An Evaluation of the Effect of App-Based Exercise Prescription Using RL on Satisfaction and Exercise Intensity

The PERFORM-RL study (Personalised Exercise Prescription for Remote Fitness Using Reinforcement Learning) was a 12-week randomised crossover trial designed to evaluate the effectiveness of an app-based exercise prescription system powered by reinforcement learning (RL). The study aimed to investigate whether exercise sessions tailored by RL would lead to greater user satisfaction and higher exercise intensity compared to generic, non-personalised exercise sessions. The trial enrolled 62 participants (27 males, 42 females; mean age 42 years) who were randomly assigned to alternate between two conditions: an RL-driven intervention, which personalised exercise sessions based on user preferences and feedback, and a control condition with non-tailored, generic exercise sessions. Participants were instructed to complete three exercise sessions per week using the i80 BPM app, which offered a variety of video-guided exercises. The RL model customised these sessions based on user feedback, including satisfaction and perceived intensity, with the goal of optimising future sessions. The primary outcome was user satisfaction, measured via the Physical Activity Enjoyment Scale (PACES-8) after each session. Secondary outcomes included exercise intensity, as assessed by the Borg Rating of Perceived Exertion (RPE) scale, and heart rate data collected through a Samsung Galaxy Fit 2 smartwatch. The trial was conducted in Dublin, Ireland, and approved by the UCD Human Research Ethics Committee (LS-21-34-Tragos-Lawlor). Participants provided informed consent and were blinded to their group allocation. The trial was not registered prospectively, but steps are being taken for retrospective registration.