# data[].attributes.headline
Technology assisted PROM study in Asthma care

# data[].attributes.subject
Technology assisted PROM study in Asthma care

# data[].attributes.attachment_link.title
Allergy - 2022 - Sousa‐Pinto - Patient‐reported outcome measures  PROMs  using the MASK‐air  app in severe asthma

# data[].attributes.headline
Laura Reilly: Patient outcomes, patient experiences and process indicators associated with the routine use of patient-reported outcome measures (PROMs) in cancer care: a systematic review

# data[].attributes.subject
Laura Reilly: Patient outcomes, patient experiences and process indicators associated with the routine use of patient-reported outcome measures (PROMs) in cancer care: a systematic review

# data[].attributes.attachment_link.title
Patient outcomes, patient experiences and process indicators associated with the routine use of patient-reported outcome measures (PROMs) in cancer care: a systematic review - Supportive Care in Cancer

# data[].attributes.attachment_link.description
Purpose In current cancer care, there is a growing debate about the value of using patient-reported outcome measures (PROMs) in daily clinical follow-up. A systematic review of the literature was conducted to assess the evidence of the effectiveness of the routine use of PROMs in daily cancer care in terms of patient outcomes, patient experiences and process indicators and to identify the effect of giving feedback about PROM findings to patients and/or health care professionals (HCPs). Methods A systematic search was performed. Studies were eligible for inclusion when they (1) used a PROM as an intervention, with or without feedback to patients and/or HCPs, compared with not using a PROM, and (2) used a PROM as an intervention with feedback to patients and/or HCPs, compared with using a PROM without giving feedback to patients and/or HCPs. Results After screening of 8341 references, 22 original studies met the inclusion criteria. Most studies found a positive effect on survival, symptoms, HRQoL and patient satisfaction. In general, using feedback to patient and/or HCPs about the PROM results led to better symptom control, HRQoL, patient satisfaction and patient-doctor communication. The majority of included studies had insufficient power to detect significant differences in the outcomes assessed. Conclusion This review shows that predominantly positive findings were found in the use of a PROM in daily cancer care. Additionally, more positive effects were seen when feedback is provided to patient and/or health care professionals, and it is thus highly recommended that this is always done.

# data[].attributes.attachment_link.title
BJS Open - 2017 - Poku - Systematic review assessing the measurement properties of patient‐reported outcomes for venous leg

# data[].attributes.headline
Benefits of PROM in cardiology - presentation

# data[].attributes.subject
Benefits of PROM in cardiology - presentation

# data[].attributes.attachment_link.readable_text
Open Heart. 2019; 6(1): e000920.

Original research article

Abstract

Introduction

Routine measurement of the outcome of myocardial infarction is usually limited to immediate morbidity and mortality. Our aim was to determine the response to patient-reported outcome measures (PROMs) 3 months later, identify response bias and explore the feasibility of comparing outcome with their recalled view of their prior health state.

Methods

Patients admitted with ST-segment-elevation myocardial infarction (STEMI) to five percutaneous coronary intervention centres were invited to complete a retrospective questionnaire containing the EQ-5D-3L and short form Seattle Angina Questionnaire (SAQ-7). Response rate for a 3-month mailed follow-up questionnaire and potential response biases were assessed. Patients' outcomes were compared with their baseline using χ ² and paired t-test to assess for differences.

Results

Of 392 patients contacted, 260 (66.3%) responded. Responders were more likely to be older, female, more affluent and have a higher EQ-5D at baseline. Three months after surgery, patients' SAQ-7 and angina symptom subscale returned to their baseline score. The physical limitation subscale score was worse than at baseline (79.9 vs 73.2, p=0.002), whereas the quality-of-life subscale was better (66.6 vs 73.9; p<0.001). The EQ-5D-3L index score was similar at 3 months to baseline (0.82 vs 0.79). Evidence of bias arising from responders being in better general health at baseline needs further investigation and, if confirmed, needs to be taken into account in interpreting PROMs data.

Conclusion

It is feasible to use PROMs routinely to assess the impact of emergency admissions of patients with STEMI. A larger demonstration project with more sites is needed to confirm these findings.

Keywords: patient reported outcome measures, health status, health-related quality of life, retrospective, response rate, feasibility, emergency admissions, STEMI, AMI

What is already known about this subject?

While there have been improvements in the management of cardiovascular disease, significant variation still exists in survival following acute myocardial infarction (AMI) between hospitals within England.

Morbidity and mortality outcomes can be supplemented by patient-reported outcome measures (PROMs), but have not been used widely in routine care.

The feasibility of recruiting patients with AMI to recall their pre-admission health status has been demonstrated, their likelihood of responding to a postdischarge mailed PROM questionnaire at 3 months is unknown.

What does this study add?

PROMs can be successfully collected in patients 3 months after STEMI with a response rate of 66.3% using mailed follow-up.

Most patients regained their prior level of cardiac health as measured by the short form Seattle Angina Questionnaire. The physical limitation subscale score was worse than at baseline, whereas the quality-of-life subscale was better.

How might it impact on clinical practice?

PROMs offer the opportunity for routinely assessing the impact of treatment from the patient's perspective.

Meaningful comparisons of hospitals based on PROMs could be undertaken to supplement clinical measures such as mortality, morbidity and complications.

Introduction

Despite the number of emergency admissions to hospital increasing and concern about variations in outcomes between providers,1 2 no attempt has been made to use patient-reported outcome measures (PROMs) to determine patients' perception of their change in health status. In England, emergencies account for about 40% of all hospital admissions, with the number of admissions having increased by 47% over the last 15 years.3 Two-thirds of hospital beds are occupied by emergencies and the cost to the National Health Service (NHS) is approximately £12.5 billion annually.4

Measuring the quality of healthcare is paramount for all health systems. PROMs is one of the ways to measure effectiveness and to determine the benefit of resources spent.5 6 PROMs are self-reported questionnaires designed to be completed by patients to capture their health at specific points in time to detect a health change over a period. They are multidimensional measures which may cover symptoms, functional status or health-related quality of life. Health status and quality of life are outcomes that are highly relevant and important to patients alongside traditional clinical outcomes and survival.6 7

While there have been improvements in the management of cardiovascular disease, significant variation still exists in survival following acute myocardial infarction (AMI) between hospitals within England.8 However, nothing is known about whether PROMs of survivors also vary between healthcare providers in England as routine assessment is limited to clinical outcomes (mortality and morbidity). Although there have been no attempts in England to routinely capture patients' recovery using PROMs, they have been used in clinical trials. However, the extent to which such outcomes reflect those obtained in routine clinical care is unclear. There have been attempts to collect longer-term outcomes of patients with AMI in the USA, but whether those results are transferable to the English NHS is unclear.9-12

If the aim of healthcare is to restore a patient to his or her full potential, we need to be able to compare patients' outcomes with their health status before the sudden and unexpected event that leads to the emergency admission. To determine the feasibility of employing PROMs in emergency NHS admissions, an exploratory feasibility study was conducted in patients admitted with ST-segment-elevation myocardial infarction (STEMI).13 Success of recruiting patients soon after admission and of obtaining their recollected state of health prior to their admission to provide a baseline assessment has been reported.13-15

In this paper, we report on the follow-up response rate for patients who, following an emergency admission, were confirmed to have suffered a STEMI, meeting the PPCI assessment checklist for inclusion and who underwent emergency (primary) percutaneous coronary intervention (PCI). Secondary objectives were to quantify any response bias as regards sociodemographic characteristics, comorbidity and health status and determine its potential impact on outcome assessment. Being an initial feasibility study, it was not powered sufficiently to make meaningful comparisons between participating centres.

Methods

Site and patient recruitment

A multisite study was carried out to ensure there would be variation in the administration of patient recruitment and data collection. This would provide insights into the relative merits of recruiting in different settings and with different personnel involved.16 For practical reasons, the study was confined to one region of England (North Thames). Five primary angioplasty centres were invited through the National Institute for Health Research (NIHR) Collaborations for Leadership in Applied Health Research and Care partnership network, and all agreed to participate.

Patients admitted with STEMI to the five centres who were alive at discharge were eligible for inclusion unless they were not literate in English, judged not to have sufficient cognitive ability or were not residents in the UK.

Patients were invited to participate soon after their primary PCI and as close to the discharge date as possible to ensure the immediate effects of the intervention were minimised. Clinical staff explained the study to patients, provided written information and obtained written consent. Questionnaires recalling their pre-admission baseline health status were completed by patients without assistance from staff or family except when they were impeded by physical disability or sensory impairment.

Full details of the study methods and feasibility of recruitment have been described elsewhere.13

Patients were sent a follow-up questionnaire by mail from the London School of Hygiene and Tropical Medicine 12 weeks after their admission to hospital. Patient vital status was checked against the Personal Demographics Service at NHS Digital prior to sending a follow-up questionnaire. Non-responders after 2 weeks were sent a reminder questionnaire.

Questionnaires

The questionnaires completed during the admission included demographic information, self-reported comorbidities, a disease-specific PROM and a generic PROM. Patients were asked to recall how they were 1 month before their admission.

The disease-specific PROM used was the short form Seattle Angina Questionnaire (SAQ-7 UK version). This is a seven-item health status measure for patients with coronary artery disease that has well-established validity, reliability, sensitivity to clinical change, and prognostic value.17-19 Scores range from 0 to 100, where higher scores indicate fewer symptoms and higher health-related quality of life. SAQ-7 has good domain coverage (symptom burden, functional status and quality of life), psychometric properties (validity, sensitivity), feasibility to implement (questionnaire length, language availability and cost to implement) and clinical interpretability (knowledge of how to interpret scores in a clinically meaningful way).20 It assesses five dimensions: exertional capacity, angina stability, angina frequency, treatment satisfaction and disease perception. Three subscales can be derived: physical limitation; angina symptoms; quality of life (SAQ-QoL). The summary scale and the three subscales extend from 0 (worst possible health state) to 100 (best possible health state). The SAQ-7 has been previously validated and applied in patients with acute coronary syndrome.17 18

The generic PROM used was the EQ-5D-3L which has five items: mobility, usual activities, personal care, pain/discomfort and anxiety/depression. It takes up to 5 min to complete.21 For each item, the patient chooses from three possible responses indicating the level of their function. A multi-attribute utility score where death and perfect health are represented by 0 and 1 is calculated.22 Scores less than 0 are considered worse than death and 1 is the maximum score possible. The EQ-5D-3L was used rather than the EQ-5D-5L as the former is still the version used in the National PROMs Programme in England.

Analysis

Participating patients' characteristics were summarised using means and SDs for continuous variables or percentages for categorical variables. Response rates were calculated and reported for patients grouped by age, sex, living arrangements, socioeconomic status (SES), baseline SAQ-7 scores and baseline EQ-5D-3L scores. SES was measured using the English Index of Multiple Deprivation (IMD) based on patients' residential postcodes23 with patients assigned to quintiles of the national ranking of IMD scores.

SAQ-7 scores and subscales were calculated according to scoring instructions of the questionnaire developers whereby partial responses were included where possible. Furthermore, individuals with non-responses to two or more items in a subscale did not contribute to the calculation of the component score as per scoring instructions provided by the developers of the SAQ-717

The likelihood of responding according to several patient characteristics (age, sex, SES, comorbidities, baseline SAQ-7 and EQ-5D-3L) was calculated. This allowed the likely impact of non-response on the observed change in SAQ-7 and EQ-5D-3L to be estimated for the patient characteristics shown have a statistically significant non-response association, based on the assumption that non-responders would have reported similar PROM changes as responders.

Patients' outcomes at 3 months were compared with their baseline using χ ² and paired t-test to assess evidence of change in health status. Change scores, with the 95% CIs, were also used to describe reasonable limits on the extent of any change in order to assess whether the results were consistent with recovery to baseline (no change or an improvement in scores).

Results

Response rates

A total of 396 patients were recruited and completed questionnaires (Q1) recalling their health state 1 month earlier ( online supplementary appendix 1). Of these, four (1%) died during the follow-up period. Of the 392 survivors, 260 patients (66.3%) responded to the follow-up PROM questionnaire (QF), 216 responded to the first request and 44 after one reminder.

The mean time between completing the baseline and the follow-up questionnaire was 89 (SD 17) days and between admission and follow-up questionnaire, 92 days.

Response bias

Responders and non-responders were similar as regards comorbidities, living arrangements and disease-specific PROM score (SAQ-7) ( table 1 ). Responders differed from non-responders in other ways: they were older (mean age 64.3 SD 12; range 35-94 vs 57.1, SD 10; range 28-79, p<0.0001) ( figure 1 ), more likely to be women, more likely to come from more affluent SES and have a higher generic PROM score (EQ-5D-3L) at baseline.

Comparing change in PROM scores

The distribution of the EQ-5D-3L at baseline has a left skew, with the majority of patients between 0.8 and 1.0 and a small minority having scores below 0.5 indicating poor health-related QoL. The SAQ-7 score distribution at baseline also has a left skew but to a lesser extent than the EQ-5D index ( figure 2 ).

Three months after AMI, patients' mean SAQ-7 score and mean angina symptom subscale was similar to their baseline score ( table 2 ). In contrast, the physical limitation subscale was worse than at baseline (79.9 vs 73.2, p=0.002) while the SAQ-QoL subscale had improved (66.6 vs 73.9; p<0.001). The EQ-5D-3L index score was slightly lower at 3 months than at baseline (0.82 vs 0.79, p<0.02) though its statistical significance, however, appears to be due to a change in the shape of the distribution rather than a shift in distribution.

Table 2

PROM

Number with complete data

Baseline
Mean (SE, 95% CI)

Follow-up
Mean (SE, 95% CI)

Change (95% CI, P value)

Influence of non-response on change in health status

Changes following STEMI and PCI in most PROM scores (the SAQ-7, SAQ-7 subscales and EQ-5D-3L) were not significantly associated with patient characteristics. The one exception was that patients in the poorest health (as determined by their baseline EQ-5D-3L score) reported significantly larger (p<0.001) improvements in their EQ-5D scores at 3 months ( table 3 ).

Table 3

Patient characteristics

Mean SAQ-7 summary score

SAQ-7 summary score difference in health change, (SD)
(n=259)

P value

Baseline (Q1) score, (SD)
(n=390)

Follow-up (QF) score, (SD)
(n=260)

Patient characteristics

EQ-5D

P value*

Baseline score
(n=385)

Follow-up score
(n=258)

Difference in health change
(n=256)

Assessment of non-response bias

Assessment of potential biases that might have been introduced by some patients not responding was based on the assumption that patients with similar baseline EQ-5D index scores would have had similar follow-up EQ-5D or SAQ scores. To illustrate the impact on non-response linked to baseline EQ-5D (mean 0.82 in responders vs 0.79 in non-responders; table 1 ), we estimated the mean change in SAQ and EQ-5D scores had there been 100% follow-up response rate, compared with the observed mean changes. The mean change in SAQ-7 would have been 1.2 (for all participants including non-responders) compared with 0.8 (observed in responders). The observed mean change in EQ-5D-3L would have been −0.02 compared with the observed mean change of −0.03.

Discussion

Main findings

Three-month follow-up PROMs can be successfully collected from two-thirds of patients admitted as emergencies with STEMI for primary PCI through mailed questionnaires. Although responders and non-responders were similar with regards to their living arrangements, number of comorbidities and baseline SAQ-7, responders were more likely to be older, female, of a higher social economic status and be in better general heath according to the EQ-5D-3L score. Apart from the latter, none of these characteristics was associated with the change in health reported at follow-up so have not introduced any possible bias to the findings. However, the higher EQ-5D-3L of responders at baseline could introduce some bias leading to an underestimation of the improvement in the cardiac health of patients 3 months after the event: change in SAQ-7 would be 1.2 instead of 0.8. Similarly, the observed deterioration in generic health (EQ-5D-3L 0.03 lower at follow-up) would be less (0.01 lower).

Three months after PCI, patients' mean SAQ-7 score and angina symptom score returned to their baseline score, suggesting patients regain their prior level of cardiac health. Although patients reported greater physical limitation than beforehand, they felt their QoL had improved. Given that a clinically meaningful difference in SAQ scores is estimated to be 5-8 points, these differences are clinically important.24

Although the EQ-5D-3L index score was lower at 3 months when compared with baseline (0.82 vs 0.79), the clinical significance of this decrement should be further explored as although this reached statistical significance, the CIs overlap.

What this study adds

This study has demonstrated the feasibility of collecting PROMs 3 months after STEMI among patients who, during their admission, had supplied retrospective accounts of the pre-event health status. It has shown that the response is subject to responder bias which, if confirmed in a larger study, would need to be taken into account when comparing the outcomes of different providers to ensure meaningful findings.

The observation that while patients' physical limitation worsens their QoL improves is surprising. There are three possible explanations. First, it may be that patients recall their prior QoL as worse than it was, although no such bias was detected in studies of elective surgery when retrospective and contemporary reports were compared.14 15 Second, it may be that patients' baseline disease-specific QoL was already lowered due to the presence of subacute symptoms prior to their AMI, but were not at the clinical threshold that warranted medical attention. Grodzinsky et al reported similar baseline SAQ-QoL scores (63.8) in patients with AMI as that reported in this study.25

Third, it may be that patients exercised caution in their physical exertion and hence imposed greater physical limitations on their function than necessary. Meanwhile, their QoL may improve from the psychological boost of having survived their AMI and had the reassurance of having had their coronary arteries stented. This may be due to a degree of 'response shift' occurring following patients' experience of an AMI. Patients may have a different appreciation of their cardiac-related QoL. All PROMs, which are subjective reports, can be influenced by response shift.26 The literature on clinical recovery trajectories after STEMI at 3 months is sparse with no studies reporting the SAQ-7. However, it is reported that patients continue to recover and improve their SAQ scores for up to 12 months.9 25

Strengths and limitations

This is the first study of using retrospective PROMs in routine clinical practice to collect patients' baseline health status and 3 months after for those admitted with STEMI in England. Conducting it in five trusts demonstrated the feasibility of PROM use in different hospital organisational cultures and environments.

One potential limitation is that despite the left skew of the EQ-5D data, we opted to use the same statistical test (t-test) for comparisons between the 3 months and baseline to preserve consistency comparisons with other measures. However, as the t-test does not take into account the skew and truncation of the EQ-5D data, the p value should be interpreted with caution in this instance and the CI is the more appropriate method of interpretation of any differences.

Implications on further research/policy

This study shows that it is feasible to collect retrospective and follow-up PROMs from patients admitted as emergencies with STEMI in NHS hospitals. This approach offers an insight into the opportunity for assessing, from the patient's perspective, the impact of treatment for the 40% of hospital admissions that are emergencies and patients' subsequent recovery after their emergency admission. The generalisability of these findings to other causes of emergency admissions needs to be established.

Further research is warranted to explore longer-term outcomes and compare these with patient risk profiles and clinical characteristics to recovery trajectories.

Routine collection of PROMs in emergency admissions is feasible using the retrospective PROMs collected during the index admission and a subsequent contemporary follow-up. Data could be linked to clinical measures known to be associated with outcome (such as Kilip classification, concentration of Tnl, infarct site and left ventricular ejection fraction) and quality dashboards to support on-going quality improvement through benchmarking, by promoting clinical effectiveness and patient-centred care. Larger studies are needed to collect PROMs in patients admitted with AMI and other patients with emergency acute coronary syndrome to enable subgroup analysis of patient and clinical characteristics, to investigate further any response bias and to develop risk adjustment models to enable comparisons of providers.

Acknowledgments

The patients and site leads and staff of five hospitals that participated in the study.

Footnotes

STEMI PROMs site and study leads: Julie Saunders, Mervyn Andiapen, Amy Richards, Mark Vertue, Joanne Riches, Jonathan Breeze, Amy Hoare, Alison Pottle, Paula Rogers, Claire Prendergast, Karen Wilson, Kirsty Gibson.

Contributors: EK was the principal investigator of the study. NB is the doctoral supervisor. EK and NB wrote the paper with input from JN and SEP.

Funding: EK is funded by Economic and Social Research Council doctoral fellowship. Grant reference: ES/J500021/1. The research was supported by the National Institute for Health Research (NIHR) Collaboration for Leadership in Applied Health Research and Care North Thames at Barts Health NHS Trust.

Disclaimer: The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.

Competing interests: None declared.

Patient consent for publication: Obtained.

Ethics approval: All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and national research committee (NHS Health Research Authority) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This study received NHS ethical approval from South East Coast-Brighton and Sussex Research Ethics Committee (REC reference: 16/LO/2053) and was incorporated in the NIHR Research Network Portfolio.

Provenance and peer review: Not commissioned; externally peer reviewed.

References

3. Steventon A, Deeny S, Friebel R. Emergency hospital admissions in England: which may be avoidable and how? Heal Found 2018:1-21. [ Google Scholar]

4. Department of Health Emergency admissions to hospital: managing the demand, 2013. [ Google Scholar]

16. Runkel PJ, McGrath JE. Research on human behavior: a systematic guide to method. American Educational Research Association, 1973. [ Google Scholar]

23. Department for Communities and Local Government English indices of deprivation, 2015. [ Google Scholar]

Articles from Open Heart are provided here courtesy of BMJ Publishing Group

# data[].attributes.attachment_link.title
Original research article: Using patient-reported outcome measures for primary percutaneous coronary intervention

# data[].attributes.attachment_link.description
Routine measurement of the outcome of myocardial infarction is usually limited to immediate morbidity and mortality. Our aim was to determine the response to patient-reported outcome measures (PROMs) 3 months later, identify response bias and explore ...

# data[].attributes.headline
Chelsea Leece - pulmonary rehab and asthma PROM 

# data[].attributes.subject
Chelsea Leece - pulmonary rehab and asthma PROM 

# data[].attributes.attachment_link.readable_text
Show authors

Respiratory Research 24, Article number: 14 (2023) Cite this article

Abstract

Background

The CaReQoL Asthma assesses the care-related quality of life outcomes of pulmonary rehabilitation retrospectively in patients with severe asthma. The questionnaire comprises five domains (physical functioning; social functioning; coping with asthma; knowledge about asthma; medication).

Aim

To investigate construct and criterion validity of the CaReQoL Asthma, as well as its responsiveness and minimal important change (MIC), in comparison with other health measures (AQLQ, ACQ and FEV ₁).

