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A qualitative study of user perceptions of mobile health apps

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A qualitative study of user perceptions of mobile health apps

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Abstract

Background

Mobile apps for health exist in large numbers today, but oftentimes, consumers do not continue to use them after a brief period of initial usage, are averse toward using them at all, or are unaware that such apps even exist. The purpose of our study was to examine and qualitatively determine the design and content elements of health apps that facilitate or impede usage from the users' perceptive.

Methods

In 2014, six focus groups and five individual interviews were conducted in the Midwest region of the U.S. with a mixture of 44 smartphone owners of various social economic status. The participants were asked about their general and health specific mobile app usage. They were then shown specific features of exemplar health apps and prompted to discuss their perceptions. The focus groups and interviews were audio recorded, transcribed verbatim, and coded using the software NVivo.

Results

Inductive thematic analysis was adopted to analyze the data and nine themes were identified: 1) barriers to adoption of health apps, 2) barriers to continued use of health apps, 3) motivators, 4) information and personalized guidance, 5) tracking for awareness and progress, 6) credibility, 7) goal setting, 8) reminders, and 9) sharing personal information. The themes were mapped to theories for interpretation of the results.

Conclusions

This qualitative research with a diverse pool of participants extended previous research on challenges and opportunities of health apps. The findings provide researchers, app designers, and health care providers insights on how to develop and evaluate health apps from the users' perspective.

Peer Review reports

Background

Currently over 97,000 health-related apps (termed as health apps hereafter) are available in the health and fitness category of the Apple app store and Google play store, with about 1000 more being created every month. This number is expected to increase by about 25% each year [ 1]. These health apps include medical apps for health providers and medical education, patient-centered apps for disease management or self-diagnosis, and general health and fitness apps for lifestyle management [ 2]. With the exponential increase of health apps, research on their use in the areas of health promotion and disease management has also increased significantly over the past 5 years. There have been a number of intervention studies based on mobile apps and most of these focused on specific medical issues such as diabetes [ 3, 4], pain management [ 5], weight loss [ 6, 7], etc. The intervention studies mostly focus on the patient population using mobile apps for treatment or disease self-management [ 3- 7].

However, a majority of the available health apps are for health and wellness promotion and disease prevention for the general public. A large number of the studies on such health apps adopted a content analysis approach [ 8- 14]. Although these content analyses provide important insight into what apps are available and whether theories and evidence-based practice inform app design, qualitative studies examining these health apps from the users' perspectives are limited. It is important to go beyond the content analysis to examine users' perceptions of these off-the-shelf health apps to gauge the differences between users who have, who will and who never will adopt mHealth [ 15]. By examining users' experience with current apps, researchers and app developers can better design future mhealth interventions to be both effective and accepted by end users.

Among the limited qualitative research on health apps, one focus group study examined user perceptions of mobile apps in supporting behavior change and provided insights from users' perspective for researchers and designers to better design and develop health apps [ 16]. Specifically, this study found that healthy young adults were interested in using health apps to support health-related behavior change. Potential users expected health apps to be accurate, legitimate, secure, and able to record and track behavior and goals. They also expected health apps to require minimal effort to operate and have the ability to acquire advice and information "on the go". These healthy young adults did not like apps with context-sensing capabilities or social media features. Despite insightful findings, one limitation of the study is that its participants are biased toward the younger population (average age was 23.8) with relatively high education and income (affiliated with a university). The most recent data from the Pew Internet Research indicate that 54% of Americans ages 50-64, 52% of Americans with less than college-level education, and 50% of Americans with less than $30,000 annual household income own a smartphone [ 17]. Smartphone penetration rate is over 80% for the 25 to 54 years old age group in the United Kingdom [ 18]. The data indicate that smartphones and health apps are not just the products used by young people with high education and high income. Therefore, it is important to extend the previous research by examining a more diverse pool of users with various age groups and social economic status to better understand users' perception of these apps.

Another limitation of existing qualitative studies on health apps is the lack of a systematic framework to guide the qualitative exploration. Although the qualitative research on user perception is exploratory in nature, having a systematic framework helps the research stay focused. Therefore, the current research attempts to systematically examine users' views about features and capabilities of health apps, based on the mobile phone features and intervention strategies identified in Klasnja and Pratt's previous research [ 19]. Specifically, their framework lays out five behavioral intervention strategies that can be achieved via various phone functions (i.e., text messaging, cameria, native applications, automated sensing, and Internet access), which include: 1) tracking health information, 2) involving the health care team, 3) leveraging social influence, 4) increasing the accessibility of health information, and 5) utilizing entertainment.

Additionally, although this qualitative research takes a bottom-up inductive approach, we discuss the findings in light of theoretical frameworks (e.g., extended unified theory of acceptance and usage of technology (UTAUT2) [ 20], self-regulation [ 21]), which pave the road for future quantitative research to systematically adopt theoretical frameworks for a top-down deductive approach. In other words, theoretical frameworks did not guide this qualitative work, as qualitative research usually takes the bottom-up inductive rather than the top-down deductive approach to analyze results. Instead, existing theoretical frameworks were brought in for the interpretation of findings because they can help explain the how and why of the findings as well as abstract the findings to guide future research. In the following section, we explain the procedure for conducting the current qualitative research. Inductive thematic analysis was used to analyze transcripts leading to the identification of nine themes. The discussion provides insights for researchers and designers to better design and implement apps for health promotion and behavior change.

Methods

Participants

To make sure we adhered to qualitative reporting standards, we followed the 32-item consolidated criteria for reporting qualitative studies (COREQ) checklist (Additional file 1). A total of 44 individuals, including 39 in six focus groups and five in interviews participated in this study. Purposive sampling was used and recruitment was not based on data saturation. All the consented participants completed the study. Inclusion criteria were the ownership and regular use of a smartphone. To get a well-rounded picture about why people used or did not use health apps, both individuals with and without prior knowledge or usage of health apps were included. Each focus group consisted of three to nine participants. Two focus groups consisted of 17 college students (11 female) were recruited from the subject pool in a large Midwestern university in the U.S. Four other focus groups were formed with 22 non-student participants (18 female) through email to staff members at the same university. Although the focus groups in this study aimed to address the limitation of previous studies by including participants with a wide age range, the educational level of the participants was still relatively high. In an attempt to include a more diverse pool, we also recruited five participants (one female) via solicitation at a local plaza. These five participants were not affiliated with the university and were of diverse social economic statuses. However, due to their scattered working schedules, we were unable to conduct a focus group and individual interviews were conducted instead. The variation in profession type among the interviewees (i.e., automobile mechanic, truck driver, hair stylist, small business owner, morning shift as medical assistant and night shift at fast food franchise) provided information about how individuals with relatively less education and physically strenuous working-hours use health apps. In general, all the participants considered themselves to be in either 'very good health' or 'good health' condition, had an average health consciousness score of 3.8 out of 5 (assessed by the health consciousness scale [ 22]), and had never used medical apps (which are more medically focused as compared to the health apps of our study). Table 1 summarizes the demographic information and app usage of the participants. The data analysis combined the six focus groups and five interviews.

