceptive failure, with higher failure rates observed among never‐married women (versus ever‐married women) for most methods except condoms (for which the opposite pattern occurred) internationally (Polis et al. 2016), and higher failure for cohabitating or formerly married women versus married women across all methods combined, in the United States (Sundaram et al. 2017).

The association of contraceptive failure with parity varied internationally and in the United States, with higher failure for some methods among lower‐parity women internationally, but higher failure for some methods (pills, condoms, withdrawal, and all hormonal methods combined plus IUDs) for higher‐parity women in the United States (Sundaram et al. 2017). Internationally, women using contraception to space (versus to limit) births tended to have higher failure rates, though estimates did not vary significantly by intention for implants, IUDs, or oral contraceptives. These patterns held regardless of parity for user‐dependent methods, but higher‐parity IUD and pill users who were limiting had higher contraceptive failure than higher‐parity IUD and pill users who were spacing (Polis et al. 2016). Women with less motivation to avoid pregnancy may both be more likely to use a method inconsistently and more likely to use less reliable methods (Black et al. 2010).

The association of wealth with contraceptive failure was similar in various geographical contexts, with higher failure rates occurring among poorer women, except for user‐independent methods such as implants, IUDs, and injectables-and in the international setting, this association held regardless of age (Black et al. 2010; Polis et al. 2016; Sundaram et al. 2017). In the international analysis, contraceptive failure was not associated with urban versus rural residence, except that urban injectable users had higher failure rates than rural injectable users. Similarly, education did not appear to be strongly associated with contraceptive failure for most methods (Polis et al. 2016).

In the United States, black women and Hispanic women had higher failure rates than white women or women of other races for some user‐dependent methods (Sundaram et al. 2017). A number of other contextual factors that have not often been specifically examined in analyses of contraceptive failure may play a role, including higher coital frequency, substance abuse, interactions between medications and hormonal contraceptive methods that may impact effectiveness or cause unexpected side effects, relationship violence, incorrect information or misperceptions about correct use stemming from miscommunication between providers and patients, barriers to access to contraceptive services, and impacts from side effects or a higher body‐mass index (Black et al. 2010).

DATA AND METHODS

We use data from 15 Demographic and Health Surveys, which are large‐scale, nationally representative household surveys of women of reproductive age (15-49). In the surveys selected, participants were asked about pregnancies, births, terminations, and episodes of contraceptive use that occurred over the past five or more years, producing a retrospective month‐by‐month reproductive calendar history for each woman, hereinafter referred to as "calendar data." For each episode of contraceptive use that was discontinued in the calendar period, women were asked, "Why did you stop using the (method)?" Women's responses are categorized into one of 14 precoded categories, including "became pregnant while using" (i.e., reported contraceptive failure). These histories allow for the use of life table methods to calculate failure rates by contraceptive method. The failure rates in this article represent typical‐use, rather than perfect‐use, failure rates, including both method‐related failures (failure of the method to work as expected) and user‐related failures (stemming from incorrect and/or inconsistent use of the method).

Selection of Datasets Included in Analysis

The collection of retrospective calendar data requires women to accurately recall episodes of contraceptive use that occurred up to seven years in the past. Women may omit failures that occurred long ago simply due to recall biases; they may report they ended contraceptive use for reasons other than failure due to social desirability bias; or they may omit episodes of use that ended in a failure to avoid discussing the failure, especially if the failure ended in an abortion. Accurate recall may be particularly difficult for contraceptive methods that are used only sporadically, such as coitus‐dependent methods. Recall may also be more difficult for older women, who have generally been sexually active for a longer time and thus need to recall episodes of use further back in time, compared to adolescents who may have only become sexually active recently. Underreporting of retrospective contraceptive use in the calendar occurs in an estimated 74 percent of comparisons between calendar data and current‐use estimates for the same time point (Bradley et al. 2015). To obtain the most reliable estimates from imperfect data, we used multiple strategies to identify the surveys likely to be of highest quality and to limit the impact of potential biases.

First, we selected surveys in which the calendar data could be validated against external information (comparisons with current‐status method‐specific contraceptive prevalence rate (CPR) data from previous DHSs, as described in Bradley and colleagues 2015). We only included surveys that showed no evidence of underreporting of any of the contraceptive methods analyzed here. This stringent selection criterion excluded more than 60 percent of surveys considered for potential inclusion. The lack of evidence of underreporting in the surveys we selected indicates that few, if any, episodes of contraceptive use were omitted due to recall, social desirability, or other biases.

Second, we examined each survey for other types of misreporting, calculating indices for multiple data quality measures including potential underreporting, heaping, and displacement of events in the reproductive calendars for each contraceptive method analyzed in each survey, described in detail in Addendum A. We examined the distribution of each index for outlying values. We considered any value greater than p75 + 1.5* interquartile range as an outlier. We excluded surveys that had outlying values in the upper tails of any of these indices. This exclusion leaves us with surveys in which women are apparently able to correctly remember contraceptive use episodes and place them accurately in time, rather than heaping their start dates on convenient months such as January, for example. If there were multiple surveys within a country that fit these selection criteria, we selected the most recent survey. These selection criteria led to a sample of 15 surveys: Armenia 2005, Bangladesh 2011, Colombia 2010, Dominican Republic 1996, Egypt 2014, Honduras 2011-12, Jordan 2009, Kenya 1998, Morocco 1992, Peru 2012, Philippines 2003, Rwanda 2010, Senegal 2012-13, Turkey 2003, and Zimbabwe 2005-06. Use of different selection criteria would clearly produce a different survey sample. For this analysis, however, we felt comfortable using this most restrictive set of selection criteria, which we believe indicates the highest‐quality survey data. The selected surveys come from a wide range of socioeconomic and demographic contexts in Africa, Asia, Eastern Europe, and Latin America.

Third, we considered which portions of the retrospective calendar-spanning a period of between five and seven years before the date of interview-would be most reliable. According to an earlier study, contraceptive use was most poorly reported for points furthest back in time (Bradley et al. 2015) suggesting that resulting failure rates using information from such periods were most likely to be underreported also. Under the theory that contraceptive failure rates for an individual method should not change dramatically within the same country across a single five‐year period, we tested this concept by splitting each calendar period (typically 5 to 7 years; see Bradley et al. 2015 for details) into two equal time segments and calculating single‐decrement failure rates separately for each time segment. In the majority of comparisons, contraceptive failure rates were substantially lower when estimated from the early time segment versus the later time segment within each survey. Although this pattern was not found in every survey, it does suggest that contraceptive failures are frequently underreported for periods further back in time. The finding further suggests that the problems with underreporting of contraceptive use episodes do affect estimates of contraceptive failure and, most likely, discontinuation rates for other reasons. We therefore decided to use only the most recent data from each survey. We exclude the most recent three months from analysis because women in their first trimesters may not yet recognize they are pregnant, which could lead to underestimation of failure rates. We use the 3-38‐month period prior to each woman's interview as the window of observation for analysis.

The final sample using the most recent 3-38 month calendar segments from 15 surveys yielded 105,322 episodes of contraceptive use collected from 97,094 women interviewed.

We conducted sensitivity analyses to test whether the inclusion of older surveys-those conducted more than 10 years ago-had any effect on the results by recalculating estimates, limiting the data to surveys conducted since 2008. We also tested whether using the 3‐year recall period versus a shorter 1‐year recall period changed the results of our analyses. Full details are shown in Appendix Tables 1-41 and described in the Sensitivity Analyses section.

Analytic Methods

Each segment of contraceptive use reported in the reproductive calendar was converted to a contraceptive episode for analysis. A single woman could contribute multiple episodes to the analysis if she stopped and started using contraception several times during the calendar period, or switched between different methods. Each episode is a segment of a single method of use. If multiple methods are used at the same time, the most effective method is recorded in the survey (ICF 2018). To calculate failure rates, we constructed episode‐based associated single decrement life tables (Preston, Heuveline, and Guillot 2000). In these calculations, all contraceptive discontinuations for reasons other than contraceptive failure were censored. Episodes of use that began prior to this window enter into the life table as late entries (see Polis et al. 2016 for details of life table calculations and left truncation). Details of these calculations and use of the Delta Method for confidence interval calculations are included in Addendum B.

To analyze factors associated with contraceptive failures, we used multilevel piecewise exponential hazard models. We partitioned the duration of contraceptive use into intervals s within which the baseline hazard is assumed to be constant. Based on graphical analyses of the baseline hazards and following previous analyses of contraceptive failure (Curtis and Blanc 1997; Moreau et al. 2007; Bradley, Schwandt, and Khan 2009), we defined intervals of three months duration for the first year of use (0-2, where month 0 is the month of uptake; 3-5; 6-8; and 9-11 months).

In preliminary analyses, we found that the baseline hazard was far more similar for the same method across countries than for different contraceptive methods within the same country. We therefore pooled all data together across countries, and estimated separate models (both unadjusted failure rate models and multilevel hazard models) for each of the seven most commonly used contraceptive methods: pills (combined or progestin‐only), injectables (combined or progestin‐only), IUDs (hormonal and nonhormonal), implants,2 male condoms, withdrawal, and periodic abstinence.3 Using separate models for each contraceptive method is in line with previous findings that different types of women select into using different contraceptive methods (WHO Task Force 1980; Frost and Darroch 2008). Frost and Darroch ( 2008) found that socioeconomic, demographic, and partner characteristics were significant predictors of the methods women chose to use, and that women with strong motivation to avoid pregnancy were more likely to choose more effective reversible methods. The WHO Task Force ( 1980) found that urban/rural residence was strongly associated with method selection in India and Turkey, and that women who intended to space, rather than limit, were more likely to select less effective short‐acting methods, rather than IUDs in India, Korea, and Turkey. Further, Steele and Curtis (2003) found that method choice is endogenous with some types of contraceptive discontinuation.

Data from all interviewed women in each of the selected surveys were pooled together for analyses described below. All analyses are weighted using sampling weights, and weights were multiplied by a survey‐specific constant defined as ∑1 n w i n w i, where wi is the weighted number of interviewed women in survey i, and n = 15 surveys included in analysis. This constant equalizes the effective weighted sample size across surveys, so each survey contributes equally to the analysis, i.e., results are not weighted more heavily toward surveys with larger sample sizes. Results are therefore interpretable as averages across all women in the surveys included in analysis.

Episodes of contraceptive use were linked with data from other sections of each woman's individual interview, allowing for examination of failures by demographic and socioeconomic characteristics. We measure age at the start of the episode of use, grouping women's ages into 5‐year categories with an open‐ended category for women aged 40 and older because failures are very rare among women in this age group.

Marital status during each episode is measured by comparing the date of the end of the contraceptive use episode to the date of the woman's (first) marriage. Each episode is then classified according to whether the woman had been married at the time of discontinuation, or whether she had never been married before she discontinued. For women who were married only once and report they are currently married at the time of survey, "ever married" is synonymous with currently married at the time of discontinuation. For formerly married women, however, we do not know the date of marital dissolution, and for women who were married more than once, we do not know the date of any marriage after the first. We therefore can only classify women as "ever married" or "never married" at the time of the episode of contraceptive use. If failure rates are substantially different between currently and formerly married women, this may lead to over‐ or under‐estimation of the failure rate for currently married women. We anticipate that women using to space, rather than limit (hereinafter called contraceptive intention) their childbearing, may experience higher levels of failure because the anticipated costs of a mistimed pregnancy are lower than an unwanted pregnancy.

Following Lightbourne ( 1985), contraceptive intention is calculated by comparing women's reported ideal number of children to the number of children they had when the episode of contraceptive use began. If their ideal number was less than or equal to their current number of children, women were assumed to have already achieved their ideal family size and the episode was classified as "using to limit." All other episodes of use were classified as "using to space." This includes non‐numeric responses to the question on ideal number of children, such as "up to God." We reason that women who do not give a numeric ideal family size, but still use contraception, are using in order to space, rather than limit, their births. This classification allows contraceptive intention to be time‐varying with each episode of contraceptive use, but assumes that reported ideal number of children is constant over time, which may not be valid.

We include educational level, using DHS standard classifications of no education, primary, and secondary or higher education based on each country's educational system (MEASURE DHS 2013), and socioeconomic status as proxies of access to information and contraceptive services and supplies, anticipating that failure rates may be lowest among the wealthiest and most highly educated. For our socioeconomic status measure, we use the DHS "wealth index" constructed from information on household ownership of durable goods and amenities using principal components analysis, placing households on a continuous scale of wealth within the country, which is then divided into equally sized quintiles by population size (Rutstein and Johnson 2004).

These final two measures are country‐specific, as educational systems vary by country and wealth quintiles are relative only to other households within the same country. These two measures are only measured at the time of the survey, and are not time‐varying. We do not expect a great deal of mis‐specification associated with this limitation, however, as it is unlikely that large proportions of women will have experienced substantial changes in educational or socioeconomic status within the three‐year period prior to interview.

We fit multilevel models of contraceptive failure for each method using Poisson regression with the logarithm of the time each woman is at risk of failure within the 3‐month interval s as an offset (Rabe‐Hesketh and Skrondal 2012). The model is

where μ s e i j is the mean parameter of the Poisson distribution, t s e i j is the time at risk of failure in the 3‐month interval s for contraceptive episode e from the reproductive calendar of woman i in country j, α 1 s is an interval‐specific intercept that allows the baseline hazard of failure for that contraceptive method to change every 3 months, α 2 j is a country‐specific fixed effect, X 1 s e i j represents each woman's age at the beginning of interval s, X 2 s e i j represents her marital status at the beginning of interval s, Y 1 e i j represents whether the intention of contraceptive use segment e was to space or limit births, Z 1 i j measures the country‐specific highest educational level achieved by woman i at the time of the survey, Z 2 i j is a measure of the woman's country‐specific socioeconomic status at the time of the survey, the random intercept ζ i j introduces dependence among the hazards for different episodes of contraceptive use for the same woman i, and exp (ζ i j) is assumed to be normally distributed and independent of the covariates. ζ i j represents an unobserved frailty shared across contraceptive episodes for the same woman, measuring constructs such as women's underlying fecundity or propensity toward failure that is not captured by her age or other sociodemographic characteristics included in the model.

Sensitivity Analysis Methods

Although the DHS program has many processes in place to aid women's recall of contraceptive events-notably, asking first about the most significant reproductive events, including births, pregnancies, and terminations, and then asking about contraceptive use episodes as they relate to the time between, say, the birth of their first child and their subsequent pregnancy (ICF 2018)-there are still understandable concerns that women do not accurately recall episodes of contraceptive use that occurred long ago. We therefore conducted a sensitivity analysis to see if the analyses above changed when we limited the recall period to a single year-specifically, months 3-14 prior to interview, excluding the most recent 3 months to avoid underestimating failures due to unrecognized pregnancies in the first trimester. Results from these sensitivity analyses are shown in Appendix Tables 1 and 2.

We also note that some of the surveys included in analysis are more than 20 years old and may not accurately depict women's current experiences. This is particularly true in the Dominican Republic, Kenya, and Morocco-countries in which the most reliable data were found in surveys conducted in the 1990s. However, two studies that examined trends in failure rates using multiple DHS surveys from the same country found failure rates for each method to remain relatively consistent within each country over time (Bradley, Schwandt, and Khan 2009; Ali, Cleland, and Shah 2012). We tested whether limiting the analysis to surveys conducted in the last 10 years (since 2008) had an impact on the results. Results from these sensitivity analyses are shown in Appendix Tables 3 and 4.

In each sensitivity analysis, we checked whether each failure rate differed significantly from the rates in the main analysis, shown in Table 1 , by checking whether the confidence intervals overlapped. Results that are significantly different from the main analysis are shown with an asterisk in the Significance column of Appendix Tables 1 and 3.

RESULTS

Failure Rates

We display unadjusted 12‐month failure rates for each method in Figure 1 and Table 1 . Twelve‐month failure rates are interpretable as the percentage of women who, on average,4 will become pregnant within the first year of typical method use. Estimates from the pooled sample used in this analysis, shown in the top bars in Figure 1 , are extremely low for implants and IUDs, with approximately one woman out of 100 becoming pregnant across a one‐year horizon (implant failure rate = 0.3 per 100 episodes, 95% CI 0.1-0.9; IUD failure rate 1.2, CI 0.9-1.5). Approximately 2 percent of injectable users would be expected to become pregnant during the first year of use (95% CI 1.7-2.3). Failure rates are higher for short‐term resupply methods of pills and condoms, which require users to have the methods on hand and use them correctly, with an estimated 6 to 9 users out of every 100 becoming pregnant in the first year of use (pill failure rate 6.3, CI 5.9-6.8; condom failure rate 8.6, CI 7.6-9.6). The highest failure rates are seen for traditional methods of withdrawal and periodic abstinence, with 17-19 percent of users becoming pregnant within a year of beginning the method (withdrawal failure rate = 17.3, CI 15.9-18.7; periodic abstinence failure rate 19.0, CI 17.4-20.6).

Figure 1 compares the estimated failure rates from this study (in the top bars) with those we previously estimated from the median values of failure rates across 43 recent DHS surveys (Polis et al. 2016, in middle bars), and the widely cited typical use estimates from Contraceptive Technology based on US clinical and survey data from 1979, 1995, and 2002 (Hatcher 2011). In Polis and colleagues ( 2016), we noted that our estimated failure rates were similar to previous studies based on a broad range of DHS data (e.g., Ali, Cleland, and Shah 2012) but diverged markedly from estimates using US data. Specifically, these estimated failure rates were substantially lower than US estimates for injectables (1.7 versus 6), oral contraceptives (5.5 versus 9), male condoms (5.4 versus 18), withdrawal (13.4 versus 22), and periodic abstinence (13.9 versus 24). We noted that one potential source of this discrepancy is that the US estimates were corrected for abortion underreporting using secondary estimates of the number of abortions resulting from each contraceptive method from abortion patients surveys (Kost et al. 2008). No such information is available in the countries where DHS surveys are conducted, so DHS‐based results cannot be corrected in this way, however such adjustments would tend to increase, rather than decrease, estimated failure rates. The estimates both in the present study and in Polis and colleagues ( 2016) remain substantially lower than the Hatcher US estimates. For example, in the absence of the abortion underreporting correction, the 2002 US estimate of condom failure rate decreases from 17.4 to 13.9-still significantly higher than our present estimate of 8.6 (CI 7.6-9.6).

As shown in Figure 1 , contraceptive failure rates estimated from calendar data that showed the lowest levels of underreporting (as described in Addendum A) are similar to the median rates across all available survey data for methods with low failure rates and thus limited variability, like implants and IUDs. Estimates of failure rates for implants and IUDs are virtually identical between the two data sources, with completely overlapping confidence intervals indicating that the results are not statistically significantly different. Our estimates of injectable failure are also similar (2.0 failures per 100 episodes of use versus 1.7), and reasonably similar for oral contraceptives (6.3 failures per 100 episodes of use versus 5.5). Differences between the present estimates and the estimates in Polis 2016 are larger for methods with higher levels of failure: condoms (8.6 versus 5.4), withdrawal (17.3 versus 13.4), and periodic abstinence (19.0 versus 13.9), though the current estimates fall within the upper bounds of the 95% confidence intervals of the 2016 analysis for condoms and periodic abstinence, and just beyond the upper bound of the 95% CI for withdrawal.

As shown in Table 1 , the strongest patterns in contraceptive failure are seen by age, with adolescents consistently experiencing the highest failure rates and women aged 40+ the lowest. Differences in failure estimates by age are substantial: condom users aged 15-19 experience contraceptive failure at more than 10 times the rate of women aged 40 and older (Figure 2 ). Pill and periodic abstinence users aged 15-19 have failure rates that are almost four times higher, and withdrawal users have failure rates that are six times higher, compared to women in their forties.

Failure rates for pills, condoms, withdrawal, and periodic abstinence are substantially higher for women in the poorest quintile of the population than for women from the wealthiest households, though results are only statistically significant for contraceptive pills (Table 1 ).5 The poorest pill users experience failure 8.4 times per 100 episodes of use (CI 7.1-9.9), while the wealthiest pill users have a significantly lower failure rate of 5.0 (CI 4.1-6.1). Women using contraceptives to space have significantly higher rates of contraceptive failure than those using to limit for IUDs (failure rate for spacing of 1.6, CI 1.2-2.2 versus limiting 0.7, CI 0.4-1.0), condoms (spacing 9.4, CI 8.3-10.8 versus limiting 6.4, CI 5.0-8.2), withdrawal (19.5, CI 17.8-21.4 versus 13.0, CI 11.4-14.8), and periodic abstinence (22.4, CI 20.3-24.6 versus 13.8, CI 11.6-16.4). Patterns of failure by education and marital status are inconsistent.

Model Results

Table 2 presents results from the multilevel multivariate hazard models of contraceptive failure. In these models, age remains by far the most consistent predictor of contraceptive failure after adjusting for the other covariates. Compared to adolescent women aged 15-19, the hazard of failure for women aged 40 and older is 99 percent lower for IUD users, 64 percent lower for injectable users, 76 percent lower for pill users, 99 percent lower for condom users, and 76-81 percent lower for traditional method users. For most methods, the hazard of failure decreases monotonically as women's age increases.

Table 2

IUD

Injectable

Pill

Condom

Withdrawal

Periodic Abstinence

Hazard ratio

95% CI

Hazard ratio

95% CI

Hazard ratio

95% CI

Hazard ratio

95% CI

Hazard ratio

95% CI

Hazard ratio

95% CI

20-24

0.95

(0.46-1.93)

0.93

(0.66-1.32)

0.83*

(0.68-1.02)

0.56***

(0.41-0.75)

0.86

(0.68-1.08)

0.82

(0.63-1.07)

25-29

0.61

(0.30-1.24)

0.69*

(0.45-1.07)

0.70***

(0.56-0.89)

0.35***

(0.25-0.51)

0.73**

(0.57-0.93)

0.85

(0.64-1.12)

30-34

0.85

(0.38-1.92)

1.05

(0.66-1.68)

0.69***

(0.52-0.91)

0.28***

(0.18-0.43)

0.54***

(0.41-0.72)

0.66**

(0.48-0.91)

35-39

0.40*

(0.15-1.05)

0.71

(0.39-1.28)

0.65**

(0.46-0.91)

0.19***

(0.1-0.37)

0.52***

(0.37-0.73)

0.46***

(0.33-0.65)

40+

0.01***

(0.00-0.11)

0.36*

(0.11-1.18)

0.24***

(0.14-0.41)

0.06***

(0.03-0.15)

0.19***

(0.1-0.35)

0.24***

(0.14-0.39)

Primary education

1.34

(0.66-2.71)

0.58***

(0.41-0.84)

1.05

(0.88-1.27)

0.94

(0.68-1.3)

1.18

(0.92-1.53)

1.11

(0.9-1.37)

Poorest quintile

0.78

(0.27-2.29)

1.09

(0.66-1.82)

1.95***

(1.39-2.74)

1.40

(0.93-2.09)

1.30

(0.94-1.81)

1.70***

(1.2-2.41)

Poorer quintile

1.41

(0.56-3.55)

1.36

(0.82-2.27)

1.57***

(1.15-2.13)

1.49**

(1.01-2.2)

1.39**

(1.03-1.88)

1.89***

(1.32-2.72)

Middle quintile

0.67

(0.31-1.46)

1.14

(0.71-1.83)

1.32*

(0.98-1.77)

1.64***

(1.16-2.32)

1.25

(0.94-1.65)

1.13

(0.84-1.53)

Intention to limit (r = space)

0.50**

(0.28-0.88)

1.31

(0.92-1.85)

0.91

(0.75-1.1)

0.94

(0.68-1.3)

0.70***

(0.58-0.85)

0.76***

(0.62-0.93)

Rural residence (r = urban)

1.01

(0.53-1.94)

0.99

(0.69-1.41)

0.80**

(0.67-0.96)

0.92

(0.71-1.2)

0.88

(0.74-1.06)

0.83

(0.65-1.05)

Never married (r = ever married)

0.90

(0.39-2.06)

0.89

(0.61-1.29)

0.77

(0.55-1.08)

0.71**

(0.53-0.94)

1.57***

(1.12-2.2)

0.72**

(0.56-0.94)

Women's socioeconomic status is associated with failure among pill, condom, withdrawal, and periodic abstinence users, with poorer women experiencing significantly higher hazards of failure than their wealthier counterparts (Table 2 ).6 Results are particularly strong for pill users, the poorest of whom have almost twice the hazard of failure of the wealthiest users. After adjusting for other variables in the model, there is no consistent relationship between failure and education. Strength of motivation to avoid pregnancy, as measured by intention to limit versus space, remains a significant correlate of failure for IUD, withdrawal, and periodic abstinence users. Urban versus rural residence appears to have little or no impact on contraceptive failure (except that rural pill users may have 20 percent lower hazards of failure than urban users), and patterns by marital status remain inconsistent.

Sensitivity Analyses

When limiting the sample to a 1‐year recall period (Appendix Table 2 , N= 42,950 episodes of contraceptive use), we observed no statistically significant differences in failure rates as compared with our main analysis. There were too few cases of failure among the 1,109 implant episodes in this sample to produce failure rates, so we are unable to compare estimates for this method. In the second sensitivity analysis, keeping the 3‐year recall period but limiting the sample to surveys conducted in the last 10 years, the sample size was larger: 85,802 episodes of contraceptive use (Appendix Table 4). There were no statistically significant differences in overall failure rates between this and the main analysis. However, out of more than 100 comparisons between failure rates by method and characteristics, there were only two estimates that were significantly lower than our main analysis: those using withdrawal to limit, at 9.3 (95% CI 7.6-11.2) versus 13.0 (95% CI 11.4-14.8) in the main analysis, and ever‐married withdrawal users, at 13.7 (95% CI 12.4-15.3) versus 16.7 (95% CI 15.4-18.0) in the main analysis.

We compared hazard models between our main analysis and under the two conditions in the sensitivity analyses (i.e., restricted to a 1‐year recall period [Appendix Table 2 ], and restricted to surveys conducted in the past 10 years [Appendix Table 4]). Some statistically significant relationships in the main analysis were no longer significant in the restricted analyses (likely due to smaller sample sizes), and a small number of minor changes occurred (such as the relationship between pill use and intention to limit becoming significant in the sample of more recent surveys), but the findings between the main analysis and the sensitivity analyses were largely consistent.

Limitations

Some covariates (particularly education and wealth quintile) were measured at the time of the survey, and not at the time of the episode of use. Since the episodes analyzed here took place in the most recent 3 years, it is unlikely that dramatic shifts occurred between categories in that time frame, but we acknowledge that there may be some misidentification in relationships with failure due to this limitation. The lack of major differences in the relationships with wealth and education in the 1‐year recall sensitivity analysis suggests that these measures are still applicable to events in the most recent 3 years.

These failure rate estimates are based on women's self‐reports, which are not validated in any clinical way, and may not precisely reflect women's actual contraceptive histories and contraceptive failures. Although we have tried to limit to surveys without evidence of underreporting of contraceptive use episodes, we are unable to adjust for potential underreporting of use episodes that end in abortion, as is done for estimates in the United States (Sundaram et al. 2017), so true failure rates could potentially be higher.

DISCUSSION AND CONCLUSIONS

Consistent with other investigations of contraceptive failure, we found failure to be more common for users of short‐acting and user‐dependent methods, with more than 6 out of every 100 pill users and more than 8 out of 100 condom users becoming pregnant within the first year of use. Failure rates are particularly high for traditional methods, with more than 17 failures per 100 episodes of withdrawal and 19 failures per 100 episodes of periodic abstinence. These high levels of failure indicate an inability of family planning programs to help women and couples meet their reproductive intentions. High‐quality service provision including counseling that may decrease user errors, and widespread access to a wide variety of methods (including long‐acting methods that reduce opportunities for human error) are clearly warranted.

We find that contraceptive failure disproportionately affects the youngest and poorest women-in other words, women who may be the least able to care for an unintended child, obtain maternal health care, and access safe abortion services (Bankole et al. 2008; Gipson, Koenig, and Hindin 2008; Rasch and Kipingili 2009; Fung 2012; Sundaram et al. 2012; Joyce, Tan, and Zhang 2013). Given the anticipated increases in global numbers of contraceptive users (Brown et al. 2014), contraceptive failure is almost certain to become a more widespread phenomenon. The increasing contribution of contraceptive failures to the health and socioeconomic status of populations warrants a better understanding of this experience and how women resolve unintended pregnancy and the associated implications.

In this article, we have instituted a robust methodology to identify the highest‐quality nationally representative data available on contraceptive discontinuation. Our sensitivity analyses seem to confirm that the data used here produce estimates of contraceptive failure that are accurate and reliable. Using higher‐quality surveys appears to produce higher estimates of contraceptive failure rates than prior analyses of DHS calendar data that did not consider quality criteria (Ali, Cleland, and Shah 2012; Polis et al. 2016), though differences between estimates were not consistently statistically significant. The higher failure rates in the quality‐selected surveys suggest that failures and other discontinuations are likely to be underreported in the many surveys that collected less reliable calendar data. DHS calendar data quality should be considered in future studies using contraceptive calendar data.

One key finding from this analysis is the striking role that age plays in contraceptive failure, with the youngest users experiencing failure rates up to 10 times higher than older women for certain methods. These differences are dramatic enough that the discrepancies between our estimates and estimates from US data could potentially be explained by differences in the age composition of users. Newly published estimates of US failure rates based on National Survey of Family Growth (NSFG) data from 2006-10 are lower than previous US rates shown in Hatcher ( 2011) for every method, though results are still higher than results from this study. For example, the new condom failure rate estimate is 12.6 (SE 1.11) (Sundaram et al. 2017), which is still likely significantly higher than our estimate of 8.6. If the age distribution of contraceptive method users skews younger in the NSFG data on which the Hatcher ( 2011) and Sundaram and colleagues ( 2017) estimates are based, as compared with the sample analyzed here, the differences between estimates could potentially be explained entirely by compositional differences in the age structure of users. Though outside the scope of the present study, this is a clear area for further examination. In future research we plan to investigate age‐adjusted failure rates to facilitate comparisons across multiple populations with varying age structures.

Demographers have long used age‐specific data to model fertility, mortality, and other life experiences, but age patterns have not been widely incorporated in models of contraceptive failure, generally due to data limitations. Most modeling exercises that incorporate failure or use‐effectiveness use single values for entire methods or method categories, either based on US data (Kost et al. 2008; Hatcher 2011) or data from the Philippines in 1978 (Liang 1978; Bongaarts and Potter 1983; Bongaarts 2015) which are still used for estimating the Proximate Determinants model today (Bongaarts 2015). The age‐ and method‐specific failure estimates presented here provide a useful opportunity to refine existing models, particularly those that aim to understand or project fertility rates in low‐ and middle‐income countries. Such evidence could also be of potential use in clinical settings, to help women at different life stages better understand their own levels of risk. The findings presented here have direct applications for modeling approaches as well as for program and policy development worldwide.

Contraceptive use is among the clearest indicators of intention to avoid unintended pregnancy. Contraceptive use generally indicates that a woman or couple believes that their current situation-their age, marital status, recency of prior birth, number of living children, or financial situation-would make them unable to care for a child at that time. Users of contraceptive methods are attempting to intentionally plan the number and spacing of their children, and family planning programs must support them by helping potential users adopt methods that are most appropriate for them and by educating them about risks of contraceptive failure that are most relevant, including, when appropriate, the explanation and provision of age‐specific failure rates. As policies and programs encourage more women to adopt contraception, there must be an increasing focus on supporting women and couples in avoiding contraceptive failure, and providing support including safe abortion services when contraceptive failure does occur.

ACKNOWLEDGMENTS

The authors would like to thank Leontine Alkema and Susheela Singh for their guidance and excellent suggestions that improved this article. The research on which this article is based was funded by UKaid from the UK government to the Guttmacher Institute. The views expressed are those of the authors and do not necessarily reflect the UK government's official policies, nor those of the authors' employers.

ADDENDUM A. EVALUATION OF CALENDAR DATA QUALITY

In evaluation of calendar data quality, we searched for ways to identify surveys that showed evidence of poor data quality. We examined the gap between the method‐specific contraceptive prevalence rate measured from retrospective calendar data and a previous current‐status estimate. Those estimates come from analyses described in Bradley, Winfrey, and Croft 2015. We excluded surveys that had outlying values on this measure for any of the contraceptive methods analyzed in this article.