Methods

Eighty three adults with severe refractory asthma filled out the CaReQoL Asthma at 6 and 12 months after a 12-week personalized multidisciplinary pulmonary rehabilitation program in a tertiary asthma centre, either in Switzerland or The Netherlands. Construct validity and responsiveness were assessed by testing pre-defined hypotheses about associations with changes in AQLQ, ACQ and FEV ₁ scores. Criterion validity and MIC was assessed using Global Perceived Effect (GPE). Factor analyses, Cronbach's alpha, Spearman's correlations, paired t-tests and Student-Newman-Keuls tests were performed.

Results

Cronbach's alphas of the questionnaire domains ranged from 0.82 to 0.95. Good construct validity and responsiveness were found; 84% of the assessed correlations confirm pre-defined hypotheses and reflect both weak and moderate to strong correlations. Good criterion validity was also identified, with CaReQol scores discriminating better than other health measures between levels of GPE at 6 months post-rehabilitation. The MIC for the total score was estimated at 0.84.

Conclusion

These study results suggest that the CaReQoL Asthma is a valid and responsive instrument and shows to be a comprehensive and tailored questionnaire for evaluating and monitoring outcomes of pulmonary rehabilitation in patients with severe refractory asthma. In order to further substantiate the reliability and validity of the CaReQoL Asthma, as well as to monitor outcomes of pulmonary rehabilitation in patients with severe asthma, it is recommended to use the CaReQoL Asthma in addition to other disease specific instruments.

Background

Severe refractory asthma is asthma that remains uncontrolled despite optimal pharmacological treatment with high dose inhaled corticosteroids plus another controller and/or systemic corticosteroids, and adequately addressing comorbidities and contributory factors [ 1]. This chronic condition and its treatment poses a substantial burden on the patients' life, due to the symptoms, exacerbations and side-effects of the medication, with profound consequences for the patients' physical and mental health, relationships and careers [ 2]. In the Netherlands, approximately 3.6% of the adults with asthma have severe refractory asthma and their care is estimated to account for more than 60% of the costs associated with asthma [ 1, 3].

Besides pharmacological treatment, non-pharmacological interventions such as allergen avoidance and pulmonary rehabilitation are recommended for severe refractory asthma [ 4, 5]. A pulmonary rehabilitation trajectory is often indicated. Rehabilitation is the process of helping a person achieve the highest possible level of function, independence and quality of life. Pulmonary rehabilitation is defined as a comprehensive intervention based on a thorough patient assessment followed by patient-tailored therapies, which include but are not limited to exercise training, education, self-management and psychological support, designed to improve the physical and psychological condition of patients with chronic respiratory disease and to promote the long-term adherence of health-enhancing behaviors [ 6]. During and after this trajectory, effects can be monitored by regularly measuring the lung function (e.g. forced expiratory volume in one second, FEV ₁) and patient-reported outcome measures (PROMs; i.e. questionnaires on the self-perceived health status or health-related quality of life) [ 7, 8].

PROMs are frequently used in asthma care, for example to assess the perceived severity or health status and quality of life of patients. Two internationally well-known, validated and frequently used instruments are the Asthma Quality of Life Questionnaire (AQLQ) [ 9, 10] and the Asthma Control Questionnaire (ACQ) [ 11]. However, these are not specifically designed for assessing the effects of pulmonary rehabilitation and might fall short in fully describing the health-related quality of life from the patients' perspective with respect to the treatment goals. In general, there is a need for more comprehensive assessments to identify treatable traits that can be addressed during rehabilitation [ 12]. But until now, specific instruments for comprehensively assessing the outcomes of multidisciplinary pulmonary rehabilitation for patients with severe asthma are missing.

Therefore, we have developed the CaReQoL Asthma to measure the Care-Related Quality of Life; a disease and care-specific questionnaire for monitoring the effects of clinical rehabilitation in patients with severe asthma [ 13]. Patients were fully involved in all stages of the development, in contrast to many previously developed PROMs (e.g. the ACQ) that have been developed by clinicians or researchers and consequently do not necessarily represent the patients' perspective [ 14]. The development process of the CaReQoL Asthma is summarized in Box 1. The items of the questionnaire were based on focus groups with patients, literature and existing questionnaires, and extensive cognitive and psychometric testing in patients [ 13].

Development process of the CaReQoL Asthma (Van Kessel et al. [ 13])

This study aims to assess the construct and criterion validity of the CaReQoL Asthma, by comparing the scores with two other self-report asthma-specific health questionnaires (ACQ and AQLQ) and a clinical measure (FEV ₁), and to assess the responsiveness and the minimal important change (MIC) of the CaReQoL Asthma. We hypothesize that this care-specific instrument assesses the care-related health outcomes of asthma rehabilitation more comprehensively and specifically than other asthma-specific questionnaires (ACQ and AQLQ), and that the CaReQoL Asthma is a more tailored, straightforward, and responsive instrument for assessing the outcomes of asthma rehabilitation longitudinally than the other instruments and the clinical measure for lung function (predicted FEV ₁).

Methods

Study design and treatment setting

Data were collected as an additional, informal part of a prospective clinical study with a 12 months follow-up period on the effects of a 12-week personalized multidisciplinary pulmonary rehabilitation program, either at a high-altitude (Dutch Asthma Centre in Davos, Switzerland) or at sea-level (Merem Asthma Centre in Hilversum, The Netherlands) [ 15]. The clinical study was originally set up as a trial and was registered at The Netherlands Trial Register ( www.trialregister.nl; NTR5182), but it fell back to an observational design because randomization turned out to be not feasible. Nevertheless, patients were assessed and evaluated during and after their pulmonary rehabilitation in accordance with a systematic protocol.

Both asthma centres supplied structured, quality-controlled personalized treatment and pulmonary rehabilitation for adults with severe asthma. The rehabilitation included attempts to achieve optimal asthma control and to reduce (oral) corticosteroids to the lowest effective level, exercise training, asthma education including self-management, and psychological support. Both centres provided standardized treatment by using a modular approach with nine basic modules: medication and inhalation; exacerbation; self-management; physical fitness; daily physical activity; functional-ADL-training, dyspnea management; food and diet; coping; psychological support.

After the 12-week rehabilitation period, patients were followed for an additional 12 months with follow-up visits every 3 months at the asthma centre in The Netherlands. During the follow-up, patients were treated by their referring pulmonologist according to the (international) guidelines.

Patients

This validation study includes 83 adults (18-75 years) with severe refractory asthma who were living in The Netherlands and who filled out the CaReQoL Asthma at follow-up at 6 and 12 months after the pulmonary rehabilitation. All selected patients had a diagnosis of severe refractory asthma according to the ERS/ATS criteria [ 1], and they were referred by their pulmonologist to a tertiary asthma clinic between October 2015 and February 2018 [ 15].

At baseline, all patients were symptomatic and had uncontrolled asthma, they used long-acting bronchodilators and high dose inhaled corticosteroids with or without oral corticosteroid, and they were either non-smokers or ex-smokers for > 6 months [ 15]. Treatment of comorbidity was optimized before taking part in the study [ 15]. Uncontrolled asthma was defined by the presence of at least two of the following criteria: (1) poor symptom control defined as an ACQ-score ≥ 1.5 or an ACT-score < 20; (2) frequent severe exacerbations defined as two or more bursts of systemic corticosteroids (> 3 days) in the previous year; (3) serious exacerbations defined as at least one hospitalization or intensive care unit stay or mechanical ventilation in the previous year because of an asthma exacerbation; and/or (4) persistent airflow limitation (post-bronchodilator FEV1 < 80% predicted or a FEV1/forced vital capacity (FCV) z-score < 1.64). More details on the recruitment and selection procedure and the exclusion criteria are described in a previous paper on the clinical study by De Nijs et al. [ 15].

Patients voluntarily completed the CaReQoL Asthma in addition to the primary and secondary outcome measures of the clinical study. All patients provided their written informed consent on taking part in the clinical study which was approved by the Ethics Committee of the Academic Medical Center of the University of Amsterdam (Amsterdam, The Netherlands) [ 15]. The CaReQoL Asthma was not officially part of the clinical study, and an amendment about the additional informal data collection with this instrument was handed over to the Ethics Committee after the start of the clinical study.

Data collection and instruments

Data collection with the CaReQoL Asthma was an additional, informal part of the clinical study. Measurement points of the clinical study itself were at baseline or entry of the study (t0) and two additional follow-up measurements after completing the 12-week pulmonary rehabilitation trajectory: after 6 months (38 weeks after entry; t38) and at 12 months (64 weeks after entry; t64).

Apart from the CaReQoL Asthma, two other asthma-specific self-report questionnaires were used: the Asthma Quality of Life Questionnaire (AQLQ) and Asthma Control Questionnaire (ACQ), and a clinical measure for lung functioning (FEV ₁). The CaReQoL Asthma was only assessed retrospectively at t38 and t64 (i.e. 38 and 64 weeks after the start of the rehabilitation), whereas the other instruments (AQLQ, ACQ) and the FEV ₁ were assessed at all three points including baseline (t0, t38 and t64).

CaReQoL asthma

The Care Related Quality of Life in Asthma (CaReQoL Asthma) is a 26-item questionnaire about the perceived effects of pulmonary rehabilitation on the quality of life of asthma patients [ 13]. This PROM aims to retrospectively evaluate the 'Care Related Quality of Life', by asking asthma patients directly to rate the effects of rehabilitation on various health aspects and their overall quality of life. The face and content validity of this questionnaire have already been pre-tested in 15 cognitive interviews and a psychometric test based on a survey among 195 patients [ 13]. See Appendix for the content of the questionnaire.

The CaReQoL Asthma comprises five domains or scales: Physical functioning (8 items), Social functioning (4 items), Coping with asthma (5 items), Knowledge about asthma (3 items), and Medication (3 items). The 23 items measuring these five domains state to what extent the pulmonary rehabilitation contributed to the patients' treatment goals and health aspects, with a 5-point answering scale: 1 = completely disagree, 2 = partly disagree, 3 = neutral (not agree, nor disagree), 4 = partly agree, 5 = completely agree. The escape option 'not applicable' was coded as missing. For each domain, an average scale score was calculated from the item scores (range 1-5), only for patients who completed more than half of the items within the corresponding domain. All five scales previously showed to have good internal consistency, with Cronbach's alpha's between 0.75 and 0.96 [ 13].

The last three items of the CaReQoL Asthma reflect on the overall effect of pulmonary rehabilitation on the quality of life (1 item; 1 = completely disagree, to 5 = completely agree), the global perceived effect (GPE [ 16]) of the rehabilitation on the patient's health (1 item (How is your health, compared to your health prior to rehabilitation?); 1 = very much deterioration, 2 = much deterioration; 3 = little deterioration; 4 = no change; 5 = little improvement; 6 = much improvement; 7 = very much improvement), and the respondent's age (in years), respectively.

Asthma quality of life questionnaire

The Asthma Quality of Life Questionnaire (AQLQ) is an asthma-specific 32-item questionnaire that measures the health-related quality of life of adults with asthma for four domains: Symptoms; Activity limitations; Emotional functioning; and Environmental stimuli [ 9]. Four domain scores and a total score were calculated (means of all item-scores). The Minimal Important Difference (MID) for the AQLQ is 0.5, which means that a change in score of 0.5 on the 7-point scale is the smallest change that can be considered clinically important [ 17].

Asthma control questionnaire

The Asthma Control Questionnaire (ACQ) measures both the adequacy of asthma control and change in asthma control, which occurs either spontaneously or as a result of treatment. In this study, a 6-item version of the ACQ questionnaire was used (without the clinic staff score of the FEV ₁ predicted) [ 18]. The ACQ score was calculated as the mean of the 6 symptom-related items (0 = totally controlled; 6 = severely uncontrolled; with scores < 0.5 indicating 'controlled asthma' and > 1.5 'uncontrolled asthma'). A change or difference in ACQ score of at least 0.5 can be considered as clinically important [ 18].

Forced expiratory volume in one second

Pulmonary function was measured according to international recommendations and included the forced vital capacity (FVC) and the forced expiratory volume in one second (FEV ₁), assessed after inhaled administration of 400 μg salbutamol and expressed as percentage of the predicted value [ 19]. The FEV ₁ at baseline (t0), t38 and t64 was used as an indicator of the pulmonary function in this paper.

Statistical analyses

The analyses focused on the construct validity (factor structure, internal consistency, convergent and divergent validity), criterion validity, responsiveness and the minimal important change (MIC). Analyses included: factor analyses, internal consistency (Cronbach's alpha), Spearman's rank correlation coefficients (r), mean scores and 95% confidence intervals, paired t-tests and Student-Newman-Keuls (SNK) tests. All analyses were performed in Stata/SE (version 15) and a p-value of < 0.05 was considered as statistically significant.

Since the response rate for the CaReQoL Asthma was the highest at t38, this measurement point was mainly used to investigate the psychometric properties of the CaReQoL Asthma and to compare the CaReQol scores with change scores of the AQLQ, ACQ and FEV ₁ (∆ t0-t38). Except for assessing the responsiveness, for which the scores at t64 were used as well.

Construct validity

Defining the five domains of the CaReQoL Asthma was based on psychometric analyses, including factor analyses, conducted in a previous study (factor loadings range: 0.35-0.93, inter-scale correlations range: 0.59-0.90, n = 195) [ 13]. To confirm the psychometric properties of the questionnaire in the current study sample, factor analyses were conducted for each subscale separately (Principal Component Analyses, with oblimin rotations; Eigenvalue > 1 and factor loadings > 0.40). Then the internal consistency of the subscales was assessed by calculating the Cronbach's alpha for each scale. An alpha of > 0.70 is generally considered as a good reliability [ 8, 20].

Subsequently, Spearman's rank correlation coefficients (r) were calculated to see whether the scales or items of the CaReQoL Asthma do significantly correlate with other similar measures (i.e. convergent validity), or do not (i.e. divergent or discriminant validity). Convergent/divergent validity was assessed by calculating Spearman's rank correlations between CaReQoL Asthma scores (t38) and change scores (Δ t0-t38) of similar domains of the AQLQ and the ACQ.

Based on the questionnaires' content and more or less similar items, moderate correlations (r = 0.40-0.59) were expected between the following CaReQoL scores and related domains or items in the AQLQ and ACQ questionnaires:

Physical functioning vs. changes in AQLQ Total score, AQLQ Symptoms, AQLQ Activity limitations, and ACQ;

Social functioning vs. changes in AQLQ Total score, AQLQ Activity limitations and ACQ;

Coping with asthma vs. changes in AQLQ Emotional functioning and ACQ;

Medication vs. changes in AQLQ Symptoms and ACQ;

Overall quality of life (1 item) vs. changes in AQLQ Total score, AQLQ Symptoms and ACQ;

Global perceived effect (1 item) vs. changes in AQLQ Total score, AQLQ Symptoms and ACQ.

Weak (r < 0.40) or insignificant (p > 0.05) correlations were expected between all other combinations of total and domain scores, which would confirm divergent validity. See Table 3 for the hypothesized relationships. Good construct validity means that at least 75% of the hypotheses are correct [ 20].

Criterion validity

Criterion validity refers to a comparison between the measure in question and an outcome assessed at the same time (concurrent validity), and the extent to which the measure is related to or predicts a concrete outcome or criterion (predictive validity) [ 20]. In this study the GPE (global perceived effect) was chosen as a criterion, because it is a concrete and global measure for the perceived effect of the pulmonary rehabilitation at t38. The criterion validity was assessed by first subdividing respondents into three groups based on the self-reported global perceived effect (GPE) at t38, and by comparing the three subgroups with respect to the five CaReQoL domain scores and the change scores of the other health measures since the start of the rehabilitation trajectory (Δ t0-t38).

The three subgroups according to the criterion variable (GPE) are categorized as follows: (1) patients who reported a deterioration (a little, much or very much); (2) patients who reported 'no change' or 'little improvement'; and (3) patients who reported '(very) much improvement'. The reason for including 'little improvement' in group 2 was the low number of patients in the category 'no improvement' (n = 6 at t38) and to create a more substantial reference group. Student-Newman-Keuls tests were performed to test the null hypotheses of 'no differences between subgroups'. It was hypothesized that the CaReQoL Asthma very well reflects the GPE-scores at t38 (by showing significant differences in mean scores between the three groups at t38), thus proving the ability of this instrument to discriminate between patients with a different level of perceived effect, whereas changes in other scores (Δ t0-t38 AQLQ, ACQ, FEV ₁) are less likely to do so.

Responsiveness

Responsiveness is either defined as the effect of treatment, or as a measure of longitudinal validity defined by the correlation of changes in the instrument with changes in other measures [ 21]. In this study, the responsiveness of the CaReQoL Asthma, which aims to directly reflect the effects of pulmonary rehabilitation on several domains of health, was assessed in three steps. In step one, to investigate statistical significance of the longitudinal changes in care related aspects of health, the average scores on subsequent time points were compared. For the CaReQoL Asthma, the scores at t38 and t64 were compared by conducting one-sample paired t-tests, with the null hypotheses being 'no difference between t38 and t64'. In step two, the CaReQoL Asthma scores at t38 were correlated with the change scores (Δ t0-t38) for other instruments, with the calculations and hypotheses being similar to those of the construct validity. In step three, the CaReQoL Asthma scores at t64 were correlated with the change scores for other instruments (Δ t0-t64), again with similar hypotheses as those formulated for the construct validity. See Table 3 for the hypothesized relationships.

Minimal important change

The minimal important change (MIC) was calculated for the CaReQoL Asthma at t38. The MIC is defined as the minimal change in a scale score that is considered to be important [ 20]. Firstly, three subgroups of the GPE were used (see " Criterion validity"). For each scale score, the average score of patients in group 2 (no or little improvement, see also " Criterion validity" for the consideration of combining these two groups) was subtracted from the average score of patients in group 3 (much or very much improvement). In addition, relevant changes or MICs of the AQLQ and ACQ scores (i.e. changes of at least 0.5 point) were also considered as external criteria for computing the MICs of the CaReQoL Asthma domains [ 17, 18].

Results

Descriptives and follow-up scores

The 83 patients who filled out the CaReQoL Asthma included 64 female (77%) and 19 male (23%) patients who had a mean age of 46 years (SD = 14). These 83 patients filled out the CaReQoL Asthma for the first time at t38 (6 months after rehabilitation), and 49 patients also filled out the CaReQol Asthma after one year (t64). The average scores for the CaReQoL Asthma, as well as for the other longitudinal measurements (AQLQ and ACQ and the predicted FEV ₁), are presented in Table 1. Highest scores of the CaReQoL Asthma at t38 were found for the subscales Knowledge about asthma (4.53) and Coping with asthma (4.23), followed by Physical functioning and Social functioning (3.76 and 3.62, respectively). On average, the pulmonary rehabilitation did not seem to affect the patients' medication management as its mean score of 3.36 resembled the 'neutral' answering category of 3.0.

Table 1 Scores for CaReQoL asthma (t38, t64), AQLQ, ACQ and FEV₁ (t0, t38, t64) (n = 83)

Missing data

The average percentage of item missing was 4% for the CaReQoL Asthma at t38. Sixty of the 83 patients (72%), provided complete data on all 26 CaReQoL items at t38. Of the 23 other respondents, 13 had one or two missing items, nine had three to 11 missing values, and one respondent had 21 missing values. Particularly one item of the domain Medication (item 23 about side effects of medication) showed missing values (16% missing compared to 7% or less for all other items). After 12 months (t64), 49 of the 62 participants in the total clinical study (79%) filled out the CaReQoL Asthma, due to incomplete follow-up as the CaReQoL Asthma was not administered to all patients at t64. Nevertheless, CaReQoL Asthma scores could be calculated for 77-82 of the 83 patients at t38, and for 47-49 patients at t64 (Table 1).

Construct validity

The factor analyses on each subscale confirmed the structure of the CareQoL Asthma by showing high factor loadings for each item per subscale (see Table 2), resembling the findings of a previous study on the psychometric properties of the questionnaire [ 13]. Cronbach's alpha for the five CaReQoL Asthma domains were: Physical functioning 0.95 (8 items), Social functioning 0.93 (4 items), Coping with asthma 0.85 (5 items), Knowledge about asthma 0.83 (3 items) and Medication 0.82 (3 items).

Table 2 Factor loadings and Cronbach's alpha for subscales of the CareQoL Asthma (t38)

Table 3 shows the correlations between CaReQoL Asthma scores at t38 and the change scores of other measures between t0 and t38, together with the predefined hypotheses. Of the 45 correlations investigated, 38 (84%) were in accordance with the hypotheses. Only seven associations were either stronger (3×) or weaker (4×) than expected. For example, Social functioning showed moderate instead of weak correlations with AQLQ Symptoms and AQLQ Environmental stimuli (r = 0.48 and 0.42, respectively). On the other hand, Coping was much weaker associated with AQLQ Emotional functioning (r = 0.18) and ACQ (r = − 0.14) than expected.

Table 3 Construct validity and responsiveness of CaReQoL Asthma (t38 or t64)

Responsiveness

The CaReQoL showed positive effects of the pulmonary rehabilitation (see Table 1), with an average total score of 3.89 on the 5-point scale (1-5) at t38. The effects of the rehabilitation appeared to remain stable over time as there were no significant differences in scores at 6 and 12 months (i.e. no changes between t38 and t64 according to paired t-tests). Similarly, the follow-up scores for the AQLQ, ACQ and FEV ₁ showed substantial and significant improvements after 6 months. These other scores also did not seem to change any further after 6 months, according to the overall scores and confidence intervals as displayed in Table 1, but paired t-tests for those who responded to all follow-up measurements indicated that the AQLQ total, AQLQ Symptom and AQLQ Activity limitations scores as well as the ACQ did significantly change and somewhat diminished between 6 and 12 months (AQLQ: n = 54, ACQ: n = 52; p < 0.05).

Table 3 shows the correlations between CaReQoL Asthma scores at t38 and t64 and change scores of all other instruments (differences between t0-t38 and t0-t64, respectively). Similar to the construct validity and as hypothesized, the scales on Physical functioning, Social functioning, and the 1-item measuring the global perceived effect (GPE) and the quality of life (QoL) were moderately and significantly associated with changes in the AQLQ scores (in particular: Total, Symptoms and Activity limitations) and the ACQ. These Spearman's coefficients ranged between 0.40 and 0.59. Weak and often non-significant correlations were found for the other CaReQoL scales and the predicted FEV ₁.

At t64, both Coping with asthma and Knowledge about asthma show more responsive, higher and significant correlations with changes in AQLQ or ACQ scores. In particular Coping with asthma, which was related to AQLQ and ACQ scores at t64, whereas these associations did not exist at t38. Medication on the other hand, showed less significant correlations at t64 compared to t38.

