TY  - JOUR
AU  - Doherty, Cailbhe
AU  - Lambe, Rory
AU  - O’Grady, Ben
AU  - O’Reilly-Morgan, Diarmuid
AU  - Smyth, Barry
AU  - Lawlor, Aonghus
AU  - Hurley, Neil
AU  - Tragos, Elias
PY  - 2024
DA  - 2024/11/26
TI  - An Evaluation of the Effect of App-Based Exercise Prescription Using Reinforcement Learning on Satisfaction and Exercise Intensity: Randomized Crossover Trial
JO  - JMIR Mhealth Uhealth
SP  - e49443
VL  - 12
KW  - reinforcement learning
KW  - exercise therapy
KW  - personal satisfaction
KW  - satisfaction
KW  - physiotherapy
KW  - physical therapy
KW  - exercise intensity
KW  - mobile apps
KW  - randomized controlled trial
KW  - crossover trial
KW  - apps
KW  - exercise
KW  - physical activity
KW  - mobile phone
AB  - Background: The increasing prevalence of sedentary lifestyles has prompted the development of innovative public health interventions, such as smartphone apps that deliver personalized exercise programs. The widespread availability of mobile technologies (eg, smartphone apps and wearable activity trackers) provides a cost-effective, scalable way to remotely deliver personalized exercise programs to users. Using machine learning (ML), specifically reinforcement learning (RL), may enhance user engagement and effectiveness of these programs by tailoring them to individual preferences and needs. Objective: The primary aim was to investigate the impact of the Samsung-developed i80 BPM app, implementing ML for exercise prescription, on user satisfaction and exercise intensity among the general population. The secondary objective was to assess the effectiveness of ML-generated exercise programs for remote prescription of exercise to members of the public. Methods: Participants were randomized to complete 3 exercise sessions per week for 12 weeks using the i80 BPM mobile app, crossing over weekly between intervention and control conditions. The intervention condition involved individualizing exercise sessions using RL, based on user preferences such as exercise difficulty, selection, and intensity, whereas under the control condition, exercise sessions were not individualized. Exercise intensity (measured by the 10-item Borg scale) and user satisfaction (measured by the 8-item version of the Physical Activity Enjoyment Scale) were recorded after the session. Results: In total, 62 participants (27 male and 42 female participants; mean age 43, SD 13 years) completed 559 exercise sessions over 12 weeks (9 sessions per participant). Generalized estimating equations showed that participants were more likely to exercise at a higher intensity (intervention: mean intensity 5.82, 95% CI 5.59‐6.05 and control: mean intensity 5.19, 95% CI 4.97‐5.41) and report higher satisfaction (RL: mean satisfaction 4, 95% CI 3.9-4.1 and baseline: mean satisfaction 3.73, 95% CI 3.6-3.8) in the RL model condition. Conclusions: The findings suggest that RL can effectively increase both the intensity with which people exercise and their enjoyment of the sessions, highlighting the potential of ML to enhance remote exercise interventions. This study underscores the benefits of personalized exercise prescriptions in increasing adherence and satisfaction, which are crucial for the long-term effectiveness of fitness programs. Further research is warranted to explore the long-term impacts and potential scalability of RL-enhanced exercise apps in diverse populations. This study contributes to the understanding of digital health interventions in exercise science, suggesting that personalized, app-based exercise prescriptions may be more effective than traditional, nonpersonalized methods. The integration of RL into exercise apps could significantly impact public health, particularly in enhancing engagement and reducing the global burden of physical inactivity. Trial Registration: ClinicalTrials.gov NCT06653049; https://clinicaltrials.gov/study/NCT06653049 
SN  - 2291-5222
UR  - https://mhealth.jmir.org/2024/1/e49443
UR  - https://doi.org/10.2196/49443
DO  - 10.2196/49443
ID  - info:doi/10.2196/49443
ER  -