In an attempt to quantify data quality for each survey, we looked for measures that could indicate that interviewers intentionally misrecorded information, usually to decrease their workload or the burden on their interviewees. One way to measure interviewer interference is to look for suspicious age patterns recorded for selected households. In most DHS surveys, interviews are conducted with all eligible women in the selected households-that is, women aged 15-49. If interviewers "push" 15‐year‐olds into age 14, or 49‐year‐olds into age 50, those women are no longer eligible and therefore do not need to be interviewed.

Absent some very unusual and specific birth or mortality patterns, we would expect to find roughly an equal number of 14‐year‐olds and 15‐year‐olds in a given population. In survey data, we would expect to find more people recorded as 15 years old than 14 years old, given uncertainty of birth dates and digit preferences for numbers ending in 0 or 5. We would not expect, however, to find a substantially higher number of 14‐year‐olds than 15‐year‐olds in a population, yet in some surveys the recorded age structure of the female population shows heaping on age 14. We detect this type of displacement by measuring:

The ratio of women age 14:15 listed in the household schedule.

The ratio of women age 50:49 listed in the household schedule.

We expect some normal heaping on age 50, given digit preference and uncertainty about dates of birth, particularly for older individuals. We wanted to classify surveys according to whether or not they had extreme values on each index, indicating potential problems with data quality. To detect extreme values on each index, we calculated the interquartile range (IQR) of the distribution of each index as p75 - p25. Following the standard statistical definition, we defined any value greater than p75 + 3/2* IQR to be an outlier.

Similar to the displacement of ages of women in eligible households, interviewers may displace dates of births of children, collected from interviewed women, outside the most recent 5‐year period, which typically coincides with the period covered by the calendar. Most DHS surveys ask a lengthy set of questions about the antenatal care, delivery assistance, vaccination record, and so on, about every child born in the past 5 calendar years prior to the survey. Children who were born 6 or more years before the survey, however, are not eligible for this set of questions (asked to the mother). Interviewers may displace births that occurred in the past 5 years to the prior year, thereby artificially shortening the questionnaire (see Bradley 2015). We detect this type of displacement by measuring:

The ratio of the number of births in the calendar year six years prior to survey: the number of births in the calendar year five years prior to survey.

To assess additional reporting issues in the calendar portion of the survey, we investigated heaping with several measures. We believe that, in most cases, there is not a reason to expect contraceptive use episodes to be exactly 12 months in length, rather than 11 or 13 months. Rather, if a woman reports that she used a method for "a year" and the interviewer does not probe for additional detail, the duration of use would be recorded as 12 months. This is particularly problematic if the episode was actually shorter than a full year, because this could bias 12‐month failure rates. Graphical analysis suggested that reported durations of contraceptive use were strongly heaped on 12 or 6 months in some surveys. We therefore measured:

The ratio of contraceptive use episodes reported to be 12 months duration: the average of episodes reported to be 10, 11, 13, or 14 months duration.

The ratio of contraceptive use episodes reported to be 6 months duration: the average of contraceptive use episodes reported to be 4, 5, 7, or 8 months duration.

The heaping ratios were all calculated separately for each contraceptive method. Because the injectable is typically effective for three months, we expect reported durations of injectable use to be heaped on 3‐month intervals (e.g., 3 months, 6 months, 9 months, 12 months, and so on). The 12‐month duration heaping index for injectables was therefore calculated as the ratio of episodes of 12‐month duration: the average of episodes of 9 or 15 months duration, and the 6‐month heaping index for injectables was calculated as the ratio of episodes of 6 months duration: the average of episodes of 3 or 9 months duration.

Graphical analysis also suggested that the start date of contraceptive use was strongly heaped on the month of January in many surveys. While this could in fact represent increases in availability of certain contraceptive methods, if stocks were resupplied at the beginning of the calendar year, we see no reason to believe that substantially more withdrawal or condom users began use in January rather than in December or March. It seems more plausible that women reported they began a use episode "in 2010," and the interviewer recorded the episode to have begun in January of that year. To indicate surveys in which the start date of contraceptive use episodes were strongly heaped on the month of January we measured:

The ratio of contraceptive use episodes reported to have begun in January: the average of contraceptive use episodes reported to have begun in February or March.7

Each index was calculated for each survey and, where applicable, each contraceptive method. To select the most reliable data available, we eliminated from our sample any survey that had outlying values on any of these indices or measures.

ADDENDUM B. LIFE TABLE AND CONFIDENCE INTERVAL CALCULATIONS

Life table failure rates are equivalent to cumulative probabilities. We calculate the probability of failing in each month x, conditional on not having failed in the previous month. The probability of "surviving" (i.e., not experiencing contraceptive failure in month x), is the complement of the failure probability. A failure rate is equivalent to 1-the cumulative product of the monthly conditional survival probabilities.

Specifically, we construct the cumulative probability of failure by month 12, or one‐year failure rate, as the complement of

where S₁₂ is the cumulative probability of "surviving" (i.e., not experiencing contraceptive failure) to and through month 12 of contraceptive use, and px is the conditional probability of failure in month x in 1, 2, ... 12, given that the user did not fail in any prior month.

We calculate the monthly conditional probabilities using logit regression. A logit regression of failure on dummy variables for each month x gives the inverse logit of the conditional probability of failure in each month. Substituting in logistic regression estimates i n v l o g i t(B^x) and taking logs gives

The estimates of B^x are produced using Stata's svy: logit commands, giving

An advantage to this regression‐based approach for failure rate calculation is that estimates of variance can be calculated taking full account of the sample design, as noted by Abatih and colleagues ( 2008) including the stratified, clustered sample design used in DHS surveys. We implement this by using Stata's svy suite of commands to produce the failure probabilities and associated variance‐covariance matrix. The variance and confidence intervals around the cumulative failure probability are then estimated using the Delta Method (Oehlert 1992; Fishman 2015). We define

According to the Delta method (Oehlert 1992),

Where V=A′∗ε ∗A

We need to ensure that the lower and upper bounds of the confidence interval around 1‐S ₁₂ are constrained to lie in (0,1). We do this by using a log(‐log) transformation, noting that the variance of log (−log(S^12)) is approximated with the Delta Method as

The confidence interval for log (−log(S^12)) is then

And the CI for 1‐ S^12 is estimated as

1− exp −exp log−log S^12 ±z 0.975 ∗V

Notes

Sarah E.K. Bradley is Senior Associate, Abt Associates, 6130 Executive Blvd., Rockville, MD 20852. Email: moc.cossatba@yeldarB_haraS. Chelsea B. Polis is Senior Research Scientist, Guttmacher Institute, New York, NY, and Associate, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD. Akinrinola Bankole is Senior Fellow, Guttmacher Institute, New York, NY. Trevor Croft is Technical Director, ICF, Rockville, MD.

Footnotes

¹Appendix tables are available at the supporting information tab at wileyonlinelibrary.com/journal/sfp.

² Although we were able to estimate failure rates for the contraceptive implant, failures are so rare that we were unable to assess correlates in multivariate models.

³ In some countries, fertility awareness methods (FAM) such as CycleBeads® are a part of the country's family planning program and such methods are included as separate categories in the DHS questionnaire. In these countries, the category of "periodic abstinence" excludes self‐identified FAM users. However, it is possible that some FAM users who self‐reported using "periodic abstinence" or "rhythm method" are included in this category.

⁴Specifically, the percentage of contraceptive use episodes beginning during the period of observation that end in failure within the first 12 months. Although these are episode‐based, and not woman‐based, we find that on average only 10 percent of women report more than one episode of use of the same contraceptive method during the observation period. We therefore refer to the percentage of women rather than episodes for ease of interpretation.

⁵ When grouped into the poorest 60 percent (poorest 3 quintiles) and wealthiest 40 percent (wealthiest 2 quintiles), failure rates for pills, condoms, withdrawal, and periodic abstinence are significantly higher for women in the poorest 60 percent of the population than for women from wealthier households.

⁶ When grouped into the poorest 60 percent and wealthiest 40 percent, hazards of failure rates for pills, condoms, and periodic abstinence are significantly higher for women in the poorest 60 percent of the population than for women from wealthier households.

⁷ February and March were chosen as the reference start months as exploratory analyses showed evidence of heaping on the month of December as a start date.

REFERENCES

Ali, Mohamed M. , Cleland John, and Shah Iqbal H.. 2012. "Causes and Consequences of Contraceptive Discontinuation: Evidence from 60 Demographic and Health Surveys." Geneva, Switzerland: World Health Organization.

Bongaarts, John and Potter Robert G.. 1983. "Applications of an Aggregate Fertility Model," in Fertility, Biology, and Behavior, pp. 103-126. San Diego: Academic Press. [Google Scholar]

Bradley, Sarah E.K. 2015. "More Questions, More Bias? An Assessment of the Quality of Data Used for Direct Estimation of Infant and Child Mortality in the Demographic and Health Surveys." Paper presented at the Population Association of America Annual Meeting, San Diego, CA.

Bradley, Sarah E.K. , Schwandt Hilary M., and Khan Shane. 2009. "Levels, Trends, and Reasons for Contraceptive Discontinuation," DHS Analytical Studies No. 20. Calverton, MD: ICF Macro. http://dhsprogram.com/pubs/pdf/AS20/AS20.pdf.

Bradley, Sarah E.K. , Winfrey William, and Croft Trevor N.. 2015. "Contraceptive Use and Perinatal Mortality in the DHS: An Assessment of the Quality and Consistency of Calendars and Histories," DHS Methodological Reports No. 17. Rockville, MD: ICF International. http://dhsprogram.com/pubs/pdf/MR17/MR17.pdf.

Curtis, Sian L. and Blanc Ann K.. 1997. "Determinants of Contraceptive Failure, Switching, and Discontinuation: An Analysis of DHS Contraceptive Histories," DHS Analytical Reports No. 6. Calverton, MD: Macro International. http://dhsprogram.com/pubs/pdf/AR6/AR6.pdf.

Fishman, Ezra . 2015. "Variance estimation for a complex life table quantity: Disease‐free life expectancy," Working Paper 15‐2. Population Studies Center Working Paper Series. University of Pennsylvania. http://repository.upenn.edu/cgi/viewcontent.cgi?article=1059&context=psc_working_papers.

Fung, Brian . 2012. "The quiet racism of abortion bans," The Atlantic, August 28, 2012. [http://www.theatlantic.com/health/archive/2012/08/the-quiet-racism-of-abortion-bans/261665/. Google Scholar]

Hatcher, Robert Anthony . 2011. Contraceptive Technology. New York, NY: Ardent Media. [Google Scholar]

ICF . 2018. Demographic and Health Survey Interviewer's Manual. Rockville, Maryland: ICF. [Google Scholar]

Joyce, Ted , Tan Ruoding, and Zhang Yuxiu. 2013. "Abortion before & after Roe," Journal of Health Economics 32(5): 804-815. 10.1016/j.jhealeco.2013.05.004. [[PubMed] [CrossRef] PMC free article] [Google Scholar]

Kost, Kathryn , Singh Susheela, Vaughan Barbara, Trussell James, and Bankole Akinrinola. 2008. "Estimates of contraceptive failure from the 2002 national survey of family growth," Contraception 77(1): 10-21. 10.1016/j.contraception.2007.09.013. [[PubMed] [CrossRef] PMC free article] [Google Scholar]

Liang, J.E. 1978. "Use Effectiveness Levels and Trends in the Philippines, 1970-1976," Research Note 14. Manila: Population Institute, University of the Philippines.

Lightbourne, Robert E. 1985. "Individual preferences and fertility behavior," in Cleland John. and Hobcraft John. (eds.), Reproductive Change in Developing Countries: Insights from the World Fertility Survey. Oxford University Press. [Google Scholar]

MEASURE DHS . 2013. "Demographic and Health Surveys Methodology: Standard Recode Manual for DHS‐6," Calverton, MD: ICF International.

Polis, Chelsea B. , Bradley Sarah E.K., Bankole Akinrinola, Onda Tsuyoshi, Croft Trevor, and Singh Susheela. 2016. "Contraceptive failure rates in the developing world: An analysis of demographic and health survey data in 43 countries," New York: Guttmacher Institute.

Preston, Samuel , Heuveline Patrick, and Guillot Michel. 2000. Demography: Measuring and Modeling Population Processes. London: Wiley; [https://books.google.com/books?id=xlv2H6LpNAwC. Google Scholar]

Rabe‐Hesketh, Sophia and Skrondal Anders. 2012. Multilevel and Longitudinal Modeling Using Stata. Third Edition. 2 volumes. College Station, Texas: Stata Press. [Google Scholar]

Rutstein, Shea O. and Johnson Kiersten. 2004. "The DHS Wealth Index," DHS Comparative Report No. 6. Calverton, MD: ORC Macro. http://dhsprogram.com/pubs/pdf/CR6/CR6.pdf.

Sundaram, Aparna , Vaughan Barbara, Kost Kathryn, Bankole Akinrinola, Finer Lawrence, Singh Susheela, and Trussell James. 2017. "Contraceptive failure in the United States: Estimates from the 2006-2010 National Survey of Family Growth," Perspectives on Sexual and Reproductive Health 49(1): 7-16. 10.1363/psrh.12017. [[PubMed] [CrossRef] PMC free article] [Google Scholar]

# data[].attributes.attachment_link.title
Global Contraceptive Failure Rates: Who Is Most at Risk?

# data[].attributes.attachment_link.description
Studies in Family Planning Contraceptive failure is a major contributor to unintended pregnancy worldwide. DHS retrospective calendars, which are the most widely used data source for estimating contraceptive failure in low‐income countries, vary in quality across countries and surveys.

# data[].attributes.attachment_link.readable_text
N Engl J Med. Author manuscript; available in PMC 2021 Jul 28.

Published in final edited form as:

David K. Turok, M.D., Alexandra Gero, M.P.H., Rebecca G. Simmons, Ph.D., Jennifer E. Kaiser, M.D., Gregory J. Stoddard, M.P.H., Corinne D. Sexsmith, M.S., Lori M. Gawron, M.D., and Jessica N. Sanders, Ph.D.

Abstract

BACKGROUND

In the United States, more intrauterine device (IUD) users select levonorgestrel IUDs than copper IUDs for long-term contraception. Currently, clinicians offer only copper IUDs for emergency contraception because data are lacking on the efficacy of the levonorgestrel IUD for this purpose.

METHODS

This randomized noninferiority trial, in which participants were unaware of the group assignments, was conducted at six clinics in Utah and included women who sought emergency contraception after at least one episode of unprotected intercourse within 5 days before presentation and agreed to placement of an IUD. We randomly assigned participants in a 1:1 ratio to receive a levonorgestrel 52-mg IUD or a copper T380A IUD. The primary outcome was a positive urine pregnancy test 1 month after IUD insertion. When a 1-month urine pregnancy test was unavailable, we used survey and health record data to determine pregnancy status. The prespecified noninferiority margin was 2.5 percentage points.

RESULTS

Among the 355 participants randomly assigned to receive levonorgestrel IUDs and 356 assigned to receive copper IUDs, 317 and 321, respectively, received the interventions and provided 1-month outcome data. Of these, 290 in the levonorgestrel group and 300 in the copper IUD group had a 1-month urine pregnancy test. In the modified intention-to-treat and per-protocol analyses, pregnancy rates were 1 in 317 (0.3%; 95% confidence interval [CI], 0.01 to 1.7) in the levonorgestrel group and 0 in 321 (0%; 95% CI, 0 to 1.1) in the copper IUD group; the between-group absolute difference in both analyses was 0.3 percentage points (95% CI, −0.9 to 1.8), consistent with the noninferiority of the levonorgestrel IUD to the copper IUD. Adverse events resulting in participants seeking medical care in the first month after IUD placement occurred in 5.2% of participants in the levonorgestrel IUD group and 4.9% of those in the copper IUD group.

CONCLUSIONS

The levonorgestrel IUD was noninferior to the copper IUD for emergency contraception. (Supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development and others; ClinicalTrials.gov number, NCT02175030.)

WORLDWIDE, A VARIETY OF METHODS of emergency contraception are used to decrease the risk of pregnancy after unprotected sexual intercourse. In the United States, the Food and Drug Administration (FDA) has approved only two methods of emergency contraception: oral levonorgestrel and oral ulipristal acetate. Although the copper intrauterine device (IUD) is not approved by the FDA for emergency contraception, substantial observational evidence supports that it is highly effective, failing to prevent pregnancy in less than 0.1% of cases,1 an order of magnitude lower than the incidence of failure with oral methods.2,3 However, persons selecting an IUD for long-term contraception have shown a strong preference for the levonorgestrel IUD over the copper IUD, probably because the levonorgestrel IUD reduces menstrual bleeding and discomfort.4-6

The availability of a greater number of contraceptive options is associated with increased satisfaction with and continuation of the use of the method and decreased unintended pregnancy.7 In addition, best practices for delivery of family planning services recommend quick-start contraception, with provision and initiation of the desired method at the point of care.7 Although patients can quick-start contraception with the levonorgestrel IUD,8 women obtaining a levonorgestrel IUD who report recent unprotected sexual intercourse are currently advised to take concomitant oral emergency contraception.9

A preliminary, prospective cohort study that offered persons seeking emergency contraception the levonorgestrel 52-mg IUD with concomitant oral levonorgestrel or the copper IUD showed user preference for the levonorgestrel IUD. Despite a high risk of emergency contraception failure among persons who chose the levonorgestrel IUD and oral levonorgestrel, with 40% of the participants in that group reporting multiple episodes of unprotected sexual intercourse and 61% having a body-mass index (the weight in kilograms divided by the square of the height in meters) of 25 or higher, there were no pregnancies resulting from failure of emergency contraception among the 121 persons who selected the levonorgestrel regimen.10 This, along with evidence of the effectiveness of inert IUDs,11,12 has suggested that the levonorgestrel IUD could be effective for emergency contraception. Moreover, laboratory data support the potential for levonorgestrel to directly interfere with sperm transport, sperm capacitation, the acrosome reaction, and oviduct transport.13-16 We designed the Randomized Controlled Trial Assessing Pregnancy for IUDs as Emergency Contraception (RAPID EC) trial to assess the 1-month pregnancy risk with the levonorgestrel 52-mg IUD as compared with the copper IUD for emergency contraception. We selected a noninferiority trial design because, given the preference for the levonorgestrel IUD shown by participants in earlier trials, establishing its noninferiority to the copper IUD for emergency contraception could increase emergency contraception options for patients.

METHODS

TRIAL DESIGN

Beginning in August 2016, trial staff recruited patients at three Utah family planning clinics for trial participation. We added three additional sites in March 2017 and concluded enrollment in December 2019. The institutional review board at the University of Utah approved the trial protocol, available with the full text of this article at NEJM.org. The last author, who was not directly involved with analysis, generated the randomization sequence using balanced blocks of four, stratified by site, and uploaded the blinded sequence to Research Electronic Data Capture (REDCap),17 a secure Web-based application system. We performed the trial in accordance with the principles of the Declaration of Helsinki. Trial sites purchased the study drugs, levonorgestrel 52-mg IUD (Liletta, Medicines360) and copper T380A IUD (ParaGard, Teva Women's Health), from the distributors. These companies had no role in any aspect of conduct of the trial. The first, second, and fifth authors vouch for the accuracy and completeness of the data and for the fidelity of the trial to the protocol. The statistical analysis plan is provided with the protocol at NEJM.org.

PARTICIPANTS

Eligible participants were women 18 to 35 years of age who were fluent in English or Spanish and were requesting emergency contraception after unprotected sexual intercourse within the previous 5 days (120 hours). Other inclusion criteria were a desire to initiate use of an IUD, a desire to prevent pregnancy for at least 1 year, a negative urine pregnancy test, a history of regular menstrual cycles (21 to 35 days), and known date of the last menstrual period (±3 days). On the basis of the menstrual history, we calculated the menstrual cycle day of unprotected sexual intercourse and IUD insertion and, if needed, an estimated date of pregnancy. Key exclusion criteria were breast-feeding, vaginal bleeding of unknown cause, current use of a highly effective method of contraception (sterilization, IUD, or contraceptive implant), intrauterine infection in the previous 3 months, untreated Neisseria gonor-rhea or Chlamydia trachomatis infection in the previous 30 days, allergy to copper, use of oral emergency contraception in the preceding 5 days, and known uterine cavity anomalies. We did not exclude persons who reported having unprotected sexual intercourse more than 5 days before IUD placement.

TRIAL PROCEDURES

All women at participating sites who presented for emergency contraception received printed information describing trial participation, randomization, concealment of group assignment, and the two types of IUD in the trial. Participants who received an IUD could have it removed at any time and could switch to another contraceptive method, including another type of IUD.

All participants had a negative urine pregnancy test (Osom card pregnancy test, Genzyme Diagnostics) before IUD placement. After the participants completed screening and provided written informed consent, REDCap randomly assigned them, in a 1:1 ratio, to placement of a levonorgestrel 52-mg IUD or a copper T380A IUD. We concealed the IUD package so that participants would be unaware of their assigned intervention, but the different appearance of the IUDs and differences in insertion methods meant that the providers were aware of the IUD type that was inserted. Nurse practitioners and certified nurse midwives with experience in IUD placement performed all insertions. Health centers provided the trial interventions and devices without cost to participants and billed health insurance companies when appropriate.

Before participants left the clinic, staff scheduled a 1-month follow-up visit (28 to 30 days after insertion) and provided a home urine pregnancy test for the participant to use the morning before the follow-up appointment. There were three opportunities for participants to report urine pregnancy test results. First, research staff sent a text message to participants 27 days after enrollment instructing them to conduct the home urine pregnancy test. This text included a link to use to submit results online and an option to upload a photo of the test results. The next day, participants received a link to complete their 1-month follow-up survey through REDCap, which also prompted the participants to enter the results of their urine pregnancy tests. Finally, at the 1-month office visit, a member of the clinical research staff performed a urine pregnancy test, informed the participant which IUD she had received, and had the participant complete the follow-up survey if she had not already done so. This task sequence was not standardized; thus, some participants were notified of the IUD type they received before they had completed the survey.

If participants had not yet provided urine pregnancy test results by their scheduled 1-month visit and did not attend the visit, trial staff called and sent text and email messages (up to three times by each means of contact) over the next week. If needed, trial staff attempted to reach alternative contacts who had been provided by the participants at enrollment. We continued to follow participants through 3-month and 6-month surveys and reviewed clinic electronic health record notes to assess IUD use and pregnancy status. Participant reimbursement included a $10 gift card for completing each follow-up survey and $30 for attending the 1-month clinic visit.

OUTCOMES AND ADVERSE EVENTS

The primary outcome was pregnancy, defined by our initial protocol as a positive urine pregnancy test conducted at home or in the clinic 1 month after IUD placement. After data collection was completed and data were unblinded, we observed that data for the primary outcome were missing for 48 participants. The protocol was then amended to consider participants who did not report pregnancy test results within 40 days after IUD placement as not pregnant if follow-up surveys up to 6 months, review of clinic notes, and review of medical records for any visits reported at other sites revealed no positive pregnancy test results. Participants who reported a pregnancy in the first 6 months of IUD use underwent pregnancy dating by means of ultrasonography to determine whether the pregnancy resulted from unprotected sexual intercourse within 5 days before placement of the trial IUD. Using responses from the 1-month survey, we assessed incidents of IUD discontinuation (including date and reason), participant satisfaction, and IUD-related pain and bleeding outcomes (see the Supplementary Appendix, available at NEJM.org, for details). The 1-month participant survey included a query regarding receipt of any medical care in any setting to assess adverse events. A data and safety monitoring committee evaluated the main safety outcome of emergency contraception failure in the levonorgestrel IUD group annually. Our prespecified plan required enrollment cessation if the annual evaluation revealed that the lower bound of the 95% confidence interval for the percent difference between pregnancy rates in the two groups exceeded 2.5 percentage points, per protocol, with the use of the planned final sample sizes of the two groups as denominators.

STATISTICAL ANALYSIS

We based our sample-size calculation for the primary outcome on the established pregnancy incidence of 0.1% among copper T380A IUD emergency contraception users.1 Because data were lacking on pregnancy risk with the use of the levonorgestrel IUD alone for emergency contraception, we conservatively estimated a 1% incidence of pregnancy among participants using levonorgestrel 52-mg IUDs, on the basis of a trial of the levonorgestrel IUD administered with oral levonorgestrel for emergency contraception.10 To achieve 80% power to detect a two-sided 95% confidence interval around an absolute difference in the incidence of pregnancy after emergency contraception (1% with levonorgestrel minus 0.1% with copper) that would not cross the noninferiority margin of 2.5 percentage points, we required 335 participants per trial group (for a total of 670). We planned to recruit an additional 5% of that total to accommodate loss to follow-up (for a total of 706 participants).

An intention-to-treat analysis was initially specified in the protocol, but the analysis plan was changed after trial completion to a modified intention-to-treat analysis in which we excluded participants assigned to each group who did not receive an IUD (since those who received an IUD were considered the relevant trial population) and included only those for whom we had 1-month outcome data (pregnancy test results or subsequent survey or clinical data indicating no evidence of pregnancy). We also performed a per-protocol analysis limited to participants still using their IUDs at 1 month and a sensitivity analysis that included only those who reported 1-month urine pregnancy test results. (See the Supplementary Appendix for details of the analyses.)

Missing data for the secondary outcomes were imputed with multivariate imputation by chained equations, which averaged regression coefficients across five imputed data sets, with standard errors computed with the use of Rubin's rules.18 Multiple imputation was not used for the primary outcome, because one group had no pregnancy outcomes and the model would not converge. Continuous secondary outcomes are reported as mean differences with two-sided 95% confidence intervals. Secondary categorical outcomes are reported as absolute risk differences expressed as percentage points with two-sided 95% confidence intervals. Confidence intervals for the secondary outcomes have not been adjusted for multiplicity and should not be used to infer definitive treatment effects. Data analysts were unaware of the treatment assignments. We conducted data analysis with Stata software, version 16 (StataCorp).

RESULTS

PARTICIPANTS

We assessed for eligibility 10,317 women who were seeking emergency contraception from August 2016 through mid-December 2019, until the prespecified sample size was met. Of the persons who were assessed, 805 were younger than 18 years of age, 547 did not meet inclusion criteria, and 8247 declined to participate, primarily because they desired only oral emergency contraception. Details of enrollment and trial participation are shown in Figure 1 . A total of 718 participants were enrolled, of whom 711 underwent randomization; 355 participants were assigned to receive the levonorgestrel IUD and 356 to receive the copper IUD. Of these, 317 and 321 participants, respectively, received the assigned intervention and provided 1-month outcome data (and thus were included in the modified intention-to-treat analysis), and 290 and 300 participants, respectively, provided a urine pregnancy test result at the 1-month follow-up.

Baseline characteristics were similar in the two groups except for imbalances in reasons for seeking emergency contraception ( Table 1 ). Participants reported an average of 2.1 episodes of unprotected sexual intercourse in the 5 days preceding IUD placement. At the 1-month follow-up, urine pregnancy test results for participants who received an IUD were obtained by home report for 172 participants (27.0%), by clinic report for 66 (10.3%), and by both reports for 352 (55.2%). Information on pregnancy was available for 45 of the remaining 48 participants on the basis of their returning at least one of the three follow-up surveys (at 1 month, 3 months, and 6 months). A review of data from electronic health record data confirmed the outcome for 17 participants, including 3 who did not complete any surveys.

Table 1.

Characteristic

Levonorgestrel IUD
(N = 327)

Copper IUD
(N = 328)

Body-mass index - no. (%) †

Race or ethnic group - no. (%) ‡

OUTCOMES

Pregnancy 1 month after IUD placement occurred in a single participant in the levonorgestrel IUD group (0.3%; 95% confidence interval [CI], 0.01 to 1.7), and in no participants in the copper IUD group (0%; 95% CI, 0 to 1.1). The between-group absolute difference was 0.3 percentage points (95% CI, −0.9 to 1.8) in both the modified intention-to-treat and per-protocol analyses ( Fig. 2 ).

The lone pregnancy occurred in a participant who reported a single episode of unprotected sexual intercourse 48 hours before IUD placement. Pregnancy dating by an ultrasound examination at 10 weeks was consistent with conception occurring as a result of an emergency contraception failure. The pregnancy ended in a spontaneous abortion at 10 weeks with the IUD still in place.

Table 2 shows results for secondary outcomes, both unadjusted and adjusted post hoc for the reason participants provided for seeking emergency contraception, given the baseline imbalance. One participant in the copper IUD group had an expulsion of her IUD and same-day reinsertion on day 11, and one participant in the levonorgestrel IUD group switched to a copper IUD on day 28. Among levonorgestrel IUD users, 5.2% sought medical care for adverse events in the first month after IUD placement as compared with 4.9% of copper IUD users ( Table 3 ).

Table 2.

Outcome

Levonorgestrel IUD
(N = 327)

Copper IUD
(N = 328)

Unadjusted Mean Difference
(95% CI)

Unadjusted Risk Difference
% (95% CI)

Adjusted Mean Difference
(95% CI)†

Adjusted Risk Difference
% (95% CI)†

Participant satisfaction level - no./total no. (%)

Pain

Cramping since insertion ‡

59.5±1.9

66.6±1.8

−9.9 (−15.2 to −4.6)**

−10.3 (−15.6 to −4.9) ††

Sharp pain since insertion ‡

66.6±1.4

72.5±1.3

−8.6 (−12.9 to −4.4)**

−9.0 (−13.2 to −4.8) ††

Bleeding and spotting days in first month of lUD use §

No. of bleeding days

10.8±0.5

7.2±0.3

3.5 (2.4 to 4.6)**

3.5 (2.4 to 4.6) ††

No. of spotting days

11.0±0.6

5.7±0.3

5.2 (3.7 to 6.4)**

5.4 (3.8 to 6.9) ††

Table 3.

Event

Levonorgestrel IUD
(N = 327)

Copper IUD
(N = 328)

number (percent)

Total events*

17 (5.2)

16 (4.9)

Concerns related to IUD †

3 (0.9)

1 (0.3)

DISCUSSION

This randomized trial showed that the levonorgestrel 52-mg IUD was noninferior to the copper T380 IUD for use as emergency contraception after unprotected sexual intercourse in the previous 5 days. Pregnancy at the 1-month follow-up was reported in 0.3% of participants in the levonorgestrel IUD group and in no participants in the copper IUD group, in both the modified intention-to-treat and per-protocol analyses, with confidence intervals around the difference excluding the noninferiority margin of 2.5 percentage points. These data can also support provision of quick-start contraception with the levonorgestrel 52-mg IUD after recent unprotected sexual intercourse and a negative pregnancy test.

We did not directly compare IUD use with other emergency contraception methods. However, the incidence of pregnancy with the use of the levonorgestrel IUD for emergency contraception in this trial (0.3%; 95% CI, 0.01 to 1.7) appears to compare favorably with that reported with oral emergency contraception (1.4 to 2.6%).2,3 Unlike the case with oral levonorgestrel, the efficacy of the levonorgestrel IUD for emergency contraception does not appear to be affected by higher body-mass index.19 Since the IUD also provides ongoing contraception, it avoids the delayed initiation of ongoing contraception that is recommended when ulipristal acetate is used for emergency contraception.9,20

Although studies of conventional oral emergency contraception commonly limit participants to those who have had a single episode of unprotected sexual intercourse occurring 3 to 5 days before emergency contraception, many of this trial's participants were at higher risk for pregnancy, reporting multiple episodes of unprotected sexual intercourse in the previous 5 days and some reporting unprotected sexual intercourse 6 to 14 days before IUD placement. Despite this, we found a lower frequency of pregnancy than expected.21 We previously reported no pregnancies at follow-up at 2 to 4 weeks or longer among a pooled cohort of 134 women who had a copper IUD placed 6 to 14 days after unprotected sexual intercourse and for whom follow-up information was available; that report included data from 27 participants in the copper IUD group in the present trial.22 Our conclusions were unchanged in a sensitivity analysis that included only participants who provided data from urine pregnancy tests.