Table 1 Participant demographics and smartphone and app usage

Procedure

The institutional review board at Michigan State University approved the study. After acquiring their consent, each participant first completed a questionnaire about demographics, smartphone and mobile app usage, and health status. The second author (female) served as the moderator and the third author (female) recorded the sessions and took notes. The fourth author (male) conducted the interviews. Both the moderator and the interviewer were doctoral students with a Master's Degree and were trained with qualitative research methods. The moderator and the interviewer did not have prior relationship with the participants. Nobody else besides the authors and participants were present during data collection. All the authors had a positive attitude towards health apps, but the authors strived to remain neutral in the conversations with participants. Each focus group session took place in a conference room and ran for about 40 to 90 min. Non-student participants were provided with a free meal and a $20 gift card for their time and student participants were provided with a free meal and extra course credit. The interviews were conducted by the last author, at participants' workplace, home or a nearby café. Each interview lasted for 30-45 min. The interviewees were provided a $40 gift card as incentive. All focus groups and interviews were audio-recorded and then transcribed verbatim. The anonymized transcriptions supporting the conclusions of this article are available at goo.gl/T9oZvk.

The moderator and the interviewer followed a discussion guide developed jointly by the authors (see Additional file 2) to direct the conversation. Participants were first provided an overarching introduction about the purpose of the study. They were then asked questions about their overall app usage, knowledge about health apps and their usage, and reasons for liking or disliking apps, including health apps. Participants freely discussed their own experiences without prompts.

Trigger materials. Next, in order to educate participants with no health apps and enlighten others about the wide variety of health apps, participants were exposed to a set of trigger materials (see Additional file 3). These materials included screen captures of various features of health apps based on Klasnja and Pratt's framework [ 19] of the five behavioral intervention strategies enabled by smartphones: 1) tracking health information (a: goal setting, b: behavior monitoring and tracking, c: reminders, d: progress visualization), 2) involving health care team (e: sensing and information sharing with health care providers), 3) leveraging social influence (f: social networking), 4) increasing the accessbility of health information (g: information such as tips, coaching, etc), and 5) utilizing entertainnment (h: use of entertainment, i: use of gamification). The participants were exposed to the above categories and sub-categories of health apps, one at a time, and asked to discuss their thoughts about them. Since our study focused on the general usage of health apps in users' day-to-day lives, we did not include any medical apps in the trigger materials, which would be disease specific or apps connected to medical devices or for provider communication [ 2, 23]. They were asked to go into as much detail as possible, explain what they liked or disliked, whether or not they had used that particular or a similar app before and what led them to continue or discontinue using it. The participants who did not have such an app were asked to provide reasons for non-use.

Data analysis

The verbatim transcripts were coded using the software NVivo. Inductive thematic analysis [ 24] was adopted to analyze the data. The focus group and interview transcriptions were analyzed as a whole. Each recording was coded separately by at least two authors who independently came up with labels to attach to text segments that appeared to indicate important user perspective. Then the team came together to compare their codes and revise the codes in an iterative fashion to develop a set of themes that captured the essence of the focus group discussions or interviews. Finally, the raw data were compared with the emerging theme labels and definitions, and further refined by merging, adding, and removing redundant themes. At the end, nine themes were identified. The results section describes them with illustrative quotations.

Results

Barriers to Adoption of Health Apps

Among the participants, only 57% had health apps. Four types of reasons were identified by the participants explaining why they did not adopt or start to use health apps.

Low Awareness of health apps. More than a quarter of the participants were unaware of or did not even think that health apps were available. Lack of need for health apps. Participants did not feel the need to use a health app either because other tools were already available (e.g., notebook, spreadsheet, a website which performed a similar function as the app) and it was not necessary to use a mobile app or they already had formed healthy habits and a tool was no longer needed (e.g., a physically active person would not need an app to motivate them to exercise). Although they did not consider a health app a necessity for themselves, they thought that a health app might be helpful for certain individuals such as patients or people who need the apps for motivation or to establish healthy habits.

"I will just go to the website. I don't know the benefit of this app. I have to see what the benefit will be." (Male, non-student, focus group 6 [MNS, FG6])

"I heard they more focus on people's chronic issues. I don't really see them useful in [my] life." (MNS, FG4)

"I do regular exercise, like dancing, so I don't need to track the calories." (Female college student [FCS], FG2)

Lack of app literacy. Participants did not know which app to choose or how to use it. One female college student stated that "health app is only developed [in] recent years. Consumers might not have a clear definition what is [a] good health app. There are so many functions. It is hard for consumers to decide. [They are] not sure which one fits; [they] eventually will just give up".

Cost was identified as an important determining factor for app adoption, across all the participants in all age groups and social economic status. Among the participants, 77% used only free apps. However, this does not mean that people are not willing to pay for apps. Participants indicated that they would pay, usually a small amount of one dollar or less, if they found the app worth buying. In other words, only if the app had highly unique functions and features, not normally found in free apps, then they would consider buying it.

Barriers to Continued Use of Health Apps

Lack of time and effort. Participants who had health apps indicated a number of barriers to their continued use. The primary reason was the required time and effort. One male non-student participant said: "You have to individually [manually] input everything. Doing that, that's a lot of time." Ease of use and simplicity is thus one of the top desired features among the participants to overcome the barrier.

"Ease of use. You want to get to the process as simple as possible. The app doesn't have to do everything, but you need to feel easy to use" (MNS, FG4)

"Some apps are so complicated. I delete 'em, because it's just too much, you know. So I'm looking for something pretty straightforward. It does what you need it to do without 10 million different things to do one thing." (Male truck driver in interview)

Lack of motivation and discipline. The participants felt that unless an individual was already motivated or had the discipline or dedication, it would be hard for them to continue using health apps.

Additional barriers to adoption and continued use of health apps brought up by the participants included radiation from the cell phone, apps that take storage space of the phone or drain battery, and the phone being carried in the purse and thus not used enough.

Motivators to Use Health Apps

Motivators are internal and external factors that help health app users either start or continue to use them.

Social competition. One of the external motivators was seeing other people using the app and sharing behavioral data that could be compared to others on social networking sites. Participants considered this comparison or 'competition' as a double-edged sword: it could be helpful and motivating for certain individuals in certain contexts yet it may be demotivating and backfire in other situations, especially when individuals fear that they are too far behind compared to their friends or family.

Intangible Rewards. Other apps provide users with virtual badges they could obtain or levels they could unlock. These intangible rewards are intended to motivate people by triggering their competitive natures.

"Earning badges [was] important when I was doing it...We learned as a kid, to consider [it] as [an] accomplishment." (FNS, FG3)

Tangible rewards. While badges, levels and motivational messages from an app provide intangible rewards, some individuals prefer a tangible one.

