Unveiling the Digital Phenotype of PA Behavior
NCT06094374 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 200
Last updated 2023-10-23
Summary
Observational data from healthy adults aged 65+ will be collected through cross-sectional and longitudinal methods to analyze physical activity patterns, identifying digital phenotypes. Measurements include self-reports, clinical assessments, and EMA, with statistical analysis using multivariate regression and time series analysis, and a neural network if needed to find digital phenotypes related to physical activity in older adults.
Conditions
- Physical Activity - Digital Phenotyping - Activity Tracking
Interventions
- BEHAVIORAL
-
observation of physical activity behavior
An observational study will be conducted to gather data on multiple levels aiming to identify diverse digital phenotypes related to PA behavior among community-dwelling older adults. A hybrid approach will be employed, combining both cross-sectional and longitudinal data collection methods. The overall aim is to employ data analysis to identify patterns of PA - behavior (referred to as phenotypes) and to pinpoint prognostic factors that affect PA outcomes. This integrated strategy will be complemented by four distinct measurement approaches, ensuring a comprehensive assessment of the research objectives. These measurement approaches include:self-reporting, clinical, ecological momentary and time series assessment.
Sponsors & Collaborators
-
Hasselt University
collaborator OTHER -
PXL University College
lead OTHER
Principal Investigators
-
Kim Daniels, MS · PXL University College of Applied Sciences and Arts
Eligibility
- Min Age
- 65 Years
- Sex
- ALL
- Healthy Volunteers
- No
Timeline & Regulatory
- Start
- 2024-01-15
- Primary Completion
- 2024-01-30
- Completion
- 2025-01-15
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