Although our enrollment met the sample size target (706), we overestimated the number of participants who would provide 1-month follow-up (638). However, the number of pregnancies in the levonorgestrel IUD group was lower than projected, and noninferiority was shown. We were able to obtain urine pregnancy testing, the prespecified primary outcome, for 92.5% (590) of the 638 participants who provided 1-month follow-up. We addressed missing tests by confirming no report of pregnancy in survey information and clinic records, although the timing of clinic record review after data unblinding could have potentially introduced bias. If participants who did not report 1-month urine pregnancy tests underwent abortions, it is unlikely that these would have been missed on record review, because the vast majority of abortions in Utah occur within the same health system as the clinic sites. For practical reasons, clinicians were aware of the IUD type the participants received, and some participants seen in a clinic for the 1-month follow-up visit may have been informed of their IUD type before they completed their follow-up survey. However, these situations would not be expected to affect rates of the primary outcome. Selection bias is possible, since only 7% of clinic patients seeking emergency contraception enrolled in this trial. The majority of persons who declined participation did not want an IUD. Enrollment rates were lower than in a previous trial in which we allowed participants to select their preferred IUD type for emergency contraception.10 Because the clinics serving as trial sites are known to provide low-cost oral emergency contraception rapidly, some women may not have wanted to spend the additional time at the clinic required for trial enrollment and IUD placement. The nature of the trial sites may also limit generalizability, as did our exclusion of persons outside the 18-to-35-year age range and those with irregular menstrual cycles.

In this multicenter randomized trial, the levonorgestrel 52-mg IUD was noninferior to the copper IUD for emergency contraception.

Acknowledgments

The content is solely the responsibility of the authors and does not necessarily represent the official view of any of the funding agencies or participating institutions, including the National Institutes of Health, the University of Utah, or Planned Parenthood Federation of America.

Supported by a grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) (1R01HD083340); the University of Utah, with funding in part from the National Institutes of Health (NIH) National Center for Research Resources and National Center for Advancing Translational Sciences through grant number UL1TR002538 (formerly 5UL1TR001067-05, 8UL1TR000105, and UL1RR025764); and awards from the NIH Office of Research on Women's Health (K12HD085852 to Dr. Sanders) and the Eunice Kennedy Shriver NICHD (K12HD085816 to Dr. Gawron and K24HD087436 to Dr. Turok).

We thank the Planned Parenthood Association of Utah, especially Penny Davies, for exceptional patient care and recruitment of participants into this trial; Marie Gibson for managing regulatory issues; Grace Mason for conducting follow-up; and Ingrid Nygaard and David Case for editing assistance and Rachael Hemmert for formatting assistance with an earlier draft of the manuscript.

Footnotes

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.

A data sharing statement provided by the authors is available with the full text of this article at NEJM.org.

REFERENCES

4. Diedrich JT, Desai S, Zhao Q, Secura G, Madden T, Peipert JF. Association of short-term bleeding and cramping patterns with long-acting reversible contraceptive method satisfaction. Am J Obstet Gynecol 2015; 212(1): 50.e1-8. [[ PubMed] PMC free article] [ Google Scholar]

12. Guillebaud J, Kubba A, Rowlands S, White J, Elder MG. Post-coital contraception with danazol, compared with an ethinyloestradiol-norgestrel combination or insertion of an intra-uterine device. J Obstet Gynaecol 1983; 3: S64-S68. [ Google Scholar]

# data[].attributes.attachment_link.title
Levonorgestrel vs. Copper Intrauterine Devices for Emergency Contraception

# data[].attributes.attachment_link.description
N Engl J Med. Author manuscript; available in PMC 2021 Jul 28. Published in final edited form as: David K. Turok, M.D., Alexandra Gero, M.P.H., Rebecca G. Simmons, Ph.D., Jennifer E. Kaiser, M.D., Gregory J. Stoddard, M.P.H., Corinne D. Sexsmith, M.S., Lori M. Gawron, M.D., and Jessica N. Sanders, Ph.D.

# data[].attributes.attachment_link.readable_text
doi: 10.1016/j.bpobgyn.2019.12.002.

Affiliations

¹ Department of Obstetrics and Gynecology, University of Campinas Faculty of Medical Sciences (UNICAMP), Campinas, SP, Brazil. Electronic address: bahamond@caism.unicamp.br.

² Department of Obstetrics and Gynecology, University of Campinas Faculty of Medical Sciences (UNICAMP), Campinas, SP, Brazil.

³ Centro Latinoamericano de Perinatologia, Salud de la Mujer y Reproductiva, Montevideo, Uruguay.

doi: 10.1016/j.bpobgyn.2019.12.002.

Affiliations

¹ Department of Obstetrics and Gynecology, University of Campinas Faculty of Medical Sciences (UNICAMP), Campinas, SP, Brazil. Electronic address: bahamond@caism.unicamp.br.

² Department of Obstetrics and Gynecology, University of Campinas Faculty of Medical Sciences (UNICAMP), Campinas, SP, Brazil.

³ Centro Latinoamericano de Perinatologia, Salud de la Mujer y Reproductiva, Montevideo, Uruguay.

Abstract

Unplanned pregnancy (UP) is a public health problem, which affects millions of women worldwide. Providing long-acting reversible contraceptive (LARC) methods is an excellent strategy to avoid or at least reduce UP, because the effectiveness of these methods is higher than other methods, and is indeed comparable to that of permanent contraception. As the initial introduction of the inert plastic intrauterine device (IUD) and of the six-rod implant, pharmaceutical companies have introduced a copper IUD (Cu-IUD), different models of levonorgestrel-releasing intrauterine system (LNG IUS), and one and two-rod implants, which certainly improved women's LARC options. The main characteristic of LARCs is that they provide high contraceptive effectiveness with a single intervention, and that they can be used for a long time. Emerging evidence from the last few years has demonstrated that it is possible to extend the use of the 52 mg LNG IUS and of the etonogestrel-implant beyond five- and three years, respectively, which adds new value to these LARCs.

Keywords: Contraception; Copper intrauterine device; LARCs; Levonorgestrel-releasing intrauterine system; Long acting contraceptives; Subdermal implants.

Conflict of interest statement

Declaration of Competing Interest LB and IM have received honoraria as members of advisory boards and have been invited as speakers at scientific meetings for Bayer Healthcare Pharmaceuticals and Merck. None of the other authors have any conflicts of interest to declare.

Similar articles

Estimated disability-adjusted life years averted by long-term provision of long acting contraceptive methods in a Brazilian clinic.

Bahamondes L, Bottura BF, Bahamondes MV, Gonçalves MP, Correia VM, Espejo-Arce X, Sousa MH, Monteiro I, Fernandes A. Bahamondes L, et al. Hum Reprod. 2014 Oct 10;29(10):2163-70. doi: 10.1093/humrep/deu191. Epub 2014 Aug 1. Hum Reprod. 2014. PMID: 25085802

Safety and Benefits of Contraceptives Implants: A Systematic Review.

Rocca ML, Palumbo AR, Visconti F, Di Carlo C. Rocca ML, et al. Pharmaceuticals (Basel). 2021 Jun 8;14(6):548. doi: 10.3390/ph14060548. Pharmaceuticals (Basel). 2021. PMID: 34201123 Free PMC article. Review.

# data[].attributes.attachment_link.keywords[]
levonorgestrel / administration & dosage

# data[].attributes.attachment_link.keywords[]
national center for biotechnology information

# data[].attributes.attachment_link.title
Long-acting reversible contraceptive (LARCs) methods - PubMed

# data[].attributes.attachment_link.description
Unplanned pregnancy (UP) is a public health problem, which affects millions of women worldwide. Providing long-acting reversible contraceptive (LARC) methods is an excellent strategy to avoid or at least reduce UP, because the effectiveness of these methods is higher than other methods, and is indee ...

# data[].attributes.attachment_link.readable_text
Abstract

Design thinking methodology is a collaborative strategy with the potential to create innovations. Design thinking is being used increasingly in health care. Design jams are interdisciplinary events that bring together experts and community members to collaborate on creative solutions to health-care problems. This article describes the design thinking process and includes reflection on the authors' participation in a design jam event aimed to address the knowledge-to-action gap that exists in health care for (LGBTQI2S) people.

Design thinking methodology is a framework to foster creative and interdisciplinary approaches to innovations. The aim is to develop effective solutions to current problems using creative thinking and a human-centered approach ( Eines & Vatne, 2017). Successful implementation of design thinking in health care can improve clinical practice, quality of care, and patient outcomes ( Rahemi, D'Avolio, Dunphy, & Rivera, 2018). This methodology was used to address a knowledge-to-action gap in health care for lesbian, gay, bisexual, transgender, queer, intersex and Two-Spirit (LGBTQI2S) people. This article presents the authors' experience at a design jam hosted by the Canadian Institutes of Health Research (CIHR) to design a tool for knowledge transfer and exchange (KTE) in nursing education. This article aims to describe design thinking, critique our experience, and highlight implications for creative development of nursing tools.

PROBLEM DESCRIPTION

Historically, research about health care for gender minorities has focused on LGBT people, with a dearth of literature about barriers to care for queer, intersex, and Two-Spirit people. LGBT people experience distinct health disparities and are one of the largest underserved populations in health care ( Eliason, Dibble, & Dejoseph, 2010; Institute of Medicine, 2011). Barriers contributing to health disparities include discrimination, lack of provider experience and knowledge, and a deficiency of services ( Institute of Medicine, 2011). Lack of knowledgeable health-care providers has been identified as a key barrier to culturally sensitive health care for the LGBT population ( Grant et al., 2010; Sanchez, Sanchez, & Danoff, 2009).

Content about caring for LGBTQI2S people is lacking in baccalaureate nursing curricula, with a median time of 2.12 hours devoted to the topic in U.S. nursing programs ( Lim, Johnson, & Eliason, 2015). Similarly, programs in Canada provided limited, inconsistent, non-standardized content in LGBTQI2S health and wellness ( Shortall, 2019). Rondahl (2009) explored nursing students' understanding of the LGBT population and found that their knowledge was inadequate, with only 10% of students achieving a passing mark on a LGBT knowledge questionnaire. Faculty who feel unprepared to teach LGBTQI2S content have been identified as a major barrier for including it in nursing curricula ( Eliason et al., 2010; Shortall, 2019). Recommendations have been made regarding key content for curricula to provide a strong foundation for cultural humility with LGBTQI2S patients ( Kelley, Chou, Dibble, & Roberston, 2008; Safer & Pearce, 2013; Strong & Folse, 2015; Walsh Brennan, Barnsteiner, De Leon Siantz, Cotter, & Everett, 2012), yet there is no evidence that these recommendations have been implemented in nursing curricula. Providing nurses with a better understanding of LGBTQI2S health issues and inequalities would reduce barriers faced by this population when accessing health care ( Mayer et al., 2008).

The CIHR Institute of Gender and Health, in conjunction with Hacking Health, a volunteer-run organization that hosts design thinking events, created a 2-day KTE development initiative based on the theme of Innovative Thinking to Support LGBTQI2S Health and Wellness ( CIHR, 2019). Trainee awards funded the authors and other graduate students and researchers from across Canada to attend this event. We were teamed with local health-care providers, LGBTQI2S community members, and marketing experts to develop campaigns, services, or products to disseminate research evidence to improve health outcomes ( Michael Smith Foundation for Health Research, 2018).

DESIGN JAM PROCESS

The format of this event was a design jam, a modality within design thinking methodology very similar to a hackathon. Design thinking involves five steps:

1. Empathize: Consider the population experiencing the health challenge.

2. Define: Focus on the specific problem.

3. Ideate: Brainstorm a large quantity and broaddiversity of ideas in rapid sessions.

4. Prototype: Develop and plan potential solutions.

5. Test: Ask experts and end users for feedback onprototypes to refine and develop the KTE product. ( Canadian Institutes of Health Research, 2018; Institute for Design at Stanford, 2010).

Within the design thinking process, design jams are a modality created by the computer science and technology industries ( Institute for Design at Stanford, 2010). In a design jam, a focused problem is framed by the organizers and sponsors, and a solution product is developed by interdisciplinary teams ( To, 2016). Hackathons, similar technology-focused events, include a competitive element ( Kos, 2019; To, 2016). The design jam we attended included prizes that supported project development, but the competition was not the main focus. Our design jam team consisted of two graduate nursing students (BC, EZ), a graduate student in health policy (CS), a facilitator from a major LGBTQI2S advocacy group, and a graphic artist.

Empathizing and Defining

The design jam started with an orientation to design thinking and LGBTQI2S health. Defining the problem more clearly was informed by the orientation, our expertise in LGBTQI2S health, and communication with the LGBTQI2S community members present. Our group decided that creating a product for nurses to increase knowledge and skill in providing positive and safe care to LGBTQI2S patients would address the knowledge-to-action gap for LGBTQI2S care.

Ideating and Prototyping

Brainstorming activities facilitated the description of the population (nurses) who would use our product, what they would need and want, and possible means by which we could impart the necessary knowledge. Our group envisioned the diversity of nurses who might engage with a KTE product to learn more about providing safe care to the LGBTQI2S population. We designed an online educational tool kit with learning objectives, curated resources, virtual simulation games, self-assessments, and reflection opportunities. We pitched this product to the judges and stakeholders at the end of the design jam.

Testing

We tested our prototype on other health-care participants at the design jam. We engaged with members of the LGBTQI2S community represented in the product plan to ensure that the format and content were accurate.

Ongoing Project Development

Following the 2-day design jam, the authors partnered with academic nurse researchers specializing in developing virtual simulation games for nurses to continue the development of the project. A detailed description of the development process is discussed in a separate manuscript ( Luctkar-Flude et al., 2019). The goal of the project is to advance nurses' cultural humility in caring for LGBTQI2S people. The objectives of the tool kit are:

The tool kit consists of four case-based scenarios and four rapid-fire scenarios (see Table 1). After each scenario the learner is presented with strategies for debriefing and self-reflective questions.

In order to allow for continual professional development, the tool kit also included curated resources that can be used in practice (a selection of key resources is available in Table 2).

Critique of the Design Jam Process

Our team reflected on the facilitators and barriers contained in the design jam process. Our experience did confer a number of facilitators and benefits common to design jams and hackathons: informal learning and practice in collaboration, communication, delegation, fast-paced and time-sensitive project management, pitching, and teamwork, as well as networking opportunities ( Kos, 2019). All of the teams at the design jam produced workable outputs in the form of a knowledge translation product prototype and a plan for development ( To, 2016). The event was well run, including a welcoming environment, play and growth opportunities for the participants that embraced a beginner's view of design thinking and creative products, non-intimidating project development, and use of prize incentives ( Kos, 2019).

Author reflection on participation:

Part of the design process involves envisioning the typical end user of the product. Our experience was that the qualities and characteristics we were asked to describe risked stereotyping health providers. Relying on stereotypes would also risk creating products that did not fit the reality of the target audience.

Author reflection on participation:

The research and testing phases of the process relied on a small number of community members local to the event, in a major Canadian city with a well-developed LGBTQI2S community and a strong health and social services net. The experiences of community members there would not necessarily translate across Canada. Considerations for LGBTQI2S people in smaller cities and rural and remote areas should be considered. Similarly, there were few middle-aged, elderly, or Two-Spirit community members.

Case 1: Older gay male seeing health-care provider related to grief following the loss of his partner.

Communication-Coming out to provider
Heterosexism
Avoiding stereotypes

Case 2: Transgender youth with anxiety.

Unsupportive parents
Creating safe space
Identifying community resources

Case 3: Queer young female presenting for annual physical exam.

Not making assumptions
Heterosexism
Asking good questions
Obtaining a sexual health history including risk for pregnancy

Case 4: Transgender male presenting for health care for a problem not related to his gender.

Legal and social transition
Understanding language of transgender person

Rapid-Fire 1: Transgender person in clinic. Preferred name not used.

Using preferred names and pronouns

Rapid-Fire 2: Ill child with two female parents.

Not making assumptions

Rapid-Fire 3: Inappropriate disclosure of a patient's sexual orientation by a health-care practitioner.

Stigma
Confidentiality

Rapid-Fire 4: Patient presenting for STI testing.

Creating a safe space
Not making assumptions

Author reflection on participation:

A series of scaffolded design thinking events could focus attention on various processes in knowledge translation product development ( To, 2016): focused networking events such as unconferences ( Budd et al., 2015) for fulsome community engagement, design jam engagement for solution ideation, and finally a hackathon for product prototyping, then testing by community members. As this process occurs over a longer time period, reflection and adjustments can be made to correct oversights. Follow-up support on product development, project management, and dissemination is a step seen as crucial by design thinking experts ( To, 2016).

Author reflection on participation:

IMPLICATIONS AND USE OF DESIGN THINKING IN NURSING

Health care requires innovations that are appropriate, evidence based, and that represent creative and imaginative solutions to problems ( MacFadyen, 2014). Design thinking allows nurses to creatively develop solutions to improve practice as well as creative KTE products. Design thinking innovations involving community collaborations may be more acceptable and effective compared to more expert-driven methods ( Altman, Huang, & Breland, 2018). Device designing, improving interventions and patients' experiences, teaching, and developing community health programs have all been implemented in health care using design thinking ( Hendricks, Conrad, Douglas, & Mutsvangwa, 2018).

CONCLUSION

Design thinking methodology has potential to develop innovative and creative products for nursing practice and education. This article explored our experience at a design thinking event to address the knowledge-to-action gap in health care for LGBTQI2S people. Through the design jam process, we were able to create an online education tool kit (in process, www.soginursing.ca) that aims to provide nurses with the knowledge and skills to provide safe, quality care for LGBTQI2S people.

REFERENCES

1. Budd, A., Dinkel, H., Corpas, M., Fuller, J. C., Rubinat, L., Devos, D. P., & Wood, N. T. (2015). Ten simple rules for organizing an unconference. PLOS Computational Biology, 11(2), 1-8. http://dx.doi.org/10.1371/journal.pcbi.1003905

2. Lim, F., Johnson, M., & Eliason, M. (2015). A national survey of faculty knowledge, experience, and readiness for teaching lesbian, gay, bisexual, and transgender health in baccalaureate nursing programs. Nursing Education Perspectives. Nursing Education Perspectives, 36 (3) 144-152. https://doi.org/10.5480/14-1355

3. Mayer, K., Bradford, J., Makadon, H., Stall, R., Goldhammer, H., & Landers, S. (2008). Sexual and gender minority health: What we know and what needs to be done. American Journal of Public Health, 98(6), 989-995. https://doi.org/10.2105/AJPH.2007.127811

Disclosure

The authors have no relevant financial interest or affiliations with any commercial interests related to the subjects discussed within this article.

Funding

The authors received funding from the CIHR Institute of Gender and Health to support the attendance at the Design Jam event and the development of the educational tool.

Tables

Case 1: Older gay male seeing health-care provider related to grief following the loss of his partner.

Communication-Coming out to provider
Heterosexism
Avoiding stereotypes

Case 2: Transgender youth with anxiety.

Unsupportive parents
Creating safe space
Identifying community resources

Case 3: Queer young female presenting for annual physical exam.

Not making assumptions
Heterosexism
Asking good questions
Obtaining a sexual health history including risk for pregnancy

Case 4: Transgender male presenting for health care for a problem not related to his gender.

Legal and social transition
Understanding language of transgender person

Rapid-Fire 1: Transgender person in clinic. Preferred name not used.

Using preferred names and pronouns

Rapid-Fire 2: Ill child with two female parents.

Not making assumptions

Rapid-Fire 3: Inappropriate disclosure of a patient's sexual orientation by a health-care practitioner.

Stigma
Confidentiality

Rapid-Fire 4: Patient presenting for STI testing.

Creating a safe space
Not making assumptions

View in Context

# data[].attributes.attachment_link.title
Design Thinking in Nursing Education to Improve Care for Lesbian, Gay, Bisexual, Transgender, Queer, Intersex and Two-Spirit People

# data[].attributes.attachment_link.description
Design thinking methodology is a collaborative strategy with the potential to create innovations. Design thinking is being used increasingly in health care. Design jams are interdisciplinary events that bring together experts and community members to collaborate on creative solutions to health-care problems. This article describes the design thinking process and includes reflection on the authors' participation in a design jam event aimed to address the knowledge-to-action gap that exists in health care for (LGBTQI2S) people.

# data[].attributes.attachment_link.readable_text
Open Access Project Report

by 3 ,

Academic Editor: Paul B. Tchounwou

Received: 28 June 2021 / Revised: 9 August 2021 / Accepted: 28 August 2021 / Published: 2 September 2021

1. Introduction

Lived-experience research in mental health is research that highlights the experiences of people who live with mental health issues and is conducted by researchers with their own lived experience or in research teams that include people with lived experience [ 1]. This involvement is understood to result in benefits including producing better quality research and enhancing capacity [ 1, 2, 3]. The engagement of individuals with lived experience (also referred to as consumers or service users and used interchangeably in this paper) in mental health research is an emerging and growing arena [ 4, 5]. Whilst there has been policy and, in principle, support for such involvement and engagement from governments around the world, lived-experience researchers conducting academic research in collaboration with other consumers and carers remains uncommon [ 3, 6]. There has been a recent rapid growth in the involvement of service users in service transformation initiatives, in mental health research and in the deliberations regarding what constitutes evidence [ 7]. The value of lived experience for the mental health knowledge base as well as quality improvement of services has been gradually more appreciated, although not without its challenges [ 8, 9, 10].

Lived experience-produced research that focuses on identifying the factors relevant to daily life from a lived experience perspective has the potential to assist people to cope with their mental health challenges, however, this lived-experience research is not universally recognised and is often difficult to find and access. Furthermore, most lived experience-generated research is directed at improving understandings of health professionals or policy makers rather than for direct and practical use by mental health consumers in their daily lives [ 11].

Beginnings (International Institute for Mental Health Leadership Event)

As part of the International Institute for Mental Health Leadership (IIMHL) conference in 2017 in Sydney, Australia, a series of pre-conference events took place, one of which was a workshop centred on expanding lived experience-led research. The event included about 20 lived-experience scholars in academia as well as other academic scholars. As part of the breakout group process, our small subgroup of six focused on 'lived-experience expertise', the collective wisdom of individuals impacted by mental illness. One of our group members, a peer worker, alerted us to the fact that knowledge and experiences of individuals who experience mental illness/distress as articulated in research rarely find their way to those who are not involved in the research world. Some individuals view researchers as intimidating, "higher up" or living in a "different space". The findings of lived-experience research, however, were recognised as being useful to a wide range of people going about their personal recovery journeys [ 7, 11]. This occurs both through learning from the wisdom, strategies and successes of other consumers and from understanding that others share/have shared similar experiences (not being alone). Our brainstorming session led to the desire to continue to work on a project together post-workshop day-we initially named ourselves the "Enhancing Experiential Literacy" workgroup.

Our workgroup began to meet bimonthly on Saturday mornings. These meetings were conducted virtually, giving us the opportunity to think about ways to get the findings from lived-experience research out to a wide range of people who experience mental illness/distress. Based on our knowledge of evidence-based knowledge translation strategies [ 12, 13] and coupled with our own experiences, we acknowledged that a wide range of methods would be needed, including written lay-summaries, YouTube videos and other creative means. These strategies are available but are infrequently dispersed, often unwieldy, and unlikely to be accessed by many individuals with lived experience who may benefit from the lived experience knowledge contained within. Groups such as those who are hospitalised, homeless, incarcerated, in long-term institutional settings, technologically challenged, or live in rural or remote areas, are particularly unlikely to know about or access lived-experience research.

Getting lived-experience research out to these individuals is challenging. Using Facebook "likes" and other social media is popular with some groups, but inaccessible to others. We recognised the need to use knowledge from marketing, knowledge translation, public health and human-centred design to devise innovative and effective strategies to get lived-experience research into consumers hands-to increase their experiential literacy. This is likely to involve targeting a range of people who have contact with people who access or work within mental health services, such as peer workers, homelessness workers, shelters and drug and alcohol services, presenting information in formats that are easy for them to share and marketing the need to share that information with people who access services on a regular basis.

In order to explore the strategies to get lived-experience research out to individuals who experience mental illness/distress in the best way possible, we began by exploring the extent to which accessible resources currently exist for those with lived experience to access lived-experience research. We examined what the existing research tells us about the best ways to get information out to a wide range of consumers. We conducted informal interviews with individuals impacted by mental illness/distress in our network to help us understand more, by asking them about the best ways of presenting and marketing lived-experience research. Based on this knowledge, we subsequently developed a proposal and received funding for what we called the STELLER (Supporting the Translation into Everyday Life of Lived-Experience Research) Study, our collaboration of lived-experience researchers and other researchers from diverse organisations, including: the University of Sydney, the Black Dog Institute, the Consumer-Led Research Network, the Local Health Network, The MHS Network and Enlightened Consultants. The overall study aims were three-fold: (i) to translate lived-experience research findings into user-friendly attractive resources, (ii) to disseminate those resources to individuals experiencing mental illness/distress via peer workers and (iii) to evaluate their accessibility and usefulness using a non-randomised experimental study. This paper describes the first aim-to translate evidence-based research authored by individuals with lived experience into user-friendly resources. Aims (ii) and (iii) are described elsewhere [ 14]. Ethical approval was obtained from the Southeast Sydney Local Health District Human Research Ethics Committee.

2. Materials and Methods

A staged design thinking approach [ 15] was used to develop resources based on the evidence base of lived experience on what helps in the day-to-day lives of people experiencing mental health challenges. Design thinking is characterised by five iterative stages: empathise, define, ideate, prototype and test [ 16, 17]. The first phase-empathise-involved consultation with people with lived experience of mental health issues to understand their perspectives on lived-experience research. This consultation involved a snowball approach, drawing on our respective networks and connections. This was followed by phase 2-defining the problem-involving a conference workshop to engage in discussion about how to address the challenge in accessing lived-experience knowledge, refine the design aim, research questions and begin to generate ideas. This was followed by a selection of exemplar lived-experience literature leading to phase 4-prototype-focused on the design and development of resources to share the main messages from the selected research. Phase 5-test-is described elsewhere [ 14].

3. Results

3.1. Phase 1: Empathy: Developing an Understanding of End Users

Lived Experience and Peer Worker Consultations

The first phase in design thinking is to empathise-to find out about and understand the perspective of the people we were designing for. We had individuals with lived experience on our research team, but they were active in research, so we needed to expand our reach. Consequently, informal interviews were conducted with a group of 14 individuals with lived experience of mental health issues, some of whom were peer workers. The intent was to gain a sense of the extent to which they were aware of and accessed the lived-experience literature, clearly specifying that this is actual research rather than simply one person's story or narrative.

Respondents indicated that they drew on a wide range of sources for information to aide their recovery, including mental health professionals, peers, support/advocacy groups, specific mental health programs, various forms of literature, both hard copies and on the Internet, through to drawing on information gained from past experience. They highly valued hearing about the lived experiences of others and thought that lived-experience research could be helpful. They felt that it could help people feel less alone, normalise their experiences and allow them to learn from the experiences of others. Some said that it would help people reframe their experiences or think in different ways, especially non-medical ways. Others talked about finding hope and inspiration, clarifying their own experiences and understanding others who have different experiences.

Very few searched for experiential literature, and those who came across it did so by accident. Respondents revealed that they felt such experiential literature is important and valuable and validates experience, helps with self-advocacy hope and wellbeing and creates alternate and new knowledge. The interviews highlighted that people who experience mental illness have little opportunity to learn and integrate knowledge that is emerging from lived-experience research. The mental health lived-experience research base contributes a wealth of collective experiential wisdom to the mental health knowledge base. Like much of the academic literature, this knowledge is not easily accessible or known to people who are negotiating the day-to-day impacts of mental distress [ 14]. Knowledge translation of existing research that is driven or determined by lived experience can play a significant role in assisting people in navigating both their recovery and the systems of care that they may be accessing [ 18].

3.2. Phase 2: Defining the Problem: Stakeholder Roundtable

Phase 2 in the design thinking process consists of defining the problem or design aim. We identified the problem and these aims based on our collective experience and the feedback we received in phase 1. Our goal was to identify how we might render lived-experience research brilliantly visible, to effectively bring it to the person, make it effortless to find and translate it into user-friendly, attractive and exciting formats. In order to do this, the problem was discussed with clinicians, researchers and individuals with lived experience. This discussion was in the form of a workshop held at a conference focused on mental health systems. It was conceived as an opportunity to present our research aim-how to make lived-experience research more well-known and accessible. In addition to obtaining general support for our aim, we had the opportunity to begin to move into the ideation stage. Together, workshop participants created an inspirational space-the problem or opportunity that motivates people to search for solutions. Small working groups ideated about the potential ways in which lived-experience research could be communicated in lay-friendly ways. This included: (i) making tangible products, (2) talking, telling and explaining and (3) acting, enacting and playing. The two-hour session was facilitated by co-author H.G.

Bringing Together Lived-Experience Research-Selecting the Research Papers

Equipped with knowledge from the first two steps, the research team then moved to the selection of the research papers to be translated into user-friendly resources. We initially focused on peer-reviewed papers only, to demonstrate that we are using high-quality research (without assuming that only/all peer-reviewed research is actually high quality). The papers we searched for included research papers (excluding anecdotal, review, conceptual, or opinion pieces), from the peer-reviewed literature (excluding reports, books, etc.) and with lived-experience authorship. A further criterion was that the topic needed to have relevance and clear implications for our end-user group (i.e., not addressing clinicians or researchers). Our team acknowledged the abundance of valuable work that does not fit our established criteria, that might be of potential use to those with lived experience. However, we also wanted to balance this with the need to adhere to fairly strict parameters, as we were mindful of our limited resources.

Initially, it was assumed that there would be sufficient rich and appropriate papers within the peer-reviewed literature to enable creation of resources that would be most useful to our target group, but we found a lack of papers in the peer-reviewed literature that had obvious and direct implications for individuals with lived experience. It is perhaps unsurprising that many articles focus on implications for clinicians, system designers, policy makers and researchers, given journal readership. Consequently, the requirement that articles be peer-reviewed was revised, as peer review was deemed to be less important than topical relevance to people's recovery process. This allowed inclusion of material from doctoral dissertations (which have undergone review), reports or book chapters, if similar content could not be found in a peer-reviewed article. In the end, all selected articles had been peer-reviewed, thus we were confident in their quality.

The process of identifying lived-experience research relevant to individuals with lived experience was not without its challenges. We concentrated on articles that highlighted strategies that individuals could consider to help with their recovery journey. The major feature was that they contain practical tips and coping strategies or suggest different ways of thinking, that individuals with lived experience could use in their daily lives (without necessarily having access to particular services or resources). Once the articles were identified, each article was summarised according to a uniform format to identify the main messages in clear, lay language. These summaries were then presented to peer workers and individuals with lived experience via a survey (on SurveyMonkey and via Facebook) to rate each article in terms of usefulness and identify those that resonated in importance and implications for the recovery process. The questions asked of ten selected articles were: select the top three that interest you, article would be useful in day-to-day life/recovery and the potential for the results/main messages to be translated in an engaging, user-friendly manner. Based on the survey findings, six research studies were identified to develop into user-friendly resources ( Table 1). The lived-experience authors from each of the six papers were next emailed to obtain the extent of their involvement in the paper, ensure they were happy for the article to be used and to read and comment on the summary we developed.

3.3. Phase 3: Ideation: Developing Creative Ways to Bring Lived-Experience Research to Those Impacted by Mental Illness/Distress

Phase 3 of the design thinking process focuses on ideation, facilitating creative opportunities for a diverse range of people to brainstorm and generate a range of ideas that could more effectively disseminate lived-experience research knowledge. We began this process with the workshop outlined in phase 2, which allowed us to identify a preliminary range of creative solutions. This was followed by a day-long design thinking lab, where design students from a local university were recruited as part of their internship program, to assist with the design, prototype and production of resources alongside the research team and peer workers. The composition of the design thinking lab was as follows: five research team members, four design students, six individuals with lived experience, five peer workers and two members of the general public. Author H.G., with experience in facilitating design thinking workshops and in writing about design thinking methods, facilitated the day. Engaging the group in this form of prototype testing encourages intervention solutions that match the contexts of the people and organisations that will be using and implementing the resources [ 19]. The team also ensured that suggested solutions were informed by the evidence base in knowledge translation [ 13]. The purpose of ideation is to generate an abundance of strategies for solving problems by targeting multiple factors. The methods we used (e.g., creative matrix) were intended to purposefully encourage both pragmatic strategies and unanticipated, novel, "blue sky" solutions. Subsequent design thinking methods (e.g., importance-difficulty matrix) were used to prioritize solutions with the greatest feasibility and impact.

The design thinking lab began with an introduction to the research project, background and purpose. This was followed by presentation pitches of the topics identified in the review, topic selection and organisation of teams to address each topic. The 90 s pitch for each topic was created to help workshop participants choose what they wanted to focus on. Each research team member had previously prepared a 90 s pitch of approximately 150-180 words on the six topics. The pitch explained the topic, why the area might be useful to people with lived experience, the problem/question and why individuals might want to engage in this particular topic.

