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App-based mobile health exercise interventions can motivate individuals to engage in more physical activity (PA). According to the Fogg Behavior Model, it is important that the individual receive prompts at the
The main objective of this study was to examine user experience, app engagement, and users’ perceptions and opinions regarding the PAUL app and its JIT prompts and to explore changes in the PA behavior, intrinsic motivation, and the perceived capability of the PA behavior of the participants.
In total, 2 versions of the closed-beta version of the PAUL app were evaluated: a basic version (Basic PAUL) and a JIT adaptive version (Smart PAUL). Both apps send JIT exercise prompts, but the versions differ in that the Smart PAUL app sends JIT adaptive reminder messages to initiate running or walking behavior, whereas the Basic PAUL app sends reminder messages at randomized times. A total of 23 participants were randomized into 1 of the 2 intervention arms. PA behavior (accelerometer-measured), intrinsic motivation, and the perceived capability of PA behavior were measured before and after the intervention. After the intervention, participants were also asked to complete a questionnaire on user experience, and they were invited for an exit interview to assess user perceptions and opinions of the app in depth.
No differences in PA behavior were observed (
The preliminary findings suggest that the PAUL apps are promising and innovative interventions for promoting PA. Users perceived the strength exercise prompts as a valuable addition to exercise apps. However, to be a feasible intervention, the app must be more stable.
Motivating individuals to engage in regular physical activity (PA) is a global interest as physical inactivity can lead to numerous serious health issues such as cardiovascular diseases, cancer, and diabetes [
A promising method to increase PA are mobile health (mHealth) PA apps [
Previous studies have indicated that mHealth PA interventions are more likely to be effective when they are grounded in theory and, as such, contain adequate persuasive strategies [
In addition, the research fields of human-computer interaction and design thinking emphasize the importance of the quality of the user experience for the success of the intervention [
A likely effective persuasive strategy is to provide a prompt to engage in a certain behavior [
Interventions that aim to send messages at the right time are often referred to as
Therefore, we set out to investigate 2 novel ways of JIT prompting for PA behaviors with an mHealth app, the Playful Active Urban Living (PAUL) app. First, to initiate running or walking behavior, the app sends JITAI reminder messages based on a reinforcement learning algorithm [
To determine the proof of concept for the design and implementation of the 2 types of prompts, we conducted a feasibility study [
The feasibility of the PAUL app was examined by exploring 4 factors [
Participants were recruited by distributing promotional materials around Sloterpark, Oosterpark, and Park Transwijk. Facebook advertisements were issued targeting individuals aged between 18 and 55 years and living close (<3.5 km) to the parks. In addition, advertisement messages were posted on Facebook resident groups close to the parks (ie, residential groups of apartment buildings). Recruitment materials were also distributed at various universities in the Netherlands and on the social networks of the researchers. The recruitment phase lasted from October 1, 2019, to November 14, 2019.
Initially, we targeted participants aged between 18 and 55 years who lived close (≤1 km; 10-minute walk) to one of the parks used with the beacons (ie, Park Transwijk [Utrecht, Netherlands], Oosterpark [Amsterdam, Netherlands], or Sloterpark [Amsterdam, Netherlands]) and did not meet the PA guidelines of 150 minutes per week (measured using the stages of change questionnaire) [
During this study, 2 closed-beta versions of the app were evaluated: Basic PAUL and Smart PAUL. The PAUL apps were developed by a multidisciplinary research team over a 2-year period [
These persuasive strategies were selected as they are theorized to increase the perceived capability and motivation of the participants based on the Capability, Opportunity, and Motivation Behavior model [
Screenshots of the five functionalities of the Playful Active Urban Living app.
A description of the modules in the Playful Active Urban Living (PAUL) app, including the implemented behavior change techniques (BCTs) and persuasive system design (PSD) principles.
