Can Methods From Computational Psychology be Used to Phenotype Individuals Most Likely to be Non-adherent to Fitness Goals?
- Conditions
- Lifestyle, Sedentary
- Registration Number
- NCT04783298
- Lead Sponsor
- Dublin City University
- Brief Summary
This is a longitudinal study combining objective sensor data, with decision-making games and contextual personality traits to identify patterns in exercise decay. The data generated will be used to build computational models to predict digital personas, and help identify those individuals most likely to abandon exercise goals.
- Detailed Description
Interested individuals to be recruited on social media and invited to download the study app. The plain language statement and informed consent are embedded in the app. Once e-consent is obtained, individuals will share their Fitbit data and complete the following questionnaires; Type D Personality, Goal Setting, and Self-Efficacy questionnaire and a decision-making game based on the IGT. After a 6 month time period, they will be requested to retake the questionnaires and decision-making game.
Recruitment & Eligibility
- Status
- UNKNOWN
- Sex
- All
- Target Recruitment
- 200
- Healthy individuals who have a Fitbit
- Individuals under the age of 18 years of age.
Study & Design
- Study Type
- OBSERVATIONAL
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method Change in physical activity measured by an increase in weekly steps measured by Fitbit. Week 1 and 6 months Fitbit is a physical activity tracker worn on the wrist and objectively measures steps taken.
- Secondary Outcome Measures
Name Time Method
Trial Locations
- Locations (1)
Dublin City University
🇮🇪Dublin, Ireland