A Mobile App to Increase Physical Activity in Students

The investigators developed a smartphone application, the DIAMANTE app, that uses machine learning to generate adaptive text messages, learning from daily participant data to personalize the frequency and type of motivation of messages. In the current study, the investigators will compare this application in undergraduate and graduate students at the University of Berkeley, to text-messaging chosen randomly. This study will provide insight into the effectiveness of this smartphone application for increasing physical activity in university students. Further, it will provide preliminary knowledge on the working mechanisms and variables that moderate the effectiveness of the intervention. This study is characterized by a factorial design with a total of 3 factors representing Motivational Messages (M), Feedback Messages (F) and the Time Frame (T) when the message was sent, of 4, 5 and 4 levels each, respectively. One level of M and F corresponded to a control treatment, i.e., no message sent. Each participant received one different combination of M, F and T every day. Both the adaptive and uniform random group will receive the same types of messages: feedback (4 active categories plus no message) and motivation (3 active categories plus no message). However, the message categories, timing and frequency will be optimized by a reinforcement learning algorithm in the adaptive group, and will be delivered with equal probabilities in the uniform random group (following a uniform random distribution). For the reinforcement learner group, the algorithm training data consists of the historical data of all participants (contextual variables), which include which messages were sent previously and within which time periods, and select clinical/demographic data (such as age, day of the week and depression scores) to improve prediction abilities. Subsequently, the message is chosen based on the predicted effectiveness of messages, combined with a sampling method. As such, it frequently picks out from the most rewarding messages and occasionally explores the messages with uncertainty in their reward. The aims of this study are: 1. to assess if participants in the reinforcement learning policy show a greater increase in daily steps after six week follow-up, than participants receiving messages with a uniform random distribution 2. to assess if sociodemographic, baseline physical activity behavior/attitudes and psychological factors influence the effect of the adaptive intervention. 3. to assess which messages are most beneficial in increasing physical activity.