Hedonic factor. Adding a gaming element or entertainment to an app was motivating for a certain group of people. However, most of the participants did not consider the entertainment element critical for adults using health apps. On the other hand, they assumed that a health app with fun elements might be a positive thing for children as long as they did not see through the game aspect of an app and know it was something being taught to them.

Internal dedication and motivation. Intrinsic factors are not motivations provided by an app, but factors internally prompting a person to use the app. Some participants indicated that an app could only do so much. Ultimately, it is an individual's internal dedication and motivation that will determine whether they would continue to use it for health enhancement or behavior change.

Information and Personalized Guidance

Most participants who liked having health apps stated that what they liked most was its ability to provide information, which they would not have otherwise. Some of the information might be directly provided by the app, some of the information might be user generated and shared on the app. They preferred to have diverse information so even individuals with special needs may be able to find relevant information.

"WebMD [app] gives a lot of information" (FNS, FG6)

"I like that it gives you routes that other people use [to run], so you can use them too" (FCS, FG2)

"Does it have different category, gluten free?" (FNS, FG3)

"How ethnically diverse are these [food] options?" (FNS, FG5)

In particular, they valued personalized and tailored information. One male college student said: "It could be an app that you type in personal info, your weight, blood pressure, then comes out a personal plan." Similarly, the male truck driver said: "I like the idea of this app making things simple for me, telling me what to eat, my goal weight...It's more tailored for you and it makes things easy."

While many people have the motivation to carry out healthy activities, sometimes they may not know the right way to go about it. Some of the information from the health apps guided people to places with more healthy food choices, or provided instructions of how to be healthy. Therefore, many participants liked apps with personalized coaching and guidance with specific plans.

On the other hand, participants did not like the idea of having to take orders or suggestions from a machine. In certain situations, for example, when they lack the ability to carry out a particular activity till the end or fail to reach their set goal, having the app continue to give negative feedback may demotivate and backfire.

Tracking for Awareness and Progress

Many health apps have a built-in feature to track the user's activity, including diet, exercise, etc. For example, users input their food intake and the app creates a log of what has been consumed on a particular day and provides the user with feedback, such as, their estimated weight loss or gain if they continue to eat in the same pattern over the next few days or how far they are from their goal weight. Most participants liked having the tracking feature in their apps because this type of self-monitoring increases their awareness. Additionally, the tracked data could be reviewed retrospectively so they were able to observe their progress, especially, when the progress was also visualized in a graphic manner for easy interpretation.

"It helps you to keep track of your calories and helps keep you under budget of what calories you want" (Female medical assistant/fast food store worker, interview)

"You get a chance to see what you do on a daily basis, something you're probably not aware of." (Male truck driver, interview)

"If I could see my progress over the day, it's like easier to see than just look back and say 'what did I do yesterday?'" (FCS, FG2)

However, the tracking feature did backfire in some cases. People might not be faithful to themselves and the app and cheat the tracking app.

"A lot of times people just try to lie to the app about the amount they ate. You just don't put everything you ate. It is not helping anybody." (FCS, FG4)

Even though tracking was a very positively favored characteristic of health apps, some participants thought its usefulness was temporary. After tracking for a while to learn about their own routines, they no longer needed to track themselves anymore. Therefore, tracking was perceived to be helpful for users to gauge everyday behavior pattern initially or for people who was undergoing changing their current behaviors for close monitoring. However, since the purpose of such a health app would be to educate oneself about the tracked health behavior, such an action can be looked upon as a positive thing. The user in this scenario would have successfully learned about themselves and therefore do not need to use the app anymore.

Many participants liked sensor-based automatic tracking, such as the accelerometer, which tracks a person's footsteps each day. The automatic tracking may partially resolve the issue of unreliable self-reported data in tracking. One male non-student participant indicated that having an app to automatically sense how much he ate or even his mood would be good. However, at the time of this study, there was no standard in the market to evaluate how accurately the sensing or tracking systems worked and participants had concerns about reliability of the automatically tracked information.

"[I like that] you don't have to count your steps; it will do it for you" (MCS, FG2)

"Personally, I have a bias against it [sensor] because I don't think it would be very accurate, so I don't use it." (FCS, FG2)

"It's really not that accurate because, I just run on a treadmill so I always get different results from what the treadmill says [and what the app says]." (MCS, FG2)

Credibility

To solve the accuracy concerns we discussed above regarding reliability of automatic sensors, participants mentioned several ways to evaluate the credibility of a health app before downloading it. Users normally perceived friends' recommendations as highly trustworthy.

"Most of mine [my apps] are friend recommendations, people with similar activities." (FNS, FG5)

Some participants reported that they would make the decisions based on the comments or reviews from others (non-friends), such as the ranking of the app in the market or the rating by other users.

"I will look for reliable ratings so I know which one would be most popular...I tend to go to that one. (FCS, FG6)

Participants also relied on sources which they already trusted or were familiar with to judge app credibility. A female college student said: "Sometimes the app is branded......so I trust it." Similarly, a male non-student participant said: "More likely to use an app by organizations that I am familiar with, not [a commercial] company."

Some participants would not rely on a single source to evaluate app credibility. They might cross validate the app by checking with other sources, such as their doctors. Besides cross checking with other sources, another way to test credibility, which was also mentioned by the majority of the participants, was testing the app by trying it out. Because the majority of health apps are free or offer trial versions, users have the opportunity to try the app and see whether it lives up to their expectations.

"Like the Babybump one [an app the participant has used]...I'll talk to my doctor or whatever and I'll read things... It's the same as MapMyFitness. You kind of know if it's lying because over time you can compare that clearly I'm not burning as many calories on the treadmill as I am in real life..." (FNS, FG6)

Goal Setting

In addition to the information and tracking features in health apps, many participants also liked the goal-setting feature in many apps. They believed that goal setting, especially small daily and weekly goals, could help them discipline themselves and slowly change their behaviors. Some participants also indicated that goal setting would work well with tracking, real time feedback and progress report.

Reminders

Another common feature, liked by most participants, was the reminder. Reminders were found particularly useful for busy individuals who tended to forget things or who had the need to remember multiple medications in a given day. Although reminders are available in other tools as well, participants found mobile apps to be one of the most convenient. However, some participants also pointed out that the timing and frequency of the reminders or push notifications needed to be well designed to be customizable, because otherwise users would just ignore them.

Sharing Personal Information

Many mobile apps have social networking features and enable sharing personal information, such as physical activity patterns, miles run and routes, food intake, or weight. By sharing information with family or friends, individuals might receive informational and emotional social support. At the same time, shared information from family or friends, might also fuse social competition. Many participants, including both the young and the old, female and male, high and low social economic status individuals, were reluctant to share personal information. The primary reason for not wanting to share personal information was because people considered health-related information such as exercise and dietary patterns as private matters, which did not warrant sharing with others. The participants not only disliked sharing their own, they even expressed annoyance when seeing their friends share such information on social networking sites.