Six design teams were formed, focusing on an article of choice. Each design team responded to the 'How might we...' question, namely, how might we create accessible, exciting, interesting, engaging resources for people experiencing mental health issues to know and benefit from lived-experience research? Each small design team contained at least one design student and one member of the research team. Design students brought in their relevant transdisciplinary expertise (nursing, marketing and communications, creative design) and outside thinking strategies. The design groups were introduced to the basics of design thinking (including the associated parameters, that is, desirability, feasibility and viability), and considered practical design constraints such as time and money (an upper limit of $500AUS was given per resource) required to produce the resource. The groups began by spending about 30 min reading the summary pages of information on their chosen topic (plus the original resources available if they needed to refer to them) and identifying the preliminary take-home messages from these.

Personas are essential in the human-centred design process to aide designers in honing their designs specifically to the needs and demographics of those they are designing for [ 17]. The design teams created two personas of an end-user of their designs, based on researcher knowledge and lived experiences. Each design team then divergently brainstormed ideas, without limits, before sorting and converging design possibilities down to those that were most desirable to the persona, feasible to create and viable to disseminate. This was followed by a rapid prototyping session that involved a process of draw-make-test-break-redo. This included feeding back to the larger group at various stages for ideas and feedback. Our aim was to have a full prototype for each of the six topics by the end of the day. Each group then presented their prototype to the group and handed it back to the team with recommendations of what else was needed to carry it forward to implementation.

Following the design lab, the ideas and prototypes were taken up by the design students for further development. The design students devised and produced the resources with regular input and feedback on both content and format from the research team and peer workers. Some of the designs changed significantly as they were further iterated by design students, peer workers and the research team. Table 2 summarises the resources developed for each of the six topic areas.

The final phase in design thinking is to trial the resources, which were introduced and facilitated by peer workers to people in inpatient and community mental health settings. This component of the project is described in detail elsewhere [ 14]. The use of peer workers was considered both important and consistent with the evidence base, acknowledging that the insights of individuals who have experienced mental health issues are fundamental to recovery frameworks. Peer support workers facilitate egalitarian spaces for non-peer staff and individuals with lived experience to candidly discuss the lived experience of mental illness [ 25] and can promote recovery at an individual and organisational level [ 26]. In brief, we used a non-randomised experimental design in two local area mental health services, where five peer workers introduced the resources over an eight-week period of time. Relevance and helpfulness of the resources were assessed before, during and after using standardised measurement tools and in-depth interviews. Although the resources were designed as stand-alone resources, in this first instance, we wanted to trial them with the assistance of a peer worker, who could facilitate the process by introducing individuals with lived experience to the resources, explain them and follow-up regarding use.

The implementation phase provided the research team with vital feedback and insights to iterate the resources even further, prior to any further dissemination and implementation. Iterations covered clarity of language, product format, explanation of where the resources came from and how they link to each other. Additional iteration of the resources is currently being undertaken so they can be scaled to be downloaded and produced to be used by peer provider groups.

4. Discussion

The mental health lived-experience research base provides a plethora of collective experiential wisdom to our mental health knowledge. As is the case with a great deal of academic literature, this knowledge is not easily available, accessible or even known to people who are living day-to-day with the impacts of mental distress. Knowledge translation of existing research that is driven or determined by lived experience can play a significant role in assisting people to navigate both their recovery and the systems of care that they may be accessing. Most approaches to sharing knowledge are linear and fail to constantly test to see if they are actually relevant in the 'real world' [ 27]. In the process of connecting to lived-experience research and drawing upon design thinking, we were able to engage in an iterative process to continually refine the resources.

Design thinking is an iterative and solutions-based process that shares many features with an integrated knowledge translation approach [ 28]. They both involve engaging stakeholders or knowledge end-users as collaborators throughout the project, address needs or priorities identified by end-users, lead to the co-creation of solutions with end-users, can be messy, non-linear and challenging, can lead to unique insights and can improve relevance to end-users.

5. Conclusions

Co-design approaches can help researchers and stakeholders to create meaningful, acceptable and novel interventions that target the complex, multilevel factors influencing the communication of evidence-based knowledge. This research adds to the knowledge base on the use of participatory design thinking strategies to enhance knowledge translation to peer workers and others impacted by mental health issues. The resources developed in this co-creation process will deliver (via peer workers) main messages and evidence-based experiential wisdom in areas of personalized medicine, physical health, hope, meaningful activities and recovery.

# data[].attributes.attachment_link.title
Making Lived-Experience Research Accessible: A Design Thinking Approach to Co-Creating Knowledge Translation Resources Based on Evidence

# data[].attributes.attachment_link.description
Mental health lived-experience research illuminates the perspectives and experiences of people who live with mental illness. However, little is known about how useful people with lived experience of mental illness/distress might find lived-experience research, nor what the best formats are to bring it to their attention.

# data[].attributes.attachment_link.readable_text
Department of Clinical Pharmacy, College of Pharmacy, King Khalid University, Abha 61441, Saudi Arabia

Department of Pharmacognosy, College of Pharmacy, King Khalid University, Abha 61441, Saudi Arabia

Medical Supportive Services, Health Affairs, Asir Region, Ministry of Health, Abha 61441, Saudi Arabia

College of Pharmacy, King Khalid University, Abha 61441, Saudi Arabia

Author to whom correspondence should be addressed.

Academic Editors: Giovanni Improta and Paolo Gargiulo

Received: 18 May 2021 / Revised: 26 June 2021 / Accepted: 2 July 2021 / Published: 6 July 2021

Introduction: Design thinking, an innovative problem-solving approach, has gained wide popularity in healthcare disciplines. The aim of this work is to improve outpatients' experiences in hospital pharmacies in two hospitals in Asir region, Saudi Arabia. Methods: The design thinking approach, adopted from Stanford University's D-School, was used in this study. Results: Several problems were identified: lack of comfortable environment in the pharmacies' waiting area, lack of a queue management system, and workflow inefficiencies related to ordering and supplies of medicines. A prototype was proposed to overcome these challenges. Discussion and Conclusion: The design thinking approach helped in identifying end-user (patients visiting outpatient pharmacies) values and desires and provided an understanding of their struggles. It also proposed tailored solutions that could improve patients' experiences while using the services of the outpatient pharmacies.

1. Introduction

Design thinking is defined as a systematic approach that prioritizes deep empathy for users' desires, needs, and challenges to fully understand a problem, with the aim of developing more comprehensive and effective solutions [ 1, 2] Others define design thinking as a solution-based approach using an iterative process that seeks to understand the user, challenge assumptions, and envision problems differently. The goal of this process is to identify alternative strategies and solutions that are not apparent initially.

Design thinking includes the following stages: empathy, define, ideation, prototype, and testing [ 1, 3].

Empathy is the first and most important step, where a deep, diverse understanding of the user group's needs, values, and desires is established. It may also include observation, which requires engaging with the user group by observing the behaviors that occur in their environment with self-documentation, such as taking pictures or recording audios or videos. Define or interruption includes describing the point of view of and needs of user group. Ideation) involves generating solutions by combining empathy and observation; this usually requires a multidisciplinary team to address a complex issue. The prototype phase involves generating and testing multiple alternative solutions before finally selecting the best solution through an iterative process. testing involves obtaining feedback about the prototypes from the user group, transferring empathy, as well as refining the solutions [ 1, 4, 5].

Design thinking has been used by various disciplines, including healthcare [ 6]. The literature showed many examples of successful use of the design thinking framework for problem solving. For example, it was used to improve patient experiences [ 6, 7]; address the challenges heath care providers faced in a nursing home [ 2]; re-design a hospital pharmacy management system [ 8]; enhance psychological interventions [ 9]; improve therapeutic outcomes, medication information, and self-management for diabetic patients [ 10, 11]; and in health education [ 12].

An evaluation of hospital pharmacy practices is essential to assure that they meet the global standards and to prioritize practice advancements [ 13]. An evaluation of hospital pharmacy practice was first conducted in 1998 by the American Society of Health-System Pharmacists (ASHP) using a national survey [ 14, 15]. The survey examined the role of the pharmacist in distinct stages: prescribing, transcribing, dispensing, administrating, monitoring, and educating patients. In 2005, the International Pharmaceutical Federation (FIP) also highlighted the importance of evaluating hospital pharmacy services by conducting a multinational survey involving 192 countries. Most recently, in 2010, the European Association of Hospital Pharmacists (EAHP) implemented a survey concerned with the same area of practice [ 14, 16, 17].

In Saudi Arabia, the hospital pharmacy is the second largest pharmacy sector, employing 18% of the total pharmacy workforce [ 18]. The country's pharmacy sector is considered relatively advanced compared to neighboring countries. The role of the pharmacist in hospital settings includes medication verification, dispensing, management of medication supply and storage, drug information services, as well as supervision of pharmacy interns. Other more specialized practice areas include chemotherapy, parenteral nutrition, and sterile medication preparation. Pharmacists are also responsible for reporting adverse drug events (pharmacovigilance). The performance indicators used to evaluate hospital pharmacy services include patient satisfaction, patient waiting time, number of prescriptions filled, and number of dispensing errors [ 19].

Hospital pharmacy services in the capital city, Riyadh, were assessed previously by Alsultan and colleagues [ 14, 16, 17], while those in Jeddah were evaluated by Altayar and colleagues [ 15]. However, the previous studies evaluating hospital pharmacy services in Saudi Arabia utilized objective tools, surveys that did not take into consideration end-users' needs, desires, and values. They were also conducted in the largest and most developed cities in the country, so their findings might not be generalizable to other regions. Hence, the current study was conducted. The aim of this study was to evaluate patient experiences while visiting outpatient pharmacies in two hospitals in the Asir region of Saudi Arabia using design thinking approaches. This would lead to the development and testing of strategies to overcome all identified challenges.

2. Methodology

An empirical investigation was conducted to understand the context of hospital pharmacy services using the design thinking approach, adopted from Stanford University's D-School. A case study from two hospitals in Asir Region, Saudi Arabia was used in this study to identify the most important issues.

The study was conducted between January and March 2020, semi-structured interviews (preset open-ended questions, Table 1) were conducted with outpatients utilizing the outpatient pharmacy services in two hospitals in Asir. The interviews were conducted by a female researcher who had previously worked on qualitative studies examining health services in the country. Participants were recruited through a convenience sample. A total of 41 patients agreed to participate in the study ( Table 2). Written informed consent was obtained from the participants before the interviews were conducted. Participants were also informed that their identities would remain anonymous. Detailed topic guides were developed and piloted with other patients prior to the interviews. The interviews ranged from 15 to 20 min in length and were conducted in Arabic. The interviews were tape recorded, transcribed verbatim shortly after being completed, and then translated into English. To ensure the quality of the translation, four of the interview scripts were double checked by a second English-Arabic bilingual investigator at the King Khalid University (KKU), College of Pharmacy. Ethical approval for this study was granted by the Ethical Committee of Scientific Research at KKU (ECM 2020-221; HAPO-06-B-001). The methodology is structured in five main stages, as illustrated in Figure 1.

2.1. Stage I (Empathize)

At the beginning of the case studies, the researcher interacted with patients visiting the outpatient pharmacies in the two hospitals in Asir. Based on the participants' perspectives and pain points, a list of key problems was identified through the following steps.

Observation: Researchers shadowed the patients and observed their behavior during their visits to the outpatient pharmacy. Observations of patients in real-life contexts, along with the interviews, provided insights into the struggles patients encounter while utilizing outpatient pharmacy services.

Engaging: Several one-to-one semi-structured interviews were conducted in the pharmacies' waiting areas. To deeply engage with patients, it was essential to allow them to lead the conversation and to elaborate on their points in order to fulfill the exploration nature of the design thinking approach.

Watching and Listening: Observation and engagement were combined, for instance, by asking patients of their experiences with the waiting time and dispensing of their medications. Pictures of the hospital pharmacies and waiting areas were also taken. Through several storytelling activities, the key issues with the pharmaceutical services process and flow raised questions on ways to improve their experience.

2.2. Stage II (Define)

After collecting information regarding the core issues experienced by patients, the final key issues were defined using the two following steps.

Contextualize: The key themes that emerged from the interviews, as well as those related to the research questions asked during the empathize stage, were combined into a set of draft analytic frameworks. Drafts were tested with a small amount of interview data and the final themes were used to code the data. Two investigators coded the data and a total of four themes (problems) were identified and are listed below.

Synthesize: During this phase, the possible solutions were identified. This was completed by synthesizing and prioritizing the core needs. The end-user (patient) perspective was structured by combining three key components-user, need, and insight-into an actionable problem statement.

2.3. Stage III (Ideate)

The previous stage (define) directed the researchers' efforts to generating possible solutions to overcome the core problems identified through the following steps.

Create: Stakeholders' expertise (pharmacy, information technology, and interior design) was combined through a series of brainstorming sessions. This allowed the synergy of the stakeholders to reach promising solutions. Adding limitations, including inspiring resources and accepting misunderstanding facilitated the development of creative ideas on improving patient experiences in outpatient pharmacies.

Prototype: Prototypes were formed to support the ideation phase. Several development stages enabled the creation of new ideas. This stage used a few ideation techniques, including brainwriting, provocation, storyboard, and mind mapping.

Separate: Evaluation of ideas was discouraged to allow the generation of more inspiration and creativity.

2.4. Stage IV (Prototype)

The collected information directed the possible solutions and strategies. The core issues and required solutions were determined, then various prototypes were developed through the following steps.

Build: The model process was started, i.e., constructing something or coding basic solutions, as a useful start towards a prototype.

Variables: Each prototype was designed to tackle a certain problem when evaluated as to the ways it enhances the patient experience in the pharmacy.

Build from insights gained: The prototypes were clearly identified and the patient behavior towards the changes will be monitored. Feedback to be collected in the testing phase.

2.5. Stage V: Testing (Validation with Collaborators)

The last stage of design thinking involves testing the proposed solutions that address the core issues and needs.

Observe and Refine: All solutions to be implemented for the end-user (patients) will be observed and patient feedback received.

Create Experiences: The newly implemented features will be tested by noting the way that patients experience them, rather than by their evaluation

3. Result

3.1. Stage I (Empathize) and Stage II (Define)

Based on the participants' perspectives and pain points, a list of key problems ( Table 3) were identified as follows:

Lack of comfortable environment in the pharmacies' waiting areas (limited seating, lack of refreshments, poor lighting, lack of adjustment for disabled patients) in both hospitals 1 and 2 ( Figure 2 and Figure 3).

Lack of a queue management system in hospital 2, causing improper queue management ( Figure 3 C).

Lack of equity in waiting times between the two genders (more windows serving male patients than females) in hospital 1 ( Figure 2 C).

Workflow inefficiencies through ordering and supplies; medicines are slow to arrive between physicians and pharmacies, and patients are using hand-written prescriptions in hospital 2 (lack of e-prescriptions) ( Figure 3 D).

3.2. Stage III (IDEATE) and Stage IV (Prototype)

The design thinking approach helped to identify and prioritize patients' needs related to software and interior design solutions, which can be summarized as follows.

○

Introduce electronic-prescribing initiatives for physicians so they can communicate directly with the pharmacy in hospital 2

○

Establish a queue management system in hospital 2.

Interior design solutions:

○

Redesign the pharmacy waiting areas, increasing the number of seats allotted for female patients in both hospitals 1 and 2 ( Figure 4 A,B).

○

Introduce adaptive arrangements for disabled patients (i.e., wheelchair accessible space in waiting rooms, dispensing windows to be height appropriate for wheelchair users in both hospitals ( Figure 4 C,D).

○

Provide vending machines for refreshments in both hospitals ( Figure 5 A).

○

Establish children's corner in the waiting room in in both hospitals ( Figure 5 A).

○

Provide TV screens in waiting rooms in both hospitals ( Figure 5 B).

○

Brighten the dark waiting area in hospital 2 ( Figure 4 A,B).

○

Provide partitions at the end of the counter ( Figure 5 C).

3.3. Stage V: Testing (Validation with Collaborators)

Focusing on the two main outcomes, the design thinking approach was integrated. Software solutions, as well as interior design solutions, were tested to address the identified healthcare needs. The aim was to overcome the shortcomings in both the software system and the interior design of the pharmacies. Having identified the major issues experienced by the patients, the possible solutions were explored further. The proposed solution to the interior design shortcoming addresses the patients' needs regarding seating capacity, waiting times, and arrangements for disabled patients. The proposed solution to the software issues addresses the patients' needs for a queue management system and a system that monitors and tracks drug-flow within the hospital system. The final single prototype was a model design that overcomes all the shortcomings in both hospitals, as shown in Figure 4

4. Discussion

Design thinking is a framework used to solve end-user problems [ 2]. This approach provides in-depth understanding of the user's feelings, challenges and values. This innovative technique was incorporated here to overcome the shortcomings experienced by patients with the pharmaceutical services provided in two hospitals. Hence, it provided an opportunity for stakeholders to further improve the patients' experiences within the pharmacy (Carroll, 2016).

In this case study, pharmaceutical services were explored in two tertiary hospitals in the Asir region of Saudi Arabia. This paper describes the need to facilitate deep understanding of patients' "realities" in the context of the services they receive during their visits to the outpatient pharmacies, thus identifying the need for software and interior design solutions.

Previous studies evaluating hospital pharmacy services used more objective quantitative evaluation tools that do not consider end users' needs. Hence, these problem-solving frameworks might have not reached the same conclusions. For example, an understanding of cultural influences in interior design, atmosphere, and patient satisfaction is crucial. In this context, cultural barriers require certain layouts that separate the waiting areas for each gender. This also applies to the filling of prescriptions; females prefer to get their prescriptions dispensed from the "female only" window that is served by female pharmacists. Initially, there was only a single female service window compared to three male service windows, so the waiting time for females was longer.

Another issue was the lack of electronic systems in one of the two hospitals. This affected patients' satisfaction as it caused disorganization in filling prescriptions, long wait times, and more difficult communication between pharmacists and prescribers. For example, if a prescribing error is identified by the pharmacist, the inquiry cannot be sent directly to the physician. Hence, the patient is required to go back the physician, which was challenging for some patients, especially the elderly.

Special attention was directed to the needs of children by providing a play area for them, while the elderly were allotted seating priority. Disabled patients were provided with spaces in the seating area designated for wheelchair users, as well as with improved physical accessibility to the service window. Other general improvements to the waiting areas were achieved by brightening the room and providing vending machines, TV screens and a queue management system.

This work highlighted the gap between healthcare stakeholders, software engineering stakeholders, and the interior design community when identifying and addressing healthcare issues. The design thinking approach narrowed the gap by bringing together the perspectives of a multidisciplinary team. This research showed that patients are generally satisfied with their interactions with pharmacists, but their overall experience could be upgraded by improving the delivery of services (workflow) and the surrounding environment. Hence, the expertise of a healthcare team, software engineers, and interior designers contributed to the design thinking framework, facilitating an innovative design.

One limitation of this research was its narrow scope of only two single case studies, with each outpatient pharmacy having a different list of problems. Therefore, the findings might not be generalizable to other settings. The proposed prototypes were implemented only in virtual forms (computer-aided design), with a testing phase to be conducted in the future.

5. Conclusions

This research shows that design thinking can provide innovative solutions, technology-enabled pharmaceutical services delivery and interior design solutions to overcome issues affecting the patient experience within outpatient pharmacies.

Author Contributions

Data curation, D.A., R.A. and S.A.; Formal analysis, D.A. and K.A.; Funding acquisition, D.A.; Methodology, D.A. and A.A.; Project administration, S.A.A.; Supervision, D.A. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Ethical approval for this study was granted by the Ethical Committee of Scientific Research at King Khalid University (ECM 2020-221; HAPO-06-B-001).

Informed Consent Statement

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

1. Valentine, L.; Kroll, T.; Bruce, F.; Lim, C.; Mountain, R. Design Thinking for Social Innovation in Health Care. Des. J. 2017. [Google Scholar] [CrossRef]

2. Kim, S.H.; Myers, C.G.; Allen, L. Health care providers can use design thinking to improve patient experiences. Harv. Bus. Rev. 2017. [Google Scholar]

3. Chanpuypetch, W.; Kritchanchai, D. A design pattern for modelling and simulation in hospital pharmacy management. In Proceedings of the 31st European Conference on Modelling and Simulation, ECMS 2017, Budapest, Hungary, 23-26 May 2017. [Google Scholar]

Figure 2. Pharmacy waiting area in hospital 1. ( A- C): An overview of the pharmacy waiting area from different angles, showing a lack of comfortable environment. ( D): Number of windows serving male patients in comparison with female patients, indicating a lack of equity in waiting times between the two genders.

No.

Questions

1

How was your experience in the outpatient pharmacy today? Could you tell us more?

2

How were the services? Waiting area, waiting time etc.?

3

Can you elaborate?

4

How was the pharmacist?

5

Was there any particular thing that you liked/disliked?

6

How would your experience be better?

7

Out of ten, how would you rate the service today?

Hospitals and Participants Information

Hospital 1

Hospital 2

Age of participants

Reason for visiting

End-User Prospective

Key Problem

Hospital

Key Management

Patient 10 (M)
I suggest expanding the female waiting area as sometimes there are no seats inside the female designated area, so I have sat in the common seats with men around.
Patient 4 (M)
I wish there was a number waiting system
Patient 15(A)
The waiting area is overcrowded and full with patients sometimes.

Lack of queue management system, causing improper queue management and overcrowded waiting area.

Hospital 2

Waiting system to be implemented.

The waiting area to be expanded.

Patient 14 (A)
There is not enough space in the pharmacy waiting area, and there is only a single female dispensing windows.
Patient 2 (A)
There is only one window for females compared to three for males which makes the place packed with people.

Female patients experience of longer waiting times (lack of equity in waiting times between the two genders).

Hospital 1

Dispensing prescriptions for both male and female patients can be done from any window regardless of the gender.

Patient 2 (M)
There are not enough seats and the waiting area is gloomy and depressing.
Patient 15(A)
The waiting area is very small and cannot accommodate the large number of patients There are no refreshments or even drinking water. I would suggest adding TV screens and kids play corner to entertain patients while waiting for their prescriptions.
Patient 16(A)
There is no drinking water for me to take my medications while I'm here.
Patient 4 (M)
The chairs are wobbly and not in suitable condition, the room is dark.
Patient 3 (M)
It would be great to have free refreshments like tea or coffee.
Patient 12 (M)
There is not enough space for my son's wheelchair.

Lack of comfortable environment in the pharmacies waiting area (limited seating, lack of refreshments, poor lighting, lack of adjustment for disabled patients)

Hospital 1, 2

The pharmacy waiting areas to be redesigned with more seating allotted for female patients.

Arrangements for disabled patients (i.e., wheelchair accessible space) to be made available for wheelchair users to park while waiting for their prescription, dispensing windows to be height appropriate for wheelchair users.

Vending machines to be installed.

Dim lighting be replaced with brighter lighting.

TV screens to be implemented.

Children's play corner to be established.

Patient 15 (M)
I wish there was a direct communication between the physician and the pharmacist as well as number waiting system.
Patient 4 (M)
It would be great if electronic prescribing was implemented as it would reduce the medication errors, and order for prescription filling

Workflow inefficiencies through ordering and supply of medicines that is slow to arrive between physicians and pharmacists (lack of e-prescribing)
Hospital 2

A system that monitors and tracks drug-flow within a hospital system to be installed.

# data[].attributes.attachment_link.title
Using Design Thinking Principles to Improve Outpatients' Experiences in Hospital Pharmacies: A Case Study of Two Hospitals in Asir Region, Saudi Arabia

# data[].attributes.attachment_link.description
Introduction: Design thinking, an innovative problem-solving approach, has gained wide popularity in healthcare disciplines. The aim of this work is to improve outpatients' experiences in hospital pharmacies in two hospitals in Asir region, Saudi Arabia. Methods: The design thinking approach, adopted from Stanford University's D-School, was used in this study.

# data[].attributes.attachment_link.readable_text
Journal List

JMIR Mhealth Uhealth

v.10(3); 2022 Mar

PMC8968622

Monitoring Editor: Lorraine Buis

Paula Voorheis, MSc, 1, ² Albert Zhao, MSc,2, ³ Kerry Kuluski, PhD,1, ⁴ Quynh Pham, PhD,1, ⁵ Ted Scott, PhD,2, ⁶ Peter Sztur, BTech,² Nityan Khanna, BTech,² Mohamed Ibrahim, BTech,² and Jeremy Petch, PhD1, 2, 7, ⁸

¹ Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

² Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada

³ Master of eHealth Program, McMaster University, Hamilton, ON, Canada

⁴ Institute for Better Health, Trillium Health Partners, Toronto, ON, Canada

⁵ Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada

⁶ Faculty of Health Sciences, School of Nursing, McMaster University, Hamilton, ON, Canada

⁷ Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada

⁸ Population Health Research Institute, Hamilton, ON, Canada

Paula Voorheis, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada, Phone: 1 7058163180, Email: ac.otnorotu.liam@siehroov.aluap.

Abstract

Background

Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. Behavioral Design (BD) and Design Thinking (DT) have emerged as best practice approaches in their respective fields. Until now, little work has been done to examine how BD and DT can be integrated throughout the mHealth design process.

Objective

The aim of this scoping review was to map the evidence on how insights from BD and DT can be integrated to guide the design of mHealth interventions. The following questions were addressed: (1) what are the main characteristics of studies that integrate BD and DT during the mHealth design process? (2) what theories, models, and frameworks do design teams use during the mHealth design process? (3) what methods do design teams use to integrate BD and DT during the mHealth design process? and (4) what are key design challenges, implementation considerations, and future directions for integrating BD and DT during mHealth design?

Methods

This review followed the Joanna Briggs Institute reviewer manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Studies were identified from MEDLINE, PsycINFO, Embase, CINAHL, and JMIR by using search terms related to mHealth, BD, and DT. Included studies had to clearly describe their mHealth design process and how behavior change theories, models, frameworks, or techniques were incorporated. Two independent reviewers screened the studies for inclusion and completed the data extraction. A descriptive analysis was conducted.

Results

A total of 75 papers met the inclusion criteria. All studies were published between 2012 and 2021. Studies integrated BD and DT in notable ways, which can be referred to as “Behavioral Design Thinking.” Five steps were followed in Behavioral Design Thinking: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users’ needs and for its behavior change potential. The key challenges experienced during mHealth design included meaningfully engaging patient and public partners in the design process, translating evidence-based behavior change techniques into actual mHealth features, and planning for how to integrate the mHealth intervention into existing clinical systems.

Conclusions

Best practices from BD and DT can be integrated throughout the mHealth design process to ensure that mHealth interventions are purposefully developed to effectively engage users. Although this scoping review clarified how insights from BD and DT can be integrated during mHealth design, future research is needed to identify the most effective design approaches.

Keywords: behavior change, design thinking, digital health, health behavior, mobile application, mobile health, mobile phone, product design, scoping review, systems design, telemedicine, user-centered design

Introduction

Background

Digital health interventions are increasingly being designed to help people manage their health [1]. Many of these digital health interventions seek to facilitate behavior change and are often referred to as “digital behavior change interventions” (DBCIs) [2]. Among the wide range of DBCIs available, mobile health (mHealth) interventions have the potential to improve the reach and efficiency of health support owing to the widespread use of mobile phones [3,4]. Despite the potential impact of mHealth DBCIs, there is mixed evidence on whether they are effective at changing health behavior and improving health outcomes [3]. One concern is that patients and the public struggle to “effectively engage” with mHealth DBCIs [5,6]. Effective engagement with mHealth DBCIs has been defined as necessitating both microengagement with the mHealth interface itself (eg, logging into the app, entering data) and macroengagement with the behavior changes the mHealth intervention aims to support (eg, performing the exercises prescribed by the app) [6]. Therefore, for an mHealth DBCI to “effectively engage” users, it must be designed with engaging user-centered features to support microengagement and evidence-based behavior change techniques (BCTs) to support macroengagement.

To design effective behavior change content, there is evidence that mHealth interventions developed using the behavior change theory and BCTs are more likely to be effective than those without [7]. Behavioral scientists have been developing methods to systematically transition from diagnosing a behavioral problem to designing a behavior change intervention [8-11], which can be referred to as Behavioral Design (BD) [12]. Bondaronek and colleagues [13] provide several examples of how BD can be operationalized in publicly available physical activity apps. For instance, the app “Movesum” uses BCT 1.1 Goal Setting (Behavior) in the form of an easily adjustable step count goal [13,14]. BCT 1.1 Goal Setting (Behavior) is particularly useful when users struggle to plan for what they want to achieve or how they want to act [15]. Overall, BD can be operationalized in diverse ways but generally involves the following steps: (1) understanding the behavioral problem, (2) making a behavioral diagnosis for the target behavior using behavioral theories, models, and frameworks, (3) identifying relevant BCTs using taxonomies and classifications, (4) translating BCTs into intervention features, and (5) evaluating behavior change outcomes.

To design engaging user-centered mHealth features, there is evidence that mHealth interventions developed using person- and user-centered design processes are more likely to facilitate user engagement and improve intervention effectiveness [16,17]. Person- and user-centered design processes vary in terms of their operationalization but always put user needs at the forefront of design. Design Thinking (DT) is a common framework used in design science to guide creative user-centric designs for mHealth [18]. Design thinkers can use a range of approaches during the design process to ensure user-centeredness, such as directly involving users in app development or referring to Nielsen’s 10 usability heuristics [19]. The Nielsen Norman Group provides several detailed examples of how different usability principles can be implemented in application design [20,21]. Overall, DT can be executed in different ways but generally involves the following steps: (1) empathizing with the user, (2) defining the user requirements, (3) ideating functional concepts, (4) prototyping the user-centered solution, and (5) testing the solution to see if users’ needs are met. Despite knowledge about what constitutes “effective engagement” with mHealth DBCIs, a recent scoping review of DBCIs developed over the past 2 decades found that most design teams make limited use of BCTs and do not adequately describe the methods they employ to meet users’ needs [22]. This scoping review concluded that DBCI practitioners have little guidance on how to integrate best practices from behavioral science and design science, and a need exists to develop guidance to support them through this process [22]. This conclusion has been reiterated across the literature, with experts agreeing that methodological guidance is required to design effectively engaging DBCIs [23].

Experts have also begun to discuss similarities and differences between approaches used by design scientists and behavioral scientists [24-26]. For example, design scientists often rely on end users to ideate content based on their stated preferences, needs, and recommendations. They iteratively build interventions by using ongoing feedback, with a focus on producing creative solutions that users will enjoy (ie, ensuring the users are microengaged). In contrast, behavioral scientists focus on producing solutions that will nudge behavior change (ie, ensuring the users are macroengaged). Behavioral scientists often rely on theory- and evidence-based linkages to understand behavioral problems and select intervention content. They rigorously test solutions against their ability to effect behavior change and not necessarily how and whether the user is engaging with them. Nonetheless, both approaches emphasize the importance of understanding and diagnosing the problem at hand before proposing, designing, and implementing a solution. Both approaches also aim to ensure that the resulting solutions are designed purposefully to achieve user engagement. Amalgamating best practices from DT and BD may be mutually beneficial and help design teams develop more “effectively engaging” DBCIs [24-26]. There is currently a knowledge gap with respect to how best practices from DT and BD can be integrated to develop “effectively engaging” mHealth DBCIs. Specifically, little is known about how DT and BD can be blended throughout the mHealth DBCI design process to ensure that microengagement and macroengagement needs will be met.

Aims and Objectives

The aim of this scoping review was to identify and map how design teams have integrated best practices from BD and DT throughout the mHealth DBCI design process. By clarifying how BD and DT can be integrated, this review aimed to provide guidance on how mHealth DBCIs can be designed to more “effectively engage” patients and the public. This scoping review addressed the following questions: (1) what are the main characteristics of the studies that integrated BD and DT during the mHealth DBCI design process? (2) what theories, models, and frameworks did design teams use during the mHealth DBCI design process? (3) what methods did design teams use to integrate BD and DT during the mHealth DBCI design process? and (4) what are the key design challenges, implementation considerations, and future directions for the integration of BD and DT during mHealth DBCI design?

Methods

Study Design

The Joanna Briggs Institute reviewer’s manual was used to guide the conduct of this scoping review [27]. The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist (see Multimedia Appendix 1) [28]. A scoping review protocol was drafted internally among key stakeholders, including mHealth software architects, mHealth design team managers, an information specialist, and several researchers with experience in the topic.