Criterion validity

Answers on the global perceived effect (GPE) of the pulmonary rehabilitation at t38 showed a deterioration in eight patients (group 1), no or a little improvement in 25 patients (group 2), and much or very much improvement in 45 patients (group 3). Five patients did not fill out the GPE and were missing. Table 4 presents the scores of the CaReQoL Asthma (total and mean scale scores, and overall quality of life) and change scores (t0-t38) for the AQLQ, ACQ and FEV ₁ for each GPE- group.

Table 4 Criterion validity of CaReQoL asthma and AQLQ, ACQ and FEV₁ (t38)

Almost all scores of the CaReQoL Asthma showed significant differences between two or three of the GPE-groups, except Coping with asthma and Knowledge about asthma. Other instruments showed few significant differences between the GPE-groups; only some changes in AQLQ scores (Total, Symptoms and Activity limitations) and the ACQ discriminated between some of the GPE-groups.

Minimal important change

The minimal important changes (MICs) for the CaReQoL Asthma scales at t38 are: Physical functioning 1.09, Social functioning 1.10, Coping with asthma 0.35, Knowledge about asthma 0.24, and Medication 1.34 (Table 5). Unfortunately, no additional analyses could be conducted to assess MICs in relation to relevant changes between t0 and t38 in AQLQ (an increase of at least 0.5) or ACQ (a decrease of at least 0.5), as external criteria, due to small numbers of patients with 'no relevant change' (n = 8 and n = 7 at t38; n = 9 and n = 12 at t64, respectively).

Table 5 Minimal important change (MIC) for CaReQoL asthma scores (t38)

Discussion

This paper describes the psychometric properties and validity of the CaReQoL Asthma, a self-report questionnaire specifically designed for evaluating the perceived outcomes of pulmonary rehabilitation in patients with severe asthma. The instrument aims to comprehensively describe the Care Related Quality of Life outcomes regarding the treatment goals of pulmonary rehabilitation that are important to patients [ 13]. Validation was based on longitudinal data collected among 83 patients with severe refractory asthma who took part in an observational clinical study on the effectiveness of pulmonary rehabilitation in two tertiary asthma clinics in Switzerland and The Netherlands [ 15]. This study confirms that the relatively short CaReQoL Asthma (26 items) is a comprehensive, valid, reliable and responsive questionnaire for assessing relevant outcomes of pulmonary rehabilitation in patients with severe refractory asthma.

Good psychometric properties

Findings show that the CaReQoL Asthma has a good construct validity and that it could reliably measure the long-term impact of multidisciplinary and personalized rehabilitation programs on the patients' lives, regarding five domains: Physical functioning (Cronbach's alpha = 0.95), Social functioning (alpha = 0.93), Coping with asthma (alpha = 0.85), Knowledge about asthma (alpha = 0.83) and Medication (0.82).

The new instrument even appears to assess outcomes of rehabilitation that are only partly or not at all assessed by the other well-known disease-specific instruments AQLQ and ACQ that were used as primary and secondary outcome of the total clinical study [ 15]. Particularly Coping with asthma and Knowledge about asthma turned out to be unique domains of the CaReQoL Asthma as these scores showed substantial effects of the pulmonary rehabilitation but were only weakly correlated with the two other questionnaires (AQLQ and ACQ).

Furthermore, the convergent and discriminant/divergent validity and the responsiveness of the CaReQoL Asthma, as compared to changes in AQLQ, ACQ and FEV ₁ scores, were in line with our expectations (i.e. 84% conform pre-defined hypotheses). The very low and insignificant correlations with the FEV ₁ underline the hypotheses that this clinical measure does not reflect the patients' self-reported health.

Moreover, the CaReQoL Asthma showed good criterion validity and discriminant scores in relation to the global perceived effect (GPE) as a criterion variable. The CaReQoL domain scores discriminated better than other measures between three levels of the perceived effect at 6 months post-rehabilitation, except for the domain Knowledge about asthma which appeared to be unrelated to the perceived effect.

The minimum important change (MIC) for the CaReQoL Asthma total scale is estimated at 0.84 and varies per domain, from 0.24 (Knowledge about Asthma) to 1.34 (Medication). However, these MICs are likely to be somewhat overestimated as real minimal differences could not be established in this study because of very low number of patients reporting 'no improvement'. While this GPE-reference group had to be merged with 'little improvement' to create a more substantial subgroup, this might have led to an overrating of the MICs. The estimated MICs for the CaReQoL Asthma resemble the findings of a previous study showing minimal important differences from 0.21 to 1.16 per domain [ 13]. Thus, an overall MIC of 0.8 and MICs between 0.2 to 1.2 for the five domains seem to be realistic.

Coping and knowledge

The CaReQoL domains Coping with asthma and Knowledge about asthma had the highest average scores at 6 and 12 months post-rehabilitation. These high scores reflect major and significant contributions of the rehabilitation trajectory to the patients' coping skills and knowledge about asthma. But these two domains appeared to be the least responsive to change, compared to other measures, and also had the lowest and least reliable MICs (0.35 for Coping and 0.24 for Knowledge) because of mainly non-significant differences between the GPE-groups (i.e. the criterion measure).

Interestingly, the increased coping and knowledge of patients due to the rehabilitation (according to the CaReQoL) seem to have more persistent long-term effects on the quality of life (AQLQ) and asthma control (ACQ), as correlations between these outcomes increased over time. Particularly coping was related to an increase in asthma control after one year. Apparently, increased coping skills and knowledge on asthma may persist over time and could still reinforce or prolong the effects of pulmonary rehabilitation by contributing to the patients' quality of life in the long run. This is in line with literature on various interventions aimed to improve asthma knowledge, self-management, medication adherence and inhalation techniques (e.g. objective monitoring, feedback, education or training and group learning) which in turn may result in an increased quality of life and clinical outcomes [ 22, 23]. But the underlying mechanism of the effects of pulmonary rehabilitation and the mediating effects of increased knowledge and coping skills on the self-management, health outcomes and quality of life of patients with severe asthma still needs further investigation [ 22, 23].

Long-term effects

Altogether, the follow-up measurements with the CaReQoL Asthma showed substantial and enduring effects following the pulmonary rehabilitation on the patients' quality of life and other health outcomes, still present after 12 months. In general, the follow-up measurements at 6 and 12 months showed a substantial improvement to the patients' knowledge and coping with asthma after rehabilitation, followed by an increased physical and social functioning, but a rather weak change in their medication management. These results are in line with the previously reported findings of the total clinical study [ 15] and other studies on the benefits of personalized pulmonary rehabilitation programs [ 5, 24, 25]. Additionally, the clinical study showed the strongest effects on quality of life and asthma control at the end of the rehabilitation trajectory (after 12 weeks), and these effects were larger and more sustainable in the high-altitude group than in patients who followed a rehabilitation program at sea-level [ 15].

Study limitations

The present study has some limitations. Firstly, the number of patients included in this study is low. A total of 83 patients were included at t38, which is normally too small for exploring internal consistency and construct validity [ 20]. However, the factor analyses as well as other psychometric analyses were also conducted in a previous study (published in dutch), consisting of 195 respondents and showing similar findings [ 13]. In addition, for testing a priori hypothesis, as is done for construct validity and responsiveness, a total of 50 respondents seems adequate, which is met for measurements at t38 [ 20]. Nevertheless, conclusions should be drawn with caution and replication is warranted. Secondly, the numbers of listwise missings are high: of the 83 who filled out the CaReQoL Asthma at t38, 60 had no missing items at t38 and only 38 patients also participated and fully completed the questionnaires at t64. This loss to follow-up was partly due to incomplete data collection as not all study participants in the total clinical study additionally received a CaReQoL Asthma at t38 and t64. But there did not seem to be selection bias since there were no differences in demographic or clinical characteristics between those who dropped out and those who completed the follow-up of the total clinical study [ 15]. Another limitation for validating the CaReQoL Asthma concerns the few and long-term measurement points at which our instrument was applied, given the focus of the clinical study [ 15]. Evaluations during and at the end of the pulmonary rehabilitation, for example half-way and after 12 weeks, would have yielded better data to study the responsiveness and criterion validity of the instrument. These measurement points are also recommended for implementing the instrument in clinical practice for the purposes of shared decision making, monitoring and quality improvement [ 26]. Finally, as the CareQol Asthma is a direct measure requiring respondents to report change in functioning as compared to baseline, baseline measures were effectively obtained retrospectively and therefore susceptible to recall bias [ 27]. However, the obvious alternative would have been an indirect measure of change where respondents only report current level of functioning at baseline (t0) and follow-up (t38 and t64) and change is calculated as the difference between baseline and follow-up. In such a design, the baseline measure would be susceptible to response shift, i.e., the phenomenon that internal standards of severity of symptoms change over time [ 28]. Although it is commonly assumed that recall bias is a bigger problem than response shift, empirical results appear to be mixed [ 27, 28],

Future research

Future studies with the CaReQoL Asthma could reveal more specific effects of the pulmonary rehabilitation in patients with severe asthma over time, including increased knowledge and coping skills. Furthermore, future research may yield more insight into the underlying mechanisms that either enhance or hamper disease management and shared decision making on the personalized treatment, not only for severe asthma but perhaps also for other respiratory disorders (i.e. mild asthma and COPD) and other treatment modalities (regular treatment or specific disciplines, e.g. physiotherapy). In addition, future studies should aim to replicate the psychometric properties of the CareQoL Athma in bigger samples.

Conclusions

Our findings show good properties for the construct validity (internal consistency, convergent and divergent validity), responsiveness and criterion validity of the CaReQoL Asthma at 6 months post-rehabilitation. This instrument is the first PROM that was specifically designed for measuring outcomes of pulmonary rehabilitation in patients with severe asthma. It has been developed in close collaboration with patients and fully describes the impact of the pulmonary rehabilitation on life domains which are important to patients. Moreover, our results suggest that this instrument may be regarded as a unique and complementary instrument as it does not measure exactly the same constructs as the AQLQ and ACQ and it comprises specific domains on knowledge and coping with asthma which are not covered by these instruments. In order to further substantiate the reliability and validity of the CaReQoL Asthma, as well as to monitor outcomes of pulmonary rehabilitation in patients with severe asthma, we recommend to use the CaReQoL Asthma in addition to other disease specific instruments.

Availability of data and materials

All relevant data is presented in the manuscript and associated tables. The data that support the findings of this study are available from UMC Utrecht; however, restrictions apply to the availability of these data, which were used under license for the current study and are not publicly available.

Abbreviations

CaReQoL:

Care Related Quality of Life in Asthma

PROM:

Patient reported outcome measure

MIC:

Minimal important change

AQLQ:

Asthma Quality of Life Questionnaire

ACQ:

Asthma Control Questionnaire

FEV₁ :

Forced expiratory volume in one second

GPE:

Global perceived effect

95CI:

95% Confidence interval

SD:

Standard deviation

SNK:

Student-Newman-Keuls test

References

Download references

Acknowledgements

The authors thank all study participants for their valuable contribution, acknowledge the two asthma centres (Dutch Asthma Centre Davos, Davos, Switzerland, and Merem, Hilversum, The Netherlands) and their pulmonologists (Lous Rijssenbeek and Eline bij de Vaate) for the recruitment of subjects and Monique Legemaat and Jeanette Ambuhl for collecting the study data. Maaike Zuurmond is also acknowledged for preparing the data for the analyses.

Funding

This study was supported by a Grant from The Netherland-Davos Society.

Author information

Authors and Affiliations

1. Nivel, Netherlands Institute for Health Services Research, PO Box 1568, 3500 BN, Utrecht, The Netherlands

Linda Springvloet, Mattanja Triemstra, Bart Knottnerus, Marlon Rolink & Dolf de Boer

2. Department of Respiratory Medicine, University Medical Center Utrecht, Utrecht, The Netherlands

Harry Heijerman

Contributions

MT and MR conceived and designed the study. LS, MT and BK analyzed the data, and all other authors (MR, HH and DdB) helped in interpreting the data. LS, MT and BK drafted the manuscript. All authors were responsible for writing the manuscript and final approval of the version to be published. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Dolf de Boer.

Ethics declarations

Ethics approval and consent to participate

All patients provided their written informed consent on taking part in the clinical study which was approved by the Ethics Committee of the Academic Medical Center of the University of Amsterdam (Amsterdam, the Netherlands) [ 15]. The CaReQoL Asthma was not officially part of the clinical study, and an amendment about the additional informal data collection with this instrument was handed over to the Ethics Committee after the start of the clinical study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

CaReQoL asthma

Questionnaire on the 'care related quality of life' in persons with severe asthma

Please answer the following propositions about the effects that you might experience after your recent pulmonary rehabilitation. The questions are about your opinion and the perceived effects during the past two weeks.

Add your response to each question starting with ' Due to the pulmonary rehabilitation... '.

You may choose 'not applicable' (N.A.) when the question is not applicable to you, or when a change or effect is not related to your asthma.

1. 25
How is your health now, compared to your health before the pulmonary rehabilitation?

2. 26
What is your age?

____ years.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Reprints and permissions

About this article

Cite this article

Springvloet, L., Triemstra, M., Knottnerus, B. et al. Validation of the CaReQoL asthma: a patient reported outcome measure for monitoring the perceived effects of pulmonary rehabilitation in adult patients with severe refractory asthma. Respir Res 24, 14 (2023). https://doi.org/10.1186/s12931-022-02281-6
Download citation

Received:

Accepted:

Published:

DOI: https://doi.org/10.1186/s12931-022-02281-6

Keywords

# data[].attributes.attachment_link.title
Validation of the CaReQoL asthma: a patient reported outcome measure for monitoring the perceived effects of pulmonary rehabilitation in adult patients with severe refractory asthma - Respiratory Research

# data[].attributes.attachment_link.description
Background The CaReQoL Asthma assesses the care-related quality of life outcomes of pulmonary rehabilitation retrospectively in patients with severe asthma. The questionnaire comprises five domains (physical functioning; social functioning; coping with asthma; knowledge about asthma; medication).

# data[].attributes.attachment_link.readable_text
Kate Churruca, PhD, Research Fellow, 1 Chiara Pomare, PhD, Postdoctoral Research Fellow, 1 Louise A. Ellis, PhD, Research Fellow, 1 Janet C. Long, PhD, Senior Research Fellow, 1 Suzanna B. Henderson, MPH, Manager, Indicator Development, 2 Lisa E. D. Murphy, MHM, Manager, Strategy Development and Intergovernment Relations, 2 Christopher J. Leahy, MHSM, Director Safety and Quality Improvement Systems, 2 and Jeffrey Braithwaite, PhD, Director 1

Abstract

Background

Patient‐reported outcome measures (PROMs) are questionnaires that collect health outcomes directly from the people who experience them. This review critically synthesizes information on generic and selected condition‐specific PROMs to describe trends and contemporary issues regarding their development, validation and application.

Methods

We reviewed academic and grey literature on validated PROMs by searching databases, prominent websites, Google Scholar and Google Search. The identification of condition‐specific PROMs was limited to common conditions and those with a high burden of disease (eg cancers, cardiovascular disorders). Trends and contemporary issues in the development, validation and application of PROMs were critically evaluated.

Results

The search yielded 315 generic and condition‐specific PROMs. The largest numbers of measures were identified for generic PROMs, musculoskeletal conditions and cancers. The earliest published PROMs were in mental health‐related conditions. The number of PROMs grew substantially between 1980s and 2000s but slowed more recently. The number of publications discussing PROMs continues to increase. Issues identified include the use of computer‐adaptive testing and increasing concerns about the appropriateness of using PROMs developed and validated for specific purposes (eg research) for other reasons (eg clinical decision making).

Conclusions

The term PROM is a relatively new designation for a range of measures that have existed since at least the 1960s. Although literature on PROMs continues to expand, challenges remain in selecting reliable and valid tools that are fit‐for‐purpose from the many existing instruments.

Patient or public contribution

Consumers were not directly involved in this review; however, its outcome will be used in programmes that engage and partner with consumers.

Keywords: patient safety, patient‐reported outcome measure, PROM, review

1. INTRODUCTION

Over the last few decades, health‐care systems have increasingly recognized patients' perspectives as fundamental to ensuring that services are of a high quality and delivered in an equitable and safe way.1 The expanding use of patient‐reported outcome measures (PROMs) has been part of this shift.2, 3 PROMs are standardized questionnaires that collect information on health outcomes directly from patients, including about symptoms, health‐related quality of life and functional status. In addition to standardization, PROMs should ideally undergo psychometric validation to ensure that they accurately reflect the outcomes they purport to measure and that they can reliably assess changes over time.4

PROMs were originally developed for use in research, particularly clinical trials assessing the effectiveness of treatments.5 Over time, their applications have broadened to include the following: supporting clinical decision making, prioritizing patients for surgical procedures, comparing outcomes among health‐care providers, stimulating quality improvement and evaluating practices and policies.2, 3, 6, 7, 8 Evidence that the routine use of PROMs, at least in an oncological setting, leads to better outcomes for patients is inconclusive, but they do appear to improve patient‐provider communication and patient satisfaction.6, 9 Potential benefits of these measures rely on them being rigorously developed, relevant to patients and well‐validated.10

Broadly, PROMs fall into two main categories: condition‐specific and generic. The latter measures health concepts that are relevant to a wide range of patient groups, enabling aggregation and comparisons across varied conditions and settings. An example is the EQ‐5D; developed by the EuroQol Group,11 it includes five questions asking after the patient's health that day, mobility, self‐care, usual activities, pain/discomfort and anxiety/depression. Condition‐specific PROMs capture elements of health relevant to a particular patient group or condition. For example, the European Organisation for Research and Treatment of Cancer12 developed a widely used PROM that assesses the quality of life of patients with cancer (EORTC QLQ‐30). There is a core generic questionnaire for all patients with cancer, as well as modules targeting symptoms and outcomes of different cancer diagnoses (eg the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Lung Cancer Module (EORTC QLQ‐LC13)13).

Condition‐specific and generic PROMs are both important for understanding and improving patient care at multiple levels of the health‐care systems. Past literature reviews on PROMs have focused on identifying measures related to specific conditions (eg epilepsy14) or populations (eg children and adolescents15). A review that critically synthesizes information on PROMs and considers trends and issues in this literature has thus far been lacking. This paper aims to address this gap by evaluating trends in PROMs and their publication and discusses contemporary issues that relate to the development, validation and application in health care.

2. METHODS

We used a rapid review methodology to synthesize the evidence on generic and condition‐specific PROMs, identifying measures and evaluating trends and issues. Rapid review is a form of evidence synthesis that streamlines traditional systematic review methods in a shortened time frame.16 The approach was selected due to the fast‐moving nature of the field and the breadth of focus of this review. The Preferred Reporting Items for Systematic Reviews and Meta‐analyses (PRISMA) guidelines were used to guide the methodological design.17 A critical review approach18 was adopted to evaluate trends and contemporary issues in the use of PROMs. This rapid review was conducted for a lead safety and quality organization in Australia, the Australian Commission on Safety and Quality in Health Care. Although consumers were not directly involved in this review, the Commission engages and partners with consumers to deliver its work programmes. This review is part of its work to support the use of PROMs to 'drive quality improvement in a way that brings patients' voices and outcomes to the fore'.19

2.1. Search strategy

Following consultation with a research librarian and an examination of past systematic reviews on PROMs,15, 20 keywords and MeSH terms were developed. The search of academic literature was limited to papers published in English and published over the last 30 years (1989‐2019). This period coincides with the rapid growth in the use of PROMs.21 However, PROMs originally developed prior to the 30‐year search window were included where they were reported on in papers published during this period. The search was also restricted to journal articles (ie conference papers and books were excluded). The search was run on four databases (MEDLINE, EMBASE, PsycINFO, CINAHL) in conjunction with a search of the grey literature and snowballing of reference lists and past reviews. An example of the search strategy used is displayed in Table 1 . The search was carried out on 15 May 2019.

TABLE 1

Database

Keywords and MeSH

MEDLINE

"patient?reported outcome" OR "PROM" OR MeSH term "patient reported outcome measures"

AND

"psychometr*" OR "reliability" OR "valid*"

AND

"questionnaire" OR "tool" OR "scale" OR "survey" OR "measure" OR "instrument" OR "interview"

2.2. Eligibility criteria

PROMs were included in the review if they met the following criteria: (1) standardized instrument/survey that is used for measuring health outcomes (eg symptoms, quality‐of‐life, functional status) reported directly by the patient and using a range of modes of delivery, including computerized‐adaptive testing (CAT); (2) validated, that is, there was published statistical analyses establishing reliability (eg Cronbach's alpha22) and validity of the scale(s), including construct validity (eg factor analyses, item‐response modelling, convergent and discriminant validity), criterion‐related validity (eg concurrent and predictive validity), or analyses of known group differences; (3) validation analyses were conducted on an English language version of the instrument, either in the original validation paper or subsequently and (4) the measure assesses generic health status OR is a condition‐specific PROM from one of the included conditions (see Results, Table 2 ). To be included, it was not required that a survey be described as a 'PROM' in its original development, but it needed to measure patient‐reported health outcomes as per criterion (1).

TABLE 2

Disease group

Diseases

PROMs

For criterion (4), conditions were selected by examining the Australian Institute of Health and Welfare's (AIHW's) list of high burden diseases,23 which largely reflected international trends.24 Reproductive and maternal conditions were also added based on consultation with the project funder. These conditions are grouped by International Classification of Diseases (ICD‐10) disease groups.25

2.3. Additional searches: Snowballing, scoping published reviews and grey literature

When reviewing validation papers, researchers noted any additional potential PROMs, adding them to the list for scoping (ie snowballing). Searches for other PROMs in Scopus, Google Scholar and Google Search were also performed using terms for both specific conditions and broader disease groups. Websites that list PROMs, such as the International Consortium for Health Outcomes Measurement (ICHOM),26 were checked. Researchers also searched for recent systematic and other reviews on the topic, reading these papers to ensure as far as possible no relevant PROMs were missed.

2.4. Screening and data extraction

Citations returned from the database searches were downloaded into Endnote and duplicates were removed. Titles and abstracts were then exported to Microsoft Excel for a simultaneous process of screening against inclusion criteria and preliminary data extraction. Preliminary data extraction involved documenting the relevant disease group (where applicable, from Table 2 ) and condition(s) reported in the abstract, plus any PROMs mentioned by name, for all citations meeting the inclusion criteria. In the few cases where there was not sufficient information in the abstract to assess eligibility or extract data, the full text of the paper was examined.

