Abstract
BACKGROUND: The incidence of chronic diseases associated with physical inactivity is on the rise, being one of the leading risk-increasing factors for early death rates throughout the world. Often, physical activity interventions fail to deliver sustained adherence over time due to limiting tailoring to individual baseline characteristics, leaving out contextual changes over time. One solution for this issue may be the use of adaptive interventions relying on contextual multiarmed bandits, a type of reinforcement learning algorithm, that can use baseline and contextual individual data to personalize aspects of the intervention, such as developing personalized workout plans. OBJECTIVE: The main objectives of this study are (1) to determine the effectiveness of contextual bandits for automated goal setting in the context of a web-based physical activity intervention, (2) to understand the role of user characteristics impacting ideal workout schedules based on adherence to predetermined goals, and (3) to explore the influence of user autonomy on recommendation effectiveness. METHODS: We developed a protocol for a web-based adaptive intervention trial to investigate the effectiveness of goal recommendation (task difficulty) based on reinforcement learning. The web application (named Apptivate) creates workout routines with 3 different difficulty levels, changing the total workout duration as well as rest times between exercises. Physical activity professionals validated the routine design, ensuring that workouts match recommended guidelines for healthy adults. An initial pilot was conducted, aiming for 800 university students to test the web application for 1 week, to provide initial data to calibrate the algorithm as well as overall feedback for the web application. For the main study, a total of 500 university students will be recruited to participate for 40 days during early 2026. Participants will be divided into 3 groups: user choice (no recommendation), user choice with automated recommendations (contextual bandits), and automated plans without choice. RESULTS: The pilot was conducted in September 2025. Data analysis for the pilot is undergoing, and the main study is planned for early 2026. Our main statistical analysis includes a direct comparison (paired tests) between success rates across intervention arms, as well as by difficulty level and individual characteristics. CONCLUSIONS: Physical activity maintenance is key to achieving long term health goals. Tailored digital interventions are promising strategies for physical activity adherence, but personalization often fails to consider dynamic contextual changes. The proposed protocol for a physical activity intervention using adaptive experimentation can provide robust causal inference on the role of choice versus autonomy when goal difficulty is tailored under an adaptive data-driven approach. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/73766.