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Automated Personalized Goal Setting for Individual Exercise Behavior: Protocol for a Web-Based Adaptive Intervention

Juan Carlos Caro1, Phuong H Nguyen2, Stefan Lipman2

  • 1Department of Industrial Engineering, Faculty of Engineering, University of Concepción, Concepcion, null, Chile.

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|November 12, 2025
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Summary
This summary is machine-generated.

This study investigates adaptive digital interventions using contextual bandits to personalize physical activity goals. Findings will inform tailored strategies for improved long-term adherence and health outcomes.

Keywords:
adaptive experimentsbehavior changecontextual banditsmachine learningpersonalized exerciseuser autonomy

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Area of Science:

  • Digital health interventions
  • Behavioral science
  • Machine learning applications in health

Background:

  • Rising chronic diseases linked to physical inactivity necessitate effective interventions.
  • Current physical activity interventions often lack sustained adherence due to poor personalization.
  • Adaptive interventions using contextual multi-armed bandits offer a solution for personalized physical activity plans.

Purpose of the Study:

  • Assess the effectiveness of contextual bandits for automated goal setting in web-based physical activity interventions.
  • Understand how user characteristics influence optimal workout schedules and goal adherence.
  • Explore the impact of user autonomy on the effectiveness of personalized recommendations.

Main Methods:

  • A web-based adaptive intervention trial protocol using reinforcement learning for goal recommendation (task difficulty).
  • The Apptivate web application offers personalized workout routines with adjustable difficulty, duration, and rest times, validated by professionals.
  • A pilot study with 800 university students preceded the main study (500 students for 40 days) comparing user choice, automated recommendations, and automated plans.

Main Results:

  • Pilot study conducted September 2025; main study planned for early 2026.
  • Data analysis is ongoing for the pilot; main study will compare success rates across intervention arms, difficulty levels, and user characteristics.
  • Statistical analysis will utilize paired tests for direct comparison between intervention groups.

Conclusions:

  • Sustained physical activity is crucial for long-term health.
  • Tailored digital interventions show promise for improving physical activity adherence.
  • This adaptive experimentation protocol can yield causal insights into choice, autonomy, and tailored goal difficulty in data-driven approaches.