Another reason for not wanting to share personal information was the concern of how the information might be exploited by a third party, such as health insurance companies or advertisers. This concern was most prominent among non-student participants. Some were even concerned that the information (e.g., routine running routes) could put individuals in danger.

Although most of the participants were not favorable to sharing personal information, they indicated that they would share some information, depending on necessity and security as well as whether they could control with whom they would share and what type of information was shared. They preferred to share the information with a small group of individuals designated by themselves. Some of them would not share information with strangers yet some were open to the idea.

"Calorie intake. Starting weight. Ending weight. That would be the things I would share only if I chose to with certain people." (Male hair stylist, interview)

"The only concern is who has my medication info. If it is secure, I will think it is helpful." (MNS, FG4)

"I am more likely to interface with strangers. I have done some fitness goal with this community of strangers that are doing the same thing, more comfortable with that." (FNS, FG3)

"You need real personal contact to do it [providing social support based on the shared information]. I don't find they [strangers online] are accountable the same way like one-on-one contact." (MNS, FG4)

Discussion

A qualitative study about health app user perceptions was conducted with a diverse pool of participants of various age groups and social economic status. Our findings add evidence to the understudied area of user experience and perspectives on health apps. Via six focus groups of 39 participants and five individual interviews, we identified barriers faced by smartphone users for adoption and continued use of health apps. Through their own experiences and by showing participants various common features of health apps, we summarized the motivating or facilitating features that drive health app usage, as well as factors hindering their use (Table 2). Many of the themes we identified were consistent with Dennison et al.'s findings [ 16], including smartphones as valuable information sources, tracking progress and raising awareness, aversion of sharing information, privacy and security concerns, skepticism over context sensing, necessity for efficiency and convenience, credibility and accuracy. The challenges to use and continue to use health apps faced by our diverse pool of participants were consistent with Dennison et al. [ 16]. The primary barrier to continuing using these apps was the required time and effort.

Table 2 Summary of identified themes and sub-themes mapped to theoretical constructs

We also discovered many new findings among our participants who were older and of lower social economic status. For instance, low app awareness and low app literacy are two factors leading to non-adoption, suggesting that increasing awareness and educating the general public about health apps is needed in order to make mHealth widely accepted. Users liked information from the apps, but more importantly, they desired personalized information. Although many of the health apps attempt to provide personalized and tailored information, currently these apps do not have adequate input from users with their personal information to provide tailored feedback. Users need to trust the apps to allow access to enough personal information in order to have more granularly tailored information and feedback. Future research is needed regarding the tension between user's desire for personalization and privacy protection. Users liked tracking, and particularly reliable automatic tracking, as it required little time and effort from the users. Automatic and accurate sensing of users' behaviors and states was among one of the top desirable features. Users tended not to want to share personal information due to privacy or security concerns. However, they would share such information if they had control over with whom they shared or could share only with a small group of individuals. Various barriers to adoption as well as motivators for continued use were identified. It is also necessary to mention, both the cost of the app and tangible rewards were identified by participants, indicating the importance of monetary factors. Besides tracking and personalized information, goal setting and reminders were also perceived to be very helpful behavior change features in the health apps.

Mapping Themes to Theories

After further examining the themes identified via inductive thematic analysis, we found that many of the themes could be mapped to existing technology adoption and behavior change theories. For instance, time and effort requirement for app adoption and continued use is related to the effort expectancy construct in [ 22]: participants did not use health apps because they did not expect health apps to be simple and easy to use. Lack of app literacy is related to the facilitating conditions construct in [ 22]: participants did not use health apps because they lacked existing knowledge or guidance of using health apps. The feature liked by the participants, tracking for awareness, ties to the self-observation process of self-regulation in social cognitive theory [ 21]. Table 2 presents an overview of how some of the identified themes and sub-themes can be applied to different theoretical constructs. We do acknowledge that this list by no means is exhaustive, and additional theories can potentially be mapped to identified themes. The theories included here are frequently used in the context of technology acceptance and behavior change. We also emphasize the added value of connecting our study results to theoretical concepts with the aim of providing theoretical guidance for researchers who intend to examine or develop mobile health technologies and their features.

Practical Implications

In the United States, only 62% of smartphone owners have used their phone to look up information about a health condition in 2015 [ 17] and only 19% of smartphone owners have a health app in 2012 [ 25]. It is possible that the low penetration rate of health apps might result from lack of awareness, just like many of our participants did not realize that they could use their phones to track food and exercise or remind them about their medication. To overcome adoption barriers, first of all, we need to increase awareness of the existence of health apps among the general public. One way to achieve this is through entertainment education-incorporating health app use among characters in popular TV shows might be one way to raise awareness [ 26]. Primary care doctors may be another important source to recommend health apps to clients. This addresses both the issue of users not knowing which app to choose and the credibility concern. However, to achieve this, health providers themselves should be aware and receptive to health apps. Currently, there is limited research on health providers' perceptions of health apps and future research regarding this is needed. As for the barrier of not knowing how to use health apps, one possible solution is to provide facilitating conditions such as community technology literacy workshops to help the non-digital native. However, what is more important actually falls on app designers. A well-designed app needs to be intuitive to use without involving users checking a manual. The participants found that many of the existing apps have too many complicated features or options that an average user might not use. A layer design that caters to both experienced and novice users are needed. This also addresses the third barrier-time and effort requirement. Designing an app, which is easy to use and reduces the time and effort put in by the users, is crucial. Thorough usability testing is necessary before app release [ 27]. The last barrier identified by our participants was cost. Most participants indicated that they only used free apps. In fact, many health apps are free. Existing content analyses indicated that most of the paid apps cost no more than 3 dollars [ 10]. The paid apps must have features that were not available in any free apps in order to convince the users that it had the price value for them to pay.

The participants also discussed other factors that might motivate them to adopt and continue to use health apps, including social influence and social competition, intangible and tangible rewards, and entertainment and hedonic factors. Participants considered hedonic motivation to be more important for children or the younger generation than for themselves. Friends or family members' recommendation of health apps was identified as an important factor to increase credibility of the app, thereby describing the importance of social influence. Therefore, designer of health apps should include features publicly demonstrating usage to a user's close social network to promote wide usage.

Two other prominent features liked or desired by the participants were personalized guidance and reminders. The participants did not just want generic information from the apps; they expected to obtain individualized information from the health apps. Tailoring has been shown to be effective for health communication and education [ 28, 29]. Health apps have the ability to capture ample user information either through self-input or through automatic sensors. However, many of the existing apps only tailor or personalize minimally. Future research should examine how we can take advantage of smartphones and health apps to capture granular individual information and provide smartly customized guidance and feedback to users [ 23]. In fact, the reminder feature can also be personalized. Future research should examine whether a personalized reminder can enable users to receive it at the most appropriate time and context to have the maximum effect.

