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

This intervention involved the use of a smartphone app, i80 BPM, which delivered personalised exercise prescriptions using a reinforcement learning (RL) model. The RL algorithm tailored the exercise sessions by adapting variables such as intensity, duration, and exercise type based on individual user preferences, real-time feedback, and performance data. This dynamic personalisation was designed to enhance user satisfaction and engagement over the 12-week study period. Participants completed three exercise sessions per week.

This intervention used the same i80 BPM smartphone app to deliver generic, pre-designed exercise sessions that did not adapt based on user preferences or feedback. The exercise sessions were standardised for all participants, with no customisation. The control arm served as a comparator to evaluate the impact of personalised, RL-driven exercise prescriptions. Participants completed three exercise sessions per week.