PAUL functionality, subcategory, and description | BCTs [ |
PSD principles [ |
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Strength exercise prompts: The user receives location-based strength exercise prompts (audio and pop-up messages) on predetermined GPS locations. The prompt contains an instruction video of the exercise (squat or push-up) in the direct environment of the user. Amenities in the park (eg, trees, benches, or lantern posts) are used for the exercises. |
Information on when and where to perform the behavior Information on how to perform the behavior Demonstrate the behavior Prompt practice |
Primary task support: Reduction Tunneling Rehearsal Normative influence |
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Reminder messages: The user receives up to 14 short reminder messages each week containing a motivational suggestion and either information on the progress toward their goal or (affective) information on performing PAa. The timing of the reminder messages depends on the group allocation (Basic vs Smart PAUL). |
Information provision (general) Provide feedback on performance Prompt practice |
Primary task support: Tunneling Tailoring Personalization Dialogue support: Reminders Suggestions |
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PA behavior: The app records and stores PA metrics during app use (frequency, duration, speed, and distance). The user must press “start” to initiate behavior tracking. The app also records and stores situational characteristics during each session and when sending a reminder (weather type, calendar availability, time, and date). After receiving a strength exercise prompt, the user must log if they performed the exercise. |
Automatic monitoring of behavior |
Primary task support: Reduction Self-monitoring |
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Behavior outcome: The user can report notes on the training session and report on a 1-to-5 scale how they are feeling and how intense the workout was. To monitor how many strength exercises the participant has done, they must log whether they performed or skipped the exercises (during the walking or running activity). |
Self-monitoring of behavior Self-monitoring of behavior outcome |
Primary task support: Self-monitoring |
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Sustained feedback: During running or walking, the user can view simple metrics on their screen (time, distance, current speed, average speed, and number of strength exercises), and the user receives audio feedback every 5 minutes on the duration of the activity. |
Provide feedback on performance |
Primary task support: Personalization Self-monitoring |
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Cumulative feedback: After performing PA with the app, the user can view a summary of their activities (ie, a PA report) with the time, distance, and average speed and a map with their route. The user can access a history view that contains all PA reports. On the home screen, users can view their progress toward their goal. |
Provide feedback on performance |
Primary task support: Personalization Self-monitoring |
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Praise: The user receives a pop-up praise message and a message on the landing page when the weekly goal is reached. |
Rewards contingent on successful behavior |
Dialogue support: Praise |
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Goal setting: To set a goal, the user must perform a short questionnaire. With this questionnaire, the user can set their own long-term walking or running goal (for frequency and duration). Furthermore, a tailored start goal (frequency and duration of activity) is given based on the current fitness level of the participant. The goal increases roughly 10% every week until the long-term goal is reached. |
Goal setting (behavior) Setting graded tasks Review of behavior goals |
Primary task support: Tailoring Dialogue support: Suggestions |
aPA: physical activity.
The Smart PAUL app differs from the Basic PAUL app in that it can optimize the timing of reminders with a self-learning module [
Both apps were programmed to send up to 14 reminders per week. However, during the intervention period, there were technical issues that prevented the app from sending the reminders (ie, the notifications were not activated in the app because of a processing error in the sent format). For Basic PAUL, this issue was resolved within the first week, whereas, for Smart PAUL, the issue was resolved after 3 weeks. Therefore, the Smart PAUL group only received the JITAI reminders in the last week of the intervention.
To determine the feasibility of the PAUL apps, a mixed methods pre-post intervention was performed. This study is part of a larger study that aimed to determine the feasibility of the PAUL apps and examine the user-app interactions with the JITAI reminders. In this paper, we describe the feasibility of the PAUL apps as a whole, whereas we have described the user-app interactions with the JITAI reminder messages in more detail in another paper [
Individuals were screened for eligibility using a web-based enrollment questionnaire on the participants’ characteristics. Eligible participants were contacted by the main researcher (KS), and a face-to-face meeting was arranged. During this meeting, the participants were informed about the main objective of the study, the study requirements, and the data handling. When an individual had no further questions, they were asked to sign the informed consent form. The participant then received an accelerometer and an information pamphlet that summarized the most important study information.
All participants started with the baseline measurement either on November 11, 2019, or November 17, 2019. On the day before the start of the baseline measurement, the participants received the baseline questionnaire to assess the determinants of PA and self-reported PA as well as a reminder to wear the accelerometer for 7 consecutive days. A reminder to fill in the web-based questionnaire was sent when needed after 2 days.