Conclusions

The current study attempts to expand the limited research on the user perspectives of health apps by conducting a qualitative research with a diverse pool of participants. Although we successfully recruited participants of various age groups, we only recruited five participants outside the university setting. Additionally, due to the constrained working hours, these five participants could not be included into any focus groups and were interviewed individually. Although both the focus group moderator and the interviewer followed a pre-determined guide to ask questions and conduct the qualitative research, the two modes of data collection could introduce biases. For instance, the method of focus groups might lead to participants' ideas bouncing off of each other and thus growing. At the same time, people might be reluctant to share their perspectives in a group setting of focus groups if they fear that others might not agree with them.

Additionally, despite our effort to recruit a gender balanced pool, about 2/3 of the participants were female. Third, not all the participants had used health apps and the discussion was based on their perception of the examples provided by the researchers in the trigger materials. Their perception might be different when they actually use the apps. Nevertheless, including non-users of health apps, was a deliberate move in order to get insights into the perceptions of those who didn't use health apps. These insights add an additional layer to the quality of findings. Furthermore, it removes the potential for a positive bias created by a dataset populated only by users (effectively ignoring the non-users). Finally, none of our participants had experience using medical apps that connect to medical devices [ 30] or for provider communication or medical education [ 2]. Therefore, the results of this study cannot be apply to medical apps defined by the U. S Food and Drug Administration [ 30] or Boulos et al. [ 2]. Despite the limitations, however, our research adds important qualitative evidence to the current research of mHealth through the perspective of a diverse pool of users beyond the young, highly educated group, which can provide important insight for both researchers and app designers to develop and deploy health apps for behavior change intervention. Interpreting the findings based on the theoretical frameworks, we recommend that future research on mHealth and app-based behavior change should focus on developing theory-guided, ease to use, personalized apps with social media and information sharing features that the users can customize and control as well as incentives to foster continued use.

Abbreviations

FCS:

Female college student

FG:

Focus group

FNS:

Female, non-student

MCS:

Male college student

MNS:

Male, non-student

UTAUT2:

Extended unified theory of acceptance and usage of technology

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Zahry NR, Cheng Y, Peng W. Content analysis of diet-related mobile apps: A self-regulation perspective. Health Commun. 2016; doi: 10.1080/10410236.2015.1072123.

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Smith A. U.S. Smartphone Use in 2015. Pew Research Center. 2015. http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-/. Accessed 30 Aug 2015.

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Smartphone ownership penetration in the United Kingdom (UK) in 2012-2015, by age. http://www.statista.com/statistics/271851/smartphone-owners-in-the-united-kingdom-uk-by-age/. Accessed 1 Dec 1 2015.

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Gould SJ. Health consciousness and health behavior: the application of a new health consciousness scale. Am J Prev Med. 1990;6(4):228-37.

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Fox S, Duggan M. Mobile Health 2012. Pew Research Center. 2012. http://www.pewinternet.org/files/old-media//Files/Reports/2012/PIP_MobileHealth2012_FINAL.pdf. Accessed 30 Aug 2015.

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Kreuter MW, Oswald DL, Bull FC, Clark EM. Are tailored health education materials always more effective than non-tailored materials? Health Educ Res. 2000;15(3):305-15.

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Noar SM, Benac CN, Harris MS. Does tailoring matter? Meta-analytic review of tailored print health behavior change interventions. Psychol Bull. 2007;133(4):673-93.

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U. S. Food and Drug Administration. Mobile medical applications: Guidance for industry and food and drug administration staff. http://www.fda.gov/downloads/MedicalDevices/.../UCM263366.pdf Accessed 28 July 2016.

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Becker MH, Radius SM, Rosenstock IM. Compliance with a medical regimen for asthma: A test of the health belief model. Public Health Rep. 1978;93:268-77.

Download references

Acknowledgements

We thank the participants for sharing their experiences and perspectives.

Funding

The study was partially funded by the Department of Media and Information, Michigan State University.

Availability of data and materials

The data (anonymized transcriptions) supporting the conclusions of this article are available at goo.gl/T9oZvk. The focus group and interview discussion guide is included in Additional file 2 and the discussion trigger materials are included in Additional file 3.

Authors' contributions

WP contributed to the conception, design, and analysis, providing oversight to the draft and interpretation of the data, and writing of the background, results, and discussion sections. SK contributed to design, data collection and analysis, and writing of the methods and results section. SY contributed to design, data collection and analysis, and writing of the results section. SAH contributed to data collection and analysis, and writing of the methods section. Each of the authors has revised the current manuscript and gives their final approval for its submission and publication.

Competing interests

The authors declare that they have no competing interests.

Consent for publication

Not applicable.

Ethics approval and consent to participate

The study was approved by the institutional review board at Michigan State University, IRB# x13-1088e. All the participants signed the consent form before taking part in this study.

Author information

Affiliations

1. Department of Media and Information, Michigan State University, 404 Wilson Road, Room 409, East Lansing, MI, 48824, USA

Wei Peng & Shaheen Kanthawala

2. Department of Advertising & Public Relations, Michigan State University, 404 Wilson Road, Room 309, East Lansing, MI, 48824, USA

Shupei Yuan

3. School of Journalism, Michigan State University, 404 Wilson Road, Room 359, East Lansing, MI, 48824, USA

Syed Ali Hussain

Corresponding author

Correspondence to Wei Peng.

Additional files

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
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# data[].attributes.attachment_link.title
A qualitative study of user perceptions of mobile health apps

# data[].attributes.attachment_link.description
Background Mobile apps for health exist in large numbers today, but oftentimes, consumers do not continue to use them after a brief period of initial usage, are averse toward using them at all, or are unaware that such apps even exist.

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Innehållet i Forskningsetiska principer fokuserar på etikgranskning av forskningsprojekt inom humaniora och samhällsvetenskap. Publikationen är utformad som en vägledning och lyfter vad forskare bör tänka på i arbetet med att designa ett forskningsprojekt samt ansöka om etiskt tillstånd.

Obs! Eftersom Forskningsetiska principer publicerades 2002 är delar av informationen inaktuell. Till exempel nämns PUL, en lag som inte längre finns.

Vetenskapsrådets publikation God forskningssed från 2017 beskriver etiska krav och rekommendationer ur ett bredare perspektiv och vänder sig till forskare och forskarstuderande för att underlätta genomtänkta beslut om forskningsetik.

# data[].attributes.attachment_link.description
Innehållet i Forskningsetiska principer fokuserar på etikgranskning av forskningsprojekt inom humaniora och samhällsvetenskap. Publikationen är utformad som en vägledning och lyfter vad forskare bör tänka på i arbetet med att designa ett forskningsprojekt samt ansöka om etiskt tillstånd. Obs! Eftersom Forskningsetiska principer publicerades 2002 är delar av informationen inaktuell.

# data[].attributes.headline
Motion-tracking workout app uses iPhone camera and AI to perfect your exercise posture

# data[].attributes.subject
Motion-tracking workout app uses iPhone camera and AI to perfect your exercise posture

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Kaia iPhone app uses camera and AI to perfect your exercise posture and routine

# data[].attributes.attachment_link.description
Kaia Health has rolled out a major update to its Perfect Squat Challenge app, changing its name to Kaia Personal Trainer and uses AI and your iPhone's camera to improve your workout posture.

