A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial (Preprint)

BACKGROUND Consistent physical inactivity among adults and adolescents poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a promising avenue for scalable and personalized physical activity promotion. However, developing and evaluating such adaptive interventions at scale, while integrating robust behavioral science, presents methodological hurdles. OBJECTIVE The PEARL study aimed to assess the feasibility and effectiveness of a reinforcement learning (RL) algorithm, informed by health behavior change theory (COM-B), to personalize the content and timing of physical activity nudges via the Fitbit app compared to fixed and random nudging strategies, and to a control group with no nudges. METHODS We conducted a large-scale, four-arm randomized controlled trial (RCT) enrolling 13,463 Fitbit users. Participants were randomized to: (1) Control (no nudges); (2) Random (random content/timing); (3) Fixed (logic based on baseline COM-B survey); and (4) RL (adaptive algorithm). The primary outcome was the change in average daily step count from baseline to 2 months. Secondary outcomes included user engagement and survey responses regarding capability, opportunity, and motivation. RESULTS 7,711 participants were included in the primary analysis (mean age 42.1 years; 86.3% female). At 1 month, the RL group showed a significant increase in daily steps compared to Control (+296 steps, P