After successful completion of the baseline period, the participants were manually randomized into the Smart and Basic PAUL groups by an independent researcher with a 1:1 ratio stratified by the 3 parks (ie, Park Transwijk, Sloterpark, and Oosterpark). The group allocation was double-blinded. In the following 4 weeks, the participants could use the PAUL app. The user was asked but not obligated to not turn off the reminder function (ie, push notifications) of the app and to give access to their digital calendar. The participants were informed that this would improve the function of the app without explaining any details of the differences between the 2 groups. During the intervention, the participant received a visit from a researcher to download the accelerometer data.
At the start of the fourth intervention week, the user was reminded to wear the accelerometer again for 1 week. After 5 weeks, the individual received a link to the final questionnaire on the usability of the PAUL app, the determinants of PA, and the self-reported PA. After the intervention, all the participants were invited for an interview at a location of their liking. As a token of appreciation for participating in this study, the participants received a voucher for a cinema visit or a sports activity with a value of €30 (US $31.32).
To gain a better understanding of the users’ perception of the PAUL app and its functionalities, all participants were invited for semistructured exit interviews of 20 to 40 minutes at a location of the participants’ choice. The topics in the interview guide covered the perceptions of the included strategies, the design and implementation of the strategies, and the user experience of the strategies. During the interviews, the researchers and participants were still blinded to their group allocation.
In addition, a web-based, 20-item, 7-point scale questionnaire on the user experience with the PAUL app was administered at the end of the intervention (acquired from the study by Mollee et al [
To determine how often the participants used the app during the intervention and, thus, were exposed to the intervention strategies (referred to as
Perceived capability and intrinsic motivation were measured independently for the 3 behaviors of the app (running, walking, and strength exercise). The 6-item perceived competence subscale of the Intrinsic Motivation Inventory [
The PA behavior of the participants was measured using a hip-worn accelerometer, the ActiGraph GT3X+ (ActiGraph LLC), 1 week before the intervention (baseline) and in the last week of the intervention (after the intervention). Accelerometer measurements were considered sufficient if the participants wore the accelerometers for a minimum of 8 hours a day and for at least 3 weekdays and 1 weekend day. A total of 35% (8/23) of the participants did not meet these requirements for either the pre- or posttest measurement and were therefore excluded from the analysis.
The interviews were audio-recorded on the researchers’ phones and transcribed verbatim. After transcribing the interviews, the text was imported into MAXQDA Plus (version 20.2.2; VERBI GmbH). Qualitative research cycle was used to code and analyze the data [
After coding all the data, the codes were analyzed by a researcher according to a cyclic process [
In addition to the interview data, the questionnaire responses were used to gain an overall perspective on the technical problems, perceived effectiveness, usability, satisfaction, and esthetics of the app. To this end, the questionnaire responses were uploaded to SPSS (version 25; IBM Corporation), and the item on technical issues was inverted. To determine the differences between the Smart and Basic PAUL apps, a Mann-Whitney
The behavioral engagement with the PAUL app data was uploaded from the servers of the PAUL app and subsequently cleaned by removing duplicate data. The data were validated by cross-checking the data sets of the PAUL app. For some participants (3/20, 15%), no data were recorded by the app. This was likely due to a connection error with the back end of the app, which could be caused by several factors such as battery failure. Participants whose data were not recorded could not be included in the analysis. Descriptive statistics were calculated for the participants in the Basic and Smart conditions. Differences between the Smart and Basic PAUL apps were calculated using a Mann-Whitney
To analyze the changes in intrinsic motivation and perceived capability, we only included the measures for behavior that the participants wanted to change. That is, if the participants had set a goal with the PAUL app to increase their walking activity, we only used their scores for walking. If they had a running and walking goal, the scores were averaged. This also included changes that the participants made during the intervention. The scores for motivation and perceived capability to perform strength exercises were calculated for all participants. The scores were then uploaded to SPSS, and a descriptive analysis was performed. To determine if there were differences between the groups, the differential scores between the pre- and postintervention measurements were calculated, and Mann-Whitney
