Background: Although the health benefits of physical activity are well established, it remains challenging for people to adopt a more active lifestyle. Mobile health (mHealth) interventions can be effective tools to promote physical activity and reduce sedentary behavior. Promising results have been obtained by using gamification techniques as behavior change strategies, especially when they were tailored toward an individual’s preferences and goals; yet, it remains unclear how goals could be personalized to effectively promote health behaviors. Objective: In this study, we aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. We hypothesize that interventions suggesting health goals that are tailored based on end users’ (self-reported) current and desired capabilities will be more engaging than interventions with generic goals. Methods: The study was designed as a 2-arm randomized intervention trial. Participants were recruited among staff members of 7 governmental organizations. They participated in an 8-week digital health promotion campaign that was especially designed to promote walks, bike rides, and sports sessions. Using an mHealth app, participants could track their performance on two social leaderboards: a leaderboard displaying the individual scores of participants and a leaderboard displaying the average scores per organizational department. The mHealth app also provided a news feed that showed when other participants had scored points. Points could be collected by performing any of the 6 assigned tasks (eg, walk for at least 2000 m). The level of complexity of 3 of these 6 tasks was updated every 2 weeks by changing either the suggested task intensity or the suggested frequency of the task. The 2 intervention arms—with participants randomly assigned—consisted of a personalized treatment that tailored the complexity parameters based on participants’ self-reported capabilities and goals and a control treatment where the complexity parameters were set generically based on national guidelines. Measures were collected from the mHealth app as well as from intake and posttest surveys and analyzed using hierarchical linear models. Results: The results indicated that engagement with the program inevitably dropped over time. However, engagement was higher for participants who had set themselves a goal in the intake survey. The impact of personalization was especially observed for frequency parameters because the personalization of sports session frequency did foster higher engagement levels, especially when participants set a goal to improve their capabilities. In addition, the personalization of suggested ride duration had a positive effect on self-perceived biking performance. Conclusions: Personalization seems particularly promising for promoting the frequency of physical activity (eg, promoting the number of suggested sports sessions per week), as opposed to the intensity of the physical activity (eg, distance or duration). Replications and variations of our study setup are critical for consolidating and explaining (or refuting) these effects. Trial Registration: ClinicalTrials.gov NCT05264155; https://clinicaltrials.gov/ct2/show/NCT05264155

The Experience Sampling Method (ESM) is gaining ground for collecting self-reported data from human participants during daily routines. An important methodological challenge is to sustain sufficient response rates, especially when studies last longer than a few days. An obvious strategy is to deliver the experiential questions on a device that study participants can access easily at different times and contexts (e.g., a smartwatch). However, responses may still be hampered if the prompts are delivered at an inconvenient moment. Advances in context sensing create new opportunities for improving the timing of ESM prompts. Specifically, we explore how physiological sensing on commodity-level smartwatches can be utilized in triggering ESM prompts. We have created Experiencer, a novel ESM smartwatch platform that allows studying different prompting strategies. We ran a controlled experiment ($N=71$) on Experiencer to study the strengths and weaknesses of two sampling regimes. One group ($N=34$) received incoming notifications while resting (e.g., sedentary), and another group ($N=37$) received similar notifications while being active (e.g., running). We hypothesized that response rates would be higher when experiential questions are delivered during lower levels of physical activity. Contrary to our hypothesis, the response rates were found significantly higher in the active group, which demonstrates the relevance of studying dynamic forms of experience sampling that leverage better context-sensitive sampling regimes. Future research will seek to identify more refined strategies for context-sensitive ESM using smartwatches and further develop mechanisms that will enable researchers to easily adapt their prompting strategy to different contextual factors.

Social gamification systems have shown potential for promoting healthy lifestyles, but applying them to occupational settings faces unique design challenges. While occupational settings offer natural communities for social interaction, fairness issues due to heterogeneous personal goals and privacy concerns increase the difficulty of designing engaging games. We explored a two-level game-design, where the first level related to achieving personal goals and the second level was a privacy-protected social competition to maximize goal compliance among colleagues. The solution was strengthened by employing occupational physicians who personalized users’ goals and coached them remotely. The design was evaluated in a 5-month study with 53 employees from a Dutch university. Results suggested that the application helped half of the participants to improve their lifestyles, and most appreciated the role of the physician in goal-setting. However, long-term user engagement was undermined by the scalability-motivated design choice of one-way communication between employees and their physician. Implications for social gamification design in occupational health are discussed.