In the second stage, PROMs labelled with conditions from our list were reviewed in detail. These were distributed amongst members of the research team by disease group (eg KC reviewed all PROMs for the included musculoskeletal conditions). Researchers searched for the original validation paper(s) for each PROM to fill in data on country, year and process of validation and PROM domains/dimensions; they documented the number of times the validation paper had been cited in Google Scholar as an indicator of how widely the PROM was used in published research. Researchers then scoped more recent papers using the PROM, selecting an exemplar study, ideally from 2012 or later to indicate currency. Reports or other clinical or policy‐focused evidence of use were also searched for online and documented when identified. While conducting the review and extracting data, researchers recorded trends and issues in the development, validation and application of PROM. These included: recurrent patterns in the types of PROMs published (generic and condition‐specific), the terminology used to describe them, modes of delivery and contemporary issues in their use (eg validity, use of technology). These were identified from reviewing the PROMs papers and examining the systematic reviews used for snowballing.

3. RESULTS

A total of 6453 citations were returned from the database search. Following removal of duplicates, 3450 titles/abstracts were exported to Excel for screening and preliminary data extraction (Figure 1 ). This led to the identification of 255 PROMs, both generic and from conditions in Table 2 .

A further 200 PROMs were identified through additional searches, amounting to 455 PROMs evaluated. Of these, 315 met all inclusion criteria and were included in this review (Appendix S1). PROMs were excluded at this stage for a number of reasons including insufficient evidence of validation or lack of validation in English, measures were a clinical assessment tool (ie used by clinicians) or not yet sufficiently validated for use as a PROM, the measure was too generally described or lacked appropriate standardization to be adequately validated (eg general reports of numeric rating scales, visual analogue scales, the Patient Global Assessment27), or the information available online was too limited to assess eligibility.

The number of PROMs included in the review by disease group is shown in Table 2 . As can be seen, the highest number of PROMs was identified for musculoskeletal conditions and for generic PROMs, followed by cancer and gastrointestinal conditions. Only two PROMs were identified covering iron‐deficiency anaemia, the only blood and metabolic condition included in the condition list.

In compiling PROMs, trends over time were examined among measures and publications. Despite the date range of 1989‐2019, no papers came up from the academic literature search prior to 1999, indicating the time dependent nature of the term 'patient‐reported outcome measure'. However, after this time publications using this terminology increased rapidly (see Figure 2 ). From reviewing information in these papers, as well as searches of grey literature, we were able to identify validated PROMs from a much wider date range, with the first validation paper for a PROM included in this review being from 1965. The number of validated PROMs being published increased until the 2000s, when this trend plateaued (see Figure 3 ).

Time trends in the publication of PROMs by disease groups are displayed in Figure 4 . Among the included conditions, development and validation of PROMs varied by disease group: the first validated mental health PROM was published in 1965, whereas PROMs for other disease groups (eg infectious, hearing and vision, respiratory, kidney, skin and blood) were not developed until the late 1980s and early 1990s.

PROMs meeting our inclusion criteria were originally developed in a range of countries including Singapore, Norway, New Zealand and the Netherlands, although almost half had been developed in the United States of America (n = 147, 46.8%), followed by the United Kingdom (n = 81, 25.8%), Canada (n = 29, 9.2%) and Australia (n = 12, 3.8%). Thirty‐three (10.5%) PROMs had been simultaneously validated in multiple countries and often numerous languages, such as the KIDSCREEN‐52, a generic PROM assessing health‐related quality of life in children and adolescents, validated in 12 European countries (Austria, Czech Republic, France, Germany, Greece, Hungary, the Netherlands, Poland, Spain, Sweden, Switzerland, the United Kingdom)28 and the InFLUenza patient‐reported outcome (FLU‐PRO), which assesses influenza symptoms and was validated in English and Spanish across the United States, United Kingdom, Mexico and Argentina.29

We also examined the number of citations received by each of the PROM validation papers as an indication of the use of the measure (averaged by year). Overall, validation papers received an average of 43.7 cites per year, but there was a high degree of skewedness (min = 0, max = 1102.4, mdn = 11.3, SD = 108.1). Table 3 displays the number of citations per year for PROM validation papers by disease group. Validation papers from mental health PROMs had the highest number of citations per year at 254.1, followed by generic PROMs (54.7 citations) and blood and metabolic PROMs (47.0 citations), of which there were only two.

TABLE 3

Condition groups

Average cites per year

Mental health

254.1

Generic PROM

54.7

Blood and metabolic

47.0

Cancer

39.0

Oral

36.2

Respiratory

34.7

Skin

30.6

Kidney and urinary

29.4

Musculoskeletal

28.4

Neurological

24.8

Reproductive and maternal

24.5

Cardiovascular

16.7

Hearing and vision

15.0

Gastrointestinal

14.2

Endocrine

11.5

Infectious

8.7

Injuries and trauma

7.7

For 51 (16.2%) PROMs, published reports were able to be found online, indicating the use of these PROMs for clinical or policy purposes. Two‐hundred and seventy (85.7%) of the included PROMs had been used in subsequent research studies. This left 45 PROMs with no evidence online to indicate subsequent use following validation, although 21 of these had been published in the last 5 years.

4. DISCUSSION

This review identified both generic and condition‐specific PROMs, evaluated trends in measures and publications and considered contemporary issues in the development, validation and application of PROMs in health care. We identified 315 validated PROMs (Appendix S1), covering a range of common conditions with high burden of disease across all major condition groups.25 Thirty‐nine of the included measures were generic PROMs.

4.1. Trends

There was growth in the academic literature on PROMs, particularly over the last decade, reflecting the broader movement to put the patient at the centre of health care. PROMs value patients as experts on their experiences and can facilitate their involvement in clinical decision making.2 The increase in publications on PROMs reflects a growing global recognition that incorporating the patient's perspective is integral to the quality and effectiveness of health care.30

The average number of citations received per year for validation papers suggests a strong appetite for using PROMs. Mental health PROMs had the highest average citations per year. This may be because of the centrality of self‐report in diagnosing and monitoring these conditions, which typically lack biological markers; hence, some of the mental health PROMs are also used for screening and diagnosing these conditions.31, 32, 33 Indeed, we found numerous mental health PROMs validated as early as the 1960s, long before PROMs for physical disorders, or before the nomenclature for these measures became established.

Citation counts are just one indicator of the use of a PROM and not one that demonstrates use in clinical practice. We found evidence in reports published online that some of the PROMs reviewed were used in clinical practice or to inform policy. For example, PROMs had been used to evaluate the effectiveness of care delivery models,34 understand the needs of particular populations,35, 36 examine care pathways37 and assess treatment effectiveness..38, 39 As part of the uptake of PROMs in practice, ICHOM, established in 2012, publishes standard sets of outcomes (including patient reported) for different medical conditions, together with implementation advice, that hospitals internationally can use to inform what they measure.

In scoping the literature, we also observed utilization of CAT over the last decade. CAT is able to differentiate respondents along the continuum of a trait (eg degree of pain) by extensive collection, content validation and calibration of item banks.40 Calibrating item banks involves advanced psychometrics using item‐response theory to determine the degree of 'difficulty' or level of the underlying trait being measured (eg 'moderate' pain) for each item.41 Using calibrated item banks in computer administered PROMs is thought to improve efficiency, making delivery of the questionnaire more dynamic and flexible because new questions are adapted to patients' prior responses.42 In that sense, CAT‐enabled PROMs are more individualized than traditional PROMs, which have been criticized as having limited flexibility,43 being potentially inappropriate for use among some groups,44 and lacking timeliness in generating prompt, clinically meaningful interpretations.45 The Patient‐Reported Outcome Measure Information System (PROMIS), a National Institute of Health initiative 46, 47 was the most notable example identified of CAT‐enabled PROMs. PROMIS item banks have been progressively created for adult and paediatric populations across physical (eg fatigue), mental (eg anxiety) and social health (eg peer relationships). In many cases, these were drawn from other validated PROMs, but their calibration, coupled with the extensive support available and limited restrictions on use, has contributed to PROMIS' popularity.48

4.2. Issues

Many of the PROMs identified by this review were well‐established-some were more than 30 years old-while papers captured from our search of academic databases were more recent. Thus, the term 'patient‐reported outcome' appears to be a fairly new designation for measures, questionnaires and inventories that might once have been described as assessing symptom severity or health‐related quality‐of‐life. It was often only in retrospect, by the attribution of other authors, that older instruments were recognized as PROMs. This may make searching for PROMs challenging.

There were differing degrees of validation and notions of what constituted validity in the PROMs reviewed. Not all forms of validity testing (eg face, content, construct, criterion‐related, known group differences) are sufficient to claim that a scale is valid. There is a growing trend to assess the methodological quality of PROM studies using the COnsensus‐based Standards for the selection of health status Measurement INstruments (COSMIN).49, 50 Increasingly systematic reviews are being conducted on PROMs for specific conditions and symptoms, and these use COSMIN or other assessment criteria to make determinations about which PROM(s) is superior in terms of validity.51, 52, 53, 54 Although it was not the purpose of this review to make such recommendations, it is clear that COSMIN is an increasingly important tool for assessing potential PROMs.

Many PROMs reviewed were not originally developed for use in clinical practice or to inform policy decisions and may have issues with responsiveness to change or potential floor and ceiling effects that limit their potential in these settings.55 Recently, there have been calls to reconceptualize the validation of PROMs as an on‐going, iterative process of evidence accumulation, with consideration of how the data will be used to make decisions about individuals and populations, instead of a static designation that a PROM is valid.56 The findings of our review must be considered in light of this idea; although we identified PROMs validated in English and for the measurement of outcomes for particular conditions, within these parameters there are a broad range of contexts (eg different countries, care settings), populations (eg older patients, condition subtypes) and purposes at the micro‐, meso‐ and macro‐level (eg decision making with individual patients, provider comparison, funding health‐care organizations) for which validity could conceivably vary.

4.3. Things to consider when selecting a PROM

As others have noted, a key challenge for researchers, policymakers and clinicians is selecting a reliable and valid tool from the hundreds of PROMs available.57 These measures are designed to be subjective and reflect patients' perspectives and experiences; they do not replace other more objective measurements. Rather, PROMs should be used to complement clinical data.30

One decision is whether to use a generic or condition‐specific PROM. Limitations exist for both. Generic PROMs can be used to generalize or compare across different conditions but lack sensitivity to condition‐specific outcomes; hence, they may have greater applicability at an organizational or system level. Condition‐specific PROMs are argued to have greater face validity, credibility and responsiveness to changes in the patient's condition.2 Hence, they are most appropriate for measuring treatment outcomes within specific clinical populations and focusing on the individual level.11 For these reasons, it has been recommended that both types of PROMs be used concurrently and applied at different levels of the health system.30 Our review identified nuances and different focuses of outcome domains within condition‐specific PROMs for the same condition. For example, some PROMs for rheumatoid arthritis focus on quality of life (eg58), while others prioritize subjective assessments of symptom impacts (eg59) or functional status (eg60), and some focus on specific symptoms (eg fatigue61) or social issues related to the condition (eg work instability62). With these issues in mind, selecting the right PROM(s) requires careful reflection on the intended purposes, including why patients' responses are being collected, what one expects or wants to find, what information is relevant to capture, and how this this information will be used.63

4.4. Strengths and limitations

The method reported here is a multipronged approach to reviewing the literature and identifying PROMs. A key strength was examining both generic and condition‐specific PROMs to consider trends and issues in this growing field. Less than two thirds of instruments evaluated came from the academic databases search, likely because this search used keywords exclusively related to PROMs rather than, for example, health‐related quality‐of‐life, symptom severity or other more specific terms that are now captured under the umbrella of 'PROMs'. This weakness was to some extent mitigated by additional searches including snowballing, scoping published reviews and searching grey literature. However, some potential instruments meeting our criterion (1) of assessing health outcomes reported directly by patients may have been missed by our search strategy if they are no longer used or have never been recognized as PROMs.

This review synthesized trends across 315 PROMs, including both generic and condition‐specific, but due to the rapid nature of the review, the collection of some information, particularly the use of a PROM in published reports or exemplary studies, was not exhaustive, nor is this aspect of the process entirely replicable. These insights should be treated as indicative of how PROMs are being used generally, rather than a clear demonstration of which PROMs are used in research and practice and which are not. New PROMs continue to be published, including for new conditions and clinical areas.64 We excluded a number of measures that are still in the early stages of validation (eg reporting on content validity) but it is likely that over time, through more extensive validation, these would have met criteria for inclusion. We also excluded PROMs where we could not find evidence of English validation, even though some had translations available.65 The research on, and use of, PROMs, as this review has shown, is constantly moving.

5. CONCLUSION

There are many PROMs and many more studies examining, using or discussing them. In this review, we identified 315 generic and condition‐specific PROMs, producing a library available for public use that will be updated over time.66 We also evaluated trends among measures and publications on PROMs and discussed contemporary issues related to their development, validation and application in healthcare. A key challenge to using PROMs is selecting a reliable and valid tool that is appropriate for one's purpose from the hundreds of instruments available. This review provides insights to assist with understanding the scope of available generic and selected condition‐specific PROMs, including trends in the development, validation and use of these PROMs. It highlights the growing global recognition that incorporating the patient's perspective is integral to the quality and effectiveness of health care and issues associated with this shift. The review demonstrates that the measurement of patient‐reported outcomes is an evolving field.

CONFLICT OF INTERESTS

None declared.

AUTHOR CONTRIBUTIONS

SH, LM and CL conceptualized the project. KC, CP and LE conducted the literature review and drafted the manuscript with support from SH and LM. KC, CP, LE, JL, SH, LM, CL and JB contributed to the final version of the manuscript.

Notes

Churruca K, Pomare C, Ellis LA, et al. Patient‐reported outcome measures (PROMs): A review of generic and condition‐specific measures and a discussion of trends and issues. Health Expect. 2021;24:1015-1024. 10.1111/hex.13254 [[ PubMed] [ CrossRef] PMC free article] [ Google Scholar]

Funding information

This work was funded by the Australian Commission on Safety and Quality in Health Care, the NHMRC Partnership Centre in Health System Sustainability (Grant ID 9100002) and NHMRC Investigator Grant APP1176620

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available in the supplementary material Appendix S1 of this article.

REFERENCES

5. U.S. Department of Health . Guidance for industry: patient‐reported outcome measures: use in medical product development to support labeling claims: draft guidance, in Health and Quality of Life Outcomes, 2006. U.S. Department of Health Human Services FDA Center for Drug Evaluation Research; U.S. Department of Health Human Services FDA Center for Biologics Evaluation Research; U.S. Department of Health Human Services FDA Center for Devices Radiological Health. p. 79. [ PMC free article] [ PubMed]

14. Nixon A, Kerr C, Breheny K, Wild D. Patient Reported Outcome (PRO) assessment in epilepsy: a review of epilepsy‐specific PROs according to the Food and Drug Administration (FDA) regulatory requirements. Health Qual Life Outcomes. 2013;11:38. [[ PubMed] PMC free article] [ Google Scholar]

18. Paré G, Trudel M‐C, Jaana M, Kitsiou S. Synthesizing information systems knowledge: a typology of literature reviews. Inform Manage. 2015;52(2):183‐199. [ Google Scholar]

23. Australian Institute of Health and Welfare . Chronic Disease. Canberra, ACT: AIHW; 2019. [ Google Scholar]

24. Institute for Health Metrics and Evaluation (IHME) . Findings from the Global Burden of Disease Study 2017; 2018. Seattle, WA: IHME.

25. Australian Institute of Health and Welfare . Australian Burden of Disease Study: impact and causes of illness and death in Australia 2015, 2019. Canberra, Australia: AIHW.

30. Devlin NJ, Appleby JGetting the Most Out of PROMS. Putting Health Outcomes at the Heart of NHS Decision‐Making. London, UK: The King's Fund; 2010. [ Google Scholar]

33. Anderson J, Michalak E, Lam R. Depression in primary care: tools for screening, diagnosis, and measuring response to treatment. B C Med J. 2002;44(8):415‐419. [ Google Scholar]

34. Varker T, Gibson K, Watson L, Metcalf O, O'Donnell M. What is the effectiveness of outreach services for improvement mental health? A Rapid Evidence Assessment. Carlton, VIC, Australia: Phoenix Australia Centre for Posttraumatic Mental Health; 2018. Report prepared for the Department of Veterans' Affairs. Available from: https://www.dva.gov.au/file/11780/download?token=IO2wj39j

35. Garcia P, McCarthy M. Measuring Health: A Step in the Development of City Health Profiles. Copenhagen, Denmark: World Health Organization, Regional Office for Europe; 1995. [ Google Scholar]

36. Byles J, MacKenzie L, Redman S, Williamson A, Parkinson L. Housing and Independent Living: Environment and Built Factors for Maintaining Independence in Older Age; 2011. Glebe, NSW: Sax Institute.

37. Arthritis Research UK . Musculoskeletal Health Questionnaire: Developing and piloting a generic patient reported outcome measure for us across musculoskeletal care pathways. United Kingdom: Versus Arthritis; 2016.

38. Churches T, Naylor J, Harris I. Arthroplasty Clinical Outcomes Registry National (ACORN) Annual Report 2016; 2017, Whitlam Orthopaedic Research Centre: Sydney, Australia.

39. New Zealand Joint Registry . Seventeen year report: January 1999 to December 2015; 2016, New Zealand Joint Registry: New Zealand.

42. Meijer RR, Nering ML. Computerized Adaptive Testing: Overview and Introduction. Thousand Oaks, CA: Sage Publications; 1999. [ Google Scholar]

64. Howard K, Clifford S, Saretsky T. Content validation of a new patient‐reported outcome (PRO) instrument in hospital‐acquired bacterial pneumonia (HABP). Value Health. 2017;20(5):A80. [ Google Scholar]

Articles from Health Expectations : An International Journal of Public Participation in Health Care and Health Policy are provided here courtesy of Wiley-Blackwell

# data[].attributes.attachment_link.title
Patient‐reported outcome measures (PROMs): A review of generic and condition‐specific measures and a discussion of trends and issues

# data[].attributes.attachment_link.description
Patient‐reported outcome measures (PROMs) are questionnaires that collect health outcomes directly from the people who experience them. This review critically synthesizes information on generic and selected condition‐specific PROMs to ...

# data[].attributes.headline
Mary Ramsbottom- Outcomes of the first global multidisciplinary consensus meeting including persons living with obesity to standardize patient-reported outcome measurement in obesity treatment research

# data[].attributes.subject
Mary Ramsbottom- Outcomes of the first global multidisciplinary consensus meeting including persons living with obesity to standardize patient-reported outcome measurement in obesity treatment research

# data[].attributes.headline
Mairead-Predicting the risk of Readmission for patients with Chronic Heart Failure

# data[].attributes.subject
Mairead-Predicting the risk of Readmission for patients with Chronic Heart Failure

# data[].attributes.body
Systematic review on the measurement properties of diabetes-specific patient-reported outcome measures (PROMs) for measuring physical functioning in people with type 2 diabetes
by Elsman, Ellen B M; Mokkink, Lidwine B; Langendoen-Gort, Marlous ; More...

# data[].attributes.body
What is important for people with type 2 diabetes? A focus group study to identify relevant aspects for Patient-Reported Outcome Measures in diabetes...
by Abdel-Rahman, Nura; Manor, Orly; Valinsky, Liora ; More...
edited by Angkurawaranon, Chaisiri
PloS one, 11/2022, Volume 17, Issue 11

# data[].attributes.attachment_link.title
Nephrology - 2020 - Willik - Patient‐reported outcome measures   PROMs   making sense of individual PROM scores and changes

# data[].attributes.attachment_link.title
Diabetic Medicine - 2022 - Speight - Impact of glycaemic technologies on quality of life and related outcomes in adults

# data[].attributes.headline
PROMs in cystic fibrosis data registry Caroline B

# data[].attributes.subject
PROMs in cystic fibrosis data registry Caroline B

# data[].attributes.attachment_link.readable_text
Evan Tang, 1, 2,† Aarushi Bansal, 1, 2,† Marta Novak, 3, ⁴ and Istvan Mucsi 1, 2,*

Abstract

Chronic kidney disease (CKD) is a complex medical condition that is associated with several comorbidities and requires comprehensive medical management. Given the chronic nature of the condition, its frequent association with psychosocial distress, and its very significant symptom burden, the subjective patient experience is key toward understanding the true impact of CKD on the patients' life. Patient-reported outcome measures are important tools that can be used to support patient-centered care and patient engagement during the complex management of patients with CKD. The routine collection and use of patient-reported outcomes (PROs) in clinical practice may improve quality of care and outcomes, and may provide useful data to understand the disease from both an individual and a population perspective. Many tools used to measure PROs focus on assessing health-related quality of life, which is significantly impaired among patients with CKD. Health-related quality of life, in addition to being an important outcome itself, is associated with clinical outcomes such as health care use and mortality. In Part 1 of this review, we provide an overview of PROs and implications of their use in the context of CKD. In Part 2, we will review the selection of appropriate measures and the relevant domains of interest for patients with CKD.

Keywords: patient-reported outcomes, chronic kidney disease, kidney transplantation, renal transplantation, patient-reported outcome measures, quality of life measurement, quality of life

Introduction

In developed countries, there has been a rapid increase in the prevalence of chronic non-communicable diseases, which are responsible for the majority of morbidity and premature deaths in these societies ( 1, 2). These health conditions require complex long-term professional care and self-management ( 1, 2). Within the context of the health care system, management of chronic health conditions has been shifting away from the traditional provider-directed care model to a patient-centered care model ( 3- 5). Patient-centered care utilizes the perspective of patients and allows them to play a key role in defining and managing their care ( 3- 5). It promises a more holistic approach, improved patient experience, as well as improved medical outcomes compared with traditional care models ( 3, 5- 12). Incorporating the patient perspective into clinical practice could improve the accuracy and completeness of assessment, provides accountability, and may play a role in outcome prognostication ( 13- 15).

Chronic kidney disease (CKD) represents a major public health problem. Its prevalence has been rapidly increasing, in part, due to the rising prevalence of obesity, diabetes and hypertension ( 16- 21). CKD is associated with substantial disease burden as both the disease and its various treatments-dietary and lifestyle modifications, renal replacement therapies (dialysis and kidney transplant)-are very intrusive. Accordingly, CKD is associated with substantially impaired quality of life (QOL) and a significant increase in the risk of cardiovascular disease and premature death ( 22- 25).

