A Mobile App to Increase Physical Activity in Students
Condition: Mobile Health; Physical Activity; Exercise; Mood; Machine Learning
Study type: Interventional

Summary: 
      Background: Insufficient physical activity is one of the leading risk factors of death
      worldwide. Behavioral treatments delivered via smartphone apps, hold great promise for
      helping people engage in healthy behaviors including becoming more physically active.
      However, similar to 'face-to-face' treatments, effects typically do not seem to be sustained
      over longer periods of time.

      Methods: the investigators developed a smartphone application that uses different types of
      motivational and feedback text-messaging to motivate individuals to increase physical
      activity. Here, participants are randomized to either receive messages by a uniform random
      distribution (n=50), or chosen by a reinforcement learning algorithm (n=50), which learns
      from daily participant data to personalize the frequency and type of motivation of messages.

      Objectives: In the current study, the investigators examine this application in undergraduate
      and graduate students at the University of California, Berkeley. The investigators compare
      whether participants in the uniform random or adaptive group have higher increases in steps
      during the study. The investigators also examine the effect of the different types of
      messages on step counts. Further the investigators assess the influence of patient
      characteristics, such as socio-demographic, psychological questionnaire scores and baseline
      physical activity on the effect of the adaptive arm and effectiveness of the messages.
      Finally, the investigators assess participant qualitative feedback on the text-messaging
      program, through feedback provided via questionnaires, text-message and phone interviews.
    

Detailed description: 
      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.
    

Intervention name(s): Uniform random message delivery; Reinforcement learning message delivery
Intervention type(s): Behavioral; Behavioral
Intervention description(s): The uniform random intervention group receives feedback and motivational messages chosen from the messaging banks with equal probabilities.; The adaptive intervention group receives messages chosen from the messaging banks by a reinforcement learning algorithm.
Allocation: Randomized
Intervention model: Parallel Assignment
Intervention model description: Participants were randomized to a uniform random group, or a reinforcement learning group. Within these groups, participants received the same types of text-messages, but the delivery schedule differed.
Masking: Single (Participant)
Primary purpose: Prevention