A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial
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| Format: | Preprint |
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2025
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| author | Lee, Amy Armento Hegde, Narayan Deliu, Nina Rosenzweig, Emily Suggala, Arun Lakshminarasimhan, Sriram He, Qian Hernandez, John Seneviratne, Martin Singh, Rahul Kalkar, Pradnesh Shanmugam, Karthikeyan Raghuveer, Aravindan Singh, Abhimanyu Nguyen, My Taylor, James Alla, Jatin Villar, Sofia S. Emir-Farinas, Hulya |
| author_facet | Lee, Amy Armento Hegde, Narayan Deliu, Nina Rosenzweig, Emily Suggala, Arun Lakshminarasimhan, Sriram He, Qian Hernandez, John Seneviratne, Martin Singh, Rahul Kalkar, Pradnesh Shanmugam, Karthikeyan Raghuveer, Aravindan Singh, Abhimanyu Nguyen, My Taylor, James Alla, Jatin Villar, Sofia S. Emir-Farinas, Hulya |
| contents | Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a promising avenue for scalable, personalized physical activity (PA) promotion. However, developing and evaluating such interventions at scale, while integrating robust behavioral science, presents methodological hurdles. The PEARL study was the first large-scale, four-arm randomized controlled trial to assess a reinforcement learning (RL) algorithm, informed by health behavior change theory, to personalize the content and timing of PA nudges via a Fitbit app.
We enrolled and randomized 13,463 Fitbit users into four study arms: control, random, fixed, and RL. The control arm received no nudges. The other three arms received nudges from a bank of 155 nudges based on behavioral science principles. The random arm received nudges selected at random. The fixed arm received nudges based on a pre-set logic from survey responses about PA barriers. The RL group received nudges selected by an adaptive RL algorithm. We included 7,711 participants in primary analyses (mean age 42.1, 86.3% female, baseline steps 5,618.2).
We observed an increase in PA for the RL group compared to all other groups from baseline to 1 and 2 months. The RL group had significantly increased average daily step count at 1 month compared to all other groups: control (+296 steps, p=0.0002), random (+218 steps, p=0.005), and fixed (+238 steps, p=0.002). At 2 months, the RL group sustained a significant increase compared to the control group (+210 steps, p=0.0122). Generalized estimating equation models also revealed a sustained increase in daily steps in the RL group vs. control (+208 steps, p=0.002). These findings demonstrate the potential of a scalable, behaviorally-informed RL approach to personalize digital health interventions for PA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_10060 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial Lee, Amy Armento Hegde, Narayan Deliu, Nina Rosenzweig, Emily Suggala, Arun Lakshminarasimhan, Sriram He, Qian Hernandez, John Seneviratne, Martin Singh, Rahul Kalkar, Pradnesh Shanmugam, Karthikeyan Raghuveer, Aravindan Singh, Abhimanyu Nguyen, My Taylor, James Alla, Jatin Villar, Sofia S. Emir-Farinas, Hulya Machine Learning Consistent physical inactivity poses a major global health challenge. Mobile health (mHealth) interventions, particularly Just-in-Time Adaptive Interventions (JITAIs), offer a promising avenue for scalable, personalized physical activity (PA) promotion. However, developing and evaluating such interventions at scale, while integrating robust behavioral science, presents methodological hurdles. The PEARL study was the first large-scale, four-arm randomized controlled trial to assess a reinforcement learning (RL) algorithm, informed by health behavior change theory, to personalize the content and timing of PA nudges via a Fitbit app. We enrolled and randomized 13,463 Fitbit users into four study arms: control, random, fixed, and RL. The control arm received no nudges. The other three arms received nudges from a bank of 155 nudges based on behavioral science principles. The random arm received nudges selected at random. The fixed arm received nudges based on a pre-set logic from survey responses about PA barriers. The RL group received nudges selected by an adaptive RL algorithm. We included 7,711 participants in primary analyses (mean age 42.1, 86.3% female, baseline steps 5,618.2). We observed an increase in PA for the RL group compared to all other groups from baseline to 1 and 2 months. The RL group had significantly increased average daily step count at 1 month compared to all other groups: control (+296 steps, p=0.0002), random (+218 steps, p=0.005), and fixed (+238 steps, p=0.002). At 2 months, the RL group sustained a significant increase compared to the control group (+210 steps, p=0.0122). Generalized estimating equation models also revealed a sustained increase in daily steps in the RL group vs. control (+208 steps, p=0.002). These findings demonstrate the potential of a scalable, behaviorally-informed RL approach to personalize digital health interventions for PA. |
| title | A Personalized Exercise Assistant using Reinforcement Learning (PEARL): Results from a four-arm Randomized-controlled Trial |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2508.10060 |