# data[].attributes.headline
App som skannar kroppen och ger procentuell analys på din kropp

# data[].attributes.subject
App som skannar kroppen och ger procentuell analys på din kropp

# data[].attributes.attachment_link.readable_text
Amazon is getting into the health gadget market with a new fitness band and subscription service called Halo. Unlike the Apple Watch or even most basic Fitbits, the Amazon Halo Band doesn't have a screen. The app that goes along with it comes with the usual set of fitness tracking features along with two innovative - and potentially troubling - ideas: using your camera to create 3D scans for body fat and listening for the emotion in your voice.

The Halo Band will cost $99.99 and the service (which is required for Halo's more advanced features) costs $3.99 per month. Amazon is launching it as an invite-only early access program today with an introductory price of $64.99 that includes six months of the service for free. The Halo service is a separate product that isn't part of Amazon Prime.

The lack of a screen on the Halo Band is the first indicator that Amazon is trying to carve out a niche for itself that's focused a little less on sports and exercise and a little more on lifestyle changes. Alongside cardio, sleep, body fat, and voice tone tracking, a Halo subscription will offer a suite of "labs" developed by partners. They're short challenges designed to improve your health habits - like meditation, improving your sleep habits, or starting up basic exercise routines.

The Halo Band "is not a medical device," Amazon tells me. As such, it hasn't submitted the device to the FDA for any sort of approval, including the lighter-touch " FDA clearance " that so many other fitness bands have used.

The Halo Band hardware

The Halo Band consists of a sensor module and a band that clicks into it on top. It's a simple concept and one we've seen before. The lack of a display means that if you want to check your steps or the time, you'll need to strap something else to your wrist or just check your phone.

The band lacks increasingly standard options like GPS, Wi-Fi, or a cellular radio, another sign that it's meant to be a more laid-back kind of tracker. It has an accelerometer, a temperature sensor, a heart rate monitor, two microphones, an LED indicator light, and a button to turn the microphones on or off. The microphones are not for speaking to Alexa, by the way, they're there for the voice tone feature. There is explicitly no Alexa integration.

It communicates with your phone via Bluetooth, and it should work equally well with both iPhones and Android phones. The three main band colors that will be sold are onyx (black), mineral (light blue), and rose gold (pink-ish).

There will of course be a series of optional bands so you can choose one to match your style - and all of them bear no small resemblance to popular Apple Watch bands. The fabric bands will cost $19.99 and the sport bands will be $15.99.

Amazon intends for users to leave the Halo Band on all the time: the battery should last a full week and the sensor is water resistant up to 5ATM. Amazon calls it "swimproof."

But where the Halo service really differentiates itself is in two new features, called Body and Tone. The former uses your smartphone camera to capture a 3D scan of your body and then calculate your body fat, and the latter uses a microphone on the Halo Band to listen to the tone of your voice and report back on your emotional state throughout the day.

Body scans

Body scans work with just your smartphone's camera. The app instructs you to wear tight-fitting clothing (ideally just your underwear) and then stand back six feet or so from your camera. Then it takes four photos (front, back, and both sides) and uploads them to Amazon's servers where they're combined into a 3D scan of your body that's sent back to your phone. The data is then deleted from Amazon's servers.

Once you have the 3D scan, Amazon uses machine learning to analyze it and calculate your body fat percentage. Amazon argues that body fat percentage is a more reliable indicator of health than either weight or body mass index. Amazon also claims that smart scales that try to measure body fat using bioelectrical impedance are not as accurate as its scan. Amazon says it did an internal study to back up those claims and may begin submitting papers to peer-reviewed medical journals in the future.

Finally, once you have your scan, the app will give you a little slider you can drag your finger on to have it show what you would look like with more or less body fat.

That feature is meant to be educational and motivational, but it could also be literally dangerous for people with body dysmorphic disorder, anorexia, or other self-image issues. I asked Amazon about this directly and the company says that it has put in what it hopes are a few safeguards: the app recommends you only scan yourself every two weeks, it won't allow the slider to show dangerously low levels of body fat, and it has information about how low body fat can increase your risk for certain health problems. Finally, although anybody 13 years of age and up can use the Halo Band, the body scan feature will only be allowed for people 18 or older.

Tracking the tone of your voice

The microphone on the Amazon Halo Band isn't meant for voice commands; instead it listens to your voice and reports back on what it believes your emotional state was throughout the day. If you don't opt in, the microphone on the Band doesn't do anything at all.

Once you opt in, the Halo app will have you read some text back to it so that it can train a model on your voice, allowing the Halo Band to only key in on your tone and not those around you. After that, the band will intermittently listen to your voice and judge it on metrics like positivity and energy.

It's a passive and intermittent system, meaning that you can't actively ask it to read your tone, and it's not listening all of the time. You can also mute the mic at any time by pressing the button until a red blinking LED briefly appears to show you it's muted.

Amazon is quick to note that your voice is never uploaded to any servers and never heard by any humans. Instead, the band sends its audio snippets to your phone via Bluetooth, and it's analyzed there. Amazon says that the Halo app immediately deletes the voice samples after it analyzes them for your emotional state.

It picks up on the pitch, intensity, rhythm, and tempo of your voice and then categorizes them into "notable moments" that you can go back and review throughout the day. Some of the emotional states include words like hopeful, elated, hesitant, bored, apologetic, happy, worried, confused, and affectionate.

We asked Amazon whether this Tone feature was tested across differing accents, gender, and cultures. A spokesperson says that it "has been a top priority for our team" but that "if you have an accent you can use Tone but your results will likely be less accurate. Tone was modeled on American English but it's only day one and Tone will continue to improve."

Data privacy

Both the Body and Tone features are innovative uses of applied AI, but they are likely to set off any number of privacy alarm bells. Amazon says that it is being incredibly careful with user data. The company will post a document detailing every type of data, where it's stored, and how to delete it.

Every feature is opt-in, easy to turn off, and it's easy to delete data. For example, there's no requirement you create a body scan and even if you do, human reviewers will never see those images. Amazon says the most sensitive data like body scans and Tone data are only stored locally (though photos do need to temporarily be uploaded so Amazon's servers can build the 3D model). Amazon isn't even allowing Halo to integrate with other fitness apps like Apple Health at launch.

Some of the key points include:

Your Halo profile is distinct from your Amazon account - and it will need to be individually activated with a second factor like a text message so that anybody else that might share your Amazon Prime can't get to it.

You can download and delete any data that's stored in the cloud at any time or reset your account to zero.

Body scans and tone data can be individually deleted separately from the rest of your health data.

Body scans are only briefly uploaded to Amazon's servers then deleted "within 12 hours" and scan images are never shared to other apps like the photo gallery unless you explicitly export an image.

Voice recordings are analyzed locally on your phone and then deleted. "Speech samples are processed locally and never sent to the cloud," Amazon says, adding that "Tone data won't be used for training purposes."