Patient-reported outcome measures (PROMs) are direct responses from patients without alteration or interpretation by a clinician ( 12, 14). Use of PROMs can improve the assessment of disease burden among patients with CKD ( 12, 14). As such, incorporation of PROMs in routine clinical care would be an important tool toward facilitating patient-centered care ( 26- 29). Moreover, involving patients in defining clinical and research priorities has the potential to enhance the relevance and acceptability of research from the perspective of all stakeholders, including patients, the public, clinicians, funding and regulating agencies, and policy makers ( 29, 30) In addition, establishing a shared platform of understanding may improve patient-provider communication, increase adherence, and improve clinical outcomes ( 31, 32). Perhaps the most crucial component of patient engagement is incorporating the lived patient experience in health care delivery.

There has been an increasing interest in the systematic collection of patient-reported outcomes (PROs) data for monitoring the impact of chronic illness and improving care among patients with chronic medical conditions such as cancer, rheumatoid arthritis, chronic obstructive pulmonary disease, and heart failure ( 14, 15, 33- 35). Patient-reported measures (PRMs) have been shown to be superior to clinician reports both in the detection of symptoms and side effects of treatment, while also, being more sensitive to changes in functional status compared with standard of care ( 14, 36- 39).

Using electronic data capture to assess PROMs may improve the feasibility of assessing PROMs in routine clinical practice ( 40- 42). It eliminates the need for subsequent data entry, storage of the questionnaires, and reduces the risk of privacy breach. It has the potential for immediate scoring and presentation of the results ( 43- 45), offers the potential to link PROMs with clinical data in electronic health records ( 46, 47), to enhance communication in multidisciplinary care ( 48, 49), and to facilitate the assessment of PROMs independently from patient-provider encounters ( 43, 44, 48, 50). Electronic collection of PROs has been linked to improved QOL, reduced rates of hospitalization, and increased adherence and survival among a large cohort of outpatients receiving chemotherapy for advanced cancer ( 39). These findings support the use and potential implications of using PROs in CKD management ( 39, 47).

The aim of this review is to explore the use of PROs in the CKD population. In Part 1, we will describe PROs and implications of use. In Part 2, we will review the selection of appropriate PROMs and the relevant domains of interest for patients with CKD.

Chronic Kidney Disease

Defining CKD

Chronic kidney disease is a chronic, frequently progressive condition, defined by structural or functional abnormalities of the kidney and/or a reduced glomerular filtration rate (GFR) of less than 60 mL/min/1.73 m ² for more than 3 months ( 51- 55). It is caused by many different conditions including diabetes mellitus, hypertension, glomerulonephritis, genetic diseases, drug toxicity, urological conditions, infections, and acute kidney injury, among others ( 55, 56). CKD is a heterogeneous condition due to the varying severity and risk of progression, cause and pathology of disease, and comorbidities experienced by individual patients ( 57).

The classification of CKD is based on GFR (G category) and abnormal urinary albumin excretion (A category) ( 10, 54, 55, 58, 59). CKD is divided into five GFR stages, ranging from G1, representing normal to high kidney function with evidence of kidney disease, to G5 (GFR < 15 mL/min/1.73 m ²), known as end-stage kidney disease (ESKD). Patients with various stages of CKD can be managed by lifestyle changes and medications with the aim of slowing disease progression ( 53, 55, 59). ESKD, however, is potentially life threatening without receiving renal replacement therapy (RRT) in the form of dialysis (peritoneal or hemodialysis) or kidney transplantation ( 10, 52, 60). Classification guidelines recommend the use of a suffix to identify the RRT modality in patients with stage 5 CKD; with "D" representing dialysis and "T" representing a functioning kidney transplant ( 52, 55). The stages of CKD enable classification of the severity of renal impairment with existing guidelines in place aimed at management of each stage ( 10, 54, 55).

Patient-Reported Measures

Traditional clinical tests do not adequately assess the health status, well-being, and functional capacity of patients as patients with similar clinical severity may have vastly different responses to the disease process ( 99, 100). Likewise, biomedical characteristics and biomarkers are often inadequate for predicting the subsequent illness trajectory ( 101, 102) or response to a specific treatment. Incorporating the patient experience and patient perspective can improve the accuracy and completeness of assessment and may improve the prediction of outcomes ( 13, 40- 42, 103, 104). These observations led to the rise of health frameworks with patient-centered care as the focus, offering a much more holistic approach to the measurement of outcomes ( 3, 5- 12).

Quality of Life

The first studies focusing on patient-centered care measured QOL: the Almeda County study ( 105), the RAND Health Insurance study ( 106), and the Medical Outcomes study ( 107, 108). These studies represented a major shift in ideology toward one, which took patient's priorities into consideration. More recently, the World Health Organization defines QOL as "the individual's perception of their position in life in the context of the culture and value systems in which they live and in relation to their goals, expectations, standards, and concerns ( 109). Recognizing the breadth of this definition, the medical community further refined this concept in the context of health and coined the term health-related quality of life (HRQOL) ( 108).

Health-Related Quality of Life

Patient-Reported Measures

Patient-reported measures can be classified into two different categories: PROs, referring to one's perception of health status, and patient-reported experiences (PREs), referring to one's perception of the care they received ( 12, 14, 35, 116, 117). Tools designed to measure PROs and PREs are thus labeled as PROMs or patient-reported experience measures (PREMs) ( 12, 14, 116, 117).

Patient-reported outcome measures can be used to assess a wide variety of health-relevant concepts and are indispensable for gathering comprehensive information about a patient ( 12, 14). There are four overarching categories of information that PROMs can assess: HRQOL, functional status, symptoms and symptom burden, and health behaviors and perceptions ( 13, 14). In contrast, PREMs provide an evaluation of quality of health care delivery from the patient's perspective, such as facility cleanliness, access to information and health care teams, communication, support received, and transportation ( 12). A core tenant of PROMs and PREMs requires that responses are received without any interpretation as this enables an accurate and unadulterated account about the patient.

Patient-Reported Outcome Measures

Structure of PROMs

Patient-reported outcome measures are composed of individual questions or statements (with standardized response options), also known as items. Items are related to particular domains, which are overarching, measurable themes of interest (e.g., physical functioning, emotional well-being, etc.). Certain PROMs have algorithms in place to aggregate individual items that are similar, into specific composite ratings or domain scores. For instance, one PROM frequently used in CKD is the Kidney Disease Quality of Life Short Form (KDQOL-SF) ( 88, 125, 126). The general domains include the following: physical functioning, physical role functioning, bodily pain, general health perceptions, vitality, emotional role functioning, social role functioning, and mental health and additional, kidney disease-related domains, such as effects of kidney disease, burden of kidney disease, symptom list, sleep problems-each with scores ranging from 0 to 100 ( 125, 126). In addition, physical and mental health composite scores are generated, also ranging from 0 to 100, with higher scores representing better overall physical or mental health, respectively ( 126).

Implications of PROMs in Clinical Care

Measurement of PROs provides information about patient health and well-being which cannot be obtained by traditional medical/biological assessments ( 11, 12, 14). Responses through a subjective lens, without clinician interpretation, facilitate a holistic approach by gaining insight into patients' values and preferences while also obtaining reports on outcomes of care ( 11, 12, 31, 32). An important goal of the extensive research in PROMs would be the eventual integration of tools into the standard of care of patients with CKD. There are many implications for clinical practice based on the results drawn from PROMs. Integration of PROMs can enhance patient-centered care, facilitate communication between patients and health care providers as well as between health care team members, and provide information for evaluating clinical outcomes ( 3, 6, 9, 40, 41, 119).

Enhanced Patient-Centered Care

Patient-reported outcome measures can also be utilized to identify which symptoms are personally relevant and important to patients, which is an essential component of assessing the impact of disease and their QOL ( 179). Considering the recent finding that 80% of current clinical research does not include the top 10 research priorities as identified by patients on or nearing dialysis, understanding patients' needs, values, and opinions are crucial to improving outcomes ( 9, 27, 31, 32). As such, PROMs can be used to address this disparity in current research, by utilizing information from PROMs to align research priorities with patient priorities ( 26, 31- 33).

Enhanced Communication

Clinical Outcomes

Paramount to the discussion of implication is that of "hard" clinical outcomes, such as mortality, hospitalization, and symptom burden. Many studies have suggested that PROs have the potential to be used for prognostication ( 103, 104, 184- 187). While links between clinical outcomes and PROs have been identified, only a few studies have explored the impact of interventions on these outcomes ( 188). Further studies are required to elucidate the impact of PRO modification on clinical outcomes.

Conclusion

In this paper, we have reviewed the relevance and importance of using PROMs in the context of CKD, largely supported by the need to address currently unexplored aspects of the illness experience of patients suffering from the multifaceted and chronic nature of this disease, which contribute to a high symptom burden and clinical outcomes. This includes both physical and psychosocial concerns, which are often overlooked in this patient population. As such, the value of patient involvement in care delivery and research is increasingly recognized. PROMs are the latest frontier as a means of collecting and utilizing patient-reported information without clinician interpretation. This not only provides a means to identify and address the patient concerns but also has benefits including the facilitation of patient-centered care, enhancing communication, and elucidating research priorities. PROMs, however, are still a growing field with implications for clinical practice that is still being uncovered in the context of CKD. In Part 2, we will explore the domains of interest for patients with CKD and the selection of appropriate PROMs.

Author Contributions

ET and AB were involved in the conception of the review, including a critical review of the literature accompanied by the drafting of the manuscript. MN and IM provided support in the form of professional appraisal and the provision of supporting literature. Critical revisions were performed and the final version was reviewed and approved by both IM and MN.

Conflict of Interest Statement

The authors declare that this review was conducted without any commercial or financial conflict of interest.

References

10. Kidney disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012. Clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl (2013) 3:1-150. [ Google Scholar]

13. Cella D, Hahn EA, Jensen SE, Butt Z, Nowinski CJ, Rothrock N, et al. Patient-Reported Outcomes in Performance Measurement. Research Triangle Park, NC: RTI Press Publication; (2015). [[ PubMed] Google Scholar]

81. Saran R, Robinson B, Abbott K. US Renal Data System 2016 Annual Data Report: Epidemiology of Kidney Disease in the United States. United States Renal Data System; (2017). [[ PMC free article] [ PubMed] Google Scholar]

82. Canadian Institute for Health Information. Canadian Organ Replacement Register Annual Report: Treatment of End-Stage Organ Failure in Canada, 2001 to 2010. (2010).

164. Hays RD. The Medical Outcomes Study (MOS) Measures of Patient Adherence. Santa Monica, California: The RAND Corporation; (Vol. 19). (1994). 2004 p. [ Google Scholar]

169. Salek S. Quality of life in patients with end-stage renal disease. J Appl Therap (1999) 2:163-70. [ Google Scholar]

174. Bradley C. Design of a renal-dependent individualized quality of life questionnaire. Adv Perit Dial (1997) 13:116-20. [[ PubMed] Google Scholar]

180. Kimmel PL. Just whose quality of life is it anyway? Controversies and consistencies in measurements of quality of life. Kidney Int (2000) 57:S113-20. 10.1046/j.1523-1755.2000.07419.x [[ CrossRef] Google Scholar]

Articles from Frontiers in Medicine are provided here courtesy of Frontiers Media SA

# data[].attributes.attachment_link.title
Patient-Reported Outcomes in Patients with Chronic Kidney Disease and Kidney Transplant-Part 1

# data[].attributes.attachment_link.description
Chronic kidney disease (CKD) is a complex medical condition that is associated with several comorbidities and requires comprehensive medical management. Given the chronic nature of the condition, its frequent association with psychosocial distress, and ...

# data[].attributes.headline
CC - Measuring outcomes of rehabilitation among the elderly - a feasibility study

# data[].attributes.subject
CC - Measuring outcomes of rehabilitation among the elderly - a feasibility study

# data[].attributes.attachment_link.readable_text
The .gov means it's official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

doi: 10.3389/frhs.2023.1187713.

Affiliations

¹ Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

² Unit for Performance Assessment of the Health and Social Service System, Finnish Institute of Health and Welfare, Helsinki, Finland.

Free PMC article

Laura J Pitkänen et al. Front Health Serv.

Free PMC article

doi: 10.3389/frhs.2023.1187713.

Affiliations

¹ Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland.

² Unit for Performance Assessment of the Health and Social Service System, Finnish Institute of Health and Welfare, Helsinki, Finland.

Abstract

A feasible system for measuring patient outcomes of rehabilitation is required for assessing the real-world cost-effectiveness of rehabilitation. This study aims to assess the feasibility of measuring outcomes of rehabilitation among elderly individuals with early-stage Alzheimer's. We used the principles of Design Science to construct a set of metrics consisting of standardized PROM (Patient-Reported Outcome Measure) questionnaires, clinician-reported measures, and observational measures of functioning. We used standardized questionnaires whenever possible to ensure the validity and reliability of the questionnaires. The set of metrics was piloted on 16 individuals living at home with regular home care services. After the pilot, we further refined the set of metrics based on relevance, sensitivity to change, and applicability. We found that measurement was feasible and we propose the final set of metrics as a minimum set, which could be further improved upon by addition of metrics relevant to each subgroup of elderly individuals. We also found that using self-reported questionnaires in this population is not without difficulties. We therefore suggest that the role of informal caregivers be considered, and that accessibility of outcome questionnaires be improved.

Keywords: Alzheimer's; effectiveness; elderly; outcomes; real-effectiveness medicine; rehabilitation; value-based healthcare.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors PT and AM declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Figures

References

Crocker T, Forster A, Young J, Brown L, Ozer S, Smith J, et al. Physical rehabilitation for older people in long-term care. Cochrane Database Syst Rev. (2013) (2):CD004294. 10.1002/14651858.CD004294.pub3 - DOI - PubMed

Gillespie LD, Robertson MC, Gillespie WJ, Sherrington C, Gates S, Clemson L, et al. Interventions for preventing falls in older people living in the community. Cochrane Database Syst Rev. (2012) 2012(1):CD012424. 10.1002/14651858.CD007146.pub3 - DOI - PMC - PubMed

Daniels R, van Rossum E, de Witte L, Kempen GIJM, van den Heuvel W. Interventions to prevent disability in frail community-dwelling elderly: a systematic review. BMC Health Serv Res. (2008) 8:278. 10.1186/1472-6963-8-278 - DOI - PMC - PubMed

Melin AL, Håkansson S, Bygren LO. The cost-effectiveness of rehabilitation in the home: a study of Swedish elderly. Am J Public Health. (1993) 83(3):313-474. 10.2105/AJPH.83.3.356 - DOI - PMC - PubMed

Larsson S. The patient priority. Solve health care's value crisis by measuring and delivering outcomes that matter to patients. New York: McGraw Hill; (2022) 56, p. 65-7

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

# data[].attributes.attachment_link.title
Measuring outcomes of rehabilitation among the elderly-a feasibility study - PubMed

# data[].attributes.attachment_link.description
A feasible system for measuring patient outcomes of rehabilitation is required for assessing the real-world cost-effectiveness of rehabilitation. This study aims to assess the feasibility of measuring outcomes of rehabilitation among elderly individuals with early-stage Alzheimer's. We used the prin ...

# data[].attributes.headline
Assessment of Patient-Reported Outcome Measures for Maternal Postpartum

# data[].attributes.subject
Assessment of Patient-Reported Outcome Measures for Maternal Postpartum

# data[].attributes.body
Depression Using the Consensus-Based Standards for the Selection of Health Measurement Instruments Guideline

A Systematic Review

# data[].attributes.attachment_caption
Skovlund, S.E., Lichtenberg, T., Hessler, D. et al. Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study. Curr Diab Rep 19, 84 (2019). https://doi.org/10.1007/s11892-019-1190-x

# data[].attributes.headline
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study

# data[].attributes.subject
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study

# data[].attributes.attachment_link.title
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study - Current Diabetes Reports

# data[].attributes.attachment_link.description
Purpose of Review In recent years, the recommendation for and use of patient-reported outcome measures (PROMs) in routine diabetes care has significantly increased. We review recent evidence and highlight key opportunities and challenges related to the active clinical use of PROMs to support person-centered diabetes care and focus areas for future research in the area. Recent Findings Recent pragmatic studies support that integration of multi-dimensional PROMs for diabetes in clinical care as part of a care improvement strategy can be acceptable for and valued by people with diabetes (PWD) and healthcare professionals (HCPs) and may improve multiple aspects of quality of care, including screening, medical care monitoring and decision support, individualization of self-management support and goal-setting, and broader benefits related to active patient participation and person-centred diabetes care. We identify multiple intervention, individual, and care setting characteristics, which influence acceptability, feasibility, implementation, and effectiveness of PROMs in routine care. Recent clinical PROM studies highlight the value of mixed methods research and systematic involvement of PWD, clinicians, and other stakeholders in the design and implementation of questionnaires for patient input in routine diabetes care. Summary We identified a new significant trend towards participatory development of multi-dimensional PROMs with the aim of IT-enabled integration into routine diabetes care to facilitate multiple components of person-centered diabetes care and better clinical, quality of life, and cost outcomes. While results from large-scale randomized controlled studies are still limited, a growing number of pragmatic implementation studies support that user-centric PROM interventions have the potential to facilitate significant improvements in care for PWD.

# data[].attributes.headline
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study

# data[].attributes.subject
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study

# data[].attributes.attachment_link.readable_text
Abstract

Purpose of Review

In recent years, the recommendation for and use of patient-reported outcome measures (PROMs) in routine diabetes care has significantly increased. We review recent evidence and highlight key opportunities and challenges related to the active clinical use of PROMs to support person-centered diabetes care and focus areas for future research in the area.

Recent Findings

Recent pragmatic studies support that integration of multi-dimensional PROMs for diabetes in clinical care as part of a care improvement strategy can be acceptable for and valued by people with diabetes (PWD) and healthcare professionals (HCPs) and may improve multiple aspects of quality of care, including screening, medical care monitoring and decision support, individualization of self-management support and goal-setting, and broader benefits related to active patient participation and person-centred diabetes care. We identify multiple intervention, individual, and care setting characteristics, which influence acceptability, feasibility, implementation, and effectiveness of PROMs in routine care. Recent clinical PROM studies highlight the value of mixed methods research and systematic involvement of PWD, clinicians, and other stakeholders in the design and implementation of questionnaires for patient input in routine diabetes care.

Summary

We identified a new significant trend towards participatory development of multi-dimensional PROMs with the aim of IT-enabled integration into routine diabetes care to facilitate multiple components of person-centered diabetes care and better clinical, quality of life, and cost outcomes. While results from large-scale randomized controlled studies are still limited, a growing number of pragmatic implementation studies support that user-centric PROM interventions have the potential to facilitate significant improvements in care for PWD.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

Papers of particular interest, published recently, have been highlighted as: * Of importance ** Of major importance

1. Inzucchi SE, Bergenstal RM, Buse JB, Diamant M, Ferrannini E, Nauck M, et al. Management of hyperglycemia in type 2. Diabetes Care. 2012;35:1364-79.

Article CAS PubMed PubMed Central Google Scholar

2. Griggs CL, Schneider JC, Kazis LE, Ryan CM. Patient-reported outcome measures. Med Care. 2017;56:265:1066-7.

3. ** Young-Hyman D, De Groot M, Hill-Briggs F, Gonzalez JS, Hood K, Peyrot M. Psychosocial care for people with diabetes: a position statement of the American Diabetes Association. Diabetes Care. 2016:39, 2126-2140. Describes the ADA guideline recommendations for psychosocial care in diabetes, including consideration of use of psychological screening questionnaires in diabetes care.

4. International Diabetes Federation - Home. Global guidelines for type 2 diabetes. 2012 [cited 2019 Jan 23]. Available from: https://www.idf.org/e-library/guidelines/79-global-guideline-for-type-2-diabetes.html

5. International Diabetes Federation. Recommendations for managing Type 2 Diabetes in Primary Care. http://www.idf.org/managing-type2-diabetes. Accesed 4 Jun 2019.

6. * International Consortium for Health Outcomes Research (ICHOM) ICHOM standard set for adults with diabetes. Type 1 and type 2 in adults. Data collection and reference guide 2018: International Consortium for Health Outcomes Management - Home; 2019. [cited 2019 Jan 23]. Available from: https://www.ichom.org/portfolio/diabetes/. Accessed 11 Jul 2019. Defines PROM measures as part of global diabetes standard outcomes set for outcomes monitoring (WHO-5, PAID, and PHQ-9).

7. * d'Emden H, McDermott B, Poulson K, McGahan G. Protocol for psychosocial screening of adolescents and young adults with chronic illness. Intern Med J. 2018. https://doi.org/10.1111/imj.14211. Provides a detailed concrete case example of development of a brief psychosocial screening tool designed for routine use with young adults with type 1 diabetes.

8. Powell P, Corathers S, Raymond J, Streisand R. New approaches to providing individualized diabetes care in the 21st century. Curr Diabetes Rev. 2015;11:222-30 Available from: http://www.eurekaselect.com/openurl/content.php?genre=article&issn=1573-3998&volume=11&issue=4&spage=222.

9. * Ishaque S, Karnon J, Chen G, Nair R, Salter AB. A systematic review of randomised controlled trials evaluating the use of patient-reported outcome measures (PROMs). Qual Life Res. 2019;28:567-92 Springer International Publishing [cited 2019 Mar 25]. Available from: http://link.springer.com/10.1007/s11136-018-2016-z. Provides a first systematic review of the effectiveness of PROM interventions across disease areas on facilitating care with focus on patient value and preferences.

10. Eton D, Elraiyah TA, Yost K, Ridgeway J, Johnson A, Egginton J, et al. A systematic review of patient-reported measures of burden of treatment in three chronic diseases. Patient Relat Outcome Meas. 2013; Dove Press [cited 2019 Mar 20];4:7. Available from: http://www.dovepress.com/a-systematic-review-of-patient-reported-measures-of-burden-of-treatmen-peer-reviewed-article-PROM.

11. Skovlund SE, Troelsen L, Dømgaard M, Jakobsen PEO, Ejskjaer N. Development of a national minimal set of patient-important outcome domains for value-based diabetes care in Denmark. Diabetes. 2018, 67 American Diabetes Association Available from: http://diabetes.diabetesjournals.org/content/67/Supplement_1/135-LB.

12. * Scholle SH, Morton S, Homco J, Rodriguez K, Anderson D, Hahn E, et al. Implementation of the PROMIS-29 in routine care for people with diabetes. 2018;41:274-87. Provides qualitative evaluation of barriers, facilitators, and impacts of use of a generic PROM for PWD in primary practice.