To process the accelerometer data, they were downloaded using ActiLife (version 6.13.4, firmware 2.2.1; ActiGraph LLC), and the triaxial counts were summed as counts per minute (cpm). Episodes of at least 90 minutes were defined as nonwear episodes. Short interruption periods of a maximum of 2 minutes of 1 to 100 cpm were allowed as nonwear time to account for the possibility of accidental accelerometer movement. Only days with at least 8 hours of wear time were included in the analysis. Freedson Adult (1998) cutoff sets were used to define the time that the participants spent on MVPA (>1951 cpm). The average MVPA time was calculated while excluding nonwear time. To account for the differences in wearing time, the average time spent performing MVPA was calculated. The PA measurements were imported into SPSS, and a Mann-Whitney
The study method was approved by the local ethical committee (GEO S-19253), and the trial was registered in the Netherlands Trial Register (trial ID: NL8166). The study was conducted and reported according to the CONSORT-eHEALTH (Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth) checklist [
Recruitment resulted in 122 individuals who were interested in participating in the study and completed the enrollment questionnaire. After checking eligibility and provision of informed consent, of the 122 interested individuals, 23 (18.9%) were enrolled in the study. The main reasons for exclusion were that the individuals did not live close enough to the parks equipped with beacons or did not own an Android phone. Of the 23 participants, 3 (13%) discontinued their participation, leaving a total of 20 (87%) participants who completed the study. The participant flow diagram is shown in
The characteristics of the included 20 participants are shown in
CONSORT (Consolidated Standards of Reporting Trials) flow diagram of participants. PAUL: Playful Active Urban Living.
Background characteristics of the participants (N=20).
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All (N=20) | Smart PAULa (n=11) | Basic PAUL (n=9) | |
Gender (female), n (%) | 17 (85) | 9 (82) | 8 (89) | |
Age (years), mean (SD) | 30.65 (8.40) | 32.09 (10.73) | 28.89 (4.17) | |
BMIb (kg/m2), mean (SD) | 24.52 (5.23) | 25.79 (6.49) | 22.79 (2.04) | |
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Secondary school (VWOc) | 3 (15) | 2 (18) | 1 (11) |
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Vocational education | 1 (5) | 1 (9) | 0 (0) |
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Higher professional education degree | 3 (15) | 2 (18) | 1 (11) |
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University degree | 13 (65) | 6 (55) | 6 (67) |
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Living alone | 8 (40) | 3 (27) | 5 (56) |
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Living alone with children and others | 1 (5) | 1 (9) | 0 (0) |
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Living with partner | 3 (15) | 0 (0) | 3 (33) |
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Living with partner and children | 3 (15) | 2 (18) | 1 (11) |
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Living with partner, children, and others | 1 (5) | 1 (9) | 0 (0) |
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Living with more adults (such as student housing) | 4 (20) | 4 (36) | 0 (0) |
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Part-time employment (<34 hours per week) | 8 (40) | 4 (36) | 4 (44) |
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Full-time employment (≥34 hours per week) | 6 (30) | 3 (27) | 3 (33) |
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Studying | 6 (30) | 4 (36) | 2 (22) |
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Maintenance phase | 13 (65) | 6 (55) | 7 (78) |
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Action phase | 1 (5) | 0 (0) | 1 (11) |
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Preparation | 2 (10) | 2 (18) | 0 (0) |
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Contemplation | 4 (20) | 3 (27) | 1 (11) |
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Maintenance phase | 5 (25) | 3 (27) | 2 (22) |
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Action phase | 0 (0) | 0 (0) | 0 (0) |
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Preparation | 5 (25) | 3 (27) | 2 (22) |
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Contemplation | 8 (40) | 4 (36) | 4 (44) |
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Precontemplation | 2 (10) | 1 (9) | 1 (11) |
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No or little running experience | 5 (25) | 3 (27) | 2 (22) |
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Experienced runner, not currently running | 12 (60) | 6 (55) | 6 (67) |
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Experienced runner, currently running | 3 (15) | 2 (18) | 1 (11) |
aPAUL: Playful Active Urban Living.
bThe weight of 1 participant was entered incorrectly and was therefore not included in this table.
cVWO: Voorbereidend wetenschappelijk onderwijs (preuniversity education).
dPA: physical activity.