Search Strategy

The search strategy was developed by the lead author in collaboration with an information specialist. Initial searches were conducted in journals highly relevant to our research topic (eg, Cochrane, JMIR journals) to identify suitable search terms. A series of initial searches in MEDLINE were completed to analyze the text words contained in the title and abstracts of retrieved papers and index terms used to describe the papers. A final list of search terms was compiled, and a search of the databases MEDLINE, PsycINFO, EMBASE, and CINAHL was completed on May 15, 2021. A handsearch of Journal of Medical Internet Research was also completed on May 15, 2021, as this was recognized as a journal highly relevant to the research topic. A gray literature search and a review of paper reference lists were not conducted. This decision was made to tighten the scope of the review around the research objectives, given time and resource constraints. Search terms combined the following topics: mHealth, behavior change, and design thinking. The full search strategy can be seen in Multimedia Appendix 2.

Eligibility Criteria

Included papers had to be primary studies, where a full-text paper described the design process of an mHealth DBCI. More specifically, included papers had to describe how BD and DT practices were integrated throughout the design of an mHealth DBCI that aimed to support behavior change in patients or the public. To meet these criteria, the papers had to clearly describe their mHealth DBCI design process, addressing at least 3 of the 5 design process steps suggested in DT (empathizing, defining, ideating, prototyping, and testing) [18]. In addition, papers had to clearly describe how behavior change theories, models, frameworks, or techniques were incorporated into the mHealth design process. Studies that only used behavior change insights to evaluate the mHealth DBCI after development were excluded. Furthermore, studies that described an mHealth intervention designed to provide a psychological treatment (eg, cognitive behavioral therapy) without describing how their design process utilized behavior change theories, models, and frameworks to support “effective engagement” were also excluded. No limitations were put on the year of publication; however, only papers published in English were included. To be eligible, the intervention must have been an mHealth intervention, defined by the World Health Organization as “health care and public health practice supported by mobile devices such as mobile phones, tablets, patient monitoring devices, and other wireless devices” [4]. The intervention must also have been designed for use by patients or the public. If the intervention was designed only for use by health care professionals, it was excluded.

Evidence Selection

Studies from the database searches were handled using Covidence (Veritas Health Innovation Ltd) reference management software. Papers were deduplicated and imported for screening using Covidence. A 2-level screening was performed after duplicate removal. During level 1 screening, titles and abstracts were screened using the eligibility criteria. Publications with title or abstract not meeting the eligibility criteria were excluded. During level 2 screening, full-text papers that passed level 1 were screened. Studies that met the eligibility criteria were included for full data extraction. Consistent with PRISMA-ScR, reasons for exclusion were recorded at the full-text level [28]. Prior to the selection of sources, 2 reviewers completed a pilot screening of 50 titles and abstracts to assess the reliability of the eligibility criteria. Interrater agreement for study inclusion was calculated using percentage agreement. If agreement was lower than 80%, the eligibility criteria would be clarified and another pilot test would occur. All interrater discrepancies during level 1 and 2 evidence selection were resolved between the 2 reviewers upon discussion. The 2 reviewers screened all titles, abstracts, and full-text papers for inclusion.

Data Extraction

The reviewers extracted data from the eligible papers by using 2 data extraction forms. The first data extraction form elicited the main study characteristics, including lead author, year of publication, journal of publication, country of origin, study design, study purpose, target user population, the health issue, the target health behavior, mHealth DBCI summary, mHealth DBCI design process duration, and the members of the mHealth DBCI design team. The second data extraction form elicited details about how BD and DT were integrated throughout the design process, which included extracting types of theories, models, and frameworks used; the approaches design teams used to integrate best practices from BD with DT over the course of the mHealth DBCI design process, key challenges in the mHealth DBCI design process, key implementation considerations for mHealth DBCIs, and future considerations for the mHealth DBCI design. The data extraction forms were drafted, revised, and agreed upon by the 2 reviewers after an iterative process of implementing the extraction forms on a sample of papers.

Analysis and Presentation of Results

A descriptive analysis of the included papers was conducted to meet the objectives of the scoping review. Narrative descriptions, frequency calculations, and visual diagrams were utilized to communicate the results.

Results

Evidence Selection

A total of 1912 papers were identified from the searches. After 651 duplicate studies were removed, 1252 papers were screened based on their titles and abstracts, with 255 full-text papers meeting the eligibility criteria. After full-text review, 75 papers fulfilled the eligibility criteria and were included in the final review. Figure 1 shows the PRISMA-ScR flow diagram illustrating the paper selection process [28].

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) flow diagram of the study selection process.

Main Characteristics of the Included Papers

All 75 primary studies were published between 2012 and 2021, with a surge from 2018 onward (51/75, 68%). The Journal of Medical Internet Research and its sister journals accounted for almost 55% (41/75) of the papers included. The United States (14/75, 19%), the United Kingdom (13/75, 17%), Australia (10/75, 13%), and Holland (9/75, 12%) were the most common study locations. The target population of the mHealth DBCIs varied, with the most common being patients with cardiovascular issues (7/75, 9%), patients with diabetes (5/75, 7%), adults with overweight and obesity (5/75, 7%), adults who smoke (5/75, 7%), adults with poor physical activity levels (5/75, 7%), and cancer survivors (5/75, 7%). The target health behaviors addressed also varied, with the most prominent being improved physical activity (18/75, 24%), improved diet (17/75, 23%), disease self-management (12/75, 16%), preventative health behaviors (6/75, 8%), adherence to medication (5/75, 7%), adherence to rehabilitation programming (5/75, 7%), and smoking cessation (5/75, 7%). mHealth DBCI design teams were multidisciplinary in their membership. Members included researchers, patients, caregivers, community partners, clinicians, technology developers, and experts in behavior change, health psychology, health communications, health promotion, and health informatics. Topic experts were also relied on depending on the type of intervention (eg, diabetes educators). Software engineers, computer scientists, videographers, product designers, and graphic designers were brought to assist with the development of the mHealth intervention, although it was usually unclear when they were included. The design process duration was usually not reported. Out of the 14 interventions that clearly reported design process duration, there was a large variation in timespan, ranging from less than 3 months to upwards of 4 years. Multimedia Appendix 3 provides an overview of the main study characteristics, and Multimedia Appendix 4 provides a full list of the 75 studies included.

Theories, Models, and Frameworks Used During Design

Studies used a variety of theories, models, and frameworks in their mHealth DBCI design process. Theories, models, and frameworks were most often used to (1) guide the design process itself, (2) conceptualize the behavior change problem, (3) identify relevant BCTs, and (4) evaluate ideas for their applicability, feasibility, or potential effectiveness. Figure 2 summarizes the types of theories, models, and frameworks used in the mHealth DBCI design process, and Multimedia Appendix 5 provides a detailed breakdown.

Theories, models, and frameworks used in mobile health digital behavior change intervention design. ABACUS: App Behavior Change Scale; APEASE: Acceptability, Practicability, Effectiveness, Affordability, Side-effects, and Equity; BCT: behavior change technique; COM-B: capability, opportunity, motivation-behavior; DBCI: digital behavior change intervention; IDEAS: Integrate, Design, Assess, and Share; mHealth: mobile health; MoSCoW: must-have, should-have, could-have, and won't-have, or will not have right now; PRECEDE: Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED: Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development.

Methods Used to Integrate BD and DT During the mHealth DBCI Design Process

Regardless of the theories, models, and frameworks teams used to design their mHealth DBCIs, they integrated best practices from BD and DT in notable ways. We refer to the mixing of BD and DT throughout the mHealth DBCI design process as the “Behavioral Design Thinking Approach” (see Figure 3). The Behavioral Design Thinking Approach presents a new method of designing mHealth DBCIs, which is the result of merging together best practices from BD and DT. Multimedia Appendix 6 provides further detail on the Behavioral Design Thinking Approach along with several specific examples on how BD and DT can be integrated.

The behavioral design thinking approach [5,6]. BCT: behavior change technique; DBCI: digital behavior change intervention; mHealth: mobile health; UI: user interface; UX: user experience.

Generally, 5 steps are followed in the Behavioral Design Thinking Approach: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users’ needs and for its behavior change potential. Across these steps, studies integrated DT and BD in different ways, often “driving” their mixing in a certain direction. The 3 ways DT and BD were mixed included (1) DT and BD approaches were equally weighted (notation = DT+BD), (2) DT drove the approach, with concepts from BD supplementing the DT approaches (notation = DT→BD), or (3) BD drove the approach, with concepts from DT supplementing the BD approaches (notation = BD→DT). Overall, studies tended to “empathize” by blending DT and BD equally (DT+BD), “define” by blending DT and BD equally (DT+BD), “ideate” by driving the process by BD (BD→DT), “prototype” by driving the process by DT (DT→ BD), and “test” by driving the process by DT (DT→BD). These approaches are summarized in the text below.

Behavioral Design Thinking Approach

Behavioral Design Thinking Step 1: Empathize With Users and Their Behavior Change Needs

Most studies described how they empathized with users while simultaneously conceptualizing the behavior change problem (66/75, 88%), blending concepts from DT and BD harmoniously (notation = DT+BD). To empathize with the users who would perform a target behavior, studies analyzed users’ experiences, perceptions, beliefs, needs, and preferences with their health issues, health behaviors, health interventions, and mobile app usage. To understand the behaviors that the users will perform, studies examined applicable target health behaviors, behavioral determinants, BCTs, and behavioral theories, models, and frameworks. Finally, to understand the context that the users perform the behavior in, studies assessed current practices and programs, relevant personal, social, environmental, and structural factors, and pertinent clinical, usage, and behavioral aims. Studies used primary research (eg, interviews, focus groups, surveys, creative workshops) and secondary research (eg, secondary data analyses, literature reviews, reviews of other interventions, guidelines, practices) to empathize. Studies not only involved patient and public end users during this step (48/75, 64%), but also involved health care practitioners, community partners, behavioral experts, design scientists, and technology developers. To directly involve patients and the public, studies used a variety of tools such as interview guides informed by behavior change models and visual presentations of apps to stimulate collaborative discussion.

Behavioral Design Thinking Step 2: Define User and Behavior Change Requirements

Studies tended to analyze the empathy results in order to define system requirements that would meet both users’ needs and behavior change needs (notation = DT+BD). Studies tended to define user-centered requirements by sorting users’ stated preferences into key themes to be addressed. Studies tended to define the behavior change requirements by using the empathy results to formulate a “behavioral diagnosis,” which outlines the behavioral determinants that need to be addressed. When studies organized the requirements into an amalgamated format, DT or BD usually drove the organization. For example, some studies used tables organized by the relevant behavioral determinants, whereby they would then list corresponding user quotes and resultant requirements alongside the behavioral determinants. Other studies opted to use the requirements to create holistic user personas and scenarios. Regardless of the approach to amalgamate and make sense of the requirements, the requirement lists were often lengthy and needed to be refined. Refining the requirements involved (1) identifying the requirements that met the project scope and objectives, (2) using feasibility criteria to refine and select certain requirements, (3) consulting experts and patient and public users to prioritize the requirements, and (4) ranking the most important requirements according to their likelihood to elicit behavior change, alignment with current practices, adaptability to a digital interface, acceptability to users, and compatibility with data collection needs.

Behavioral Design Thinking Step 3: Ideate User-Centered Features and Behavior Change Content

Most studies described how they translated requirements into design content and features by using ideation methods (67/75, 89%). Generally, studies drove their ideation methods using BD (notation = BD→DT). For instance, several studies relied only on BD to ideate the relevant behavior change content (19/67, 28%). These studies used researcher-led “behavioral mapping” to match relevant behavior change determinants with BCTs by using evidence-based taxonomies and tested linkages. It was often unclear, however, exactly how these studies translated the identified BCTs into content for the mobile app. Other studies attempted to completement BD approaches with DT methods during their ideation (48/67, 72%). In these cases, studies would creatively ideate design content and features that would support behavior change while also meeting users’ needs and preferences. These studies relied on different techniques such as design team brainstorming, expert stakeholder panels, technology partner consultation, and end user co-design efforts. Design teams tended to brainstorm how the behavior change insights could be integrated with users’ preferences, existing programming, norms in mHealth design, and clinical management needs. Stakeholder panels would discuss all the requirements and consider the most appropriate strategies (dosage, delivery, organization, and personalization). Technology partners would specify how requirements could be operationalized within an mHealth app. Patient and public partners would contribute to co-design sessions to create and reflect on ideas, content, and designs. Approximately 34% (23/75) of the studies appeared to directly involve patient and public users during this step to ensure that the ideated content met their unique needs.

Behavioral Design Thinking Step 4: Prototype a Solution That Is User-Centered and Supports Behavior Change

Many studies did not clearly describe the prototyping methods they used to translate ideated content and features into functional solutions (28/75, 36%). Studies that described their prototyping approach tended to drive their prototyping by using DT (notation = DT→BD). For instance, a large proportion of studies only used prototyping methods, tools, and aids traditional to DT (37/47, 79%). DT prototyping methods usually involved iterative prototyping, feedback, and refinement, often utilizing sprint, scrum, or agile methods. DT prototyping tools included wireframing, paper prototyping, application flowcharts, and use case scenarios. DT prototyping aids included tools such as Nielsen’s usability heuristics [19] and the Eight Golden Rules [29]. Despite reliance on DT for prototyping, several studies supplemented DT methods with BD considerations to ensure that behavior change content would be operationalized within the prototyped solution (10/47, 21%). For example, to supplement DT prototyping methods, some studies utilized BCT codevelopment to ensure that BCTs were not lost in translation during technological development. To supplement DT prototyping tools, some studies elected to make BCT flowcharts to clarify how the BCTs would be interacted with. To supplement DT prototyping aids, some studies also used the Behavioral Intervention Technology Model [30] or the Persuasive Systems Design Framework [31], which were behaviorally informed models used to guide feature selection. Among the studies that clearly described their prototyping methods, just over half appeared to directly involve patient and public end users in their prototyping process (26/47, 55%).

Behavioral Design Thinking Step 5: Test Solution Against User Needs and for Its Behavior Change Potential

A large proportion of studies did not clearly describe how they tested their mHealth DBCI solution within the design process (18/75, 24%). Studies that described their testing methods tended to drive their testing by methods traditional to DT (notation = DT→BD). For instance, a considerable number of studies relied exclusively on DT evaluation approaches such as heuristic evaluation, usability testing, expert evaluation, and pilot testing to evaluate the solutions against users’ needs (33/57, 58%). Despite reliance on DT for testing, some studies supplemented these testing methods with BD considerations to evaluate the solution for its behavior change potential (24/57, 42%). For instance, in addition to traditional DT heuristic evaluation, some studies conducted a BCT evaluation to assess the final solution for the presence of known BCTs. In addition to traditional DT usability testing, some studies “tagged” BCT components within the app to follow how users engaged, accepted, and perceived the intended BCTs. Some studies also conducted posttest user interviews directed by interview guides based on constructs of a behavioral model. In addition to traditional DT expert evaluations, some studies brought on behavioral science experts to assess the extent to which the intervention content had fidelity to the intended BCTs. Experts also could assess the quality of the mHealth DBCI by using the App Behavior Change Scale [32]. If pilot tests were conducted, specific behavior change outcomes could be evaluated in addition to traditional usage metrics. Metrics included change in knowledge, change in intentions, state of behavior change, user experience with BCTs, perceived potential of BCTs, and user engagement with BCTs.

Design Challenges, Implementation Considerations, and Future Directions

In addition to describing their design process, studies also identified key implementation considerations, design challenges, and future directions for mHealth DBCI design. These have been summarized in Textbox 1. Multimedia Appendix 7 expands on these results in further detail.

Key design challenges, implementation considerations, and future directions.

Design challenges

Design process can be time and resource consuming, especially when the design approach is unclear.

Recruiting and involving representative end users and key stakeholders can be difficult.

Conflicting ideas can result from integrating behavioral theory, user needs, and stakeholder views.

The translation of behavior change techniques into actual mobile health (mHealth) digital behavior change intervention (DBCI) features and content can be confusing.

The design process is time limited and usually may not allow for comprehensive evaluation.

Integrating the mHealth DBCI into clinical practice can be complex.

mHealth platforms come with their own technical challenges and limiting factors.

Implementation considerations

Evaluate potential implementation barriers and facilitators during the “testing” step.

Facilitate early stakeholder buy-in.

Plan for the integration of the mHealth DBCI into clinical systems.

Use feasibility criteria throughout the entire design process.

Use an implementation plan (marketing, dissemination, onboarding, adoption, usage, sustainability).

Future directions

Guidance on the design process for mHealth DBCIs.

Guidance on how to operationalize behavioral change techniques within mHealth DBCIs in a user-friendly way.

Guidance on how to meaningfully involve users and stakeholders in the design process.

Guidance on how to tailor mHealth DBCIs to meet behavioral, personal, and clinical needs.

Discussion

Primary Findings

This paper presents a systematic scoping review of 75 papers that described their design process for developing an mHealth DBCI. This review addressed a gap in the literature about how mHealth interventions can be designed to integrate practices from DT and BD. Although the number of mHealth DBCIs seems to be growing, the results highlight substantial heterogeneity in the methods studies used to design them. This scoping review aimed to clarify, map, and synthesize the different methods that can be used to design mHealth DBCIs. A new consolidated approach to mHealth DBCI design is presented—the Behavioral Design Thinking Approach. A large proportion of mHealth DBCIs were designed to facilitate diet- and exercise-related behavior change. Similar findings were noted in a scoping review on DBCI design over the past 2 decades [22]. Nonetheless, the results point to a lack of clarity about how mHealth DBCI content and features were ideated and operationalized. Several studies in this review struggled to translate BCTs into user-friendly mHealth features. Studies also ended up trying to fit several BCTs within the mHealth app, increasing app complexity. To simplify the user experience, several studies noted that personalization may be an important future direction for the field. Tailoring mHealth content to users’ unique behavioral, clinical, and personal needs can help facilitate the delivery of features to support effective engagement [33]. Ensuring that design teams use appropriate questions and metrics to inform personalization will be essential. This further exemplifies the need for the meaningful involvement of patients and the public in mHealth DBCI design. Regarding the meaningful involvement of patients and the public in the mHealth DBCI design process, this review found that patients and the public were most often involved as participants (eg, in user interviews, surveys, testing) rather than as partners on the design team itself. Patients and the public appeared to have minimal direct input on design decision-making throughout the mHealth DBCI design process. Growing calls are being made to prioritize the unique perspectives of patients and the public during design [16,17]. Involving end users in the design process has been suggested to increase the effectiveness, relevance, and appropriateness of mHealth DBCIs [16,17]. Nonetheless, design teams appear to have little guidance on how to meaningfully engage patient and public end users in the design process itself [34-37].

Theories, Models, and Frameworks Used During Design

Regarding the theories, models, and frameworks that studies used during mHealth DBCI design, design teams appeared to face a vast array of these “tools” from different fields, created for different purposes. Although studies reported benefiting from the structure that these tools offered, a best practice approach for integrating DT and BD insights to develop “effectively engaging” mHealth DBCIs did not appear to exist. This scoping review offers a classification of the types of theories, models, and frameworks that teams can use during mHealth DBCI design and offers a consolidated approach for guiding the design process in general.

Methods Used to Integrate BD and DT During the mHealth DBCI Design Process

During the integration of DT and BD throughout the mHealth DBCI design, key similarities and differences between the 2 approaches were observed. During the empathizing step, users’ preferences often differed from what was needed to change their behavior. Balancing user-stated preferences with evidence-based behavior change strategies and mHealth platform requirements was a challenge for teams. During the defining step, it was clear that blending DT and BD perspectives resulted in lengthy and complex requirements. Involving users and topic experts to refine these requirements appeared to be a helpful strategy. During ideation, BD relied on evidence-based linkages to ideate content, whereas DT relied on iterative brainstorming and collaborative creation. Studies reconciled these different approaches by starting with BD to develop a list of relevant BCTs and then using DT to creatively integrate BCTs and other requirements into the mHealth DBCI. The prototyping step was the least well described among studies but usually relied on approaches traditional to DT. Studies that brought on their technology partner only at the prototyping stage often had to add additional methods to ensure that BCTs were operationalized as imagined. The testing step was also dominated by DT, as it was challenging to meaningfully test for behavior change outcomes within the time constraints imposed by the design process. Nonetheless, several measures were used by studies to assess the solution’s potential to support behavior change.

Design Challenges, Implementation Considerations, and Future Directions

Finally, regarding key implementation considerations, design challenges, and future directions, several cross-cutting themes were identified. In addition to ensuring that BD and DT insights were incorporated into the mHealth DBCI design, many teams noted that planning for implementation of the mHealth DBCI was just as important for ensuring success. Using insights from BD and DT may also be relevant in designing an implementation plan for the mHealth DBCI. Implementation planning appears to be particularly complex in this context, as many mHealth DBCIs need to be integrated with existing clinical systems and norms. Regardless of how evidence-based and user-friendly the mHealth DBCI may be, if stakeholder buy-in, system adaptation, and clinical sustainability issues are not taken into consideration, the mHealth DBCIs are likely to fail. Future research must address the lack of guidance design teams have in developing and implementing effective mHealth DBCIs. Particularly, design teams have little guidance on the “tools” (ie, theories, models, and frameworks) they can use, how to meaningfully involve patient and public end users, and how to tailor mHealth DBCI content to meet behavioral, personal, and clinical needs.

Limitations

The most significant limitation of this scoping review was that the inclusion criteria necessitated that each included paper had to be a full-text primary study that clearly described the mHealth design process. This decision was made to tighten the scope of this review and owing to limited time and resources. Although the systematic database search was not supplemented by a gray literature search or a review of reference lists, the 75 included papers offer a diverse range of insights that meet the research objectives while addressing a prominent gap in the literature. It should be noted that the original scope of this review included a database, reference list, and gray literature search to identify available design frameworks that could be used by design teams to integrate BD and DT insights during mHealth development. Presenting these results alongside the data extracted from 75 primary studies was beyond the scope of this paper. The results of this scoping review are only inclusive of studies written in English; therefore, findings may not be generalizable internationally.

Implications

The Behavioral Design Thinking Approach offers a way forward in the field of mHealth DBCI design. mHealth design teams may consider using the insights presented in the Behavioral Design Thinking Approach to inform their future work. mHealth design teams may also find it helpful to reflect on the different types of theories, models, and frameworks they can use during the design process, as well as the key challenges they may face along the way. The findings presented in this review may also be relevant to researchers in the fields of behavioral science and design science who are interested in interdisciplinary collaboration. It is reasonable to assume that breaking down silos between these 2 fields may improve the success of mHealth DBCIs. Overall, the main benefits of this research include (1) clarifying what approaches can be used to design mHealth DBCIs, (2) promoting transparency in the choices that studies must make during the mHealth DBCI design process, and (3) enabling future researchers to test what design approaches are the most effective to develop “effectively engaging” mHealth DBCIs.

Conclusion

The number of mHealth interventions designed to support behavior change is increasing. Integrating best practices from BD and DT may allow for the development of mHealth DBCIs that more effectively engage patients and the public. This paper has helped identify and conceptualize the methods that can be used to integrate BD and DT throughout the mHealth DBCI design process. Raising the standard of mHealth design methods will be essential to ensure confidence in the impact mHealth interventions can have on improving health outcomes. If more mHealth DBCIs are purposefully designed to address effective engagement, it is likely that patients, the public, and health care practitioners will be more confident in adopting mHealth. Given the predicted increase in the demand of mHealth interventions, the time is now to ensure mHealth design methods are appropriately suited to increase effective engagement.

Acknowledgments

This review was funded from the core operating budget of the Centre for Data Science and Digital Health (CREATE) at Hamilton Health Sciences. The most responsible author is the Director of CREATE. The larger organization of Hamilton Health Sciences had no role in the design, conduct, or content of the review.

Abbreviations

BCT | behavior change technique
BD | Behavioral Design
DBCI | digital behavior change intervention
DT | Design Thinking
mHealth | mobile health
PRISMA-ScR | Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews

Multimedia Appendix 1

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist.

Multimedia Appendix 2

Search strategy.

Multimedia Appendix 3

Main characteristics of the studies.

Multimedia Appendix 4

Summary of all the studies included.

Multimedia Appendix 5

Theories, models, and frameworks used in design.

Multimedia Appendix 6

The behavioral design thinking approach.

Multimedia Appendix 7

Design challenges, implementation considerations, and future directions.

Footnotes

Contributed by

Authors' Contributions: PV was responsible for leading all stages of the scoping review. KK, QP, and JP reviewed the scoping review protocol to ensure its research rigor. TS, PS, NK, and MI ensured that the scoping review objectives and protocol were appropriate and applicable to the field. PV and AZ completed the study screening and data extraction. PV was responsible for writing the scoping review draft. All authors were involved in reviewing and preparing the manuscript for publication.

Conflicts of Interest: None declared.

References

1. Murray Elizabeth, Hekler Eric B, Andersson Gerhard, Collins Linda M, Doherty Aiden, Hollis Chris, Rivera Daniel E, West Robert, Wyatt Jeremy C. Evaluating Digital Health Interventions: Key Questions and Approaches. Am J Prev Med. 2016 Nov;51(5):843–851. doi: 10.1016/j.amepre.2016.06.008. http://europepmc.org/abstract/MED/27745684 .S0749-3797(16)30229-X [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017 Jun;7(2):254–267. doi: 10.1007/s13142-016-0453-1. http://europepmc.org/abstract/MED/27966189 .10.1007/s13142-016-0453-1 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The Impact of mHealth Interventions: Systematic Review of Systematic Reviews. JMIR Mhealth Uhealth. 2018 Jan 17;6(1):e23. doi: 10.2196/mhealth.8873. https://mhealth.jmir.org/2018/1/e23/ v6i1e23 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

4. Kay M, Santos J, Takane M. Global Observatory for eHealth Series, vol 3. Geneva, Switzerland: World Health Organization; 2011. [2021-05-15]. mHealth: new horizons for health through mobile technologies based on the findings of the second global survey on eHealth. http://www.who.int/goe/publications/goe_mhealth_web.pdf . [Google Scholar]

6. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, Merchant GC, Naughton F, Blandford A. Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. Am J Prev Med. 2016 Nov;51(5):833–842. doi: 10.1016/j.amepre.2016.06.015.S0749-3797(16)30243-4 [PubMed] [CrossRef] [Google Scholar]

7. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010 Feb 17;12(1):e4. doi: 10.2196/jmir.1376. https://www.jmir.org/2010/1/e4/ v12i1e4 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Michie S. Designing and implementing behaviour change interventions to improve population health. J Health Serv Res Policy. 2008 Oct;13 Suppl 3:64–9. doi: 10.1258/jhsrp.2008.008014.13/suppl_3/64 [PubMed] [CrossRef] [Google Scholar]

11. Cash PJ, Hartlev CG, Durazo CB. Behavioural design: A process for integrating behaviour change and design. Design Studies. 2017 Jan;48:96–128. doi: 10.1016/j.destud.2016.10.001. [CrossRef] [Google Scholar]

14. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013 Aug;46(1):81–95. doi: 10.1007/s12160-013-9486-6. [PubMed] [CrossRef] [Google Scholar]

15. Carey Rachel N, Connell Lauren E, Johnston Marie, Rothman Alexander J, de Bruin Marijn, Kelly Michael P, Michie Susan. Behavior Change Techniques and Their Mechanisms of Action: A Synthesis of Links Described in Published Intervention Literature. Ann Behav Med. 2019 Jul 17;53(8):693–707. doi: 10.1093/abm/kay078. http://europepmc.org/abstract/MED/30304386 .5126198 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Yardley L, Morrison L, Bradbury K, Muller I. The person-based approach to intervention development: application to digital health-related behavior change interventions. J Med Internet Res. 2015 Jan 30;17(1):e30. doi: 10.2196/jmir.4055. https://www.jmir.org/2015/1/e30/ v17i1e30 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. McCurdie T, Taneva S, Casselman M, Yeung M, McDaniel C, Ho W, Cafazzo J. mHealth consumer apps: the case for user-centered design. Biomed Instrum Technol. 2012;Suppl:49–56. doi: 10.2345/0899-8205-46.s2.49. [CrossRef] [Google Scholar]

23. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop. J Med Internet Res. 2017 Jun 29;19(6):e232. doi: 10.2196/jmir.7126. https://www.jmir.org/2017/6/e232/ v19i6e232 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: Scoping reviews (2020 version) In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide, Australia: JBI; 2020. [Google Scholar]

28. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MD, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp. Straus SE. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 02;169(7):467–473. doi: 10.7326/m18-0850. [PubMed] [CrossRef] [Google Scholar]

29. Shneiderman B. Designing for fun. Interactions. 2004 Sep;11(5):48–50. doi: 10.1145/1015530.1015552. [CrossRef] [Google Scholar]

30. Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res. 2014 Jun 05;16(6):e146. doi: 10.2196/jmir.3077. https://www.jmir.org/2014/6/e146/ v16i6e146 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Oinas-Kukkonen H, Harjumaa M. Persuasive Systems Design: Key Issues, Process Model, and System Features. CAIS. 2009;24:pp–pp. doi: 10.17705/1cais.02428. [CrossRef] [Google Scholar]

33. Lustria MLA, Noar SM, Cortese J, Van Stee Stephanie K, Glueckauf RL, Lee J. A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun. 2013;18(9):1039–69. doi: 10.1080/10810730.2013.768727. [PubMed] [CrossRef] [Google Scholar]

37. Birnbaum F, Lewis D, Rosen RK, Ranney ML. Patient engagement and the design of digital health. Acad Emerg Med. 2015 Jun;22(6):754–6. doi: 10.1111/acem.12692. doi: 10.1111/acem.12692. [PMC free article] [PubMed] [CrossRef] [CrossRef] [Google Scholar]

Articles from JMIR mHealth and uHealth are provided here courtesy of JMIR Publications Inc.

# data[].attributes.attachment_link.title
Integrating Behavioral Science and Design Thinking to Develop Mobile Health Interventions: Systematic Scoping Review

# data[].attributes.attachment_link.description
Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. ...

# data[].attributes.attachment_link.readable_text
MLA

Peck, Adam,et al. "Using Design Thinking to Develop and Guide Institutional Strategy." Applying Design Thinking to the Measurement of Experiential Learning, edited by Adam Peck and Danielle DeSawal, IGI Global, 2021, pp. 245-270. https://doi.org/10.4018/978-1-7998-7768-4.ch016

APA

Peck, A., Antonen, J., & Preston, M. (2021). Using Design Thinking to Develop and Guide Institutional Strategy. In A. Peck, & D. DeSawal (Ed.), Applying Design Thinking to the Measurement of Experiential Learning (pp. 245-270). IGI Global. https://doi.org/10.4018/978-1-7998-7768-4.ch016

Chicago

Peck, Adam and Jessica Antonen, and Michael Preston. "Using Design Thinking to Develop and Guide Institutional Strategy." In Applying Design Thinking to the Measurement of Experiential Learning. edited by Peck, Adam, and Danielle DeSawal, 245-270. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-7768-4.ch016

Export Reference

# data[].attributes.attachment_link.title
Using Design Thinking to Develop and Guide Institutional Strategy

# data[].attributes.attachment_link.description
Strategic planning is a routine activity for institutions of higher education. However, these processes do not always result in strategy that reflects the complexity of these organizations or the challenges that they face. By applying design thinking to the challenge of creating robust strategy, the...

# data[].attributes.attachment_link.readable_text
Journal List

JMIR Mhealth Uhealth

v.10(3); 2022 Mar

PMC8968622

Monitoring Editor: Lorraine Buis

Paula Voorheis, MSc, 1, ² Albert Zhao, MSc,2, ³ Kerry Kuluski, PhD,1, ⁴ Quynh Pham, PhD,1, ⁵ Ted Scott, PhD,2, ⁶ Peter Sztur, BTech,² Nityan Khanna, BTech,² Mohamed Ibrahim, BTech,² and Jeremy Petch, PhD1, 2, 7, ⁸

¹ Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada

² Centre for Data Science and Digital Health, Hamilton Health Sciences, Hamilton, ON, Canada

³ Master of eHealth Program, McMaster University, Hamilton, ON, Canada

⁴ Institute for Better Health, Trillium Health Partners, Toronto, ON, Canada

⁵ Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada

⁶ Faculty of Health Sciences, School of Nursing, McMaster University, Hamilton, ON, Canada

⁷ Division of Cardiology, Department of Medicine, McMaster University, Hamilton, ON, Canada

⁸ Population Health Research Institute, Hamilton, ON, Canada

Paula Voorheis, Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, 155 College St 4th Floor, Toronto, ON, M5T 3M6, Canada, Phone: 1 7058163180, Email: ac.otnorotu.liam@siehroov.aluap.