13. Nanayakkara N, Pease A, Ranasinha S, Wischer N, Andrikopoulos S, Speight J, et al. Depression and diabetes distress in adults with type 2 diabetes: results from the Australian National Diabetes Audit (ANDA) 2016. Sci Rep. 2018;8:7846 [cited 2019 Mar 23] Available from: http://www.nature.com/articles/s41598-018-26138-5.

14. * Nicolucci A, Kovacs Burns K, Holt RIG, Comaschi M, Hermanns N, Ishii H, et al. Diabetes Attitudes, Wishes and Needs second study (DAWN2™): cross-national benchmarking of diabetes-related psychosocial outcomes for people with diabetes. Diabet Med. 2013;30:767-77 Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/dme.12245. Presents multi-national data on an international set of pragmatic PROM indicators for person-centered diabetes care.

15. Snoek FJ. Monitoring of individual needs in diabetes (MIND)-2: follow-up data from the cross-national Diabetes Attitudes, Wishes, and Needs (DAWN) MIND study. 2012;35:2128-32.

16. De Wit M, de Waal HA, Bokma JA, Haasnoot K, Houdijk MC, Gemke RJ, et al. Follow-up results on monitoring and discussing health-related quality of life in adolescent diabetes care: benefits do not sustain in routine practice. Pediatr Diabetes. 2010;11:175-81 Available from: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1399-5448.2009.00543.x.

17. Pouwer F, Tack CJ, Geelhoed-Duijvestijn PHLM, Bazelmans E, Beekman AT, Heine RJ, et al. Limited effect of screening for depression with written feedback in outpatients with diabetes mellitus: a randomised controlled trial. Diabetologia. 2011;54:741-8.

Article CAS PubMed PubMed Central Google Scholar

18. * Santana MJ, Haverman L, Absolom K, Takeuchi E, Feeny D, Grootenhuis M, et al. Training clinicians in how to use patient-reported outcome measures in routine clinical practice. Qual Life Res. 2015;24:1707-18. https://doi.org/10.1007/s11136-014-0903-5. Provides a framework for the evaluation of potential impacts of implementation of PROMs in chronic healthcare.

19. ** Corathers SD, Mara CA, Chundi PK, Kichler JC. Psychosocial patient-reported outcomes in pediatric and adolescent diabetes: a review and case example. Curr Diab Rep Current Diabetes Rep. 2017;17. Provides insights into a concrete model for multi-dimensional PRO assessment in routine pediatric diabetes care and utilization of PRO data also at group level.

20. Frost MH, Bonomi AA, Cappelleri JC, Schünemann H, Moynihan TJ, et al. Applying quality-of-life data formally and systematically into clinical practice. 2003;25:D10.

21. Deane K, Delbecque L,Gorbenko O,HamoirAM, Hoos A, Nafria B, et al. Co-creation of patient engagement quality guidance for medicines development: an international multistakeholder initia-1073 tive. BMJ Innov. 2019;bmjinnov-2018-000317. Available from: https://innovations.bmj.com/lookup/doi/10.1136/bmjinnov-2018-10750003177

22. Prinsen CAC, Mokkink LB, Bouter LM, Alonso J, Patrick DL, de Vet HCW, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res Springer International Publishing. 2018;27:1147-57.

Article CAS PubMed PubMed Central Google Scholar

23. Gandek B, Ware JE, Aaronson NK, Apolone G, Bjorner JB, Brazier JE, et al. Cross-validation of item selection and scoring for the SF-12 health survey in nine countries. 1998;51:1171-8.

24. Roth AJ. Rapid screening for psychologic distress in men with prostate carcinoma: a pilot study. 1998;82:1198-904.

25. Lin C-Y, Lee T-Y, Sun Z-J, Yang Y-C, Wu J-S, Ou H-T. Development of diabetes-specific quality of life module to be in conjunction with the World Health Organization quality of life scale brief version (WHOQOL-BREF). Health Qual Life Outcomes. 2017;15:167 [cited 2019 Mar 29] Available from: http://hqlo.biomedcentral.com/articles/10.1186/s12955-017-0744-3.

26. Schmitt A, Reimer A, Hermanns N, Huber J, Ehrmann D, Schall S, et al. Assessing diabetes self-management with the diabetes self-management questionnaire (DSMQ) can help analyse behavioural problems related to reduced glycaemic control. PLoS One. 2016;11:1-12.

Google Scholar

27. Laursen DH, Christensen KB, Christensen U, Frølich A. Assessment of short and long-term outcomes of diabetes patient education using the health education impact questionnaire (HeiQ). BMC Res Notes. 2017;10:213 [cited 2019 Mar 29] Available from: http://bmcresnotes.biomedcentral.com/articles/10.1186/s13104-017-2536-6.

28. Ylitalo KR, Meyer MRU, Lanning BA, During C, Laschober R, Griggs JO. Simple screening tools to identify limited health literacy in a low-income patient population. Med (United States). 2018;97.

29. National Association of Community Health Centers (NACHC). PRAPARE Implementation and Action Toolkit. 2016 [cited 2019 Mar 1]. Available from: http://www.nachc.org/research-and-data/prapare/toolkit/. Accessed 6 Jun 2019.

30. Billioux A, Verlander K, Anthony S, Alley D. Standardized screening for health-related social needs in clinical settings: the accountable health communities screening tool. NAM Perspect. 2017;7(5). https://doi.org/10.31478/201705b

31. Speight J, Barendse SM, Singh H, Little SA, Inkster B, Frier BM, et al. Characterizing problematic hypoglycaemia: iterative design and preliminary psychometric validation of the Hypoglycaemia Awareness Questionnaire (HypoA-Q). 2016;33:376-85.

Download references

Author information

Authors and Affiliations

1. Steno Diabetes Center North Denmark, Aalborg University Hospital, Aalborg, Denmark

Soren E. Skovlund & N. Ejskjaer

2. Department of Clinical Medicine, Aalborg University, Søndre Skovvej 15, DK-9000, Aalborg, Denmark

Soren E. Skovlund & N. Ejskjaer

3. Danish Health Data Authority, Copenhagen, Denmark

TH Lichtenberg

4. Family & Community Medicine, School of Medicine, University of California, San Francisco, San Francisco, CA, USA

D. Hessler

Corresponding author

Correspondence to Soren E. Skovlund.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any intervention studies with human or animal subjects performed by any of the authors. Qualitative research involving people with diabetes and caregivers was undertaken by Soren E. Skovlund for this study. All participants in this research gave informed written consent and study protocol was conducted according to local and national scientific regulations.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Psychosocial Aspects

About this article

Cite this article

Skovlund, S.E., Lichtenberg, T., Hessler, D. et al. Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study. Curr Diab Rep 19, 84 (2019). https://doi.org/10.1007/s11892-019-1190-x
Download citation

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

# data[].attributes.attachment_link.title
Can the Routine Use of Patient-Reported Outcome Measures Improve the Delivery of Person-Centered Diabetes Care? A Review of Recent Developments and a Case Study - Current Diabetes Reports

# data[].attributes.attachment_link.description
Purpose of Review In recent years, the recommendation for and use of patient-reported outcome measures (PROMs) in routine diabetes care has significantly increased. We review recent evidence and highlight key opportunities and challenges related to the active clinical use of PROMs to support person-centered diabetes care and focus areas for future research in the area.

# data[].attributes.attachment_caption
Patient Reported Outcome Measures to inform care of People with Dementia - A Systemic Scoping Review by Ayton et al. 2021

# data[].attributes.headline
E Health research trial in advancing wound care.

# data[].attributes.subject
E Health research trial in advancing wound care.

# data[].attributes.attachment_link.readable_text
Objective To pilot test the proposed DiaPROM trial components and address uncertainties associated with conducting a full-scale randomised controlled trial (RCT) to evaluate whether such a trial is feasible.

Design Two-arm pilot RCT.

Participants Adults aged ≥18-39 years, with minimum 1 year type 1 diabetes duration, attending outpatient follow-up. Exclusion criteria were pregnancy, severe cognitive, somatic or psychiatric conditions and impaired vision.

Randomisation and intervention All participants completed electronic Patient-Reported Outcome Measures (PROMs) prior to the annual diabetes consultation. Using computer-generated block-randomisation without blinding, we assigned participants in a 1:1 ratio stratified by sex to receive standard care or an intervention. Physicians reviewed diabetes distress scores ( Problem Areas In Diabetes scale) and referred individuals with scores ≥30 or single item(s) ≥3 to minimum two diabetes nurse consultations where reported problems were reviewed and discussed.

Outcomes Recruitment and retention rates; participants perceptions about intervention components. Variance and estimated between-group differences in follow-up scores ( Diabetes Distress Scale (DDS), WHO 5-Well-being Index, Perceived Competence for Diabetes Scale and glycaemic control) and DDS correlation with baseline scores, to assist sample size calculations.

Results We randomised 80 participants to the control or intervention arm (one participant was later excluded). 23/39 intervention arm participants qualified for additional consultations and 17 attended. 67/79 attended the 12-month follow-up (15.2% attrition); 5/17 referred to additional consultations were lost to follow-up (29.4% attrition). Participants reported PROMs as relevant (84.6%) and acceptable (97.4%) but rated the usefulness of consultations as moderate to low. Baseline mean±SD DDS score was 2.1±0.69; DDS SD was 0.71 (95% CI: 0.60 to 0.86) at follow-up; correlation between baseline and follow-up DDS scores was 0.8 (95% CI: 0.7 to 0.9).

Conclusions The pilot trial revealed need for intervention modifications ahead of a full-scale trial to evaluate use of PROMs in diabetes consultations. Specifically, participant acceptability and intervention implementation need further investigation.

Aim

To pilot test the proposed DiaPROM trial components and address uncertainties associated with conducting a full-scale RCT in order to evaluate whether the trial methods and the intervention are feasible. The pilot trial objectives were thus to:

1. Evaluate the recruitment procedures, randomisation procedure and attrition rates.

2. Evaluate the acceptability, appropriateness and implementation of the intervention components.

3. Estimate variance and between-group differences in participant outcomes (diabetes distress; general well-being; perceived diabetes competence and glycaemic control) following intervention or standard care, and correlation between participants' diabetes distress scores at baseline and 12 months, in order to assist future sample size calculations.

Design

The study was designed as a single-centre two-arm pilot RCT.

Setting and participants

In Norway, people with T1D are followed up at hospital clinics. We conducted the pilot trial at a university hospital endocrinology outpatient clinic where approximately 80% of the patients with diabetes have T1D. Eligible participants aged ≥18-39 years with T1D duration for at least 1 year were identified using the clinic's attendance list. We sent invitation letters with consent forms by mail 10-14 days prior to the patients' annual diabetes consultations. Informed by pilot trial sample size guidance and the diabetes distress proportions documented in our feasibility study, 34 38 we aimed to recruit 80 participants, 40 in each arm. Using information from the electronic patient records (EPR), we applied the following exclusion criteria: ongoing pregnancy, severe cognitive deficiency, severe somatic comorbidity (eg, end-stage renal disease, severe heart failure, severe cancer), major psychiatric diagnosis (eg, severe depression or bipolar disorder, schizophrenia) and/or impaired vision.

Pilot trial intervention

We have described the intervention in detail in our protocol paper. 33 Briefly, DIPS, eHealth systems supplier to Norwegian hospitals, developed the technical application for capturing and transferring electronic PROMs to the diabetes-specific EPR. 39 We asked all participants to arrive 15 min early to complete PROMs on a stationary touchscreen computer located in the outpatient clinic's waiting area prior to two annual diabetes consultations (baseline and 12 months). While completing PROMs, participants received an individual four-character code which was used to download the PROMs to the EPR. The length of the annual consultations was increased from 30 to 45 min. Furthermore, we used the 20-item Problem Areas In Diabetes (PAID) scale to assess diabetes distress. 40-42 PAID items are rated on a 5-point Likert-like scale (0, 'not a problem' to 4, 'serious problem'), and an overall diabetes distress score of 0-100 is calculated, with higher scores indicating greater distress. A score ≥40 suggests serious diabetes distress. 10 41 The PAID is widely used, and the Norwegian version is available in the diabetes-specific EPR. 43

We developed a manual to guide the physicians to download PROMs and review and discuss PAID scores with intervention arm participants, and to identify moderate and serious distress, specifically PAID total score ≥30 or at least one item scored 3 or 4. Next, the physicians were to offer individuals with such scores a minimum of two 30-min diabetes specialist nurse consultations; the first within 4 weeks after randomisation and the second within a further 3 months. We also developed a communication manual where we guided the nurses to review baseline PAID and discuss reported problem areas with the participants using person-centred, empowerment-based communication skills; 'asking open questions', 'active listening', 'responding', 'summing up' and 'agreeing on goals and actions to take'. In addition, we requested the nurses to record problem areas discussed, goals, action strategies and plans in the EPR. In the second consultations, we asked the nurses and participants to discuss the problem areas, goals and actions and to decide whether to continue with consultations (optional number) until the next annual physician consultation. Intervention arm participants with lower PAID scores received follow-up according to standard clinical protocols after the brief review of their PAID scores with the physicians. Control arm participants, whose scores were inaccessible to the clinicians in the EPR, received 'care as usual'.

Sample characteristics

Sociodemographic and diabetes-related information was gathered from the participants' EPR: age, sex, ethnic origin, diabetes duration, diabetes long-term complications, comorbidities, body mass index, glycosylated haemoglobin (Haemoglobin A 1c (HbA 1c)) level, number of self-reported symptomatic hypoglycaemic events in the previous month, history of hypoglycaemia requiring assistance and hospitalisation due to ketoacidosis and insulin injection device. We also obtained self-report data on current type of glucose monitoring device, daily glucose measurement count, first language, educational level, cohabitation status and work affiliation. In addition, we received ethical approval to record age, sex and HbA 1c of eligible participants who declined participation.

Primary outcome measure

To avoid using the same questionnaire for diabetes distress assessment as an element of the intervention and as an outcome measure, we chose the Diabetes Distress Scale (DDS) as our primary outcome. 43 The 17-item DDS measures diabetes-specific problems rated on a 6-point Likert-like scale (1, 'no problem' to 6, 'serious problem'). 44 The scale yields an overall diabetes distress score and four subscales: emotional burden (five items; eg, 'Feeling that diabetes controls my life'), physician-related distress (four items; eg, 'Feeling that my doctor doesn't take my concerns seriously enough'), regimen-related distress (five items; eg, 'Feeling that I am not testing my blood sugars frequently enough') and diabetes-related interpersonal distress (three items; eg, 'Feeling that friends or family are not supportive enough of self-care efforts'). Item scores are averaged to form a total and subscale scores from 1 to 6, with higher values indicating greater distress. 45 Scores are then categorised as little or no distress (<2.0), moderate distress (2.0-2.9) and high distress (≥3.0). Moderate and high distress is considered clinically relevant. 45

Secondary outcomes measures

We used the WHO 5-Well-being Index (WHO-5) 5-item measure of current general well-being. 46 Items are scored on a 6-point Likert-like scale (0, 'at no time' to 5, 'all the time'). A 0-100 score is calculated and scores <50 suggest impaired well-being, while ≤28 indicate likely depression. 46 47 A 10-point change is considered clinically relevant. 46 The measure is reported to be psychometrically sound, acceptable and suitable for diabetes outpatient settings. 48-50 The 4-item Perceived Competence for Diabetes Scale (PCDS) assesses the degree to which people with diabetes feel they can manage daily aspects of diabetes care (1, 'strongly disagree' to 7, 'strongly agree'). 51 Item scores are averaged to form a score. Finally, we obtained information about glycaemic control from routinely performed blood samples measuring HbA 1c (mmol/mol) recorded in the EPR.

Experiences with the pilot trial intervention

After each annual consultation, participants were asked to complete a paper questionnaire, which included the DDS (primary outcome measure) and questions about experiences with and perceptions about the pilot trial components. We asked all participants PROMs acceptability questions (five response options from 'not at all' to 'very large degree'): relevance, number of items and willingness for annual completion. In addition, we asked about preferred completion method (electronic or paper). Intervention arm participants were also asked about PAID use and consultation usefulness. Finally, we reviewed the nurses' EPR notes for intervention arm participants referred to additional follow-up, to evaluate intervention consultation fidelity (per-protocol).

Randomisation

We randomised participants in a 1:1 ratio to an intervention or control arm using computer-generated block-randomisation at the patient level, developed and administered by DIPS. 39 The computerised allocation took place when the physicians downloaded PROMs to the EPR. Group allocation information appeared on the computer screen, and the physicians told the participants. Furthermore, we stratified by sex to ensure equal numbers (20) of male and female participants in each arm. Due to the nature of the intervention, blinding of group allocation to participants, healthcare providers and research personnel was not possible.

Analyses

All analyses were carried out using Stata SE 16 for Windows. 52 At each timepoint, we estimated means, SD and 95% CI of SDs of outcome measures for both groups. To examine within and between-group variation of paired differences in outcome measures from baseline to 12-month follow-up, we estimated means and SDs, and means and 95% CIs, respectively. Using Spearman's correlation coefficient, we estimated correlation with 95% CI between participants' primary outcome measure scores at baseline and 12 months. The primary outcome measure SD, 95% CI of SD and correlation coefficient was used to assist in full trial sample size calculations. In all analyses, we computed missing items using person-mean substitution if at least 50% of the items per scale were completed. 53 54

Patient and public involvement

In the protocol paper, 33 we have provided a detailed description of health service user involvement based on the Guidance for Reporting Involvement of Patients and the Public 2 (GRIPP2) short form. 55

Recruitment, randomisation, sample characteristics, and retention

Between 15 January and 7 May 2018, we assessed 149 patients with T1D for eligibility and randomised 80 participants, 40 (50%) to each trial arm ( figure 1). The randomisation procedure yielded two groups with equal distribution of men and women. Baseline characteristics for the total sample and trial arms are presented in table 1. Compared with the included participants, the 22 who declined had longer diabetes duration (13.7±7.0 years (95% CI: 12.2 to 15.3) vs 18.6±10.2 years (95% CI: 14.1 to 23.1)), while there were no differences in gender distribution, age or HbA 1c level. Furthermore, 24/40 (60.0%) intervention arm participants qualified for additional nurse follow-up ( figure 1). One participant was later excluded due to newly discovered language problems. In total, 17/23 (73.9%) were referred and attended 1-5 consultations (mean±SD 2.2±1.1); 12/17 (70.6%) attended the per-protocol minimum. After reviewing the nurses' EPR notes, we registered that 28/38 consultations were performed according to the protocol, while 10/38 focused on other aspects than diabetes distress assessed by the PAID. Therefore, a mean of 1.65 (0-2) intervention consultations was conducted, and 9/17 received per-protocol follow-up of minimum two sessions. The 12-month follow-up was performed from 5 December 2018 to 17 June 2019. Twelve participants were lost to follow-up (overall attrition rate 15.2%; intervention arm: 8 (20.5%); control arm: 4 (10%)), but none withdrew consent ( figure 1). Furthermore, 5/17 referred to additional nurse consultations were lost to follow-up (attrition rate 29.4%).

Acceptability, appropriateness and implementation of the intervention components

At baseline, 21/79 (26.6%) participants located the touchscreen computer without guidance, 43 (54.4%) confirmed they had read the written study information. At 12 months, five participants completed PROMs on paper; four because of a defective touchscreen and one asked for a telephone consultation. Of the remaining 62 participants, we had to remind 30 (48.4%) to complete PROMs. Furthermore, 2/17 participants referred to additional nurse follow-up delayed the first consultation for 4-6 months. The remaining 15/17 were offered the first consultation within 27.0±4.8 (19-35) days after randomisation. However, due to five participants postponing at least once, the consultations were conducted after 42.5±27.7 (22-123) days. The second appointments (n=15) were offered after 85.5±30.6 (20-133) days and attended by 12 participants after 100.8±35.3 (20-153) days.

Total WHO-5, PAID and PCDS completeness was 99.4% at baseline and 99.2% at 12 months. When asked about preferred method for completing PROMs in the future, two (2.6%) individuals chose paper-completion, whereas 42 (54.5%) opted for in-clinic computerised PROMs and 33 (42.9%) favoured home-based web-completion ( online supplemental figure 1). Seventy-five (97.4%) reported that number of items were acceptable to a large or very large degree, 72 (92.3%) found the items relevant and 66 (84.6%) were willing to complete PROMs annually ( online supplemental figure 1).

Among intervention arm participants, 23/39 (59.0%) and 13/31 (41.9%) reported PAID items scored ≥3 and/or a total score ≥30 (moderate to high distress) at baseline and follow-up, respectively ( online supplemental table 1). The control arm participants' corresponding proportions were 19/40 (47.5%) and 20/36 (55.6%). Thirty (76.9%) intervention group participants reported that the PAID results were discussed at baseline, of which 15 (38.5%) found it useful to a large or very large degree and 10 (25.6%) to some degree. At 12 months, 20/24 (83.3%) reported that PAID was discussed and 11 (45.8%) found it useful to a large or very large degree. Only 10/17 referred to additional follow-up completed all items about PAID use; five found the discussions useful; four reported to have benefitted to a large degree, whereas three had not benefitted at all. In total, 17/53 (32.1%) participants stated that completing PROMs had to some degree led to discussions related to diabetes-related challenges which would not otherwise have been discussed (similar in both trial arms). Furthermore, 14 (26.4%) reported that completing PROMs had been a positive experience, while 24/53 (45.2%) found it somewhat positive (similar in both trial arms).

Outcome measures

In total, 67/79 (84.8%) participants responded to all DDS items at baseline and 58/67 (86.6%) at 12 months ( online supplemental table 2). Mean scores and SDs of the outcome measures at baseline and follow-up for each trial arm are reported in tables 2 and 3. At follow-up, the sample's SD of DDS score was 0.71 (95% CI: 0.60 to 0.86) ( table 2). From baseline to follow-up, we observed a reduction in DDS overall score by an average of 0.25 (SD: 0.42) in the intervention arm but no apparent reduction in the control arm (0.00, SD: 0.47). The intervention arm's DDS subscale scores were all improved (−0.14 to −0.39, SDs: 0.66 to 0.86), while the control arm's changes in subscales scores ranged from −0.07 to 0.09 (SDs: 0.54 to 0.82). For other outcome variables (WHO-5, PCDS and HbA 1c), only small changes were seen ( table 3). The correlation coefficient between baseline and follow-up DDS scores was 0.8 (95% CI: 0.7 to 0.9) ( online supplemental table 3).