To examine to what extent the PA behavior of the participants changed over time, the accelerometer data of the participants were analyzed. A total of 35% (7/20) of the participants were excluded from the analysis as they did not meet the required wear time. For the remaining 65% (13/20) of the participants, there were no significant differences between the participants in the Smart PAUL group and the Basic PAUL group (
Next, intrinsic motivation and perceived capability were examined for running or walking and strength exercises (
To determine the differences in determinants before and after the intervention, a Wilcoxon signed-rank test was performed. As there were no differences between the 2 groups, we analyzed the 2 user groups as 1. Significant differences were found in running and walking motivation (
The pre- and postintervention MVPA time for the Smart and Basic groups. MVPA: moderate to vigorous physical activity; ns: not significant; PAUL: Playful Active Urban Living.
The pre- and postintervention intrinsic motivation (4A) and perceived capability scores (4B) of the participants enrolled in the Basic and Smart Playful Active Urban Living groups. ns: not significant.
The user experience was examined using a questionnaire in which the user was asked to rate the app on a score from 1 (lowest) to 7 (highest). As shown in
User experience ranging from 1 (low) to 7 (high) of the Smart and Basic PAUL apps. PAUL: Playful Active Urban Living.
To examine whether the participants used the app and, thus, were exposed to the included persuasive strategies, we explored the frequency of opening the app. As can be seen in
The daily frequency of opening the PAUL app from the day the Basic and Smart PAUL apps were downloaded. PAUL: Playful Active Urban Living.
In this section, we describe the findings of the exit interviews. The perceptions and experiences of both PAUL apps are reported simultaneously to a large extent as the app functionalities are also largely similar. Only the results regarding the timing of the reminders are reported separately.
Overall, the participants thought that the included persuasive strategies were useful and that the most important features were present in the app. The app was perceived by most as “simple” and “basic,” which was liked by some of the participants as it made using the app clear with a simple goal:
And furthermore, it’s nice that it’s so clear, that you’re not lost, or that you’re somewhere in 7 steps and think: where am I? And that was nice. He was just clear.
Other participants indicated that they preferred a more elaborate app:
Um, all right in the general sense. But, um, in many ways, just a little basic. In terms of what you can do with it, of course.... I went running with my girlfriend during this research and she has a Nike running app, and yes, that's super interactive.
Well, I thought it was, in its essence, a really nice app to use, for instance for running. Here and there, there were some features of which I thought: “Oh those should be further developed.”
The improvements to the app mainly lay in the development of the implementation of a strategy rather than the addition of another strategy (eg, earning coins or a leaderboard). For instance, participant 18 (female, aged 22 years) would have liked more “interactions” with the app (eg, controlling whether she could perform the strength exercises in a particular exercise session and how many she could perform). By doing so, the users can personalize the apps themselves. Furthermore, the improvements to features that participant 5 (male, aged 32 years) mentioned were to provide more detailed and graphic information about his activities and add more types and locations for the strength exercises. Other participants would have liked “to know the idea behind” the goal setting functionality (participant 8, female, aged 30 years); thus, she would have liked additional information on how the goal was determined.
A frequently mentioned improvement in functionality was to automatically track all the PA activities of the user. Some participants hoped for an app that helped them integrate more PA into their daily activities (eg, cycling slightly further than normal or taking the stairs more often) as this fit better in their life than going for a recreational run or walk. Furthermore, as not all their activities were recorded, they felt as though the app did not give them credit for all their PA behavior:
What I also find unfortunate about it was that you physically had to say that you’re going to move now. And a lot of my movements just happen in life, so to speak. So, when I walk to the supermarket, or when I go there or there for a bit. And I’m not gonna enter that. And he won’t record that. Whereas for me, those are the moments that I could make a profit, that if he would record it.
