Ignite Creation Date:
2024-10-26 @ 3:43 PM
Last Modification Date:
2024-10-26 @ 3:43 PM
Study NCT ID:
NCT06653049
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-15
Brief Title:
An Evaluation of the Effect of App-Based Exercise Prescription Using RL on Satisfaction and Exercise Intensity
Sponsor:
None
Organization:
None
Study Overview
Official Title:
An Evaluation of the Effect of App-Based Exercise Prescription Using RL on Satisfaction and Exercise Intensity Randomized Crossover Trial
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PERFORM-RL
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
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 users 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 users fitness level goals and real-time feedback The models reward function aimed to maximise user satisfaction and session effectiveness by adapting the exercise difficulty and content in response to the users 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
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 users 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 users fitness level goals and real-time feedback The models reward function aimed to maximise user satisfaction and session effectiveness by adapting the exercise difficulty and content in response to the users 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None