Evaluation of the Impact of Adaptive Goal Setting on Engagement Levels of Government Staff With a Gamified mHealth Tool

Lifestyle, Lifestyle, Healthy, Lifestyle Risk Reduction, Lifestyle, Sedentary

Promoting Healthier Lifestyles, mHealth

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, the investigators aim to evaluate the impact of personalized goal setting in the context of gamified mHealth interventions. The investigators 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 Noorderkempen governmental organization. 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. Note: Eindhoven University of Technology is not an official GCP sponsor. Hence, this study is not a medical clinical trial.

Belgium