Abstract

Background

Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. Behavioral Design (BD) and Design Thinking (DT) have emerged as best practice approaches in their respective fields. Until now, little work has been done to examine how BD and DT can be integrated throughout the mHealth design process.

Objective

The aim of this scoping review was to map the evidence on how insights from BD and DT can be integrated to guide the design of mHealth interventions. The following questions were addressed: (1) what are the main characteristics of studies that integrate BD and DT during the mHealth design process? (2) what theories, models, and frameworks do design teams use during the mHealth design process? (3) what methods do design teams use to integrate BD and DT during the mHealth design process? and (4) what are key design challenges, implementation considerations, and future directions for integrating BD and DT during mHealth design?

Methods

This review followed the Joanna Briggs Institute reviewer manual and PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Studies were identified from MEDLINE, PsycINFO, Embase, CINAHL, and JMIR by using search terms related to mHealth, BD, and DT. Included studies had to clearly describe their mHealth design process and how behavior change theories, models, frameworks, or techniques were incorporated. Two independent reviewers screened the studies for inclusion and completed the data extraction. A descriptive analysis was conducted.

Results

A total of 75 papers met the inclusion criteria. All studies were published between 2012 and 2021. Studies integrated BD and DT in notable ways, which can be referred to as “Behavioral Design Thinking.” Five steps were followed in Behavioral Design Thinking: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users’ needs and for its behavior change potential. The key challenges experienced during mHealth design included meaningfully engaging patient and public partners in the design process, translating evidence-based behavior change techniques into actual mHealth features, and planning for how to integrate the mHealth intervention into existing clinical systems.

Conclusions

Best practices from BD and DT can be integrated throughout the mHealth design process to ensure that mHealth interventions are purposefully developed to effectively engage users. Although this scoping review clarified how insights from BD and DT can be integrated during mHealth design, future research is needed to identify the most effective design approaches.

Keywords: behavior change, design thinking, digital health, health behavior, mobile application, mobile health, mobile phone, product design, scoping review, systems design, telemedicine, user-centered design

Introduction

Background

Digital health interventions are increasingly being designed to help people manage their health [1]. Many of these digital health interventions seek to facilitate behavior change and are often referred to as “digital behavior change interventions” (DBCIs) [2]. Among the wide range of DBCIs available, mobile health (mHealth) interventions have the potential to improve the reach and efficiency of health support owing to the widespread use of mobile phones [3,4]. Despite the potential impact of mHealth DBCIs, there is mixed evidence on whether they are effective at changing health behavior and improving health outcomes [3]. One concern is that patients and the public struggle to “effectively engage” with mHealth DBCIs [5,6]. Effective engagement with mHealth DBCIs has been defined as necessitating both microengagement with the mHealth interface itself (eg, logging into the app, entering data) and macroengagement with the behavior changes the mHealth intervention aims to support (eg, performing the exercises prescribed by the app) [6]. Therefore, for an mHealth DBCI to “effectively engage” users, it must be designed with engaging user-centered features to support microengagement and evidence-based behavior change techniques (BCTs) to support macroengagement.

To design effective behavior change content, there is evidence that mHealth interventions developed using the behavior change theory and BCTs are more likely to be effective than those without [7]. Behavioral scientists have been developing methods to systematically transition from diagnosing a behavioral problem to designing a behavior change intervention [8-11], which can be referred to as Behavioral Design (BD) [12]. Bondaronek and colleagues [13] provide several examples of how BD can be operationalized in publicly available physical activity apps. For instance, the app “Movesum” uses BCT 1.1 Goal Setting (Behavior) in the form of an easily adjustable step count goal [13,14]. BCT 1.1 Goal Setting (Behavior) is particularly useful when users struggle to plan for what they want to achieve or how they want to act [15]. Overall, BD can be operationalized in diverse ways but generally involves the following steps: (1) understanding the behavioral problem, (2) making a behavioral diagnosis for the target behavior using behavioral theories, models, and frameworks, (3) identifying relevant BCTs using taxonomies and classifications, (4) translating BCTs into intervention features, and (5) evaluating behavior change outcomes.

To design engaging user-centered mHealth features, there is evidence that mHealth interventions developed using person- and user-centered design processes are more likely to facilitate user engagement and improve intervention effectiveness [16,17]. Person- and user-centered design processes vary in terms of their operationalization but always put user needs at the forefront of design. Design Thinking (DT) is a common framework used in design science to guide creative user-centric designs for mHealth [18]. Design thinkers can use a range of approaches during the design process to ensure user-centeredness, such as directly involving users in app development or referring to Nielsen’s 10 usability heuristics [19]. The Nielsen Norman Group provides several detailed examples of how different usability principles can be implemented in application design [20,21]. Overall, DT can be executed in different ways but generally involves the following steps: (1) empathizing with the user, (2) defining the user requirements, (3) ideating functional concepts, (4) prototyping the user-centered solution, and (5) testing the solution to see if users’ needs are met. Despite knowledge about what constitutes “effective engagement” with mHealth DBCIs, a recent scoping review of DBCIs developed over the past 2 decades found that most design teams make limited use of BCTs and do not adequately describe the methods they employ to meet users’ needs [22]. This scoping review concluded that DBCI practitioners have little guidance on how to integrate best practices from behavioral science and design science, and a need exists to develop guidance to support them through this process [22]. This conclusion has been reiterated across the literature, with experts agreeing that methodological guidance is required to design effectively engaging DBCIs [23].

Experts have also begun to discuss similarities and differences between approaches used by design scientists and behavioral scientists [24-26]. For example, design scientists often rely on end users to ideate content based on their stated preferences, needs, and recommendations. They iteratively build interventions by using ongoing feedback, with a focus on producing creative solutions that users will enjoy (ie, ensuring the users are microengaged). In contrast, behavioral scientists focus on producing solutions that will nudge behavior change (ie, ensuring the users are macroengaged). Behavioral scientists often rely on theory- and evidence-based linkages to understand behavioral problems and select intervention content. They rigorously test solutions against their ability to effect behavior change and not necessarily how and whether the user is engaging with them. Nonetheless, both approaches emphasize the importance of understanding and diagnosing the problem at hand before proposing, designing, and implementing a solution. Both approaches also aim to ensure that the resulting solutions are designed purposefully to achieve user engagement. Amalgamating best practices from DT and BD may be mutually beneficial and help design teams develop more “effectively engaging” DBCIs [24-26]. There is currently a knowledge gap with respect to how best practices from DT and BD can be integrated to develop “effectively engaging” mHealth DBCIs. Specifically, little is known about how DT and BD can be blended throughout the mHealth DBCI design process to ensure that microengagement and macroengagement needs will be met.

Aims and Objectives

The aim of this scoping review was to identify and map how design teams have integrated best practices from BD and DT throughout the mHealth DBCI design process. By clarifying how BD and DT can be integrated, this review aimed to provide guidance on how mHealth DBCIs can be designed to more “effectively engage” patients and the public. This scoping review addressed the following questions: (1) what are the main characteristics of the studies that integrated BD and DT during the mHealth DBCI design process? (2) what theories, models, and frameworks did design teams use during the mHealth DBCI design process? (3) what methods did design teams use to integrate BD and DT during the mHealth DBCI design process? and (4) what are the key design challenges, implementation considerations, and future directions for the integration of BD and DT during mHealth DBCI design?

Methods

Study Design

The Joanna Briggs Institute reviewer’s manual was used to guide the conduct of this scoping review [27]. The scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist (see Multimedia Appendix 1) [28]. A scoping review protocol was drafted internally among key stakeholders, including mHealth software architects, mHealth design team managers, an information specialist, and several researchers with experience in the topic.

Search Strategy

The search strategy was developed by the lead author in collaboration with an information specialist. Initial searches were conducted in journals highly relevant to our research topic (eg, Cochrane, JMIR journals) to identify suitable search terms. A series of initial searches in MEDLINE were completed to analyze the text words contained in the title and abstracts of retrieved papers and index terms used to describe the papers. A final list of search terms was compiled, and a search of the databases MEDLINE, PsycINFO, EMBASE, and CINAHL was completed on May 15, 2021. A handsearch of Journal of Medical Internet Research was also completed on May 15, 2021, as this was recognized as a journal highly relevant to the research topic. A gray literature search and a review of paper reference lists were not conducted. This decision was made to tighten the scope of the review around the research objectives, given time and resource constraints. Search terms combined the following topics: mHealth, behavior change, and design thinking. The full search strategy can be seen in Multimedia Appendix 2.

Eligibility Criteria

Included papers had to be primary studies, where a full-text paper described the design process of an mHealth DBCI. More specifically, included papers had to describe how BD and DT practices were integrated throughout the design of an mHealth DBCI that aimed to support behavior change in patients or the public. To meet these criteria, the papers had to clearly describe their mHealth DBCI design process, addressing at least 3 of the 5 design process steps suggested in DT (empathizing, defining, ideating, prototyping, and testing) [18]. In addition, papers had to clearly describe how behavior change theories, models, frameworks, or techniques were incorporated into the mHealth design process. Studies that only used behavior change insights to evaluate the mHealth DBCI after development were excluded. Furthermore, studies that described an mHealth intervention designed to provide a psychological treatment (eg, cognitive behavioral therapy) without describing how their design process utilized behavior change theories, models, and frameworks to support “effective engagement” were also excluded. No limitations were put on the year of publication; however, only papers published in English were included. To be eligible, the intervention must have been an mHealth intervention, defined by the World Health Organization as “health care and public health practice supported by mobile devices such as mobile phones, tablets, patient monitoring devices, and other wireless devices” [4]. The intervention must also have been designed for use by patients or the public. If the intervention was designed only for use by health care professionals, it was excluded.

Evidence Selection

Studies from the database searches were handled using Covidence (Veritas Health Innovation Ltd) reference management software. Papers were deduplicated and imported for screening using Covidence. A 2-level screening was performed after duplicate removal. During level 1 screening, titles and abstracts were screened using the eligibility criteria. Publications with title or abstract not meeting the eligibility criteria were excluded. During level 2 screening, full-text papers that passed level 1 were screened. Studies that met the eligibility criteria were included for full data extraction. Consistent with PRISMA-ScR, reasons for exclusion were recorded at the full-text level [28]. Prior to the selection of sources, 2 reviewers completed a pilot screening of 50 titles and abstracts to assess the reliability of the eligibility criteria. Interrater agreement for study inclusion was calculated using percentage agreement. If agreement was lower than 80%, the eligibility criteria would be clarified and another pilot test would occur. All interrater discrepancies during level 1 and 2 evidence selection were resolved between the 2 reviewers upon discussion. The 2 reviewers screened all titles, abstracts, and full-text papers for inclusion.

Data Extraction

The reviewers extracted data from the eligible papers by using 2 data extraction forms. The first data extraction form elicited the main study characteristics, including lead author, year of publication, journal of publication, country of origin, study design, study purpose, target user population, the health issue, the target health behavior, mHealth DBCI summary, mHealth DBCI design process duration, and the members of the mHealth DBCI design team. The second data extraction form elicited details about how BD and DT were integrated throughout the design process, which included extracting types of theories, models, and frameworks used; the approaches design teams used to integrate best practices from BD with DT over the course of the mHealth DBCI design process, key challenges in the mHealth DBCI design process, key implementation considerations for mHealth DBCIs, and future considerations for the mHealth DBCI design. The data extraction forms were drafted, revised, and agreed upon by the 2 reviewers after an iterative process of implementing the extraction forms on a sample of papers.

Analysis and Presentation of Results

A descriptive analysis of the included papers was conducted to meet the objectives of the scoping review. Narrative descriptions, frequency calculations, and visual diagrams were utilized to communicate the results.

Results

Evidence Selection

A total of 1912 papers were identified from the searches. After 651 duplicate studies were removed, 1252 papers were screened based on their titles and abstracts, with 255 full-text papers meeting the eligibility criteria. After full-text review, 75 papers fulfilled the eligibility criteria and were included in the final review. Figure 1 shows the PRISMA-ScR flow diagram illustrating the paper selection process [28].

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) flow diagram of the study selection process.

Main Characteristics of the Included Papers

All 75 primary studies were published between 2012 and 2021, with a surge from 2018 onward (51/75, 68%). The Journal of Medical Internet Research and its sister journals accounted for almost 55% (41/75) of the papers included. The United States (14/75, 19%), the United Kingdom (13/75, 17%), Australia (10/75, 13%), and Holland (9/75, 12%) were the most common study locations. The target population of the mHealth DBCIs varied, with the most common being patients with cardiovascular issues (7/75, 9%), patients with diabetes (5/75, 7%), adults with overweight and obesity (5/75, 7%), adults who smoke (5/75, 7%), adults with poor physical activity levels (5/75, 7%), and cancer survivors (5/75, 7%). The target health behaviors addressed also varied, with the most prominent being improved physical activity (18/75, 24%), improved diet (17/75, 23%), disease self-management (12/75, 16%), preventative health behaviors (6/75, 8%), adherence to medication (5/75, 7%), adherence to rehabilitation programming (5/75, 7%), and smoking cessation (5/75, 7%). mHealth DBCI design teams were multidisciplinary in their membership. Members included researchers, patients, caregivers, community partners, clinicians, technology developers, and experts in behavior change, health psychology, health communications, health promotion, and health informatics. Topic experts were also relied on depending on the type of intervention (eg, diabetes educators). Software engineers, computer scientists, videographers, product designers, and graphic designers were brought to assist with the development of the mHealth intervention, although it was usually unclear when they were included. The design process duration was usually not reported. Out of the 14 interventions that clearly reported design process duration, there was a large variation in timespan, ranging from less than 3 months to upwards of 4 years. Multimedia Appendix 3 provides an overview of the main study characteristics, and Multimedia Appendix 4 provides a full list of the 75 studies included.

Theories, Models, and Frameworks Used During Design

Studies used a variety of theories, models, and frameworks in their mHealth DBCI design process. Theories, models, and frameworks were most often used to (1) guide the design process itself, (2) conceptualize the behavior change problem, (3) identify relevant BCTs, and (4) evaluate ideas for their applicability, feasibility, or potential effectiveness. Figure 2 summarizes the types of theories, models, and frameworks used in the mHealth DBCI design process, and Multimedia Appendix 5 provides a detailed breakdown.

Theories, models, and frameworks used in mobile health digital behavior change intervention design. ABACUS: App Behavior Change Scale; APEASE: Acceptability, Practicability, Effectiveness, Affordability, Side-effects, and Equity; BCT: behavior change technique; COM-B: capability, opportunity, motivation-behavior; DBCI: digital behavior change intervention; IDEAS: Integrate, Design, Assess, and Share; mHealth: mobile health; MoSCoW: must-have, should-have, could-have, and won't-have, or will not have right now; PRECEDE: Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation; PROCEED: Policy, Regulatory, and Organizational Constructs in Educational and Environmental Development.

Methods Used to Integrate BD and DT During the mHealth DBCI Design Process

Regardless of the theories, models, and frameworks teams used to design their mHealth DBCIs, they integrated best practices from BD and DT in notable ways. We refer to the mixing of BD and DT throughout the mHealth DBCI design process as the “Behavioral Design Thinking Approach” (see Figure 3). The Behavioral Design Thinking Approach presents a new method of designing mHealth DBCIs, which is the result of merging together best practices from BD and DT. Multimedia Appendix 6 provides further detail on the Behavioral Design Thinking Approach along with several specific examples on how BD and DT can be integrated.

The behavioral design thinking approach [5,6]. BCT: behavior change technique; DBCI: digital behavior change intervention; mHealth: mobile health; UI: user interface; UX: user experience.

Generally, 5 steps are followed in the Behavioral Design Thinking Approach: (1) empathize with users and their behavior change needs, (2) define user and behavior change requirements, (3) ideate user-centered features and behavior change content, (4) prototype a user-centered solution that supports behavior change, and (5) test the solution against users’ needs and for its behavior change potential. Across these steps, studies integrated DT and BD in different ways, often “driving” their mixing in a certain direction. The 3 ways DT and BD were mixed included (1) DT and BD approaches were equally weighted (notation = DT+BD), (2) DT drove the approach, with concepts from BD supplementing the DT approaches (notation = DT→BD), or (3) BD drove the approach, with concepts from DT supplementing the BD approaches (notation = BD→DT). Overall, studies tended to “empathize” by blending DT and BD equally (DT+BD), “define” by blending DT and BD equally (DT+BD), “ideate” by driving the process by BD (BD→DT), “prototype” by driving the process by DT (DT→ BD), and “test” by driving the process by DT (DT→BD). These approaches are summarized in the text below.

Behavioral Design Thinking Approach

Behavioral Design Thinking Step 1: Empathize With Users and Their Behavior Change Needs

Most studies described how they empathized with users while simultaneously conceptualizing the behavior change problem (66/75, 88%), blending concepts from DT and BD harmoniously (notation = DT+BD). To empathize with the users who would perform a target behavior, studies analyzed users’ experiences, perceptions, beliefs, needs, and preferences with their health issues, health behaviors, health interventions, and mobile app usage. To understand the behaviors that the users will perform, studies examined applicable target health behaviors, behavioral determinants, BCTs, and behavioral theories, models, and frameworks. Finally, to understand the context that the users perform the behavior in, studies assessed current practices and programs, relevant personal, social, environmental, and structural factors, and pertinent clinical, usage, and behavioral aims. Studies used primary research (eg, interviews, focus groups, surveys, creative workshops) and secondary research (eg, secondary data analyses, literature reviews, reviews of other interventions, guidelines, practices) to empathize. Studies not only involved patient and public end users during this step (48/75, 64%), but also involved health care practitioners, community partners, behavioral experts, design scientists, and technology developers. To directly involve patients and the public, studies used a variety of tools such as interview guides informed by behavior change models and visual presentations of apps to stimulate collaborative discussion.

Behavioral Design Thinking Step 2: Define User and Behavior Change Requirements

Studies tended to analyze the empathy results in order to define system requirements that would meet both users’ needs and behavior change needs (notation = DT+BD). Studies tended to define user-centered requirements by sorting users’ stated preferences into key themes to be addressed. Studies tended to define the behavior change requirements by using the empathy results to formulate a “behavioral diagnosis,” which outlines the behavioral determinants that need to be addressed. When studies organized the requirements into an amalgamated format, DT or BD usually drove the organization. For example, some studies used tables organized by the relevant behavioral determinants, whereby they would then list corresponding user quotes and resultant requirements alongside the behavioral determinants. Other studies opted to use the requirements to create holistic user personas and scenarios. Regardless of the approach to amalgamate and make sense of the requirements, the requirement lists were often lengthy and needed to be refined. Refining the requirements involved (1) identifying the requirements that met the project scope and objectives, (2) using feasibility criteria to refine and select certain requirements, (3) consulting experts and patient and public users to prioritize the requirements, and (4) ranking the most important requirements according to their likelihood to elicit behavior change, alignment with current practices, adaptability to a digital interface, acceptability to users, and compatibility with data collection needs.

Behavioral Design Thinking Step 3: Ideate User-Centered Features and Behavior Change Content

Most studies described how they translated requirements into design content and features by using ideation methods (67/75, 89%). Generally, studies drove their ideation methods using BD (notation = BD→DT). For instance, several studies relied only on BD to ideate the relevant behavior change content (19/67, 28%). These studies used researcher-led “behavioral mapping” to match relevant behavior change determinants with BCTs by using evidence-based taxonomies and tested linkages. It was often unclear, however, exactly how these studies translated the identified BCTs into content for the mobile app. Other studies attempted to completement BD approaches with DT methods during their ideation (48/67, 72%). In these cases, studies would creatively ideate design content and features that would support behavior change while also meeting users’ needs and preferences. These studies relied on different techniques such as design team brainstorming, expert stakeholder panels, technology partner consultation, and end user co-design efforts. Design teams tended to brainstorm how the behavior change insights could be integrated with users’ preferences, existing programming, norms in mHealth design, and clinical management needs. Stakeholder panels would discuss all the requirements and consider the most appropriate strategies (dosage, delivery, organization, and personalization). Technology partners would specify how requirements could be operationalized within an mHealth app. Patient and public partners would contribute to co-design sessions to create and reflect on ideas, content, and designs. Approximately 34% (23/75) of the studies appeared to directly involve patient and public users during this step to ensure that the ideated content met their unique needs.

Behavioral Design Thinking Step 4: Prototype a Solution That Is User-Centered and Supports Behavior Change

Many studies did not clearly describe the prototyping methods they used to translate ideated content and features into functional solutions (28/75, 36%). Studies that described their prototyping approach tended to drive their prototyping by using DT (notation = DT→BD). For instance, a large proportion of studies only used prototyping methods, tools, and aids traditional to DT (37/47, 79%). DT prototyping methods usually involved iterative prototyping, feedback, and refinement, often utilizing sprint, scrum, or agile methods. DT prototyping tools included wireframing, paper prototyping, application flowcharts, and use case scenarios. DT prototyping aids included tools such as Nielsen’s usability heuristics [19] and the Eight Golden Rules [29]. Despite reliance on DT for prototyping, several studies supplemented DT methods with BD considerations to ensure that behavior change content would be operationalized within the prototyped solution (10/47, 21%). For example, to supplement DT prototyping methods, some studies utilized BCT codevelopment to ensure that BCTs were not lost in translation during technological development. To supplement DT prototyping tools, some studies elected to make BCT flowcharts to clarify how the BCTs would be interacted with. To supplement DT prototyping aids, some studies also used the Behavioral Intervention Technology Model [30] or the Persuasive Systems Design Framework [31], which were behaviorally informed models used to guide feature selection. Among the studies that clearly described their prototyping methods, just over half appeared to directly involve patient and public end users in their prototyping process (26/47, 55%).

Behavioral Design Thinking Step 5: Test Solution Against User Needs and for Its Behavior Change Potential

A large proportion of studies did not clearly describe how they tested their mHealth DBCI solution within the design process (18/75, 24%). Studies that described their testing methods tended to drive their testing by methods traditional to DT (notation = DT→BD). For instance, a considerable number of studies relied exclusively on DT evaluation approaches such as heuristic evaluation, usability testing, expert evaluation, and pilot testing to evaluate the solutions against users’ needs (33/57, 58%). Despite reliance on DT for testing, some studies supplemented these testing methods with BD considerations to evaluate the solution for its behavior change potential (24/57, 42%). For instance, in addition to traditional DT heuristic evaluation, some studies conducted a BCT evaluation to assess the final solution for the presence of known BCTs. In addition to traditional DT usability testing, some studies “tagged” BCT components within the app to follow how users engaged, accepted, and perceived the intended BCTs. Some studies also conducted posttest user interviews directed by interview guides based on constructs of a behavioral model. In addition to traditional DT expert evaluations, some studies brought on behavioral science experts to assess the extent to which the intervention content had fidelity to the intended BCTs. Experts also could assess the quality of the mHealth DBCI by using the App Behavior Change Scale [32]. If pilot tests were conducted, specific behavior change outcomes could be evaluated in addition to traditional usage metrics. Metrics included change in knowledge, change in intentions, state of behavior change, user experience with BCTs, perceived potential of BCTs, and user engagement with BCTs.

Design Challenges, Implementation Considerations, and Future Directions

In addition to describing their design process, studies also identified key implementation considerations, design challenges, and future directions for mHealth DBCI design. These have been summarized in Textbox 1. Multimedia Appendix 7 expands on these results in further detail.

Key design challenges, implementation considerations, and future directions.

Design challenges

Design process can be time and resource consuming, especially when the design approach is unclear.

Recruiting and involving representative end users and key stakeholders can be difficult.

Conflicting ideas can result from integrating behavioral theory, user needs, and stakeholder views.

The translation of behavior change techniques into actual mobile health (mHealth) digital behavior change intervention (DBCI) features and content can be confusing.

The design process is time limited and usually may not allow for comprehensive evaluation.

Integrating the mHealth DBCI into clinical practice can be complex.

mHealth platforms come with their own technical challenges and limiting factors.

Implementation considerations

Evaluate potential implementation barriers and facilitators during the “testing” step.

Facilitate early stakeholder buy-in.

Plan for the integration of the mHealth DBCI into clinical systems.

Use feasibility criteria throughout the entire design process.

Use an implementation plan (marketing, dissemination, onboarding, adoption, usage, sustainability).

Future directions

Guidance on the design process for mHealth DBCIs.

Guidance on how to operationalize behavioral change techniques within mHealth DBCIs in a user-friendly way.

Guidance on how to meaningfully involve users and stakeholders in the design process.

Guidance on how to tailor mHealth DBCIs to meet behavioral, personal, and clinical needs.

Discussion

Primary Findings

This paper presents a systematic scoping review of 75 papers that described their design process for developing an mHealth DBCI. This review addressed a gap in the literature about how mHealth interventions can be designed to integrate practices from DT and BD. Although the number of mHealth DBCIs seems to be growing, the results highlight substantial heterogeneity in the methods studies used to design them. This scoping review aimed to clarify, map, and synthesize the different methods that can be used to design mHealth DBCIs. A new consolidated approach to mHealth DBCI design is presented—the Behavioral Design Thinking Approach. A large proportion of mHealth DBCIs were designed to facilitate diet- and exercise-related behavior change. Similar findings were noted in a scoping review on DBCI design over the past 2 decades [22]. Nonetheless, the results point to a lack of clarity about how mHealth DBCI content and features were ideated and operationalized. Several studies in this review struggled to translate BCTs into user-friendly mHealth features. Studies also ended up trying to fit several BCTs within the mHealth app, increasing app complexity. To simplify the user experience, several studies noted that personalization may be an important future direction for the field. Tailoring mHealth content to users’ unique behavioral, clinical, and personal needs can help facilitate the delivery of features to support effective engagement [33]. Ensuring that design teams use appropriate questions and metrics to inform personalization will be essential. This further exemplifies the need for the meaningful involvement of patients and the public in mHealth DBCI design. Regarding the meaningful involvement of patients and the public in the mHealth DBCI design process, this review found that patients and the public were most often involved as participants (eg, in user interviews, surveys, testing) rather than as partners on the design team itself. Patients and the public appeared to have minimal direct input on design decision-making throughout the mHealth DBCI design process. Growing calls are being made to prioritize the unique perspectives of patients and the public during design [16,17]. Involving end users in the design process has been suggested to increase the effectiveness, relevance, and appropriateness of mHealth DBCIs [16,17]. Nonetheless, design teams appear to have little guidance on how to meaningfully engage patient and public end users in the design process itself [34-37].

Theories, Models, and Frameworks Used During Design

Regarding the theories, models, and frameworks that studies used during mHealth DBCI design, design teams appeared to face a vast array of these “tools” from different fields, created for different purposes. Although studies reported benefiting from the structure that these tools offered, a best practice approach for integrating DT and BD insights to develop “effectively engaging” mHealth DBCIs did not appear to exist. This scoping review offers a classification of the types of theories, models, and frameworks that teams can use during mHealth DBCI design and offers a consolidated approach for guiding the design process in general.

Methods Used to Integrate BD and DT During the mHealth DBCI Design Process

During the integration of DT and BD throughout the mHealth DBCI design, key similarities and differences between the 2 approaches were observed. During the empathizing step, users’ preferences often differed from what was needed to change their behavior. Balancing user-stated preferences with evidence-based behavior change strategies and mHealth platform requirements was a challenge for teams. During the defining step, it was clear that blending DT and BD perspectives resulted in lengthy and complex requirements. Involving users and topic experts to refine these requirements appeared to be a helpful strategy. During ideation, BD relied on evidence-based linkages to ideate content, whereas DT relied on iterative brainstorming and collaborative creation. Studies reconciled these different approaches by starting with BD to develop a list of relevant BCTs and then using DT to creatively integrate BCTs and other requirements into the mHealth DBCI. The prototyping step was the least well described among studies but usually relied on approaches traditional to DT. Studies that brought on their technology partner only at the prototyping stage often had to add additional methods to ensure that BCTs were operationalized as imagined. The testing step was also dominated by DT, as it was challenging to meaningfully test for behavior change outcomes within the time constraints imposed by the design process. Nonetheless, several measures were used by studies to assess the solution’s potential to support behavior change.

Design Challenges, Implementation Considerations, and Future Directions

Finally, regarding key implementation considerations, design challenges, and future directions, several cross-cutting themes were identified. In addition to ensuring that BD and DT insights were incorporated into the mHealth DBCI design, many teams noted that planning for implementation of the mHealth DBCI was just as important for ensuring success. Using insights from BD and DT may also be relevant in designing an implementation plan for the mHealth DBCI. Implementation planning appears to be particularly complex in this context, as many mHealth DBCIs need to be integrated with existing clinical systems and norms. Regardless of how evidence-based and user-friendly the mHealth DBCI may be, if stakeholder buy-in, system adaptation, and clinical sustainability issues are not taken into consideration, the mHealth DBCIs are likely to fail. Future research must address the lack of guidance design teams have in developing and implementing effective mHealth DBCIs. Particularly, design teams have little guidance on the “tools” (ie, theories, models, and frameworks) they can use, how to meaningfully involve patient and public end users, and how to tailor mHealth DBCI content to meet behavioral, personal, and clinical needs.

Limitations

The most significant limitation of this scoping review was that the inclusion criteria necessitated that each included paper had to be a full-text primary study that clearly described the mHealth design process. This decision was made to tighten the scope of this review and owing to limited time and resources. Although the systematic database search was not supplemented by a gray literature search or a review of reference lists, the 75 included papers offer a diverse range of insights that meet the research objectives while addressing a prominent gap in the literature. It should be noted that the original scope of this review included a database, reference list, and gray literature search to identify available design frameworks that could be used by design teams to integrate BD and DT insights during mHealth development. Presenting these results alongside the data extracted from 75 primary studies was beyond the scope of this paper. The results of this scoping review are only inclusive of studies written in English; therefore, findings may not be generalizable internationally.

Implications

The Behavioral Design Thinking Approach offers a way forward in the field of mHealth DBCI design. mHealth design teams may consider using the insights presented in the Behavioral Design Thinking Approach to inform their future work. mHealth design teams may also find it helpful to reflect on the different types of theories, models, and frameworks they can use during the design process, as well as the key challenges they may face along the way. The findings presented in this review may also be relevant to researchers in the fields of behavioral science and design science who are interested in interdisciplinary collaboration. It is reasonable to assume that breaking down silos between these 2 fields may improve the success of mHealth DBCIs. Overall, the main benefits of this research include (1) clarifying what approaches can be used to design mHealth DBCIs, (2) promoting transparency in the choices that studies must make during the mHealth DBCI design process, and (3) enabling future researchers to test what design approaches are the most effective to develop “effectively engaging” mHealth DBCIs.

Conclusion

The number of mHealth interventions designed to support behavior change is increasing. Integrating best practices from BD and DT may allow for the development of mHealth DBCIs that more effectively engage patients and the public. This paper has helped identify and conceptualize the methods that can be used to integrate BD and DT throughout the mHealth DBCI design process. Raising the standard of mHealth design methods will be essential to ensure confidence in the impact mHealth interventions can have on improving health outcomes. If more mHealth DBCIs are purposefully designed to address effective engagement, it is likely that patients, the public, and health care practitioners will be more confident in adopting mHealth. Given the predicted increase in the demand of mHealth interventions, the time is now to ensure mHealth design methods are appropriately suited to increase effective engagement.

Acknowledgments

This review was funded from the core operating budget of the Centre for Data Science and Digital Health (CREATE) at Hamilton Health Sciences. The most responsible author is the Director of CREATE. The larger organization of Hamilton Health Sciences had no role in the design, conduct, or content of the review.

Abbreviations

BCT | behavior change technique
BD | Behavioral Design
DBCI | digital behavior change intervention
DT | Design Thinking
mHealth | mobile health
PRISMA-ScR | Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews

Multimedia Appendix 1

PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist.

Multimedia Appendix 2

Search strategy.

Multimedia Appendix 3

Main characteristics of the studies.

Multimedia Appendix 4

Summary of all the studies included.

Multimedia Appendix 5

Theories, models, and frameworks used in design.

Multimedia Appendix 6

The behavioral design thinking approach.

Multimedia Appendix 7

Design challenges, implementation considerations, and future directions.

