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

NCT ID: NCT06653049

Last Updated: 2024-10-22

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 69 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-09-01
• Study Completion Date: 2022-11-30

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.

Detailed Description

Study Overview The PERFORM-RL study was a 12-week randomised crossover trial conducted to evaluate the impact of app-based personalised exercise prescription on user satisfaction and exercise intensity, using reinforcement learning (RL) as the key mechanism for customisation. This study sought to determine whether RL-generated exercise sessions could improve user satisfaction and increase exercise intensity compared to non-personalised, generic sessions. The findings are expected to contribute to the growing field of digital health interventions, particularly in the area of scalable, app-based exercise programmes designed to enhance user engagement and long-term adherence.

Study Objectives The primary objective of the study was to compare user satisfaction between RL-generated exercise sessions and generic sessions. The hypothesis was that RL-driven personalisation would result in higher user satisfaction, as measured by the Physical Activity Enjoyment Scale (PACES-8). The secondary objective was to evaluate the effect of RL on exercise intensity, as measured by the Borg Rating of Perceived Exertion (RPE) scale. It was hypothesised that participants would exercise at a higher intensity in the RL condition due to the tailored nature of the sessions, which aligned with their preferences and fitness levels.

Study Design

This was a randomised, assessor-blinded crossover trial. Each participant completed two different conditions during the 12-week study:

Intervention Condition: Exercise sessions were personalised using a reinforcement learning model that adapted session difficulty and content based on participant preferences, past performance, and feedback.

Control Condition: Participants completed generic, non-personalised exercise sessions. These sessions were pre-designed and did not adapt based on the user's feedback or preferences.

Participants alternated weekly between the intervention and control conditions, meaning that each week they experienced a different approach to exercise prescription. This design ensured that each participant acted as their own control, reducing variability and increasing the robustness of the findings. All exercise sessions were delivered through the i80 BPM app, developed by Samsung, which also facilitated data collection.

Participants Participants were recruited from Dublin, Ireland, and its surrounding areas via word of mouth and social media. A total of 62 participants (27 males, 42 females; mean age 42 years) completed at least one exercise session, with 559 sessions completed overall during the 12-week trial period. Participants were healthy, recreationally active adults aged 18 to 65 years. They were screened for eligibility using the "Exercise Preparticipation Health Screening Questionnaire for Exercise Professionals." Exclusion criteria included physical disability, severe cognitive impairment, or an inability to read and write in English.

Interventions Reinforcement Learning (RL) Intervention

The RL model employed by the i80 BPM app personalised exercise sessions based on user preferences, perceived exertion, and feedback from previous sessions. The RL framework used a decision-making agent, which selected exercises from a pre-existing database based on the user's fitness level, goals, and real-time feedback. The model's reward function aimed to maximise user satisfaction and session effectiveness by adapting the exercise difficulty and content in response to the user's evolving needs.

For example, if a participant reported high satisfaction and moderate intensity in a previous session, the RL model would suggest similar or slightly more challenging exercises for the next session. The RL system continuously learned from user interactions, adjusting the exercise prescription to maintain optimal engagement and effectiveness.

Control Condition

In the control condition, participants received generic exercise sessions that were not personalised or adapted based on user feedback. These sessions included a fixed selection of exercises and did not evolve over time, providing a baseline against which the personalised RL intervention could be compared.

Outcome Measures Primary Outcome: User Satisfaction

User satisfaction was assessed after each session using an abbreviated 8-item version of the Physical Activity Enjoyment Scale (PACES-8). This validated scale measures the extent to which participants enjoyed their exercise sessions. Scores range from 1 (low satisfaction) to 5 (high satisfaction), with higher scores indicating greater enjoyment and overall satisfaction with the exercise experience.

Secondary Outcome: Exercise Intensity

Exercise intensity was measured using the Borg Rating of Perceived Exertion (RPE) scale, which ranges from 1 (very easy) to 10 (maximum exertion). Participants were prompted to rate their perceived exertion after each session via the app, ensuring real-time capture of their experience. Heart rate data, collected using a Samsung Galaxy Fit 2 smartwatch, served as an additional measure of intensity, validating the subjective RPE scores.

Additional Outcome: Heart Rate

Heart rate was continuously monitored during each session using the Samsung Galaxy Fit 2 smartwatch. The heart rate data was automatically relayed to the i80 BPM app and used to track physiological responses to the exercise sessions.

Data Collection and Management Data were collected through the i80 BPM app, which recorded user satisfaction, exercise intensity, heart rate, and the duration of each exercise session. To ensure participant confidentiality, all data were anonymised before analysis. Participants were assigned a unique identification code, and all sensitive documents were stored securely, with only authorised personnel granted access.

Sample Size and Power Calculations Based on a previous feasibility study, the sample size was estimated to be a minimum of 40 participants to detect a mean difference of 8 points on the PACES-8 scale with 80% power. A total of 69 participants were recruited to account for potential dropouts, and 62 participants completed at least one exercise session. The final sample size provided sufficient power to detect differences between the RL and control conditions.

Statistical Analysis Generalised Estimating Equations (GEE) were used to analyse the primary and secondary outcomes. GEE models were applied to account for the correlation of repeated measures within subjects. The dependent variables in the models were user satisfaction (PACES-8 scores) and perceived exertion (Borg scale scores), while the independent variables were condition (RL vs control) and trial week. Covariates included age, gender, and baseline physical activity levels, with participant ID included as a subject effect.

Conditions

Exercise; Mobile Applications

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: CROSSOVER
• Primary Study Purpose: TREATMENT
• Blinding Strategy: DOUBLE

Study Groups

Reinforcement Learning (RL) Personalised Exercise Prescription

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.

Group Type: EXPERIMENTAL

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

Intervention Type: 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.

Generic Non-Personalised Exercise Prescription

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.

Group Type: ACTIVE_COMPARATOR

Generic Non-Personalised Exercise Prescription via i80 BPM App

Intervention Type: 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.

Interventions

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

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.

Intervention Type: DEVICE

Generic Non-Personalised Exercise Prescription via i80 BPM App

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.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

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.

Exclusion Criteria

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.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University College Dublin

OTHER

Sponsor Role: lead

Responsible Party

Cailbhe Doherty

Assistant Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

University College Dublin

Dublin, Co. Dublin, Ireland

Countries

Ireland

Other Identifiers

12/RC/2289_P2

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

LS-21-34-Tragos-Lawlor

Identifier Type: -

Identifier Source: org_study_id