Lack of physical activity is an increasingly important health risk. Modern mobile technology, such as smartphones and digital measurement devices, provides new opportunities to tackle physical inactivity. This paper describes the design of a system that aims to encourage young adults to be more physically active. The system monitors the user’s behavior, uses social comparison and provides tailored and personalized feedback based on intelligent reasoning mechanisms. As the name suggests, social processes play an important role in the Active2Gether system. The design choices and functioning of the system are described in detail. Based on the experiences with the development and deployment of the system, a number of lessons learnt are provided and suggestions are proposed for improvements in future developments.

Background Insufficient physical activity (PA) is highly prevalent and associated with adverse health conditions and the risk of noncommunicable diseases. To increase levels of PA, effective interventions to promote PA are needed. Present-day technologies such as smartphones, smartphone apps, and activity trackers offer several possibilities in health promotion. Objective This study aimed to explore the use and short-term effects of an app-based intervention (Active2Gether) to increase the levels of PA in young adults. Methods Young adults aged 18-30 years were recruited (N=104) using diverse recruitment strategies. The participants were allocated to the Active2Gether-Full condition (tailored coaching messages, self-monitoring, and social comparison), Active2Gether-Light condition (self-monitoring and social comparison), and the Fitbit-only control condition (self-monitoring). All participants received a Fitbit One activity tracker, which could be synchronized with the intervention apps, to monitor PA behavior. A 12-week quasi-experimental trial was conducted to explore the intervention effects on weekly moderate-to-vigorous PA (MVPA) and relevant behavioral determinants (ie, self-efficacy, outcome expectations, social norm, intentions, satisfaction, perceived barriers, and long-term goals). The ActiGraph wGT3XBT and GT3X+ were used to assess baseline and postintervention follow-up PA. Results Compared with the Fitbit condition, the Active2Gether-Light condition showed larger effect sizes for minutes of MVPA per day (regression coefficient B=3.1; 95% CI −6.7 to 12.9), and comparatively smaller effect sizes were seen for the Active2Gether-Full condition (B=1.2; 95% CI −8.7 to 11.1). Linear and logistic regression analyses for the intervention effects on the behavioral determinants at postintervention follow-up showed no significant intervention effects of the Active2Gether-Full and Active2Gether-Light conditions. The overall engagement with the Fitbit activity tracker was high (median 88% (74/84) of the days), but lower in the Fitbit condition. Participants in the Active2Gether conditions reported more technical problems than those in the Fitbit condition. Conclusions This study showed no statistically significant differences in MVPA or determinants of MVPA after exposure to the Active2Gether-Full condition compared with the Active2Gether-Light or Fitbit condition. This might partly be explained by the small sample size and the low rates of satisfaction in the participants in the two Active2Gether conditions that might be because of the high rates of technical problems.

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