Footnotes

Contributed by

Authors' Contributions: PV was responsible for leading all stages of the scoping review. KK, QP, and JP reviewed the scoping review protocol to ensure its research rigor. TS, PS, NK, and MI ensured that the scoping review objectives and protocol were appropriate and applicable to the field. PV and AZ completed the study screening and data extraction. PV was responsible for writing the scoping review draft. All authors were involved in reviewing and preparing the manuscript for publication.

Conflicts of Interest: None declared.

References

1. Murray Elizabeth, Hekler Eric B, Andersson Gerhard, Collins Linda M, Doherty Aiden, Hollis Chris, Rivera Daniel E, West Robert, Wyatt Jeremy C. Evaluating Digital Health Interventions: Key Questions and Approaches. Am J Prev Med. 2016 Nov;51(5):843–851. doi: 10.1016/j.amepre.2016.06.008. http://europepmc.org/abstract/MED/27745684 .S0749-3797(16)30229-X [PMC free article] [PubMed] [CrossRef] [Google Scholar]

2. Perski O, Blandford A, West R, Michie S. Conceptualising engagement with digital behaviour change interventions: a systematic review using principles from critical interpretive synthesis. Transl Behav Med. 2017 Jun;7(2):254–267. doi: 10.1007/s13142-016-0453-1. http://europepmc.org/abstract/MED/27966189 .10.1007/s13142-016-0453-1 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

3. Marcolino MS, Oliveira JAQ, D'Agostino M, Ribeiro AL, Alkmim MBM, Novillo-Ortiz D. The Impact of mHealth Interventions: Systematic Review of Systematic Reviews. JMIR Mhealth Uhealth. 2018 Jan 17;6(1):e23. doi: 10.2196/mhealth.8873. https://mhealth.jmir.org/2018/1/e23/ v6i1e23 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

4. Kay M, Santos J, Takane M. Global Observatory for eHealth Series, vol 3. Geneva, Switzerland: World Health Organization; 2011. [2021-05-15]. mHealth: new horizons for health through mobile technologies based on the findings of the second global survey on eHealth. http://www.who.int/goe/publications/goe_mhealth_web.pdf . [Google Scholar]

6. Yardley L, Spring BJ, Riper H, Morrison LG, Crane DH, Curtis K, Merchant GC, Naughton F, Blandford A. Understanding and Promoting Effective Engagement With Digital Behavior Change Interventions. Am J Prev Med. 2016 Nov;51(5):833–842. doi: 10.1016/j.amepre.2016.06.015.S0749-3797(16)30243-4 [PubMed] [CrossRef] [Google Scholar]

7. Webb TL, Joseph J, Yardley L, Michie S. Using the internet to promote health behavior change: a systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. J Med Internet Res. 2010 Feb 17;12(1):e4. doi: 10.2196/jmir.1376. https://www.jmir.org/2010/1/e4/ v12i1e4 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

8. Michie S. Designing and implementing behaviour change interventions to improve population health. J Health Serv Res Policy. 2008 Oct;13 Suppl 3:64–9. doi: 10.1258/jhsrp.2008.008014.13/suppl_3/64 [PubMed] [CrossRef] [Google Scholar]

11. Cash PJ, Hartlev CG, Durazo CB. Behavioural design: A process for integrating behaviour change and design. Design Studies. 2017 Jan;48:96–128. doi: 10.1016/j.destud.2016.10.001. [CrossRef] [Google Scholar]

14. Michie S, Richardson M, Johnston M, Abraham C, Francis J, Hardeman W, Eccles MP, Cane J, Wood CE. The behavior change technique taxonomy (v1) of 93 hierarchically clustered techniques: building an international consensus for the reporting of behavior change interventions. Ann Behav Med. 2013 Aug;46(1):81–95. doi: 10.1007/s12160-013-9486-6. [PubMed] [CrossRef] [Google Scholar]

15. Carey Rachel N, Connell Lauren E, Johnston Marie, Rothman Alexander J, de Bruin Marijn, Kelly Michael P, Michie Susan. Behavior Change Techniques and Their Mechanisms of Action: A Synthesis of Links Described in Published Intervention Literature. Ann Behav Med. 2019 Jul 17;53(8):693–707. doi: 10.1093/abm/kay078. http://europepmc.org/abstract/MED/30304386 .5126198 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

16. Yardley L, Morrison L, Bradbury K, Muller I. The person-based approach to intervention development: application to digital health-related behavior change interventions. J Med Internet Res. 2015 Jan 30;17(1):e30. doi: 10.2196/jmir.4055. https://www.jmir.org/2015/1/e30/ v17i1e30 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

17. McCurdie T, Taneva S, Casselman M, Yeung M, McDaniel C, Ho W, Cafazzo J. mHealth consumer apps: the case for user-centered design. Biomed Instrum Technol. 2012;Suppl:49–56. doi: 10.2345/0899-8205-46.s2.49. [CrossRef] [Google Scholar]

23. Michie S, Yardley L, West R, Patrick K, Greaves F. Developing and Evaluating Digital Interventions to Promote Behavior Change in Health and Health Care: Recommendations Resulting From an International Workshop. J Med Internet Res. 2017 Jun 29;19(6):e232. doi: 10.2196/jmir.7126. https://www.jmir.org/2017/6/e232/ v19i6e232 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

27. Peters MDJ, Godfrey C, McInerney P, Munn Z, Tricco AC, Khalil H. Chapter 11: Scoping reviews (2020 version) In: Aromataris E, Munn Z, editors. JBI Manual for Evidence Synthesis. Adelaide, Australia: JBI; 2020. [Google Scholar]

28. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, Moher D, Peters MD, Horsley T, Weeks L, Hempel S, Akl EA, Chang C, McGowan J, Stewart L, Hartling L, Aldcroft A, Wilson MG, Garritty C, Lewin S, Godfrey CM, Macdonald MT, Langlois EV, Soares-Weiser K, Moriarty J, Clifford T, Tunçalp. Straus SE. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med. 2018 Oct 02;169(7):467–473. doi: 10.7326/m18-0850. [PubMed] [CrossRef] [Google Scholar]

29. Shneiderman B. Designing for fun. Interactions. 2004 Sep;11(5):48–50. doi: 10.1145/1015530.1015552. [CrossRef] [Google Scholar]

30. Mohr DC, Schueller SM, Montague E, Burns MN, Rashidi P. The behavioral intervention technology model: an integrated conceptual and technological framework for eHealth and mHealth interventions. J Med Internet Res. 2014 Jun 05;16(6):e146. doi: 10.2196/jmir.3077. https://www.jmir.org/2014/6/e146/ v16i6e146 [PMC free article] [PubMed] [CrossRef] [Google Scholar]

31. Oinas-Kukkonen H, Harjumaa M. Persuasive Systems Design: Key Issues, Process Model, and System Features. CAIS. 2009;24:pp–pp. doi: 10.17705/1cais.02428. [CrossRef] [Google Scholar]

33. Lustria MLA, Noar SM, Cortese J, Van Stee Stephanie K, Glueckauf RL, Lee J. A meta-analysis of web-delivered tailored health behavior change interventions. J Health Commun. 2013;18(9):1039–69. doi: 10.1080/10810730.2013.768727. [PubMed] [CrossRef] [Google Scholar]

37. Birnbaum F, Lewis D, Rosen RK, Ranney ML. Patient engagement and the design of digital health. Acad Emerg Med. 2015 Jun;22(6):754–6. doi: 10.1111/acem.12692. doi: 10.1111/acem.12692. [PMC free article] [PubMed] [CrossRef] [CrossRef] [Google Scholar]

Articles from JMIR mHealth and uHealth are provided here courtesy of JMIR Publications Inc.

# data[].attributes.attachment_link.title
Integrating Behavioral Science and Design Thinking to Develop Mobile Health Interventions: Systematic Scoping Review

# data[].attributes.attachment_link.description
Mobile health (mHealth) interventions are increasingly being designed to facilitate health-related behavior change. Integrating insights from behavioral science and design science can help support the development of more effective mHealth interventions. ...

# data[].attributes.attachment_link.readable_text
Resumo

Objetivos

Relatar experiências vivenciadas durante um estágio de Pós-Doutorado, o conhecimento adquirido sobre o método de Design Thinking e a socialização desse método em evento científico de Enfermagem.

Método

Trata-se do relato de experiência de um Pós-Doutorado realizado no Canadá e da aquisição de novos conhecimentos na área da pesquisa e do ensino.

Resultados

A oportunidade mais desafiadora foi a aproximação com o Design Thinking, pois esse conceito promoveu a consciência sobre a urgência de adotar um novo paradigma para pensar, colaborar, ensinar, desenhar, planejar, executar e avaliar as atividades de pesquisa. Após constantes reflexões sobre Design Thinking, houve a oportunidade de promover uma iniciativa-piloto de tradução de conhecimento do método e observar a excelente receptividade dos participantes no evento.

Conclusão

A experiência permitiu a aquisição de conhecimentos além da Enfermagem, estimulando o pensamento crítico e fortalecendo a destemida capacidade de pensar e inovar na produção de conhecimento.

Implicações para a prática

É inegável que o Design Thinking poderá revolucionar a educação, mediante sua inserção nos cursos da área da saúde, configurando uma ferramenta cognitiva que reconstrói a engenhosidade humana inspirada em valores humanísticos e empáticos, assegurando a qualidade de serviços e produtos, e respeitando o perfil do cliente.

Palavras-chaves:
Difusão de inovações; Conhecimento; Educação em Enfermagem; Enfermagem Baseada em Evidências

Resumen

Objetivos

Informar experiencias durante una pasantía postdoctoral, como también el conocimiento adquirido sobre el método Design Thinking y la socialización de este método en un evento de enfermería científica.

Método

Se trata de un informe de experiencia postdoctoral realizada en Canadá y adquisición de nuevos conocimientos en investigación y docencia.

Resultados

La pasantía permitió la adquisición de nuevos conocimientos en el campo de la investigación y la docencia. La oportunidad más difícil fue el acercamiento al concepto de Design Thinking, porque promovía la conciencia de la urgencia de adoptar un nuevo paradigma para pensar, colaborar, enseñar, diseñar, planificar, ejecutar y evaluar actividades de investigación. Después de reflexiones constantes sobre Design Thinking, surgió la oportunidad de promover una iniciativa piloto para traducir el conocimiento del método y observar la excelente receptividad de los participantes en el evento.

Conclusión

La experiencia permitió la adquisición de conocimiento más allá de la enfermería, estimulando el pensamiento crítico y fortaleciendo la intrépida capacidad de pensar e innovar en la producción de conocimiento.

Implicaciones para la práctica

Es innegable que Design Thinking puede revolucionar la educación, a través de su inserción en los cursos del área de salud y configurarse en una herramienta cognitiva que reconstruye el ingenio humano inspirado en valores humanísticos y empáticos, asegurando la calidad de los servicios y productos, y respetando el perfil del cliente.

Palabras clave:
Difusión de innovaciones; Conocimiento; Educación en Enfermería; Enfermería Basada en Evidencia

ABSTRACT

Objectives

To report experiences during a post-doctoral fellowship which included acquired knowledge about the method of Design Thinking and its dissemination in a scientific nursing event.

Method

A report of experience conducted in Canada and the acquisition of new knowledge in research and teaching.

Results

The fellowship established new knowledge in the fields of research and teaching. The most challenging opportunity was the introduction and adoption of Design Thinking. It increased awareness of the urgency to adopt a new paradigm to think, collaborate, teach, design, plan, execute and evaluate research-related activities. After participants reflected about Design Thinking, there was an opportunity to promote a pilot initiative to translate the knowledge about this concept and observe the participants’ positive reception in a scientific event.

Conclusion

The experience allowed the acquisition of knowledge beyond nursing and the stimulation of critical thinking; this strengthened the confidence to think and innovate in the production of knowledge.

Implications for practice

It is undeniable that Design Thinking may revolutionize education if it is added to health courses as a cognitive tool, inspired by humanistic and empathic values, that reconstructs human inventiveness; this method assures the quality of services and products while respecting the customer's profile.

Keywords:
Diffusion of Innovation; Evidence-Based Nursing; Knowledge; Nursing Education

Introdução

A realização de um Pós-doutorado de Enfermagem em uma área multidisciplinar e transdisciplinar, envolvendo o conceito de inovação tecnológica, é um grande desafio. Isso se deve à necessidade de construir pontes de transferência de conhecimento entre outras perspectivas científicas para a ciência da Enfermagem. Nesse contexto, observa-se a necessidade de aprendizado de uma linguagem específica para o efetivo diálogo e troca de saberes entre pesquisadores de diferentes áreas, tornando o conhecimento do outro algo compreensível para os profissionais da Enfermagem, principalmente na pesquisa.

O Pós-doutorado iniciou-se em dezembro de 2018, na Escola de Enfermagem Daphne Cockwell, da Universidade Ryerson, localizada na cidade de Toronto, província de Ontário, no Canadá. O estágio foi iniciado com o propósito de instrumentalizar o refinamento de um projeto de pesquisa, entretanto, caracterizou-se por rumos inesperados promovendo novos conhecimentos. Na equipe de pesquisa inicial, composta por 2 enfermeiras, a pós-doutoranda e sua supervisora, foram adicionados um pesquisador da área de informática, um da área de comunicação e design, assim como cinco mestrandas de Enfermagem peritas em Enfermagem Pediátrica e uma doutoranda em comunicação e design, e, ainda, um consultor especial para jogos eletrônicos e realidade virtual. Houve a oportunidade de se discutir o projeto numa perspectiva multidisciplinar com o reconhecimento de sua importância social, sendo assumida a liderança de conteúdo nas discussões entre os profissionais dessa equipe no aperfeiçoamento do projeto.

Na área de inovação tecnológica, focalizando no letramento eletrônico em saúde de crianças portadoras de doenças crônicas, o projeto possibilitou o enfrentamento de dificuldades múltiplas. Devido ao alto custo operacional para o desenvolvimento de protótipos, enfrentou-se o desafio de procura e estabelecimento de parcerias com profissionais da área de tecnologia e inovação da universidade, onde está sendo realizado o Estágio de Pós-Doutorado, na Universidade Ryerson. Para tal, aprendeu-se uma nova maneira de refletir sobre os problemas de pesquisas usando um método de pensar/fazer científico ainda inédito para a Enfermagem brasileira.

Essa nova maneira de pensar ressalta fatos, eventos e reflexões, e ainda, apresenta o conceito do método de Design Thinking (DT), durante um treinamento adicional de 12 semanas de curso. Cabe ressaltar que DT também guia os trabalhos da equipe de pesquisadores, alunos e colaboradores associados a Science Discovery Zone da Universidade Ryerson.

O presente artigo tem o objetivo de relatar as diversas experiências de um estágio de Pós-Doutorado de Enfermagem extensivas ao conhecimento adquirido sobre o método de Design Thinking, que se caracteriza por buscar soluções inovadoras para problemas baseado nas necessidades das pessoas.¹ 1 Plattner H, Leifer L, Meinel C. Design thinking research: building innovators. 1st ed. Cham: Springer International Publishing; 2015. 289 p. vol. 1. http://dx.doi.org/10.1007/978-3-319-06823-7.
http://dx.doi.org/10.1007/978-3-319-0682... Essa busca para a solução de problemas deve acontecer de forma coletiva e colaborativa, em uma perspectiva de empatia máxima com os participantes do processo. Este relato propõe algumas ideias preliminares que podem incentivar o diálogo entre pesquisadores brasileiros para integrarem esse método, globalmente utilizado por pesquisadores internacionais, e revolucionarem a agenda de pesquisa de Enfermagem do Brasil.

A Universidade Ryerson

A Universidade Ryerson é composta por 20 mil estudantes em diversos cursos de graduação e sua Escola de Enfermagem possui dois programas de graduação que, juntos, constituem o maior contingente de graduandos da Enfermagem canadense, com mais de 2.300 estudantes. A Escola possui ainda duas cátedras de pesquisa, a Research Chair in Urban Health e a Canada Research Chair in Design and Evaluation of Health Interventions Tier 1, e dois programas de mestrado (um com opções de dissertação ou cursos, e ainda, um outro levando ao título de Nurse Practitioner). A escola aguarda a implantação de seu programa de doutorado multidisciplinar em saúde urbana, o primeiro do país nessa área. O intenso ambiente de pesquisa deve-se à presença de várias equipes de pesquisa lideradas por enfermeiros pesquisadores em rede local, nacional e internacionais.

A Universidade Ryerson dispõe de uma série de laboratórios e recursos que podem apoiar ideias inovadoras em mídia digital e tecnologia. Esses recursos oferecem orientação, suporte e conselhos para todas as áreas de pesquisa, desde artes criativas até engenharia biomédica. A universidade investe intensamente no empreendedorismo, em especial visando seu impacto social e sua forte tendência à inovação. Tal fato contribuiu para a criação do Digital Media Zone (DMZ), que é considerado um líder mundial para startups de tecnologia no Canadá. O DMZ caracteriza-se por ser um programa que ajuda a comunidade acadêmica a construir grandes negócios, conectando-os com clientes, capital, especialistas e uma comunidade de empreendedores e influenciadores. Para isso, a universidade cede um espaço para trabalho, equipamento, treinamento, gera a oportunidade de conhecer pessoas do ramo (o famoso networking), além de possibilitar que o empreendedor consiga fundos para dar início a projetos de pesquisas. Outros exemplos do apoio técnico na área de informática para a saúde da universidade em questão, que estariam à disposição da pós-doutoranda, foram a Ryerson Transmedia Zone (Faculdade de Comunicação e Design) e o SynLab (Faculdade de Comunicação e Design).

Inclusão na comunidade científica da Universidade Ryerson

No primeiro semestre do estágio, participou-se de inúmeras atividades que proporcionaram atualização e expansão de conhecimentos. Eventos e visitas técnicas organizados por diversos setores da universidade, assim como pela supervisora no cargo de Associate Director – Scholarly, Research and Creative Activity, possibilitaram o desenvolvimento de networking com cientistas internacionais.

No ambiente intelectual da Ryerson, rico em oportunidades de aprendizado multidisciplinar, em um período de seis meses, as atividades realizadas no âmbito de eventos científicos, culturais e sociais, atividades acadêmicas, de colaboração científica, de consulta técnica e de treinamento prático para pesquisa totalizaram 189 horas, além das dezenas de horas de leituras.

Oportunidades e desafios no Pós-doutorado

Inicialmente, estar realizando o estágio em um país multicultural, com o alto nível de identidade de uma comunidade multilinguística, favoreceu a tolerância ao outro, vivenciada na comunidade acadêmica estabelecida pela Ryerson, composta por docentes e discentes, além de visitantes e estudantes estrangeiros. A respeitosa aceitação da imagem do professor pesquisador visitante, automaticamente, coloca-o em uma posição especialmente privilegiada para superar tais desafios linguísticos.

Entre todas as oportunidades apresentadas, a mais desafiadora foi a aproximação com o conceito de DT, que desestabilizou o estado de conhecimento enquanto pesquisadora já que promoveu a consciência a respeito da urgência de se adotar um novo paradigma para pensar, colaborar, ensinar, desenhar, planejar, executar e avaliar nas atividades de pesquisa. As constantes reflexões sobre DT para enfermagem durante as atividades com os discentes do curso abriram perspectivas inovadoras para uma futura prática docente e de pesquisa.

Além disso, outro desafio foi ressignificar o papel de professor assumindo a posição de discente no curso de DT, juntamente com alunos graduandos. Toda essa vivência propiciou a oportunidade de ter que pensar “outside the box”, enfrentando desafios intelectuais, criando novas parcerias intelectuais com alunos de graduação de outras disciplinas tais como negócios, engenharia biomédica, administração de empresas, entre outros, descobrindo novas maneiras de pensar até então desconhecidas, revelando talentos pessoais e, assim, ampliando o conhecimento conceitual e teórico quanto às possibilidades de soluções para diversos problemas, inclusive na área da saúde e da ciência e prática científica de Enfermagem.

Conhecimentos adquiridos na perspectiva multidimensional

O estágio em sua primeira fase possibilitou a aquisição de novos conhecimentos na área da pesquisa e do ensino. Participando de oficinas promovidas pela Registered Nurses Association of Ontario (RNAO) sobre Best Practice Guidelines conheceu-se sobre a dinâmica de elaboração das normas para a prática baseada em evidências, por enfermeiros da prática assistencial, da docência e da pesquisa, que juntos conduzem revisão sistematizada de publicações de Enfermagem nacionais e internacionais. Ainda, conheceu-se métodos sistematizados de sua implementação em ambientes de trabalho, incluindo marketing, engajamento de partes interessadas e desenvolvimento de planos e propostas, além de métodos de avaliação pertinentes.

Específico na área da pesquisa, expandiu-se conhecimento prático no treinamento do software para análise qualitativa, NVivo 11 com exercícios com transcrições e dados de pesquisas eletrônicas na web, áudio, vídeo e mídia social. Quanto à análise de dados quantitativos, foi oportuno a treinamento prático com técnicas de transformação, gerenciamento de dados, automatização de dados, arquivos de dados e exportação de saídas para outros programas de computador manipulando-se o software SPSS.

Já na Conferência de Aprendizagem e Ensino na Universidade Ryerson, Learning Together: Collaboration and Community at the Centre, aprendeu-se que, na Ryerson, o aprender resulta da colaboração entre alunos, professores, instrutores e funcionários. Juntos, criam e compartilham conhecimento, conectando o aprendizado intra e extra sala de aula, reconhecendo assim as maneiras pelas quais a aprendizagem torna-se centro de todas as ações. No decurso da conferência intitulada Protecting the Rights of Children and Youth in Central America, the Caribbean, and Canada Conference, adquiriu-se conhecimento a respeito das questões sobre direitos e imigração de crianças e jovens na América Central, no Caribe, e no Canadá.

A participação como ouvinte nas disciplinas Health: A Global Perspective, e Nursing Trends and Issues, ambas ministradas pela supervisora do estágio, proporcionou a expansão dos conhecimentos sobre saúde da criança e do adolescente, em uma escala global. Por meio da temática lecionada, que abordou saúde global, a pós-doutoranda revisou os métodos de ensino, aprendeu sobre recursos educacionais digitais, e os conteúdos ensinados para os alunos de graduação e de pós-graduação de Enfermagem. Ademais, pôde-se atualizar conhecimentos na área da didática que direcionava as discussões em sala de aula, incentivando os alunos a pensarem dentro de um paradigma de mundo amplo, interligado e eticamente desafiador.

Uma área promissora, mas ainda desconhecida da Enfermagem brasileira é a do uso medicinal da cannabis. Participando de três oficinas oferecidas por neurologista, psicólogo e enfermeiros e com a participação especial de pacientes adultos usuários da cannabis medicinal, pôde-se conhecer sobre o sistema endocanabinoide, condições comuns de uso, reconhecimento dos pacientes de cannabis medicinal, as diferenças nas cepas, e pesquisas atuais. Ademais, o papel inovador do enfermeiro foi apresentado, incluindo a consulta de Enfermagem online oferecida por eles, para assegurar privacidade e anonimato dos clientes. Importante frisar que se encontra em desenvolvimento as melhores práticas de Enfermagem, além da construção de regulamentos para tal prática em consolidação no Canadá.

O método de Design Thinking

Um destaque especial do novo conhecimento é o conceito de Design Thinking, durante horas de exercícios e reflexões no curso de Evidence-based Innovation. O curso abordando o DT foi uma experiência acadêmica compartilhada com graduandos de diversas disciplinas. Dentre eles, área de exatas, humanas e biológicas, com a múltiplas perspectivas de administradores, engenheiros, bioquímicos, biólogos, psicólogos, advogados, desenvolvedores/programadores. Sua duração foi 12 semanas e o curso foi composto por quatro problemas de negócio (Ideia Shop), além do problema final, totalizando cinco ciclos de solução de algum problema. Ao final de cada problema, após o utilizar todo o método, apresentávamos a solução com um diferente tipo de apresentação.

O conceito de DT veio para revolucionar a maneira de encontrar soluções inovadoras para os problemas, soluções criativas focadas nas necessidades reais e não em pressuposições estatísticas. É um método científico aplicado a problemas de negócios, com o pensamento baseado nesses problemas reais, refinando as ideias e não os argumentos.

Nessa perspectiva, observa-se que essa abordagem colabora na condução da pesquisa, pois caracteriza-se como uma forma sistematizada de solucionar diversos problemas, inclusive, na área da saúde. O DT provoca uma forma sistematizada e não linear de pensar para solucionar problemas de forma coletiva e colaborativa, fundamentada em valores de empatia máxima entre os participantes do processo.¹ 1 Plattner H, Leifer L, Meinel C. Design thinking research: building innovators. 1st ed. Cham: Springer International Publishing; 2015. 289 p. vol. 1. http://dx.doi.org/10.1007/978-3-319-06823-7.
http://dx.doi.org/10.1007/978-3-319-0682... Kelley² 2 IDEO U. Design thinking [Internet]. 2020 [citado 2020 fev 3]. Disponível em: https://www.ideou.com/pages/design-thinking
https://www.ideou.com/pages/design-think... declara que “Design thinking is not a linear path. It’s a big mass of looping back to different places in the process”.

É necessário ter empatia para entender as necessidades daqueles para quem se está desenvolvendo a solução. Após a definição de problemas, é importante oportunizar soluções criativas e gerar uma gama de soluções possíveis para a escolha da melhor solução. A criatividade é uma premissa básica e, para revolucionar a maneira de encontrar soluções inovadoras para os problemas, as pressuposições estatísticas tornam-se secundárias, priorizando-se soluções criativas focadas nas necessidades reais. Nesse sentido, todas as pessoas são consideradas criativas e capazes de contribuir com boas ideias e são colocadas no centro de desenvolvimento da solução ou do produto, e não somente o consumidor final. Assim, todas as ideias são bem-vindas e podem ser aproveitadas no processo de criação da solução do problema. No que diz respeito a problemas relacionados à área da saúde, a metodologia de DT não se limita à equipe multidisciplinar envolvida no processo de cuidado ao paciente: abrange os pacientes, familiares, administradores, profissionais de apoio das instituições, entre outros.³ 3 Thompson MR, Barcott DS. The role of the nurse scientist as a knowledge broker. J Nurs Scholarsh. 2019;51(1):26-39. http://dx.doi.org/10.1111/jnu.12439. PMid:30354032.
http://dx.doi.org/10.1111/jnu.12439...

A convergência de ideias de diferentes agentes, oriundos de diversos contextos, permite a emergência de soluções transdisciplinares, caracterizando uma inovação do trabalho em uma equipe multidisciplinar. O DT pode, ainda, favorecer o incentivo e facilitar a contribuição de diversos profissionais que trabalham em conjunto com a Enfermagem, harmonizando a capacidade de resposta aos problemas, mediada por conflito de saberes.⁴ 4 Ribeiro-da-Silva M, Oliveira-Silva D, Andrade-Teles-Monteiro N, Matos-Santana R, Ramos-da-Cruz-Almeida T, Santos-Rocha S. Diagnoses, results, and nursing interventions in c-sections. Rev Enferm UFPE Online. 2018 dez 2;12(12):3221-30. http://dx.doi.org/10.5205/1981-8963-v12i12a237549p3221-3230-2018.
http://dx.doi.org/10.5205/1981-8963-v12i...

Outro benefício está na flexibilidade de usar ou ceder a liderança no processo. O fato de trabalhar com pessoas de outras áreas de atuação faz com que o papel como observador seja mais presente, desenvolvendo a habilidade de escuta. Observa-se que as habilidades de ouvir as pessoas, facilitar a contribuição dos outros e melhorar a capacidade de resposta ao conflito são desenvolvidas e fomentam um clima construtivo de equipe durante todo o processo de busca de soluções para os problemas.⁵ 5 Zancheta MS, Santos WS, Felipe ICV, Lucchese S. Formação audaciosa do enfermeiro-cientista. Online Braz. J. Nurs. 2018;16(3):238-40. http://dx.doi.org/10.17665/1676-4285.20176144.
http://dx.doi.org/10.17665/1676-4285.201...

Tradução de conhecimento

Um dos papéis profissionais diferenciados do enfermeiro na área de impacto social de conhecimentos científicos, constitui hoje o de ser knowledge-broker,⁶ 6 Yates P. Design thinking can it enhance nursing research? Cancer Nurs. 2018;41(4):344-5. http://dx.doi.org/10.1097/NCC.0000000000000630. PMid:29939884.
http://dx.doi.org/10.1097/NCC.0000000000... ou seja, o de introduzir públicos específicos a novos conhecimentos por meio de ações de tradução de conhecimentos. Assim, com a expansão do conhecimento adquirido, identificou-se a oportunidade ímpar de socializá-lo entre os pares para testar a aceitabilidade do conceito do DT. Buscou-se, ainda, testar a viabilidade de sua introdução nos diversos campos de prática da Enfermagem para transpassar o status quo do modo sistematizado da resolução de problemas clínicos, administrativos e da produção de conhecimentos. Historicamente, a Enfermagem brasileira tem se guiado pela sistematização da assistência, como uma forma de incorporação do método científico no pensar e no agir, principalmente no contexto clínico.⁷ 7 Beaird G, Geist M, Lewis EJ. Design thinking: opportunities for application in nursing education. Nurse Educ Today. 2018 maio;64:115-8. http://dx.doi.org/10.1016/j.nedt.2018.02.007. PMid:29471271.
http://dx.doi.org/10.1016/j.nedt.2018.02...

Atualmente, nosso desafio concentra-se em consolidar uma visão de vanguarda quanto à formação de pesquisadores para que esses ocupem seus lugares na comunidade global de pesquisadores da Enfermagem.⁸ 8 Eines TF, Vatne S. Nurses and nurse assistants’ experiences with using a design thinking approach to innovation in a nursing home. J Nurs Manag. 2018;26(4):425-31. http://dx.doi.org/10.1111/jonm.12559. PMid:29057548.
http://dx.doi.org/10.1111/jonm.12559... O movimento recente de DT no ensino e na pesquisa de Enfermagem em alguns países⁹ 9 McLaughlin JE, Wolcott M, Hubbard D, Umstead K, Rider TR. A qualitative review of the design thinking framework in health professions education. BMC Med Educ. 2019;19(1):98. http://dx.doi.org/10.1186/s12909-019-1528-8. PMid:30947748.
http://dx.doi.org/10.1186/s12909-019-152... indica que podemos dele participar demonstrando as particularidades de ensinar e pesquisar em Enfermagem no seio da sociedade brasileira, embora no Brasil o DT seja pouco conhecido entre os profissionais de Enfermagem e nenhuma iniciativa educacional para enfermeiros esteja disponível.

Entretanto, na ocasião de um evento comemorativo de pós-graduação do programa de mestrado profissional em Enfermagem na Universidade Federal Fluminense (agosto de 2019), com a participação por vídeo conferência pela Dra. Camilla Londoño (Science Discovery Zone) e a presenca da Profa. Margareth Zanchetta, promoveu-se uma iniciativa-piloto de tradução de conhecimento de DT com palestras. A audiência foi composta de 85 participantes (estudantes de graduação em Enfermagem, professores e profissionais). A palestra conduzida pela autora e com sua supervisora obteve uma excelente receptividade da utilização do método pelos estudantes e profissionais presentes.

Foi notório o entusiasmo transmitido pela audiência na realização dos exercícios em grupo, motivação no compartilhamento de soluções e ideias. A audiência reconheceu a alta aplicabilidade do DT para superar desafios e buscar novos caminhos para soluções de problemas complexos crescentes no contexto do trabalho da Enfermagem brasileira. Além disso, todos analisaram aplicações possíveis e viáveis do DT em projetos de pesquisa por estudantes de graduação e pós-graduação, assim como em atividades de equipes e profissionais de pesquisa consolidados.