In addition, we asked about the perceptions of 3 frequently used persuasive strategies in apps that were not included in the PAUL apps: rewards (eg, victory points, digital coins, or digital awards), social support, and competition. A few participants indicated that they had nothing against rewards but that they also did not see their added value. More than half of the participants indicated that they would like to be rewarded, but they explained that receiving feedback on their progress or on the number of activities they had done was already enough of a reward. The participants expected that such rewards would strengthen the feeling of having a competition with themselves. For some participants, this was perceived as motivating, whereas others were afraid to disappoint themselves.
Competition with others was disliked by most participants. A total of 10% (2/20) indicated that they would like competition with their friends as this was a fairer comparison and, therefore, more achievable. Sharing exercise outcomes on social media was disliked as this was viewed as a call for attention. A participant indicated that sharing running routes (within the app) would be a good addition to the app. Some participants also suggested other functionalities by themselves. These were to have a selection of running routes, information on why it is important to engage in PA, emails regarding progress, and a game element to motivate users to visit certain exercise locations.
Although the participants in general explained that the app contained the most important strategies, most participants reported that they only used the app a few times. Mostly, they stopped using the app because they encountered technical issues. There were also participants who explained “that’s not the apps fault” (participant 6, female, aged 35 years) that they stopped using the app as they encountered barriers such as lack of time or bad weather. For instance, various participants explained that, because of the short winter days, it was already dark when they got home from work. This, together with colder and wetter weather circumstances, made it unpleasant to go outside for a run or walk:
Only I have to say that I used it less than I had hoped, because it was often bad weather, and very dark. And then you’re less inclined to go outside. Normally earlier in the summer I would do something more quickly anyway.
When asking the participants how an app could help them overcome these barriers, they found it difficult to give an answer. After debating the issue, some suggested receiving encouragement to go for walks when there was still daylight; for instance, during lunch. Another participant suggested including a module that enabled them to perform the strength exercises simply at home in case they were bound to stay there to watch their children or when the weather was bad.
Although some barriers to the uptake of the app lay outside the app, the biggest issue with using it were the technical problems the participants experienced. When encountering a technical problem, this evoked
Many participants did try out the location-based strength exercises. Of all the strategies that were implemented in the app, the participants were generally the most enthusiastic about the strength exercise prompts:
Well, the best part was that when I just walked through the park, that I always got one, one.... What’s that called? That I got a sound [strength exercise prompt], and then I had to do something. I really liked that about it.
Owing to the novelty of this functionality, the participants became curious and motivated to try it out. Some participants were motivated to perform these strength exercises to increase their strength and fitness. According to these participants, complementing their running activity with strength exercises resulted in a more complete workout in which they trained not only their endurance but also their strength. Furthermore, receiving a strength exercise prompt was perceived as receiving a surprise, some kind of
But I liked the mix, and that it’s bound to certain parts of the park, so to speak. That also, when you do an exercise, it can tell you where to do it. So, yeah, I actually really liked that.
The app could give better suggestions for the strength exercises as it “knew” where the participant was. Some participants explained that it helped them view the park amenities (eg, benches) in a different light, as something they could use during their workout:
Well, what I found very interesting was the part of, uhm, location. That it would indicate those strength exercises at the right places, so to speak.
To
Well, if it’s not commercial, then maybe I would. It depends. Where’s it all going, huh? What does that app need to analyze it all?
In addition, some participants also considered that the privacy-sensitive data that are collected by the app must be of added value to them. In other words, they were willing to share data if they obtained something they wanted from them in return. For instance, one of the participants explained what he liked about the strength exercise prompts:
So, I guess that it shows you: you’re here now, so it shows you the [strength exercise] possibilities. Somewhere it’s a bit freaky, that he can follow me anywhere, but assuming that privacy is well guaranteed, it delivers a lot.
