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

NCT06653049 | Completed

• 2 other identifiers: LS-21-34-Tragos-Lawlor12/RC/2289_P2
• Study Type: interventional
• Target: 69
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Exercise; Mobile Applications

Keywords

Reinforcement Learning; mHealth; Exercise therapy

Outcome Measures

Primary Outcomes (1)

• User Satisfaction with Exercise Sessions (PACES-8 Scale)

  User satisfaction was measured using the Physical Activity Enjoyment Scale (PACES-8), an abbreviated 8-item version. Participants rated their satisfaction with each exercise session on a scale from 1 (low satisfaction) to 5 (high satisfaction). Higher scores indicate greater enjoyment and overall satisfaction with the exercise experience. The measure was administered after each session.

  At the end of each exercise session during the 12-week study.

Secondary Outcomes (1)

• Exercise Intensity (Borg Rating of Perceived Exertion Scale)

  At the end of each exercise session during the 12-week study.

Study Arms (2)

Reinforcement Learning (RL) Personalised Exercise Prescription

EXPERIMENTAL

Participants in this arm received personalised exercise sessions via the i80 BPM app, which utilised a reinforcement learning (RL) model. The RL algorithm adapted exercise parameters (e.g., intensity, type of exercise, duration) based on individual preferences, performance, and feedback to optimise user satisfaction and engagement. Sessions were tailored specifically to each participant over the 12-week trial period.

Device: Reinforcement Learning (RL) Personalised Exercise Prescription via i80 BPM App

Generic Non-Personalised Exercise Prescription

ACTIVE COMPARATOR

Participants in this arm received generic, non-personalised exercise sessions using the same i80 BPM app. These sessions did not adapt to user preferences or feedback and consisted of pre-designed exercises that were uniform across all participants. This arm served as a control to compare against the RL-personalised intervention.

Device: Generic Non-Personalised Exercise Prescription via i80 BPM App

Interventions

Reinforcement Learning (RL) Personalised Exercise Prescription via i80 BPM App DEVICE

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.

Reinforcement Learning (RL) Personalised Exercise Prescription

Generic Non-Personalised Exercise Prescription via i80 BPM App DEVICE

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.

Generic Non-Personalised Exercise Prescription

Eligibility Criteria

• Age: 18 Years - 65 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Healthy, recreationally active adults aged 18 to 65 years. Able to engage in aerobic activity for a total of 80 minutes at moderate intensity, no more than two times per week.
• Able to provide informed consent. Ability to read and understand English to engage with the study materials and complete the app-based exercises.
• Access to a compatible smartphone to download and use the i80 BPM app.

You may not qualify if:

• Individuals with any physical disability or motor impairment that prevents participation in exercise.
• Individuals with severe cognitive impairment. Individuals unable to read or write in English. Known contraindications to exercise, as identified by the 'Exercise Preparticipation Health Screening Questionnaire for Exercise Professionals'.
• Participation in another clinical trial or study that could interfere with the trial results.
• Engaging in high-level or professional athletic training or more than three days of vigorous physical activity per week.

Sponsors & Collaborators

• University College Dublin: lead

Study Sites (1)

University College Dublin

Dublin, Co. Dublin, D04C7X2, Ireland

Location

MeSH Terms

Conditions

Motor Activity

Condition Hierarchy (Ancestors)

Behavior

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, OUTCOMES ASSESSOR
• Masking Details: In this trial, participants were blinded to their condition (whether they were in the RL intervention or control group) throughout the study. The outcomes assessor, who analysed the data, was also blinded to the participants' condition to reduce potential bias in interpreting the results. Neither the care providers nor the investigators were masked, as they were responsible for managing the intervention and monitoring the trial's progress, ensuring the smooth operation of the app and data collection. No other parties were masked in this trial.
• Purpose: TREATMENT
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor

Study Record Dates

• First Submitted: October 15, 2024
• First Posted: October 22, 2024
• Study Start: September 1, 2022
• Primary Completion: November 16, 2022
• Study Completion: November 30, 2022
• Last Updated: October 22, 2024

Record last verified: 2024-10

Data Sharing

• IPD Sharing: Will not share

Locations

IE (1)