Data can be shared with third parties, including some partners like WW (formerly Weight Watchers). Data generated by the "labs" feature is only shared as anonymous aggregate info.

Activity and sleep tracking

The body scanning and tone features might be the most flashy (or, depending on your perspective, most creepy) parts of Halo, but the thing you'll likely spend the most time watching is your activity score.

Amazon's Halo app tracks your cardio fitness on a weekly basis instead of daily - allowing for rest days. It does count steps, but on a top level, what you get is an abstracted score (and, of course, a ring to complete) that's more holistic. Just as Google did in 2018, Amazon has worked with the American Heart Association to develop the abstracted activity score.

The Halo Band uses its heart monitor to distinguish between intense, moderate, and light activity. The app combines those to ensure you're hitting a weekly target. Instead of the Apple Watch's hourly "stand" prompts, the Halo app tracks how long you have been "sedentary." If you go for more than eight hours without doing much (not counting sleep), the app will begin to deduct from your weekly activity score.

The Halo Band can automatically detect activities like walking and running, but literally every other type of exercise will need to be manually entered into the app. The whole system feels less designed for workout min-maxers and more for people who just want to start being more active in the first place.

Speaking of heart tracking, the Halo Band doesn't proactively alert you to heart conditions like atrial fibrillation, nor does it do fall detection.

The Halo Band's sleep tracking similarly tries to create an abstracted score, though you can dig in and view details on your REM sleep and other metrics. One small innovation that the Halo Band shares with the new Fitbit is temperature monitoring. It uses a three-day baseline when you are sleeping and from there can show a chart of your average body temperature when you wake up.

Halo Labs, partnerships, and the subscription

Finally, Amazon has partnered with several third parties to create services and studies to go along with the Halo service. For example, if your health care provider's system is compatible with Cerner, you can choose to share your body fat percentage with your provider's electronic medical records system. Amazon says it will also be a fully subsidized option for the John Hancock Vitality wellness program.

The flagship partnership is with WW, which syncs up data from Halo into WW's own FitPoints system. WW will also be promoting the Halo Band itself to people who sign up for its service.

There are dozens of lower-profile partnerships, which will surface in the Halo app as "Labs." Many of the labs will surface as four-week "challenges" designed to get you to change your health habits. Partners creating Labs range from Mayo Clinic, Exhale, Aaptiv, Lifesum, Headspace, and more. So there might be a lab encouraging you to give yoga a try or a set of advice on sleeping better like kicking your pet out of your bedroom.

Amazon says each Lab needs to be developed with "scientific evidence" of its effectiveness and Amazon will audit them. Data created from these challenges will be shared with those partners but only in an aggregated, anonymous way.

Virtually all the features discussed here are part of the $3.99 / month Halo subscription. If you choose to let it lapse, the Halo Band will still do basic activity and sleep tracking.

Too chill for fitness buffs, but will casual users want to pay a monthly subscription?

In charging a monthly subscription, Amazon is out on a limb compared to most of its competitors. Companies like Fitbit and Withings offer some of the same features you can get out of the Halo system, including sleep tracking and suggestions for improving your fitness. They also have more full-featured bands with displays and other functionality. And of course there's the Apple Watch, which will have deeper and better integrations with the iPhone than will ever be possible for the Halo Band.

Overall, Halo is a curious mix. Its hardware is intentionally less intrusive and less feature-rich than competitors, and its pricing strategy puts Amazon on the hook for creating new, regular content to keep people subscribed (exercise videos seem like a natural next step). Meanwhile, the body scanning feature goes much further than other apps in directly digitizing your self-image - which is either appealing or disturbing depending on your relationship to your self-image. And the emotion tracking with Tone is completely new and more than a little weird.

The mix is so eclectic that I can't possibly guess who it might appeal to. People who are more serious about exercise and fitness will surely want more than what's on offer in the hardware itself, and people who just sort of want to be a little more active may balk at the subscription price. And since the Halo Band doesn't offer the same health alerts like fall detection or abnormal heart rate detection, using it as a more passive health monitor isn't really an option either.

That doesn't mean the Halo system can't succeed. Amazon's vision of a more holistic health gadget is appealing, and some of its choices in how it aggregates and presents health data is genuinely better than simple step counting or ring completion.

We won't really know how well the Halo system does for some time, either. Amazon's opening it up as an early access program for now, which means you need to request to join rather than just signing up and buying it.

# data[].attributes.attachment_link.title
Amazon announces Halo, a fitness band and app that scans your body and voice

# data[].attributes.attachment_link.description
Amazon is creating an entirely new fitness band and health service, called Halo. The Halo Band is a simple gadget with no display, but the Halo app is more interesting. It can use your camera to create a 3D scan of your body and calculate your body fat.

# data[].attributes.attachment_link.readable_text
Recent years have witnessed an unprecedented growing of sport videos, as different types of sports activities can be widely-observed (i.e., from professional athletics to personal fitness). Existing approaches by computer vision have predominantly focused on creating experiences of content browsing and searching by video tagging and summarization. These techniques have already enabled a wide-range of applications for sports enthusiasts, such as text-based video search, highlight generation, and so on. In this paper, we take one step further to create an AI coach system to provide personalized athletic training experiences. Especially for sports activities which the training quality largely depends on the correctness of human poses in a video sequence. As sports videos often involve grand challenges of fast movement (e.g., skiing, skating) and complex actions (e.g., gymnastics), we propose to design the system with several distinct features: (1) trajectory extraction for a single human instance by leveraging deep visual tracking, (2) human pose estimation by proposing a novel human joints relation model in spatial and temporal domains, (3) pose correction by abnormal detection and exemplar-based visual suggestions. We have collected sports training videos from 30 sports enthusiasts, namely Freestyle Skiing Aerials dataset (63 clips). We show that the proposed system can lead to a remarkably better user training experience by extensive user studies.

# data[].attributes.attachment_link.title
AI Coach: Deep Human Pose Estimation and Analysis for Personalized Athletic Training Assistance

# data[].attributes.attachment_link.description
Recent years have witnessed an unprecedented growing of sport videos, as different types of sports activities can be widely-observed (i.e., from professional athletics to personal fitness). Existing approaches by computer vision have predominantly focused on creating experiences of content browsing and searching by video tagging and summarization.