Although the app removed some barriers, other barriers remained. Some female participants mentioned that they would not perform the strength exercises as they did not like to do them in such public spaces were other people could “look at you like that” (participant 1, female, aged 27 years and participant 3, female, aged 23 years). Other participants expressed their concern about performing strength exercises without receiving feedback on their posture from a professional. Offered solutions to this problem were to organize a group training, only implement easy exercises, or motivate the participants to practice the exercises at home in front of a mirror. Another improvement that was suggested was offering the strength exercises in more locations so the participants did not have to travel to the location before they could start their run or walk. The participants would also like to have more types of strength exercises and combinations of strength exercises that could be tailored to their capabilities. Some participants indicated that they would like to see where the strength exercises were located so they were motivated to run there, “explore” the neighborhood, and discover new exercise locations.
Reminder messages were perceived as important initiators of behavior by almost all participants. The participants said that the reminders “trigger” them (participant 20, female, aged 23 years) and that it “lowers the threshold” to engage in PA (participant 5, male, aged 32 years) and, therefore, increases the chances of engaging in PA. Thus, to some extent, the reminders function as a coach (ie, something that pulls the participants over when they have difficulty in performing the behavior themselves). Unfortunately, not all the participants received the reminder messages. However, these participants also explained that they thought they would have used the app more if they had received them. As they did not receive the reminder messages, they often forgot the app and their intention to exercise more.
Some participants explained that a reminder message in itself was not enough to motivate PA. Rather, the motivation must come from within, and the reminder message can help overcome barriers or remind them of their plans:
Yes, then it’s nice that one of those things reminds you of it, but then you think “yes, but I just don’t have time for it right now”.... So I’m more like, “yes, I’d like to try,” but it doesn’t really fit in my schedule. So, then it should be a bigger mind set of “okay, I think this is very important,” that you really make that app a part of your daily rhythm, yes.
The participants highlighted the need for well-timed and highly personalized reminder messages. Although receiving a reminder message at a good time could serve as a trigger, receiving one at a bad time could lead to irritation or feeling like they were failing (participant 20, female, aged 23 years and participant 17, male, aged 25 years). The timing of the reminder message also seemed to influence the perception of its content. For instance, a participant explained that the reminder message was annoying and “preachy.” Owing to a busy schedule, she did not have the time to go for a walk even though she had the motivation to exercise. Thus, a motivating message was not appropriate. However, she explained that she would design similar reminder messages herself as, if they were well timed, they entertained her. Furthermore, some participants explained that the content of the reminder message did not really matter at all and that receiving a prompt in itself was already sufficient. In line with these comments, several participants had issues with recalling the content of the reminder messages, indicating that the content indeed was not the most important quality of the reminder.
As the timing of the reminder message was perceived as important, the participants enjoyed the idea that the app knew their schedule by reading their agenda:
I think that’s a plus compared to other things. That you can then, that you can link it that way.
However, some participants did express privacy concerns or did not use a digital agenda that could be integrated into the app.
During the interviews, participants in both groups were generally not positive about the timing of the reminder messages. The perceptions of the timing of the JITAI reminder messages were not more positive than those of the randomly timed reminder messages. A possible explanation for these findings is that the participants in the Smart PAUL group received too few reminder messages as they only received them in the last week of the intervention. Furthermore, owing to the short study duration, the reinforcement learning model could only use a prelearned delivery strategy to determine the timing of the messages. Thus, it was not able to adjust the strategy at an individual level.
As the participants were not satisfied with the timing, we discussed what they would have preferred. It seems that there were roughly 2 groups of participants—one group that liked to set their own times and one group that wanted to receive regular reminder messages throughout the day:
...if I already know that I can’t run that day at all, because I must do all kinds of other things, then I think it would only be annoying that I would still get reminders for something.... But I am also someone who then, plans in advance which days she wants to walk, so to speak.
I’m not a planner, so, um, I get a bit itchy when I’m very tightly planned and know what I’m gonna do on what day. Especially when it’s in my spare time. Um, so, I’d rather get [a reminder] every day. That sometimes you think: “oh, yeah, I want to work out.” And sometimes I don’t.
Participants who claimed that they always planned their (physical) activities liked to set times at which they wanted to receive the reminder message. In contrast, participants with a more flexible agenda or who did not like to plan liked to receive regular and well-timed reminder messages throughout the day and decide on the spot whether they wanted to exercise.