Cabe ressaltar que, o DT enfatiza a criatividade e a inovação, além de ousar na proposição de novos objetos de estudos, redesenhando, modificando e construindo novos métodos científicos. Este argumento pareceu plenamente entendido por essa audiência, que corroborou a importância do estímulo à criatividade e à empatia nos programas de Enfermagem. Para a capacitação dos docentes e discentes da graduação e pós-graduação, autores sugerem a utilização de workshops de iniciação ao DT, o trabalho com instituições de saúde para aplicação real da metodologia do DT para resolver problemas complexos, e a exploração de colaborações multidisciplinares usando DT.⁷ 7 Beaird G, Geist M, Lewis EJ. Design thinking: opportunities for application in nursing education. Nurse Educ Today. 2018 maio;64:115-8. http://dx.doi.org/10.1016/j.nedt.2018.02.007. PMid:29471271.
http://dx.doi.org/10.1016/j.nedt.2018.02... Tal sugestão teve ressonância na audiência confirmando uma visão consensual de que o DT pode levar a uma busca pelo conhecimento além da Enfermagem, ampliando assim o escopo do pensamento crítico. Ainda, discutiu-se que novas formas de aplicar DT podem resultar em ferramentas criativas para aumentar a inovação em diferentes sistemas organizacionais na área da saúde. Ademais, pesquisas são necessárias sobre como a equipe e os gestores poderiam cooperar no desenvolvimento de todo esse processo de inovação.⁴ 4 Ribeiro-da-Silva M, Oliveira-Silva D, Andrade-Teles-Monteiro N, Matos-Santana R, Ramos-da-Cruz-Almeida T, Santos-Rocha S. Diagnoses, results, and nursing interventions in c-sections. Rev Enferm UFPE Online. 2018 dez 2;12(12):3221-30. http://dx.doi.org/10.5205/1981-8963-v12i12a237549p3221-3230-2018.
http://dx.doi.org/10.5205/1981-8963-v12i...

Interessante ainda expor que a comunicação espontânea pós-evento via e-mail com a autora revelou um crescente interesse em receber assessoria e aconselhamento sobre como o DT seria usado em assuntos atuais relacionados à preparação de alunos de graduação para o campo de estágio.

Planejamento de transferência de conhecimento

Vista a experiência positiva com uma audiência diversificada e de relevância, a segunda fase do estágio de pós-doutorado concentrar-se-á na criação de um plano de disciplina inédita optativa para uma população alvo multidisciplinar.

Prospectivamente, o DT e os novos conhecimentos a serem gerados, uma vez incorporados à prática de Enfermagem e de outros profissionais, promoveriam a articulação do diálogo com outros pesquisadores. Defende-se que, assim, os enfermeiros brasileiros poderiam desenvolver habilidades futuras de escuta atenta, sem julgamento; abrir suas mentes à criatividade e desenvolver a habilidade de pensar de uma forma não linear para o fazer científico.

Na concepção da referida disciplina, será considerado que o objetivo de uma abordagem de DT é definir e desenvolver soluções abrangentes e eficazes para problemas através do pensamento criativo, satisfazendo três fatores-chave: viabilidade, factibilidade e desejabilidade.⁷ 7 Beaird G, Geist M, Lewis EJ. Design thinking: opportunities for application in nursing education. Nurse Educ Today. 2018 maio;64:115-8. http://dx.doi.org/10.1016/j.nedt.2018.02.007. PMid:29471271.
http://dx.doi.org/10.1016/j.nedt.2018.02... Pensando em uma perspectiva multidisciplinar, os alunos poderiam apreender que a viabilidade está relacionada à estrutura organizacional e ao modelo de negócios subjacentes. A factibilidade refere-se às competências internas, ao potencial tecnológico de mudança e à capacidade de fornecer uma ideia inovadora, os alunos buscariam novos caminhos no pensar. E, finalmente, a desejabilidade diz respeito a como as partes interessadas e, em particular, os usuários se conectam emocionalmente a uma inovação,⁴ 4 Ribeiro-da-Silva M, Oliveira-Silva D, Andrade-Teles-Monteiro N, Matos-Santana R, Ramos-da-Cruz-Almeida T, Santos-Rocha S. Diagnoses, results, and nursing interventions in c-sections. Rev Enferm UFPE Online. 2018 dez 2;12(12):3221-30. http://dx.doi.org/10.5205/1981-8963-v12i12a237549p3221-3230-2018.
http://dx.doi.org/10.5205/1981-8963-v12i... os alunos vivenciariam a inovação como sendo uma experiência de valorização empática das particularidades, necessidades e potencialidades da clientela.

Autores sugerem que, além das habilidades desenvolvidas através da metodologia de DT, esse método pode ser usado para redesenhar currículos e moldar processos organizacionais.⁹ 9 McLaughlin JE, Wolcott M, Hubbard D, Umstead K, Rider TR. A qualitative review of the design thinking framework in health professions education. BMC Med Educ. 2019;19(1):98. http://dx.doi.org/10.1186/s12909-019-1528-8. PMid:30947748.
http://dx.doi.org/10.1186/s12909-019-152... O cuidar de enfermagem pode produzir, desde o saber da enfermagem até novas formas de lidar com a diversidade humana e com a capacidade de resposta de indivíduos e coletividades, em distintas maneiras de operar o cuidado em saúde.¹⁰ 10 Toso BRGO, Padilha MI, Breda KL. The euphemism of good practice or advanced nursing practice. Esc Anna Nery. 2019;23(3):e20180385. http://dx.doi.org/10.1590/2177-9465-ean-2018-0385.
http://dx.doi.org/10.1590/2177-9465-ean-...

Dessa forma, observa-se a importância de criar disciplinas que possam redesenhar o pensar do agir científico para o campo da Enfermagem e da área da saúde. Com base na experiência intelectual vivenciada pela autora com outros indivíduos, pensando em suas próprias perspectivas profissionais, pode-se afirmar que existe uma grande aceitação e motivação pela parceria intelectual com profissionais de Enfermagem. É crescente o consenso de que a Enfermagem detém um conhecimento único das experiências de saúde, vida e morte dos indivíduos.¹¹ 11 Elmore J, Wright DK, Paradis M. Nurses’ moral experiences of assisted death: a meta-synthesis of qualitative research. Nurs Ethics. 2018;25(8):955-72. http://dx.doi.org/10.1177/0969733016679468. PMid:28027675.
http://dx.doi.org/10.1177/09697330166794... Portanto, outros profissionais percebem a Enfermagem como fator catalisador para as soluções técnicas para as complexas situações nos sistemas de saúde.

Considerações finais e Implicação para a prática

A oportunidade de realização do estágio de pós-doutorado no exterior é uma experiência enriquecedora, pois além de estimular a internacionalização da pesquisa entre universidades e a troca de conhecimentos entre pesquisadores, pode promover um melhor entendimento na maneira de pensar dos envolvidos.

A participação em diversos eventos da área de Enfermagem canadense possibilitou a aquisição de novos conhecimentos, em especial, na área de inovação baseada em evidências científicas e a aproximação e a incorporação do conceito de DT. A primeira fase do estágio de pós-doutorado permitiu a aquisição de conhecimentos outros além daqueles da Enfermagem, e estimulou o pensamento crítico além do campo da área da saúde. O curso frequentado teve como resultados concretos o desenvolvimento da habilidade de uma escuta mais crítica, com mais atenção e com menos julgamento, e a afirmação do saber e da respeitabilidade da Enfermagem em um contexto de troca multidisciplinar de ideias.

Sendo assim, no que tange ao ensino do DT para a Enfermagem, é inegável que essa metodologia poderá revolucionar a educação de uma nova geração de pesquisadores, educadores, gestores e profissionais assistenciais. O compromisso social e a atuação como cidadão da Enfermagem brasileira, na era digital, em realidades constantemente complexas, serão fortalecidos pela destemida capacidade de pensar e inovar na produção de conhecimentos. Atualmente, DT é uma ferramenta cognitiva que reconstrói a engenhosidade humana inspirada em valores humanísticos, empáticos, que asseguram a qualidade de serviços e produtos e confirmam o respeito ao perfil do cliente. Assim, qualquer ideia emergida para solucionar o problema será sempre bem-vinda, podendo ressignificar o pensar e o agir científico para a Enfermagem brasileira futurista.

Referências

¹
Plattner H, Leifer L, Meinel C. Design thinking research: building innovators. 1st ed. Cham: Springer International Publishing; 2015. 289 p. vol. 1. http://dx.doi.org/10.1007/978-3-319-06823-7
» http://dx.doi.org/10.1007/978-3-319-06823-7

²

³
Thompson MR, Barcott DS. The role of the nurse scientist as a knowledge broker. J Nurs Scholarsh. 2019;51(1):26-39. http://dx.doi.org/10.1111/jnu.12439 PMid:30354032.
» http://dx.doi.org/10.1111/jnu.12439

⁴
Ribeiro-da-Silva M, Oliveira-Silva D, Andrade-Teles-Monteiro N, Matos-Santana R, Ramos-da-Cruz-Almeida T, Santos-Rocha S. Diagnoses, results, and nursing interventions in c-sections. Rev Enferm UFPE Online. 2018 dez 2;12(12):3221-30. http://dx.doi.org/10.5205/1981-8963-v12i12a237549p3221-3230-2018
» http://dx.doi.org/10.5205/1981-8963-v12i12a237549p3221-3230-2018

⁵
Zancheta MS, Santos WS, Felipe ICV, Lucchese S. Formação audaciosa do enfermeiro-cientista. Online Braz. J. Nurs. 2018;16(3):238-40. http://dx.doi.org/10.17665/1676-4285.20176144
» http://dx.doi.org/10.17665/1676-4285.20176144

⁶
Yates P. Design thinking can it enhance nursing research? Cancer Nurs. 2018;41(4):344-5. http://dx.doi.org/10.1097/NCC.0000000000000630 PMid:29939884.
» http://dx.doi.org/10.1097/NCC.0000000000000630

⁷
Beaird G, Geist M, Lewis EJ. Design thinking: opportunities for application in nursing education. Nurse Educ Today. 2018 maio;64:115-8. http://dx.doi.org/10.1016/j.nedt.2018.02.007 PMid:29471271.
» http://dx.doi.org/10.1016/j.nedt.2018.02.007

⁸
Eines TF, Vatne S. Nurses and nurse assistants’ experiences with using a design thinking approach to innovation in a nursing home. J Nurs Manag. 2018;26(4):425-31. http://dx.doi.org/10.1111/jonm.12559 PMid:29057548.
» http://dx.doi.org/10.1111/jonm.12559

⁹
McLaughlin JE, Wolcott M, Hubbard D, Umstead K, Rider TR. A qualitative review of the design thinking framework in health professions education. BMC Med Educ. 2019;19(1):98. http://dx.doi.org/10.1186/s12909-019-1528-8 PMid:30947748.
» http://dx.doi.org/10.1186/s12909-019-1528-8

¹⁰
Toso BRGO, Padilha MI, Breda KL. The euphemism of good practice or advanced nursing practice. Esc Anna Nery. 2019;23(3):e20180385. http://dx.doi.org/10.1590/2177-9465-ean-2018-0385
» http://dx.doi.org/10.1590/2177-9465-ean-2018-0385

¹¹
Elmore J, Wright DK, Paradis M. Nurses’ moral experiences of assisted death: a meta-synthesis of qualitative research. Nurs Ethics. 2018;25(8):955-72. http://dx.doi.org/10.1177/0969733016679468 PMid:28027675.
» http://dx.doi.org/10.1177/0969733016679468

# data[].attributes.attachment_link.title
Inovando no pensar e no agir científico: o método de Design Thinking para a enfermagem

# data[].attributes.attachment_link.description
Resumo Objetivos Relatar experiências vivenciadas durante um estágio de Pós-Doutorado, o...

# data[].attributes.headline
Modelo de Treinamento para Inserção de Dispositivos Intrauterinos

# data[].attributes.subject
Modelo de Treinamento para Inserção de Dispositivos Intrauterinos

# data[].attributes.attachment_link.readable_text
Introdução

Aprender praticando exclusivamente em pacientes é cada vez menos aceitável no ensino médico. O ensino baseado em simulação é uma ferramenta capaz de gerar aquisição de habilidades e competências de forma eficaz e em ambiente controlado, sem causar dano ao paciente. A Ginecologia é uma especialidade médica que depende de treinos bem estruturados, e entre os procedimentos ginecológicos destaca-se a inserção dos dispositivos intrauterinos de cobre. Para aumentar a oferta deste método contraceptivo, é preciso capacitação adequada dos profissionais de saúde. Os simuladores físicos imitam a realidade e auxiliam na aquisição de habilidades e competências, tornando de fundamental importância o ensino baseado em simulação para inserção de dispositivos intrauterinos.

Objetivo

Desenvolver um modelo de treinamento para aquisição de habilidades na inserção de dispositivos intrauterinos.

Método

Foi desenvolvido um simulador (modelo de utilidade) para inserção de dispositivos intrauterinos, composto por uma base rígida retangular com 17 cm x 15 cm x 21 cm (A x L x P), com peso de 420 gramas, confeccionada em acrinolitrilabutadieno estireno (ABS). Acoplada a este suporte, existe uma estrutura semiflexível, composta por um protótipo de canal vaginal, com colo uterino e região vulvar semelhantes à anatomia humana, confeccionada em ácido poliático flexível (PLA-FLEX). Foram selecionados 15 médicos residentes de Ginecologia e Obstetrícia para treinamento teórico-prático com o simulador proposto pelo estudo para capacitação em inserção de dispositivo intrauterino de cobre. O simulador utilizado neste estudo foi avaliado e validado por especialistas em Ginecologia e Obstetrícia.

Resultados

Houve melhora estatisticamente significante dos parâmetros avaliados comparando-se a avaliação inicial (pré-treinamento) e final (pós-treinamento) dos residentes, com homogeneidade no desempenho final. Na avaliação inicial, apenas 40% dos dispositivos ficaram bem posicionados, em contraste com 93,3% de sucesso de inserção após implementado o modelo de treinamento.

Conclusão

Desenvolveu-se um modelo de treinamento baseado em simulação que contribuiu para a aquisição de habilidades de inserção de dispositivo intrauterino de cobre quando aplicado aos residentes de Ginecologia e Obstetrícia.

Educação Médica; Simulação; Dispositivos Intrauterinos

Introduction

Learning by practicing exclusively on patients is becoming increasingly unacceptable in medical training. Simulation-based teaching is a tool capable of generating skills and competences efficiently and in a controlled environment, without causing harm to patients. Gynecology is a medical specialty that depends on well-structured training; an important gynecological procedure is the insertion of copper intrauterine devices. In order to increase the supply of this contraceptive method to the population the relevant health professionals need to be adequately trained. Physical simulators imitate reality and assist in skills acquisition. Therefore, structured training based on simulation to teach the insertion of intrauterine devices is of fundamental importance to the teaching of gynecology.

Objective

To develop a training model for skills acquisition for the insertion of intrauterine devices.

Method

A simulator (utility model) was developed for the insertion of intrauterine devices, composed of a rigid rectangular base made of acrylonitrile butadiene styrene, measuring 17 cm x 15 cm x 21 cm and weighing 420 g. Attached to this support is a semi-flexible structure made of flexible polylactic acid, composed of a prototype vaginal canal, with a uterine cervix and vulvar region, similar to the human anatomy. Fifteen gynecology and obstetrics residents were selected for theoretical and practical training with the simulator proposed by the study for the training of intrauterine copper device insertion. The simulator used in this study was evaluated by specialists in gynecology and obstetrics, who validated it for use in teaching.

Results

A statistically significant improvement was found in the evaluated parameters, comparing the initial evaluation and the final evaluation of the residents, whose final performances displayed homogeneity. In the initial evaluation only 40% of the devices were well positioned, in contrast to a 93.3% rate of successfull insertion following implementation of the training model.

Conclusion

A training model was developed to simulate the performance of copper intrauterine device insertion and contributed to the acquisition of skills for this task when applied to gynecology and obstetrics residents.

Medical Education; Simulation; Intrauterine Devices

Durante anos, o ensino médico foi baseado no modelo proposto inicialmente por Halsted: see one, do one, teach one (veja, faça, ensine), fazendo com que aprender praticando em pacientes se tornasse uma prática ainda presente na atualidade. Com a evolução das metodologias de ensino na área da saúde, surgiram os conceitos habilidades e competências, que trouxeram em conjunto o ensino baseado em simulação,.

A simulação como forma de aprendizado surgiu nos anos 1930 com o primeiro simulador de voo, Link Trainer. Já o primeiro simulador em medicina foi desenvolvido no início da década de 1960, um boneco para treinamento de respiração boca a boca, que ficou conhecido como Ressuci-Anne. A simulação substitui temporariamente o encontro com o paciente real por modelos artificiais, permitindo que o processo de ensino e aprendizagem ocorra em ambiente seguro, de forma ativa, diminuindo o medo de causar dano durante o aprendizado, com maior retenção de conhecimento, tornando "aprender praticando em pacientes" cada vez menos aceitável,,.

A literatura mundial aponta que as habilidades médicas em indicar e inserir corretamente o DIU de cobre estão abaixo do esperado, muitas vezes por imperícia ou por falsas contraindicações, o que reforça a necessidade do desenvolvimento de modelos de treinamento,.

Desenvolver um modelo de treinamento para aquisição de habilidades na inserção de dispositivos intrauterinos.

Após aprovação do projeto de pesquisa pelo Comitê de Ética e Pesquisa em Seres Humanos (CAAE: 75841617.0.0000.5174), foram selecionados os 15 residentes do primeiro ano do programa de residência médica em Ginecologia e Obstetrícia do Hospital Maternidade Leonor Mendes de Barros (HMLMB), na cidade de São Paulo. Todos assinaram o Termo de Consentimento Livre e Esclarecido. Não houve desistências no decorrer do estudo. Todos os residentes selecionados referiram ausência de treinamento prévio para inserção de DIU de cobre tanto com simulação quanto diretamente em pacientes.

Desenvolvimento do modelo de treinamento

Trata-se de um simulador físico que configura um modelo de utilidade para inserção de DIU, visando à capacitação teórico-prática na inserção de todos os tipos de DIU em humanos. Os autores depositaram o pedido de patente de modelo de utilidade junto ao Instituto Nacional de Proteção Intelectual (número de processo: BR 20 2018 002915 6).

O modelo de utilidade está representado nas Figuras 1 e 2. A Figura 1 representa uma vista tridimensional do suporte rígido de formato retangular contendo um protótipo anatômico de útero humano hemisseccionado, no interior do qual existe uma cavidade que se assemelha à cavidade uterina normal. Esse suporte rígido tem uma base retangular com 17 cm no maior ponto de altura, 15 cm de largura e 21 cm de profundidade, com peso de 420 gramas. Para o presente estudo, foi confeccionada em acrinolitrilabutadieno estireno (ABS).

Está acoplada a este suporte rígido uma estrutura semiflexível, composta por um protótipo de canal vaginal, colo uterino (com local de engate para pinçamento) e região vulvar semelhantes à anatomia humana. Esta peça foi confeccionada em ácido poliático flexível (PLA-FLEX). A Figura 2 apresenta uma vista tridimensional (frontal e lateral, respectivamente) da peça semiflexível.

O modelo de utilidade foi projetado com o programa Autodesk 3DS Max 2015 ©, e a confecção foi realizada com impressão tridimensional, utilizando-se a impressora Sethi 3D-S3, com custo final de R$ 487,00.

Graças às características do modelo, é possível realizar exame especular, identificação do colo uterino, pinçamento do colo uterino e tração com auxílio de pinça cirúrgica (Pozzi), aferição da histerometria e, em seguida, treinamento da técnica para inserção do DIU de cobre. No estudo foi utilizado o DIU TCU 380-A e foram considerados bem posicionados aqueles de localização fúndica, colocados no centro da cavidade uterina e com os braços do "T de cobre" abertos horizontalmente em direção à topografia dos óstios tubários.

O simulador utilizado no presente estudo foi avaliado por especialistas em Ginecologia e Obstetrícia com auxílio da escala de Likert com cinco pontos. Foi atribuído o conceito excelente ao simulador, caracterizando-o como validado para uso em ensino.

Treinamento teórico-prático

O primeiro contato com o simulador visou reconhecer e interagir com o simulador, gerando hipóteses de como deveria ser a técnica correta para inserção do DIU de cobre. Cada residente realizou uma tentativa de inserir o DIU de cobre e foi avaliado segundo quesitos do roteiro com os passos preconizados pelo Ministério da Saúde do Brasil sobre a técnica de inserção do DIU de cobre, transcrito a seguir.

Roteiro para inserção de DIU TCU 380-A

- Higiene das mãos e paramentação

- Toque vaginal para verificar posição uterina

- Inserção do especulo vaginal

- Antissepsia adequada

- Pinçamento do colo uterino com Pozzi às 12h

- Histerometria

- Carregar DIU no dispositivo de inserção

- Delimitar histerometria no dispositivo de inserção

- Introdução do dispositivo de inserção

- Liberação dos braços do DIU

- Certificar o posicionamento do DIU (fúndico)

- Retirada do dispositivo de inserção

- Secção dos fios do DIU (2-3 cm)

- Retirar pinça Pozzi e verificar a hemostasia

- Retirar espéculo vaginal

- DIU bem posicionado?

Após o primeiro contato com o simulador, foram ministradas aulas conceituais com recursos audiovisuais que abordavam introdução geral sobre o DIU de cobre e seu mecanismo de ação, eficácia, tempo de duração, vantagens, desvantagens, critérios de elegibilidade, efeitos colaterais, complicações e seus manejos.

Por fim, foi descrita a técnica adequada para inserção do DIU TCU 380-A, com elaboração de um roteiro. O instrutor inicialmente demonstrou a técnica de inserção do dispositivo intrauterino com o auxílio de um vídeo demonstrativo e em seguida com demonstração prática no simulador físico.

Passados 15 dias da exposição teórico-prática, cada aluno participante da pesquisa teve um período de treinamento livre controlado em posse do roteiro, com 30 minutos de prática no simulador. Nessa etapa, foi fornecido também um feedback quanto a erros e acertos anotados na primeira avaliação.

Após 15 dias do treino livre controlado, cada aluno, separadamente, simulou a inserção do DIU de cobre no modelo de utilidade, fazendo nesse momento a narrativa de cada passo, que foi avaliado pelo instrutor com auxílio do roteiro para colocação de DIU do protocolo de pesquisa.

Avaliação estatística

A amostra foi estabelecida por conveniência. Os dados da pesquisa foram armazenados em planilhas do Microsoft Excel 2016 © e submetidos a análise estatística no programa Bioestat 5.3 ©. Foi utilizado o teste de normalidade de Shapiro-Wilk, o teste t de Student pareado e o teste G Aderência. Foram considerados significantes resultados com p < 0,05.

Participaram do estudo 15 alunos e não houve desistência. Quanto à média de acertos, foi observada melhora estatisticamente significante ao término do treinamento proposto em relação à avaliação inicial dos residentes ( Figura 3). O diagrama de extremos e quartis ( Figura 4) demonstra que durante a avaliação inicial havia grande dispersão do conhecimento, em contraste com a homogeneidade do desempenho final.

No primeiro contato com o simulador, a média de acertos dos residentes foi de 59,2% por questão, e a média geral de acertos por aluno foi de 63,3%. Já na avaliação final, os resultados obtidos foram 92,1% e 98%, respectivamente ( Tabela 1).

Na avaliação inicial, apenas 40% dos dispositivos ficaram bem posicionados, em contraste com 93,3% de sucesso de inserção após implementado o modelo de treinamento ( Figura 5).

Houve melhora progressiva estatisticamente comprovada na média de acertos, tanto por questão quanto por aluno, após o treinamento com simulação, corroborando resultados da literatura sobre ensino baseado em simulação. Bartz et al. descrevem um aumento de conhecimento sobre métodos contraceptivos intrauterinos com auxílio de simulação prática no consultório de Ginecologia e Obstetrícia, gerando relações mais favoráveis quanto ao método.

O modelo de treinamento baseado em simulação proposto por Oti et al. gerou importante ganho de habilidades de atividades laparoscópicas, em concordância com a literatura, que evidencia a redução do nível de dificuldade técnica após treinos em simuladores de alta ou baixa fidelidade,.

Os autores do presente estudo destacam a importância do feedback durante o treinamento como ferramenta de construção do conhecimento, estimulando o pensamento crítico dos alunos acerca dos erros cometidos na avaliação inicial, o que possibilitou um conhecimento mais homogêneo.

Desenvolveu-se um modelo para treinamento baseado em simulação que contribuiu para a aquisição de habilidades na inserção de dispositivo intrauterino de cobre quando aplicado a residentes de Ginecologia e Obstetrícia.

¹

Torkington J, Smith SG, Rees BI, Darzi A. The role of simulation in surgical training. Ann R Coll Surg Engl. 2000; 82: 88-94.

²

Miller GE. The assessment of clinical skills/competence/performance. Acad Med. 1990; 65: 63-7.

³

Aggarwal R, Darzi A. Simulation to enhance patient safety: why aren't we there yet? Chest. 2011; 140(4):854-58.

⁴

Cooper JB, Taqueti VR. A brief history of the development of manequin simulators for clinical education and training. PostgradMed J. 2008; 84(997):563-70.

⁵

Dourado ASS, GiannellaI TR. Ensino baseado em simulação na formação continuada de médicos: análise das percepções de alunos e professores de um Hospital do Rio de Janeiro. RevBrasEduc Méd. 2014; 38 (4): 460-69

⁶

Flato UAP, Guimarães HP. Educação baseada em simulação em medicina de urgência e emergência: a arte imita a vida. Rev Bras Educ Méd. 2011; 9(5):360-64.

⁷

Giordano MV, Giordano LA, Panisset KS. Dispositivo intrauterino de cobre. Femina. 2015; 43 (1): 15-20.

⁸

Schubert FD, Herbitter C, Fletcher J, Gold M. IUD Knowledge and Experience Among Family Medicine Residents. Fam Med. 2015;47(6):474-77.

⁹

Dermish AI, Turok DK, Jacobson JC, Murphy PA, Saltzman HM, Sanders. Evaluation of an intervention designed to improve the management of difficult IUD insertions by advanced practice clinicians. Contraception. 2016; 93 (6): 533-38.

¹⁰

Dermish AI, Turok DK, Jacobson JC, Flores MES, McFadden M, Burke K. Failed IUD insertions in community practice: an under-recognized problem? Contraception. 2013;87(2):182-86.

¹¹

Bartz D, Paris A, Maurer R, Gardner R, Johnson N. Medical student simulation training in intrauterine contraception insertion and removal: an intervention to improve comfort, skill, and atitudes. Contraception. 2016; 1:3

¹²

Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Saúde sexual e saúde reprodutiva / Ministério da Saúde, Secretaria de Atenção à Saúde, Departamento de Atenção Básica. Brasília: Ministério da Saúde. 2010. p.191-205.

¹³

Oti AT et al. Developmentof a laparoscopic training modelusing a smartphone. RevColBrasCir 2017; 44(5): 471-75.

¹⁴

Bouet PE et al.Training of medical students for pelvic examination: Benefits of teaching on anatomic models. J Gynecol Obstet Biol Reprod. 2016;45(7):679-84

¹⁵

Hugon-Rodin J et al. Learning the gynecological examination on low-fidelity simulation: Impact on the feelings of medical students. Gynecol Obstet Fertil Senol. 2017;45(5):291-98.

¹⁶

Varga CRR et al. Relato de experiência: o uso de simulações no processo de ensino-aprendizagem em medicina. Rev Bras Educ Méd. 2009; 33(2):291-97.

¹⁷

Timms C, Speirs K. Student perspectives on post-simulation debriefing. Clin Teach 2015;12(6):418-22.

¹⁸

Nystrom S, Dahlberg J, Edelbring S, Hult H, Dahlgren MA. Debriefing practices in interprofessional simulation with students: a sociomaterial perspective. BMC Med Educ. 2016; 16:148-55.

¹⁹

D'Arcangues C. Worldwide use of intrauterine devices for contraception. Contraception. 2007;75(6 Suppl):2-7.

²⁰

Thompson KM, Stern L, Gelt M, Speidel JJ, Harper CC. Counseling for IUDs and implants: are health educators and clinicians on the same page? Perspect Sex Reprod Health. 2013;45(4):191-5.

²¹

Lichowski AT, Anderson BL, Power ML, Raglan GB, Espey E, Schulkin J. Obstetrician-gynecologists and contraception: long-acting reversible contraception practices and education. Contraception. 2014;89(6):578-83.

²²

Gomez AM, Hartofelis EC, Finlayson S, Clark JB.Do Knowledge and Attitudes Regarding Intrauterine Devices Predict Interest in Their Use? Womens Health Issues. 2015;25(4):359-65.

# data[].attributes.attachment_link.title
Modelo de Treinamento para Inserção de Dispositivos Intrauterinos

# data[].attributes.attachment_link.description
Aprender praticando exclusivamente em pacientes é cada vez menos aceitável no ensino médico. O ensino baseado em simulação é uma ferramenta capaz de gerar aquisição de habilidades e competências de forma eficaz e em ambiente controlado, sem causar dano ao paciente.

# data[].attributes.attachment_link.readable_text
CC BY-NC-ND 4.0 · Journal of Coloproctology 2021; 41(S 01): S1-S235
DOI: 10.1055/s-0041-1741924

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil

1   UNIFESP/EPM, São Paulo, São Paulo, Brasil
› Author Affiliations

Área: MISCELÂNEA

Data: 02/09/2021

Horário: 11:00 às 13:30

Sala: Sala 02

Forma de Apresentação: tema livre

Objetivo Este trabalho tem como objetivo demonstrar a aplicação da metodologia do Design Thinking na área da saúde.

Método Revisão bibliográfica sistemática, a partir do descritor "Design Thinking and Health", com artigos publicados entre os anos de 2015 a 2020, no Pubmed e Lilacs. Ao pesquisar o termo no LILACS apareceram 78 artigos, que com o filtro do período, diminuiu para 56 artigos, finalizando 7 artigos após a aplicação dos fatores de inclusão e exclusão. Já no Pubmed foram mostrados 280 resultados, que ao filtrar a busca dos anos 2015 a 2020, caíram para 200 resultados. Foram excluídos os artigos repetidos nas bases de dados, os que mesmo citando o termo "design thinking" não tinham relação com o tema, e os que não apresentavam acesso aberto. Foram incluídos artigos com termos afins como "mobile app" e "smartphone app" que estivessem relacionados com saúde e que fizeram uso do método design thinking.

Resultados O Design Thinking é um método que, por ser multiprofissional e contar com profissionais de várias áreas, não apenas da saúde, é capaz de buscar soluções criativas e inovadoras para solução de problemas. Ele tem sido utilizado nos últimos anos na área da saúde, sobretudo na construção de app móveis, uma vez que sua elaboração é centrada no usuário e atende às suas necessidades. O design thinking faz uso de etapas para atingir seu objetivo na busca de soluções de problemas criativos e multifacetados. Embora há a descrição do design thinking em 3 e em 4 etapas, a de 5 etapas é mais completa, sendo elas: 1) Empatia ou descoberta; 2) Definição ou interpretação; 3) Idealização ou ideação; 4) Protótipo ou experimentação; 5) Teste ou evolução. Ainda, o design thinking pode contar com o brainstorming, que é uma tempestade de ideias, na qual podem surgir ideias inovadoras. Os app móveis têm a vantagem de serem acessados em praticamente todos os ambientes, desse modo, possuem potencial para influenciar os pacientes de forma positiva, tanto para que eles adotem um estilo de vida saudável, como também para que possam autogerenciar suas doenças e comorbidades. Fatores como idade, alfabetização digital e suporte digital podem limitar o acesso aos mHealth (aplicativos móveis em saúde), no entanto, o design thinking pode oferecer um suporte para tentar aniquilar o abismo entre o usuário e o aplicativo, haja vista que uma de suas qualidades é a empatia, e por meio dela, ao ouvir os usuários finais, pode proporcionar melhorias capazes de deixar o protótipo acessível ao público-alvo.

Conclusões O design thinking se mostrou uma ferramenta eficiente para o desenvolvimento de mHealth, principalmente por fazer uso da empatia, a qual proporciona que o app seja construído de acordo com as necessidades do próprio utilizador. Ainda, estudos sugerem a associação do design thinking com outras ferramentas metodológicas, para que os mHealth possam ser adaptados e acessíveis a longo prazo.

Publication History

Publication Date:
04 January 2022 (online)

© 2022. Sociedade Brasileira de Coloproctologia. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commecial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Thieme Revinter Publicações Ltda.
Rua do Matoso 170, Rio de Janeiro, RJ, CEP 20270-135, Brazil

# data[].attributes.attachment_link.title
Thieme E-Journals - Journal of Coloproctology / Abstract

# data[].attributes.attachment_link.description
CC BY-NC-ND 4.0 · Journal of Coloproctology 2021; 41(S 01): S1-S235DOI: 10.1055/s-0041-1741924 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil 1   UNIFESP/EPM, São Paulo, São Paulo, Brasil › Author Affiliations Área: MISCELÂNEA Data: 02/09/2021 Horário: 11:00 às 13:30 Sala: Sala 02 Forma de Apresentação: tema livre Objetivo Este trabalho tem como objetivo demonstrar a aplicação da metodologia do Design Thinking na área da saúde.