Although a vast number of mobile health interventions has been discussed in the scientific literature, the scientific evidence for engineering gamified health interventions is too meager. Depending on the context, this leads to immature investments on one side of the spectrum and delayed implementations on the other side. GameBus is a web platform for increasing the rigor of gamified health experiments in living lab settings. This article clarifies the scientific design rationale of this platform and provides an eight-year evaluation of its performance. We demonstrate that a large variety of studies has been performed, leading to a promising data set for analyzing the impact of gamification techniques in the context of health promotion. While various articles on such studies have been published, along with data sets, this article shares novel information to enable other scholars to scale up such efforts. We also highlight critical gaps to guide future work on the platform.

Background: Contemporary mobile health (mHealth) interventions use various behavior change techniques to promote healthier lifestyles. Social comparison is one of the techniques that is consensually agreed to be effective in engaging the general population in mHealth interventions. However, it is unclear how this strategy can be best used to engage preadolescents. Nevertheless, this strategy has great potential for this target audience, as they are particularly developing their social skills. Objective: This study aims to evaluate how social comparison drives preadolescents’ engagement with an mHealth app. Methods: We designed a 12-week crossover experiment in which we studied 3 approaches to implementing behavior change via social comparison. This study was hosted in a school environment to leverage naturally existing social structures among preadolescents. During the experiment, students and teachers used an mHealth tool that awarded points for performing healthy activities. Participants could read their aggregated scores on a leaderboard and compare their performance with others. In particular, these leaderboards were tweaked to implement 3 approaches of the social comparison technique. The first approach focused on intragroup comparison (ie, students and teachers competing against each other to obtain the most points), whereas the other two approaches focused on intergroup comparison (ie, classes of students and their mentoring teachers collaborating to compete against other classes). Additionally, in the third approach, the performance of teachers was highlighted to further increase students’ engagement through teachers’ natural exemplary function. To obtain our results, we used linear modeling techniques to analyze the dropout rates and engagement levels for the different approaches. In such analyses, we also considered individual participant traits. Results: Our sample included 313 participants—290 students (92.7%) and 23 teachers (7.3%). It was found that student engagement levels dropped over time and declined during holidays. However, students seemed to monitor the intergroup competitions more closely than the intragroup competitions, as they, on average, checked the mHealth app more often when they were engaged in team-based comparisons. Students, on average, performed the most unique activities when they were engaged in the second intergroup setting, perhaps because their teachers were most active in this setting. Moreover, teachers seemed to play an important role in engaging their students, as their relationship with their students influenced the engagement of the students. Conclusions: When using social comparison to engage preadolescents with an mHealth tool, an intergroup setting, rather than an intragroup competition, motivated them to engage with the app but did not necessarily motivate them to perform more activities. It seems that the number of unique activities that preadolescents perform depends on the activeness of a role model. Moreover, this effect is amplified by preadolescents’ perceptions of closeness to that role model.

Background: Financial rewards can be employed in mHealth apps to effectively promote health behaviors. However, the optimal reinforcement schedule—with a high impact, but relatively low costs—remains unclear. Methods: We evaluated the impact of different reinforcement schedules on engagement levels with a mHealth app in a six-week, three-arm randomized intervention trial, while taking into account personality differences. Participants (i.e., university staff and students, N = 61) were awarded virtual points for performing health-related activities. Their performance was displayed via a dashboard, leaderboard, and newsfeed. Additionally, participants could win financial rewards. These rewards were distributed using a fixed schedule in the first study arm, and a variable schedule in the other arms. Furthermore, payouts were immediate in the first two arms, whereas payouts in the third arm were delayed. Results: All three reinforcement schedules had a similar impact on user engagement, although the variable schedule with immediate payouts was reported to have the lowest cost per participant. Additionally, the impact of financial rewards was affected by personal characteristics. Especially, individuals that were triggered by the rewards had a greater ability to defer gratification. Conclusion: When employing financial rewards in mHealth apps, variable reinforcement schedules with immediate payouts are preferred from the perspective of cost and impact.