The feasibility of the Smart PAUL and Basic PAUL apps was examined by exploring the users’ perceptions of the app, experiences, behavioral engagement and changes in PA behavior, and determinants of PA behavior. The main findings of the study were that the participants appreciated the included persuasive strategies, especially the strength exercise prompts. The strength exercises were motivating because of their novelty and because they offered variety during a run or walk and a more complete workout. Some participants even perceived the strength exercise prompt as a reward. Furthermore, the reminder messages were perceived as important initiators for PA by most participants, but they were not perceived as well timed.
Another finding was that there were little to no differences between the Smart PAUL and Basic PAUL groups regarding perceptions, opinions, and user experience. This is likely the result of the small difference between the 2 versions of the app, which became even smaller as the reminder messages were not sent during the first part of the study. Owing to this short duration, the Smart PAUL app could only apply a prelearned strategy to determine the timing of the reminders and was not able to adjust the timing to each individual participant. Finally, we found no improvement in the PA behavior of the participants, but we did find an increase in the perceived capability to perform strength exercises, and the intrinsic motivation for walking, running, and performing strength exercises did increase during the intervention. Taken together, we conclude that the PAUL apps are not feasible interventions in their closed-beta state but, if they were more stable, they could be effective in promoting PA.
The increased motivation for running, walking, and performing strength exercises and capability to perform strength exercises supports the theoretical assumptions on which the PAUL apps were based [
By examining the feasibility of the PAUL app, we could explore the perceptions of 2 different types of JIT prompts: one to
By including prompts to initiate a behavior during exercise, the PAUL app is among the first to make a combination of strength exercises and aerobic exercises. Thus far, only 2 other apps have been developed and evaluated: MOPET [
The participants appeared to be less receptive to the second type of prompts, the reminder messages. The reminder messages were often sent when the participants did not have the opportunity or capability to exercise even though they were motivated to do so. Moreover, as the motivation message only aimed to motivate the participants and not to increase their capability, it was not
The interviews suggested several options to improve the reminder messages that support the FBM [
The need for a smart and personalized system contrasts with the need for privacy. The participants were very clear on why it is important to have state-of-the-art technology—to provide very personal support that contains the exact types of support they need. This recognizes that every individual is different and has different needs. However, some participants were hesitant to share the data that are needed for this type of support. In line with earlier research [
There are some limitations to this study. A major limitation of this study were the technical issues that overshadowed the results of the feasibility trial. This demonstrates that the beta version of the PAUL app is not stable enough in all devices and Android versions. Nonetheless, the participants could still experience and engage with the apps and their functionalities and, therefore, provide valuable insights into the apps. Second, the participants were mostly highly educated women (13/20, 65%), which limits our understanding of the perceptions of other potential user groups. Finally, as no control group was used, the quantitative analysis should be interpreted with caution [
There are also several strengths to our study. For instance, the dropout rate was relatively low, with 87% (20/23) of the participants completing the study. Furthermore, the app was designed based on theories of change and input from potential end users to increase motivation and capability [
Description of the Playful Active Urban Living apps.
Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications and Online Telehealth
counts per minute
Fogg Behavior Model
just-in-time
just-in-time adaptive intervention
mobile health
moderate to vigorous physical activity
physical activity
Playful Active Urban Living
The authors thank Victor Zuniga Dourado, Paula Castro, and Maria do Socorro Simões for their contributions in the design of the study. They also thank David Tuhumena for assisting with participant recruitment and data collection. Furthermore, they would like to thank Victor Brunst for his support on the data management plan. Next, they thank
All authors have read and approved this manuscript. KS performed the study. KS, MS, DDFE, and NN contributed to the study design and methodology. SW developed the reinforcement learning module. RDDdB developed the Playful Active Urban Living apps. SW cleaned the data. KS performed the analysis and prepared the original draft. RDDdB, SW, MS, and DDFE reviewed and edited the manuscript. MD, BJAK, MS, and DDFE contributed to the funding of this research.
None declared.