# data[].attributes.headline
Vad gör en hemsida tilltalande tar upp design teorier

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Vad gör en hemsida tilltalande tar upp design teorier

# data[].attributes.attachment_link.readable_text
Vad gör en webbsida tilltalande?: En jämförelse mellan tre stora teorier inom webbdesign. 2009 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis

Abstract [sv]

Syftet med denna undersökning är att ta reda på till vilken grad olika teorier påverkar hur webbsidor uppfattas. Detta för att kunna utveckla mer tilltalande webbsidor. Vi valde att undersöka sex webbsidor: Aftonbladet, Nerikes Allehanda (NA), Dagens Nyheter (DN), IKEA, Hennes & Mauritz (H&M) och NetOnNet.Undersökningen är uppdelad i tre delar: en enkätundersökning, en expertutvärdering och en jämförelseanalys. Enkäten skickades ut till studenter vid Örebro Universitet via studentmailen och resultatet har sedan sammanställts i ett antal frekvenstabeller. Efter detta utfördes expertutvärderingarna utifrån litteraturstudien. De teorier vi valde att analysera webbsidor utifrån är användbarhet, grafisk design och flow. Efter att enkätundersökningen och expertutvärderingen utförts gjordes en analysjämförelse av mönster och samband mellan respondenternas svar och expertutvärderingarna.Vi kom fram till att vad som vad viktigast för användaren var den grafiska designen, men att om man vill skapa en riktigt bra webbsida så borde man använda flera teorier. Vi kom även fram till att de riktlinjer som finns för de olika teorierna fungerar och hjälper till för att skapa en bättre webbsida.

Place, publisher, year, edition, pages

2009. , p. 48

Keywords [sv]

användbarhet, grafisk design, flow, webbdesign National Category Computer and Information Sciences

Identifiers

URN: DiVA, id: urn:nbn:se:oru:diva-5086 ISRN: ORU-HHS/INF-GK-2009/0003--SE OAI: oai:DiVA.org:oru-5086 diva2:139751

Presentation

(English)

Uppsok
Social and Behavioural Science, Law

Supervisors

Examiners

2009-10-122009-01-262018-01-13 Bibliographically approved

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# data[].attributes.attachment_link.title
Vad gör en webbsida tilltalande?: En jämförelse mellan tre stora teorier inom webbdesign.

# data[].attributes.attachment_link.description
Vad gör en webbsida tilltalande?: En jämförelse mellan tre stora teorier inom webbdesign. 2009 (Swedish)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis Abstract [sv] Syftet med denna undersökning är att ta reda på till vilken grad olika teorier påverkar hur webbsidor uppfattas.

# data[].attributes.attachment_link.title
Designing Wearable Systems for Sports: A Review of Trends and Opportunities in Human-Computer Interaction

# data[].attributes.attachment_link.description
This paper presents a literature review of human- computer interaction works on wearable systems for sports. We selected a corpus of 57 papers and analyzed them through the grounded theory for literature review approach. We identified five themes across the papers: the different research perspectives, the type of sports and sportspeople, the roles of wearables in sports, their wearability, and the different types of feedback.

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smart workout assistant using motion detection 

# data[].attributes.subject
smart workout assistant using motion detection 

# data[].attributes.body
tar upp  andra företag som har skapat smarta assistant anlys program?

# data[].attributes.attachment_link.title
(PDF) Fitness Mate: Intelligent workout assistant using motion detection

# data[].attributes.attachment_link.description
Figures - uploaded by Tharindu Dilshan Perera Author content All figure content in this area was uploaded by Tharindu Dilshan Perera Content may be subject to copyright. Discover the world's research 19+ million members 135+ million publications 700k+ research projects Join for free January 2006 · Intangible Capital The knowledge society gives a remarkable role to human potentialities into organizations.

# data[].attributes.body
workout from home ,  få sökresultat

• Human-centered computing~Interaction devices

motion analysis

Shadow casting , Spatial augmented reality

# data[].attributes.headline
20 st designpricinper att tänka på när man designar wearable devices

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20 st designpricinper att tänka på när man designar wearable devices

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Design principer

# data[].attributes.attachment_link.readable_text
Wearable devices have great potential to support several application domains ranging from medical and safety critical, to leisure and entertainment. Wearable devices' solutions are promising, and extensive research has been conducted in this domain since the early 90's. However most of these works focuses on the feasibility of individual solutions. As such, the human aspects are often neglected, which can decrease not only the acceptance levels for novel devices, but also their sustained engagement. To facilitate the consideration of human factors in the early design stage, we present and define a list of 20 human-centered design principles. We explain how each principle can be incorporated during the design phase of the wearable device creation process. By adopting these principles, we expect practitioners to achieve better wearable solutions, improving the user acceptance, satisfaction and engagement for novel applications.

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Human Factors Considerations in the Design of Wearable Devices - Vivian Genaro Motti, Kelly Caine, 2014

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Wearable devices have great potential to support several application domains ranging from medical and safety critical, to leisure and entertainment. Wearable devices' solutions are promising, and extensive research has been conducted in this domain since the early 90's. However most of these works focuses on the feasibility of individual solutions.

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3290607.3312830

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Påpekan på att informera på distans via digitala verktyg är svårt

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Påpekan på att informera på distans via digitala verktyg är svårt

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3406865.3418360

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3401956.3404243
de har gjort en undersökning på hur visuellfedback påverkar användaren och i sin tur resultatet av träningen genom en produkt som matar in informationen ch gör om dem till visuella grafer.

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Att designa kvalitativa feedback system istället för kvantitativa feedbacksystem

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Att designa kvalitativa feedback system istället för kvantitativa feedbacksystem

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3313831.3376478
De pratar ganska mycket om hur kvantitativa feedback system ofta inte ger önskad effekt av användare utan kan leda till att användare mår sämre av dessa hjälper.

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AI i mobilen som analyserade utövandet och jämförde med foton i en databas

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AI i mobilen som analyserade utövandet och jämförde med foton i en databas

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3395035.3425302
"to the best of our knowledge, there is no work in the literature for general-purpose physical exercise correction using commodity hardware (smart phones).

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BodyLights: en produkt som ska med visuell förstärkning korrigera träningen

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BodyLights: en produkt som ska med visuell förstärkning korrigera träningen

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https://dl-acm-org.ezproxy.hkr.se/doi/pdf/10.1145/3313831.3376268

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fortsätta läsa forskningsartiklr

förberedd frågor till handledare

samhällsvinkeln

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Smart Home Gym Equipment Review: Mirror, Tonal, Peloton 2021 | The Strategist | New York Magazine (nymag.com)

Yi - the interactive yoga mat - YouTube

Can Simulated Partners Boost Workout Effort in Long-Term Exercise? - Abstract - Europe PMC _ vetenskaplig artikel. "Exercising with an always superior partner leads to greater work efforts during the hardest interval training compared to exercising alone."

24/1 Sökord : workout together online ACM , summon
Alone Together (hkr.se).   Alone Together | Proceedings of the 18th ACM Conference Companion on Computer Supported Cooperative Work & Social Computing (hkr.se)

Trip Together | Proceedings of the 5th International Conference on Human Agent Interaction (hkr.se)

Losing It Online | Proceedings of the 18th International Conference on Supporting Group Work (hkr.se)

25/1 Soma-Based Design
Background (mobilelifecentre.org)

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Vilka frågor_ämnen vill vi diskutera kring

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Se om andra har gjort experiment om ensamhet.

Vad skapar problem? vad löser problem? finns det digitala lösningar?

Vad är ensamhet?

kolla med förskolelärare i högskolan ?