In addition, 18/33 (54.5%) and 11/26 (42.3%) intervention arm participants reported moderate to high distress measured by the DDS overall score at baseline and follow-up, respectively ( online supplemental table 4). Corresponding proportions for control arm participants were 15/39 (38.5%) and 17/34 (50.0%). Regarding DDS subscales, the percentage of participants reporting moderate to high emotional burden and regimen-related distress was persistent at ~60%, across groups and timepoints.

Strengths and limitations

The pilot trial's key strengths were that it systematically addressed uncertainties associated with designing a large-scale RCT. Moreover, well-known, validated tools for measuring primary and secondary outcomes allowed for comparison with other studies. The results inform technical and practical issues of conducting a full-scale trial. Similar to our feasibility study, 34 findings suggest that completing electronic PROMs was generally accepted and technically feasible. We were able to recruit and randomise 80 participants over 15 weeks. However, one fundamental limitation was that the 12-month follow-up lasted nearly twice as long (28 weeks), mainly caused by cancelled appointments, non-attendance and loss to follow-up. Another limitation was not having predefined criteria for retention and attrition progression rates. However, this is not yet common. 56 Furthermore, complete follow-up was not achieved, and attrition differed by trial arm: 10% in the control arm, 20% in the intervention arm and 29% among those who were referred to additional follow-up. Systematic differences between completers and drop-outs may have introduced attrition bias. 57 However, clinic non-attendance is not uncommon among young adults with T1D and has been linked to difficulties communicating with the services, conflicting schedules, low perceived value of attendance and challenges with developing relationships. 58 59 The retention, implementation and acceptability issues are further explored in qualitative interviews. In summary, these issues will impact power calculations by increasing the target sample needed, in addition to affecting intervention implementation and the duration of a full-scale RCT.

Generalisability and transferability to other settings and populations may be limited due to our use of electronic technologies for completing PROMs, our choice of only including young adults with T1D, and that the Norwegian health insurance system differs from other countries. Finally, although PAID scores were not accessible in the control arm participants' EPRs, we cannot rule out contamination. Since all participants completed PROMs in the same manner, control arm participants' consultations may have been influenced by individual responses and thoughts about the questionnaires. Moreover, consulting styles within a service probably differs between clinicians. For ethical reasons, we could not instruct the physicians to avoid discussing diabetes distress in the control arm if participants requested it.

Implications and future research

Using current pilot trial data and a conservative DDS SD estimate to calculate the minimal clinically important difference (0.5 x SD), 60 and assuming that SD (0.71) is equal for each trial arm in a full-scale, single-site RCT, we estimate at least 107 participants will be required per arm to provide 90% power based on a two-sided 5% significance level. The calculation was based on the formula of a two-sample t-test for difference post-intervention and allowed for 25% attrition.

Since 10% of the participants did not complete the paper-based measures, and there was considerably more missing DDS items than other PROM items, we will strive for capturing all future data electronically. Furthermore, only a minority of participants approached the touchscreen computer by themselves. Therefore, we will continue with in-clinic guidance and e-mail or SMS reminders to support data collection. A web-based PROMs platform, recently available in Norway, will possibly enable more complete data collections in future studies. Moreover, we observed that 36 (45.6%) participants had not read the study information prior to coming to the clinic but still consented. The drop-out rates and other findings suggest that consultations were not considered useful, adequate or appropriate by the participants. This could in part be explained by protocol inflexibility and/or the waiting time between PROMs completion and additional consultations. In addition, we may not have provided sufficient detailed information about the nature of the intervention components, especially the additional follow-up. Also, this key intervention component may not have fitted the participants' personal beliefs, preferences, capabilities and/or life circumstances. 61 We may also have underestimated the contribution of the baseline review of scores and discussions between intervention arm participants and physicians. Furthermore, our criteria for offering additional follow-up may have led to overinclusion of cases but we must also consider barriers to clinic attendance and dissatisfaction with the follow-up. 14 58 Another aspect which requires consideration, is that simply answering questions for assessment purposes, such as PROMs, may affect research participants by stimulating new thinking about problem areas or behaviours, which then may lead to action-taking. 62 This question-behaviour effect makes it even more difficult to evaluate complex interventions.

Diabetes distress scores were similar to previous studies. 20 21 63 Approximately half of the participants reported moderate to serious distress, which supports statements that diabetes distress is common and worthy of individual attention in diabetes care. 4 12 15 Although the pilot trial was not powered for inferential statements, the observed between-group differences in DDS scores suggest promising effects of assessing and addressing diabetes distress. Compared with lack of assessment and follow-up, education-based or emotion-focused interventions targeting diabetes distress in adults with T1D have been found clinically effective. 24 64 65 In the pilot trial, we focused on real-life clinical consultations. Hence, the clinicians meet individuals with different needs which may entail applying either education-based or emotion-focused interventions or both, depending on individual diabetes distress foci, discussions with each individual and clinical experience. Personalising diabetes care by addressing diabetes distress systematically, may increase healthcare providers' attentiveness towards the individual experiences of living with diabetes.

Implementation fidelity and difficulties in delivering the intervention as designed appeared challenging for the clinic. One aspect was providing the consultations within the specified timeframe. Recommendations of 2-week to 1-month intervals between consultations 23 66 may be difficult to achieve within regular working hours unless telephone or digital communication are used. 67 The observed lack of intervention fidelity, for example, not reviewing the PAID during annual consultations, may be partly explained by low sense of project ownership from the clinicians. This highlights the importance of organisational incentives, management facilitation of new intervention initiatives and possibly cultural aspects in this setting. Our efforts to encourage intervention fidelity by providing information, developing manuals and arranging meetings and training for the clinicians may not have been sufficient. Consequently, we must seek to further identify key contextual, organisational and behavioural factors and mechanisms of impact. The pilot trial results show that we must involve health service users and clinicians in further development of the intervention and undertake more feasibility work with process evaluations to inform the design of a full-scale trial. 68

Patient consent for publication

Not required.

Ethics approval

The study was approved by the Regional Committee for Medical and Health Research Ethics (reference number 2017/1506/REK vest) and was undertaken in accordance with the Declaration of Helsinki. All study data have been stored on a secure research server at Haukeland University Hospital, the responsible research institution. Participation was voluntary and participants gave informed written consent. Participants were free to withdraw their consent at any time without explanation, and withdrawal would not affect their further follow-up at the outpatient clinic.

# data[].attributes.attachment_link.title
Use of patient-reported outcome measures (PROMs) in clinical diabetes consultations: the DiaPROM randomised controlled pilot trial

# data[].attributes.attachment_link.description
Objective To pilot test the proposed DiaPROM trial components and address uncertainties associated with conducting a full-scale randomised controlled trial (RCT) to evaluate whether such a trial is feasible. Design Two-arm pilot RCT. Participants Adults aged ≥18-39 years, with minimum 1 year type 1 diabetes duration, attending outpatient follow-up.

# data[].attributes.headline
Overweight and Obese Diabetics - Weight Management Intervention. PREM Miriam Oates

# data[].attributes.subject
Overweight and Obese Diabetics - Weight Management Intervention. PREM Miriam Oates

# data[].attributes.attachment_link.title
PREM OVerweight and Obese Daibetics Weight Management

# data[].attributes.headline
Prem wgt mgmt of overweight diabetics Katie Delahunty

# data[].attributes.subject
Prem wgt mgmt of overweight diabetics Katie Delahunty

# data[].attributes.attachment_link.readable_text
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing on behalf of La Trobe University. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ( CC BY-NC-ND)

References

Abouzeid M, Philpot B, Janus ED, Coates MJ, Dunbar JA (2013) Type 2 diabetes prevalence varies by socio-economic status within and between migrant groups: analysis and implications for Australia. BMC Public Health 13, 252
| Crossref | Google Scholar |

Adriaanse MA, de Ridder DTD, de Wit JBF (2009) Finding the critical cue: implementation intentions to change one's diet work best when tailored to personally relevant reasons for unhealthy eating. Personality and Social Psychology Bulletin 35, 60-71.
| Crossref | Google Scholar |

Adriaanse MA, Vinkers CDW, De Ridder DTD, Hox JJ, De Wit JBF (2011) Do implementation intentions help to eat a healthy diet? A systematic review and meta-analysis of the empirical evidence. Appetite 56, 183-193.
| Crossref | Google Scholar |

Adriaanse MA, De Ridder DTD, Voorneman I (2013) Improving diabetes self-management by mental contrasting. Psychology & Health 28, 1-12.
| Crossref | Google Scholar |

Afzali HHA, Karnon J, Beilby J, Gray J, Holton C, Banham D (2014) Practice nurse involvement in general practice clinical care: policy and funding issues need resolution. Australian Health Review 38, 301-305.
| Crossref | Google Scholar |

AIHW (2020) Diabetes. In 'Diabetes'. (Ed. Welfare AIOHA) pp. 8-14. (Australian Institute of Health and Welfare: Canberra)

Ammerman A, Lindquist C, Hersey J, et al. (2000) 'Efficacy of interventions to modify dietary behavior related to cancer risk: summary.' (Agency for Healthcare Research and Quality: Rockville, MD, USA)

Boyle E, Saunders R, Drury V (2016) A qualitative study of patient experiences of type 2 diabetes care delivered comparatively by general practice nurses and medical practitioners. Journal of Clinical Nursing 25, 1977-1986.
| Crossref | Google Scholar |

Braun V, Clarke V (2006) Using thematic analysis in psychology. Qualitative Research in Psychology 3, 77-101.
| Crossref | Google Scholar |

Britt H, Miller GC, Joan H, Bayram C, Harrison C, Valenti L, Pan Y, Charles J, Pollack AJ, Wong C, Gordon J (2016) 'General practice activity in Australia 2015-16.' General Practice Series Number 40. pp. 200. (Sydney University Press: Sydney)

Cunningham J (2010) Socio-economic gradients in self-reported diabetes for Indigenous and non-Indigenous Australians aged 18-64. Australian and New Zealand Journal of Public Health 34, S18-S24.
| Crossref | Google Scholar |

Cyril S, Nicholson JM, Agho K, Polonsky M, Renzaho AM (2017) Barriers and facilitators to childhood obesity prevention among culturally and linguistically diverse (CALD) communities in Victoria, Australia. Australian and New Zealand Journal of Public Health 41, 287-293.
| Crossref | Google Scholar |

Dao J, Spooner C, Lo W, Harris MF (2019) Factors influencing self-management in patients with type 2 diabetes in general practice: a qualitative study. Australian Journal of Primary Health 25, 176-184.
| Crossref | Google Scholar |

Desborough J, Bagheri N, Banfield M, Mills J, Phillips C, Korda R (2016) The impact of general practice nursing care on patient satisfaction and enablement in Australia: a mixed methods study. International Journal of Nursing Studies 64, 108-119.
| Crossref | Google Scholar |

Ezzy D (2002) 'Qualitative analysis: practice and innovation.' (Allen & Unwin: Sydney)

Hagger MS, Luszcznska A (2014) Implementation intention and action planning interventions in health contexts: state of the research and proposals for the way forward. Applied Psychology: Health and Well-Being 6, 1-47.
| Crossref | Google Scholar |

Hampson SE, Glasgow RE, Toobert DJ (1990) Personal models of diabetes and their relations to self-care activities. Health Psychology 9, 632-646.
| Crossref | Google Scholar |

Hegney DG, Patterson E, Eley DS, Mahomed R, Young J (2013) The feasibility, acceptability and sustainability of nurse-led chronic disease management in Australian general practice: the perspectives of key stakeholders. International Journal of Nursing Practice 19, 54-59.
| Crossref | Google Scholar |

Kelly LM (2016) Perceptions of poverty and the perpetuation of the capability-judgment cycle in outer southeast Melbourne. Community Development 47, 584-601.
| Crossref | Google Scholar |

Lean MEJ, Leslie WS, Barnes AC, Brosnahan N, Thom G, McCombie L, Peters C, Zhyzhneuskaya S, Al-Mrabeh A, Hollingsworth KG, Rodrigues AM, Rehackova L, Adamson AJ, Sniehotta FF, Mathers JC, Ross HM, McIlvenna Y, Stefanetti R, Trenell M, Welsh P, Kean S, Ford I, McConnachie A, Sattar N, Taylor R (2018) Primary care-led weight management for remission of type 2 diabetes (DiRECT): an open-label, cluster-randomised trial. The Lancet 391, 541-551.
| Crossref | Google Scholar |

Lenzen SA, Daniëls R, van Bokhoven MA, van der Weijden T, Beurskens A (2017) Disentangling self-management goal setting and action planning: a scoping review. PLoS ONE 12, e0188822
| Crossref | Google Scholar |

Mathur R, Michael Roybal G, Peters AL (2005) Short and longer term outcomes of a diabetes disease management program in underserved Latino patients. Current Medical Research and Opinion 21, 1935-1941.
| Crossref | Google Scholar |

Menigoz K, Nathan A, Turrell G (2016) Ethnic differences in overweight and obesity and the influence of acculturation on immigrant bodyweight: evidence from a national sample of Australian adults. BMC Public Health 16, 932
| Crossref | Google Scholar |

Parker S, Fuller J (2016) Are nurses well placed as care co-ordinators in primary care and what is needed to develop their role: a rapid review? Health & Social Care in the Community 24, 113-122.
| Crossref | Google Scholar |

Rosenbek Minet LK, Wagner L, Lønvig EM, Hjelmborg J, Henriksen JE (2011) The effect of motivational interviewing on glycaemic control and perceived competence of diabetes self-management in patients with type 1 and type 2 diabetes mellitus after attending a group education programme: a randomised controlled trial. Diabetologia 54, 1620-1629.
| Crossref | Google Scholar |

Stadler G, Oettingen G, Gollwitzer PM (2010) Intervention effects of information and self-regulation on eating fruits and vegetables over two years. Health Psychology 29, 274-283.
| Crossref | Google Scholar |

SWSPHN (2018) 'An in depth study of our health now and into the future.' (SWSPHN: Sydney)

Walker C, Weeks A, McAvoy B, Demetriou E (2005) Exploring the role of self-management programmes in caring for people from culturally and linguistically diverse backgrounds in Melbourne, Australia. Health Expectations 8, 315-323.
| Crossref | Google Scholar |

Wermeling M, Thiele-Manjali U, Koschack J, Lucius-Hoene G, Himmel W (2014) Type 2 diabetes patients' perspectives on lifestyle counselling and weight management in general practice: a qualitative study. BMC Family Practice 15, 97
| Crossref | Google Scholar |

Whitehead L, Glass CC, Abel SL, Sharp K, Coppell KJ (2020) Exploring the role of goal setting in weight loss for adults recently diagnosed with pre-diabetes. BMC Nursing 19, 67
| Crossref | Google Scholar |

Williams A, Manias E, Cross W, Crawford K (2015) Motivational interviewing to explore culturally and linguistically diverse people's comorbidity medication self-efficacy. Journal of Clinical Nursing 24, 1269-1279.
| Crossref | Google Scholar |

# data[].attributes.attachment_link.title
Experiences of overweight and obese patients with diabetes and practice nurses during implementation of a brief weight management intervention in general practice settings serving Culturally and Linguistically Diverse disadvantaged populations

# data[].attributes.attachment_link.description
Background To explore the perspectives of overweight and obese patients with diabetes from culturally and linguistically diverse, and disadvantaged backgrounds, as well as practice nurses (PNs) during implementation of a brief PN-supported self-regulation nutrition strategy for weight management in general practice settings serving disadvantaged populations.Methods During intervention implementation, semi-structured interviews were conducted with 12 patients and four nurses in two general practices located in metropolitan suburbs in Sydney, Australia.Results Patients and PNs found challenges related to cultural expectations and the requirement of patients to set and adhere to dietary change goals and behaviours.

# data[].attributes.headline
Prom pt outcome measures T2 Diabetes - Katie Delahunty

# data[].attributes.subject
Prom pt outcome measures T2 Diabetes - Katie Delahunty

# data[].attributes.attachment_link.readable_text
Implications of Patient-Reported Outcome Measures among patients with recently diagnosed type 2 diabetes

Abstract

Background

For the past two decades, the assessment of the quality of diabetes care has mostly relied on clinical quality indicators. These have not included Patient-Reported Outcome Measures (PROMs) which provide information on outcomes deemed valuable by patients. We aimed to examine the potential utility of PROMs in type 2 diabetes care and to study the association of PROMs with patients' characteristics and clinical quality indicators.

Methods

A cross-sectional survey of recently (≤ 4 years) diagnosed patients with type 2 diabetes (n = 392) in the setting of a large health plan. PROMs were based on two well-validated questionnaires, the Problem Areas in Diabetes (PAID) one-page questionnaire that measures diabetes-related distress, and the ten item PROMIS-10 global health questionnaire that measures general health. Additional items were added following a previous qualitative study among Israeli patients with diabetes. The survey was carried out using phone interviews, and data collected were linked to the electronic medical records. Multivariable regression models were used to assess the associations of socio-demographic variables and clinical quality indicators with the PROMs.

Results

About a fifth of participants (22%) had high diabetes-related distress (PAID score ≥ 40), a third reported that they did not feel confident in self-management of diabetes and about a third reported having sexual dysfunction. Women, younger patients, and those with a low education level (≤ 12 years) reported worse general health, were more likely to experience high diabetes-related distress, and to have low confidence in diabetes self-management. Interestingly, performance of all seven diabetes quality indicators was associated with worse general health and high diabetes-related distress. Of note, levels of glycated hemoglobin, LDL-cholesterol, or blood pressure were not associated with PROMs.

Conclusions

PROMs provide important information on patient self-reported health status and are likely to reflect aspects of the quality of care that are not otherwise available to clinicians. Thus, the use of PROMs has the potential to expand the evaluation of diabetes care and promote patient-centered care. We recommend that policy-makers in the Ministry of Health and health maintenance organizations implement PROMs for assessing and improving the care for patients with type 2 diabetes.

Similar content being viewed by others

Article Open access

Caroline B. Terwee, Petra J. M. Elders, ... Femke Rutters

Yu Ting Chen, Yan Zhi Tan, ... Hwee-Lin Wee

Article Open access

Marlous Langendoen-Gort, Lenka Groeneveld, ... Femke Rutters

Introduction

The current study is part of a larger study that was conducted in the framework of the Israeli National Program for Quality Indicators in Community Healthcare which aimed to incorporate PROMs for diabetes within the Israeli national quality indicator set for diabetes. Since 2002, the Quality Indicators in Community Healthcare program has measured quality of community healthcare provided to all Israeli patients with indicators measuring mostly process of care. The assessment of quality indicators for diabetes constitute the flagship of the program [ 24, 28], with 11 indicators dedicated to diabetes care. We aimed to examine the potential utility of PROMs in type 2 diabetes care and to study the association of PROMs with patients' characteristics and clinical quality indicators.

Methods

Study design and setting

We conducted a cross-sectional study of recently (≤ 4 years) diagnosed patients with type 2 diabetes. We chose to focus on recently diagnosed patients as the first step in future routine use of PROMs. The study was conducted in the setting of Maccabi Healthcare Services, Israel's second largest health plan that provides community healthcare to 27% (2.4 Million) Israeli residents [ 29]. Phone interviews were conducted using structured questionnaires by trained interviewers, between March and July 2019. Each interview required about 10 min to complete.

Study sample

Diabetes was ascertained between June 1, 2015 and December 31, 2016 (i.e. diabetes duration ≤ 4 years in 2019) based on one or more of the following criteria (according to the Israeli national definition of diabetes): (a) purchase of at least three prescriptions of anti-diabetic medications in 3 different months, (b) two random glucose measurements ≥ 200 mg% conducted at least 30 days apart, or (c) HbA1c ≥ 6.5% measured at least once. We included patients with diabetes aged 45-85 years who were fluent in Hebrew or Arabic. Potential participants were randomly sampled from the population of patients with diabetes using a stratified random sample. Strata were defined by ethnicity and age groups to ensure a representative sample for those who met the inclusion criteria. Of the 807 who were contacted, 392 agreed to participate in the survey (response rate; 48.6%). Participants and non-responders were similar with respect to age (60.5 ± 8.1 vs. 61.9 ± 8.5 years, respectively), socio-economic position score (5.8 ± 2.2 vs. 6.3 ± 2.0, respectively) and quality indicators achievement. The proportion of women among responding participants was non-significantly higher compared with non-responders (41.3% vs. 34.9%, respectively; p = 0.07).

Socio-demographic variables and covariates

Socio-demographic data included gender (female vs. male), age in 2019 (categorized: 48.0-55.0, 55.1-64.9, and 65.0-84.0, based on the year of birth), and ethnicity (Jewish vs. Arab, based on participant's spoken language). Socio-economic position was defined on the basis of the residential address, using scores ranging from 1 (low) to 10 (high). The scores are allocated to small statistical areas by the Israeli Central Bureau of Statistics [ 30] and updated by the POINTS Location Intelligence Company using current sociodemographic and commercial data [ 31]. These variables were retrieved from the electronic medical records.

Also, the data included marital status (married/cohabited vs. others: single, divorced, or widowed), country of birth (categorized: Israel vs. others), years of education (categorized: < 12, 12, and > 12 years), smoking (never, ever and current smokers), religion (secular, traditional, religious and orthodox), and diabetes duration (number of years with diabetes), these variables were patient-reported. Missing data of education (8.7%) were imputed using multiple imputations by chained equations (MICE) using age, gender, ethnicity, and socio-economic position.

Quality indicators and clinical data

Data on seven process indicators and three intermediate-outcome indicators were collected according to the Israeli national quality indicator set [ 32]. Process indicators included measurement of HbA1c, LDL-cholesterol, blood pressure, urinary protein, serum creatinine, ophthalmological visit, and administration of influenza vaccine. Attainment of each indicator was defined as performance at least once in the year following diabetes diagnosis (2017). In addition, a composite score for all process indicators was calculated, indicating the total number of performed process indicators in 2017, categorized as (0-4, 5, 6, or 7).

Intermediate-outcome indicators assessed whether patients achieved adequate control (adequate vs. poor control), using the last measurement in 2017. Adequate control of glycemic control was an age-specific target (HbA1c ≤ 7% for patients aged ≤ 74 years and HbA1c ≤ 8% for patients aged ≥ 75 years) [ 33]. Adequate control of blood pressure was defined as systolic blood pressure ≤ 140 mmHg and diastolic blood pressure ≤ 90 mmHg. For LDL-cholesterol, control was defined as a level ≤ 100 mg/dl [ 33].

Comorbidities (presence vs. absence), based on diagnosis and procedures, included cardiac disease (ischemic heart disease or heart failure), retinopathy, visual loss, end stage renal disease, or lower limb amputation.

Quality indicators and clinical data were retrieved from the electronic medical records.

Questionnaire construction and PROMs

The questionnaire construction was based on incorporating both the standard set recommended by ICHOM [ 20] and the results of our previous qualitative study that identified valuable aspects for patients with type 2 diabetes [ 34]. We adopted the Problem Areas in Diabetes (PAID) [ 35] questionnaire as the diabetes-specific questionnaire. PAID covered most of the domains that arose in our previous qualitative study and ICHOM recommended using PAID as the standardized diabetes-specific tool for PROMs [ 20]. PAID evaluates diabetes-related distress, i.e. patient's worries, fears, and burdens related to diabetes [ 35, 36]. The answers of the 20-items of PAID were summed and multiplied by 1.25 to generate a total score ranging from 0 to 100. And then dichotomized (PAID ≥ 40 vs. < 40), a score ≥ 40 indicates a high diabetes-related distress [ 37].

We selected the Patient-Reported Outcomes Measurement Information System (PROMIS-10) [ 38] as the generic tool for measuring general health, in line with other Israeli program that has been collecting PROMs for other diseases [ 39]. The PROMIS-10 questionnaire included 4-items that were summed to generate the global physical health (GPH) score and 4-items that generated the global mental health (GMH) score. These scores were transformed to T-score distributions, where higher scores indicate better health. Also the PROMIS-10 questionnaire included two items; the one for rating general health and the second for rating social activity [ 40].

According to our qualitative study [ 34], there were aspects deemed valuable to patients with diabetes that were not covered in the above-mentioned questionnaires. To get a full scope of these aspects, 3-items were added from the Diabetes Distress Scale [ 41] (confident in ability to manage diabetes, doctor doesn't give clear enough directions and doctor doesn't take my concerns seriously) and 4-items were added by the authors (medication costs, shared decision-making, sexual dysfunction and integrated care under one roof). All questions referred to whether the issue was a problem, with answers rated on a 5-point Likert scale, and then dichotomized (yes vs. no problem); the answers "serious problem", "somewhat serious" and "moderate problem" were classified as "yes" whereas, "no", "minor" and "irrelevant" were classified as "no".

Finally, the questionnaire collected sociodemographic data, information on smoking, and duration of diabetes. The questionnaire was translated to Hebrew and Arabic and back-translated. The Cronbach's α-coefficient of PAID and PROMIS-10 indicated satisfactory reliability (0.92 and 0.84, respectively).

Statistical analysis

Multivariable regression models were constructed to assess the associations between socio-demographic and quality indicators with PROMs scores. The following independent variables were studied: gender, age, ethnicity, level of education, presence of comorbidities, adherence to each clinical quality indicator (11 indicators including the process composite score), and the continuous value of HbA1c, blood pressure, and LDL-cholesterol.

Binary and multinomial logistic regressions were constructed to explore the associations between the independent variables and the categorical dependent variables; PAID and the additional items. Whereas generalized linear models were used to explore the associations between the independent variables and the continuous dependent variables; GPH and GMH. Model 1 included the socio-demographic variables and Model 2 included the socio-demographic variables and the clinical variables. Variables included in the models if they had a significant association with one of the dependent variables in the univariate analyses.

Statistical analysis was performed with IBM SPSS Statistics for Windows, Version 25.0 (Armonk, NY: IBM Corp). Two-sided p value < 0.05 was considered to be statistically significant. Regression coefficients β and odds ratios (ORs) along with 95% confidence intervals (CIs) were reported.

Ethical approval was obtained from the Institutional Review Board of Maccabi Healthcare Services.

Results

Table 1 presents the characteristics of the study population. Mean age of participants was 60.5 years (SD:8.1) at the time of the interview, 41.3% were women, 18.9% were Arab, and 45.7% with more than 12 years of education. Almost 30% had at least one comorbidity, 73.0% were treated with oral anti-diabetic medications (none was treated with insulin), and 64.2% performed at least six of the seven process indicators in 2017.

Table 1 Socio-demographic and clinical characteristics of the study population (n = 392)

Physical and mental health

Mean scores of the physical and mental health scales were 43.9 (SD:10.5) and 50.1 (SD:9.3), respectively. Table 2 presents the associations between socio-demographic and clinical variables with physical and mental health scores. Female sex and younger age were negatively associated with physical health score (β female = − 4.96, 95% CI [− 7.00, − 2.91] and β 48-55years = − 3.26, 95% CI [− 5.88, − 0.64] vs. 65-84 years). Moreover, lower level of education was associated with worse physical health score (β<12 years = − 7.73, and β 12 years = − 2.85 vs. > 12 years). Regarding clinical characteristics, presence of comorbidities (β = − 3.82, 95% CI [− 6.10, − 1.53]) and performance of at least six of the process indicators (β 6 indicators = − 5.05 and β 7 indicators  = − 5.79) were associated with worse physical health.

Table 2 Associations between socio-demographic and clinical factors with physical and mental health (n = 392)

While age was not associated with mental health score, female sex was associated with worse mental health (β = − 2.73, 95% CI [− 4.61, − 0.84]). Lower level of education was associated with worse mental health score (β<12 years = − 5.96, 95% CI [− 8.54, − 3.38] and β 12 years  = − 2.54, 95% CI [− 4.56, − 0.51] vs. > 12 years). Regarding clinical characteristics, presence of comorbidities (β = − 2.11, 95% CI [− 4.21, 0.00], P-value = 0.05) and performance of all seven process indicators (β = − 4.59, 95% CI [− 7.99, − 1.19]) were negatively associated with mental health. It is worth mentioning that in a separate PROMIS-10 question of rating their general health, a higher proportion of those who achieved 6-7 process indicators rated their general health as poor or fair, compared to those who achieved 0-5 indicators (34.5% vs. 24.3%, respectively, P-value = 0.023).

Diabetes-related distress

Among the participants, 22.2% had a high level of diabetes-related distress (PAID ≥ 40). Diabetes-related distress correlated negatively and significantly (p < 0.01) with physical and mental health (Spearman-r₌ − 0.51 and − 0.46, respectively, Additional file 1: Table S1).

Table 3 presents the associations between the independent variables and PAID. Women and younger patients were more likely to experience high diabetes-related distress (OR Female = 1.84, OR 48.0-55.0years = 4.41, and OR 55.1-64.9years = 2.81 vs. 65-84 years). Patients with a low education level were more likely to experience high diabetes-related distress (OR<12years = 5.27 and OR 12years  = 2.20 vs. > 12 years). Patients who achieved 5-7 process indicators were more likely (ORs = 3.07-3.60) to experience high diabetes-related distress compared to patients who achieved only 0-4 indicators.

Table 3 Adjusted odds ratios (ORs) of high diabetes-related distressa, multivariable logistic regression (n = 392)

Sexual dysfunction, self-management of diabetes, integrated care, costs of care, and shared decision making

Of the participants, 45% reported that cost of anti-diabetic medication presented a problem for them (Additional file 1: Fig. S1). Approximately 30% of the participants reported that they suffered from sexual dysfunction, 29% reported lack of integrated care and 30% being unconfident in their management of the disease. Almost 45% reported a lack of shared decision-making (Additional file 1: Fig. S1).

Table 4 presents the associations between the independent variables, including the demographic and clinical variables, and the PROMs. Arabs and those with low education level (≤ 12 years) were independently more likely (ORs≈2) to face problems with anti-diabetic medications' costs compared to Jews and those with high education, respectively. Men, Arabs, and patients with low education level were more likely to report on sexual dysfunction. Low reported self-management ability was significantly associated with female sex, young age, and lower level of education (ORs = 2-3). Lack of shared decision-making was more likely to concern women, older patients, and Jews. A substantial number of patients answered 'irrelevant' in the items, lack of integrated care under one roof (24.0%) and shared decision-making (25.5%). Therefore, we repeated the analyses using multinomial logistic regressions with 'irrelevant' as a separate category. These analyses yielded similar results. Comorbidities and composite score were not associated significantly with these issues.

Table 4 Adjusted odds ratios (ORs) of problems (yes vs. no), multivariable logistic regressions (n = 392)

No significant associations were found between PROMs (diabetes-related distress, physical and mental health as well as the additional items) and levels of HbA1c, blood pressure, or LDL-cholesterol, as continuous variables or intermediate-outcome indicators (data not shown).

Discussion

In this cross-sectional study, we assessed the potential utility of PROMs in type 2 diabetes care for the first time in Israel. We investigated the associations between PROMs and numerous clinical quality indicators. While adherence with traditional process indicators (e.g., HbA1c tests) was associated with worse general health and high diabetes-related distress, the measured levels of HbA1c, LDL-cholesterol, or blood pressure were not associated with PROMs, suggesting that PROMs capture additional dimensions, i.e. outcomes, not reflected in clinical indicators.

Interestingly, our study showed that performance of all process clinical indicators was associated with poor physical and mental health. Previous studies showed different results; one study showed that neither physical nor mental health was associated with performance of process indicators [ 27], but another study showed that performance of process indicators was associated with better mental health [ 58]. Given the cross-sectional nature of this study, we cannot establish the direction of associations between indicators' performance and poor general health. It is worth mentioning two potential explanations. First, it may well be that patients characterized with high level of anxiety evaluate their health status as poor and also tend to perform all the process indicators. Second, the presence of comorbidities could explain part of the association under consideration. Indeed, we have controlled for comorbidities, yet the adjustment may have been insufficient as we did not consider neither illness severity nor the number of comorbidities. In addition, we found that patients who achieved most (5-7) of the process indicators were more likely to have high diabetes-related distress compared to those who achieved less. The aforementioned potential explanations, anxiety and presence of comorbidities, may partly explain the association between indicators' performance and diabetes-related distress. Moreover, possibly increased medical testing, per se, increases patient's diabetes-related distress. Indeed, previous studies indicated that a higher frequency of self-monitoring of blood glucose was associated with higher levels of distress and worries [ 52, 59]. Thus, health providers should be aware that adherence to clinical indicators, may not indicate better health status or less diabetes-related distress among patients recently diagnosed with type 2 diabetes.

In this study, costs of medications were considered a problem for almost half of the participants. In Israel, there is a national health insurance law that subsidizes only some of the medications with relatively low co-payments. Several factors may explain this finding, including the purchase of medications not covered by health insurance; the presence of comorbidities; low socioeconomic position; and, since the patients were recently diagnosed with type 2 diabetes, the fact that medication cost was a new, additional financial burden. The issue of medication cost was also highlighted in our previous qualitative study, where patients with type 2 diabetes expressed concerns about the financial burden of medications, stating: "There's a new medication that's effective. But the packet costs 250 shekels [73 USD]. What about the low-paid workers or the elderly who live off of their pensions, how could they pay for that? They cannot." [ 34]. Another Israeli study showed that 10% of patients with diabetes were non-adherent with pharmacotherapy due to cost; and this behavior was associated with low socioeconomic position, unemployment, and lack of physician explanation about the prescribed medication [ 69].

Our findings showed that almost a third of the participants reported that they suffered from sexual dysfunction. In a multinational study, sex life was found to be the fourth most important issue out of 19 diabetes-specific domains [ 42]. Nevertheless, the current standard questionnaires do not include information on sexual dysfunction. Moreover, sexual dysfunction is often overlooked by health practitioners: 63% of patients with diabetes reported that their physicians had never addressed their sexual problems [ 70]. PROMs could be a useful tool to identify patients with sexual dysfunction and to follow them up.

Integrating PROMs into the routine clinical practice is a challenge[ 71]. Health providers need effective approaches to use PROMs without disrupting traditional care. Multidisciplinary healthcare teams are required for diabetes care (e.g., primary care physicians, and mental health professionals) and some aspects are more relevant to some professionals than others. Thus we recommend specifying who will address each of the PROMs. Moreover, technology (e.g. tablets in the clinics) seems to play a central role in the routine collection and use of PROMs in clinics [ 12]. PROMs in diabetes care should be monitored regularly, ICHOM has suggested to collect PROMs at baseline and annually [ 20] and we suggest considering the collection of PROMs also surrounding a change in treatment plan and when a complication is diagnosed.

Limitations of this study should be acknowledged. First, this study used a cross-sectional design, which allows assessing associations but not causal relationships and can't determine the temporal relationship among the variables examined. A longitudinal study is needed to gain an in-depth understanding of the associations. Second, the clinical indicators were assessed in 2017 and the PROMs were assessed in 2019. Third, 4-items were added by the authors (medication costs, sexual dysfunction, shared decision-making, and integrated care under one roof). Although these items were validated by experts, almost 25% of the participants have answered "irrelevant" for the last two items. Notably, sensitivity analyses were conducted using multinomial logistic regressions with "irrelevant" as a separate category, and the results were similar to those achieved from the main analyses. Lastly, the study focused on patients relatively recently diagnosed with type 2 diabetes, and the conclusions are limited to this group.

However, our study also has some important strengths. First, the results suggest that PROMs are applicable for routine measurement of diabetes quality care; the examined questionnaires are free for use in clinical practice, with acceptable length and are easily scored. Second, the examined PROMs were based on incorporating both the standard set recommended by ICHOM and aspects that are somewhat neglected in the existing PROMs, such as sexual dysfunction and financial burden, which were found valuable for patients with type 2 diabetes in our previous qualitative study [ 34]. Third, we used well-validated questionnaires and the combination of generic and diabetes-specific PROMs provided a comprehensive assessment. Lastly, the study estimated the associations between PROMs and comprehensive clinical quality indicators that commonly used in diabetes care, enabling the estimation of the added value of PROMs.

Conclusions and policy implications

Our study suggests that PROMs capture important information on patient health status and quality of type 2 diabetes care that are not reflected in the examined clinical quality indicators. PROMs are powerful tools, with the potential to both expand the evaluation of the outcomes of diabetes care and promote person-centered care.

Managing diabetes is a demanding task, requiring self-management (e.g., healthy lifestyle, blood glucose monitoring and medication adjustment) and individualized multi-disciplinary medical and psychosocial care in order to prevent complications and maintain a satisfactory quality of life. Active clinical use of PROMs and responding to the collected PROMs may improve multiple aspects of diabetes quality care. PROMs could aid health providers in identifying patient's needs, responsiveness to interventions, and enable outcome monitoring. PROMs promote patient-provider collaboration, which in turn may improve patient's adherence and quality of life. Aggregate PROMs of the primary clinic could assist medical directors in identifying at-risk groups and their needs, enhancing team members education and allocating resources accordingly.

In conclusion, this study suggests that PROMs and clinical quality indicators reflect different aspects of the quality of diabetes care and both should be considered to promote person-centered care, to improve the quality of diabetes care, and to achieve a comprehensive evaluation of diabetes quality care. Thus, we recommend that policy-makers within the Ministry of Health and health maintenance organizations promote the implementation of PROMs in type 2 diabetes care and allocate resources for further evaluation of how to implement it most efficiently, which potentially could be within the Israeli National Program for Quality Indicators in Community Healthcare.

Availability of data and materials

The datasets analyzed during the current study are not publicly available due to ethical restrictions on sharing de-identified data sets, participants had not provided informed consent on sharing data publicly. The data that support the findings of this study are available upon reasonable request and with the approval of the Institutional Review Board of Maccabi Healthcare Services.

Abbreviations

PROMs:

Patient-Reported Outcome Measures

PAID:

Problem Areas in Diabetes

ICHOM:

International Consortium for Health Outcomes Measurement

GPH:

Global physical health

GMH:

Global mental health

References

1. Hargraves JL, Palmer RH, Zapka J, Nerenz D, Frazier H, Orav EJ, Warner C, Ingard J, Neisuler R. Using patient reports to measure health care system performance. Clin Perform Qual Heal Care. 1993;1:208-13.

CAS Google Scholar

2. NHS England. Patient Reported Outcomes Measures. 2017 [cited 25 Nov 2020]. https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/patient-reported-outcome-measures-proms

3. HEDIS Quality Measure: Comprehensive Diabetes Care. [Cited 16 Jan 2024] Available: https://www.premera.com/wa/provider/news/medicare-advantage/hedis-diabetes-care/

4. Standards and Indicators | NICE. [Cited 16 Oct 2020]. Available: https://www.nice.org.uk/standards-and-indicators/index/All/Diabetes

5. Israel National program for quality indicators in community healthcare. [Cited 26 Aug 2020]. Available: https://en.israelhealthindicators.org/

6. Harman JS, Scholle SH, Ng JH, Pawlson LG, Mardon RE, Haffer SC, et al. Association of Health Plans' Healthcare Effectiveness Data and Information Set (HEDIS) Performance with outcomes of enrollees with diabetes 48(3):217-23;2010. https://doi.org/10.1097/MLR.0b013e3181ca3fe6

7. Ministry of Health. Summary Report on the Activity of Health Funds in 2020 (Hebrew). 2021. Available: https://www.health.gov.il/PublicationsFiles/dochHashvaatui2020.pdf

8. Central Bureau of Statistics Israel. Characterization and classification of geographical units by the socio-economic level of the population 2008. 2013. [Cited 16 Jan 2024] Available: https://www.cbs.gov.il/en/publications/Pages/2013/CHARACTERIZATION-AND%C2%A0CLASSIFICATION-OF%C2%A0GEOGRAPHICAL-UNITS%C2%A0BY-THE-SOCIO-ECONOMIC-LEVEL-OF-THE-POPULATION-2008.aspx

9. Points Location Intelligence. [Cited 7 Jul 2020]. Available: https://points.co.il/en/points-location-intelligence/

10. Calderon-Margalit R, Manor O, Shmueli A, Ben-Yehuda A, Paltiel O, Krieger M. National Program for Quality Indicators in Community Healthcare in Israel, Report 2013-2017. 2017. Available: https://en.israelhealthindicators.org/

11. PROMIS. [Cited 26 Feb 2021]. Available: https://www.healthmeasures.net/score-and-interpret/interpret-scores/promis

12. Kalda R, Rätsep A, Lember M. Predictors of quality of life of patients with type 2 diabetes. Patient Prefer Adherence. 2008;2:21-6.

13. Quah JH, Luo N, Ng WY, How CH, Tay EG. Health-related quality of life is associated with diabetic complications, but not with short-term diabetic control in primary care. Ann Acad Med Singapore. 2011;40:276-86.

Download references

Funding

This study was supported by The Israeli National Institute for Health Policy Research.

This study was funded by a research grant from the Israel National Institute for Health Policy Research (NIHP, grant #R/289/2017). NIHP had no role in the design or conduct of the study.

Author information

Authors and Affiliations

1. Braun School of Public Health, The Hebrew University of Jerusalem Hadassah Medical School, 91120, Jerusalem, Israel

Nura Abdel-Rahman, Orly Manor & Ronit Calderon-Margalit

2. Maccabi Healthcare Services, Tel Aviv, Israel

Einat Elran

3. The New York Academy of Medicine, New York, USA

David Siscovick

Contributions

All authors contributed to the design of the study. EE contributed to the data acquisition from the Maccabi Healthcare Services. RC, OM and NA contributed to the conception of the study, conducted the data analyses and drafted the manuscript. All authors contributed to the interpretation of the data. DS critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nura Abdel-Rahman.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the Institutional Review Board of Maccabi Healthcare Services (0012-18-ASMC). All methods were performed in accordance with the ethical standards as laid down in the Declaration of Helsinki and its later amendments or comparable ethical standards. Verbal informed consent was obtained from all participants before starting the interview.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

. Table S1. Correlations (Spearman) between PROMs, socio-demographics and quality indicators, (n = 392). Figure S1. Distribution of facing problems in selected items, (n = 392).

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Reprints and permissions

About this article

Cite this article

Abdel-Rahman, N., Manor, O., Elran, E. et al. Implications of Patient-Reported Outcome Measures among patients with recently diagnosed type 2 diabetes. Isr J Health Policy Res 13, 6 (2024). https://doi.org/10.1186/s13584-024-00592-1
Download citation

Received:

Accepted:

Published:

DOI: https://doi.org/10.1186/s13584-024-00592-1

Share this article

Anyone you share the following link with will be able to read this content:

Get shareable link

Provided by the Springer Nature SharedIt content-sharing initiative

Keywords

# data[].attributes.attachment_link.title
Implications of Patient-Reported Outcome Measures among patients with recently diagnosed type 2 diabetes - Israel Journal of Health Policy Research

# data[].attributes.attachment_link.description
Background For the past two decades, the assessment of the quality of diabetes care has mostly relied on clinical quality indicators. These have not included Patient-Reported Outcome Measures (PROMs) which provide information on outcomes deemed valuable by patients.

# data[].attributes.headline
Glycaemia and correlates of patient-reported outcomes in ACCORD trial participants

# data[].attributes.subject
Glycaemia and correlates of patient-reported outcomes in ACCORD trial participants

# data[].attributes.attachment_caption
Articles and 'grey' literature (reports and websites) were sources of information. They analyzed using 5 online databases, which were the following; Web of Science, Scopus, Pubmed, JSTOR, and Google Advanced Search.

# data[].attributes.headline
lateefat        Alzheimer's disease and Dementia           

# data[].attributes.subject
lateefat        Alzheimer's disease and Dementia           

# data[].attributes.attachment_caption
bmjopen-2019-036249 . The use of a Low Cost Patient- Held Communication tool for people living with Dementia.

# data[].attributes.headline
Dementia Communication Tool Sarah Walshe

# data[].attributes.subject
Dementia Communication Tool Sarah Walshe

# data[].attributes.body
Questionnaire: The PDQ-Exercise

# data[].attributes.attachment_link.title
Movement Disorders - 2021 - Morley - Development of an Exercise‐Specific Parkinson s Disease Questionnaire  The

# data[].attributes.headline
Suzie Coombes . Patients' experiences across the trajectory of atrial fibrillation: A qualitative systematic review

# data[].attributes.subject
Suzie Coombes . Patients' experiences across the trajectory of atrial fibrillation: A qualitative systematic review

# data[].attributes.attachment_caption
Type 2 Diabetics were given a combination of metformin and sulphanjurec  in a double blinded randomised placebo controlled study.. A 5 dimension EuroQol questionnaire, sheild weight questionnaire, impact of weight on quality of life questionnaire and diabetes treatment questionnaire were used. Patients with T2D reported better outcomes when Diapagaliflzon was given as a third agent in triple therapy regieme.