Ignite Creation Date:
2024-05-06 @ 7:41 PM
Last Modification Date:
2024-10-26 @ 3:11 PM
Study NCT ID:
NCT06094374
Status:
NOT_YET_RECRUITING
Last Update Posted:
2023-10-23
First Post:
2023-10-17
Brief Title:
Unveiling the Digital Phenotype of PA Behavior
Sponsor:
PXL University College
Organization:
PXL University College
Study Overview
Official Title:
Unveiling the Digital Phenotype A Protocol for a Prospective Study on Physical Activity Behavior in Community-dwelling Older Adults
Status:
NOT_YET_RECRUITING
Status Verified Date:
2023-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
MIADP
Brief 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
Detailed Description:
Observational data will be collected in healthy older adults aged 65 or above combining both cross-sectional and longitudinal data collection methods to analyze patterns of PA behavior and identify prognostic factors affecting PA outcomes in order to identify digital phenotypes related to PA
The measurements are based on the Behavioral Change Wheel and include self-reporting assessments clinical assessments for cross-sectional data collection and ecological momentary assessment EMA as well as time series data collection for longitudinal data The statistical analysis will involve multivariate regression analysis and time series analysis with a Bonferroni correction to account for multiple comparisons A machine learning algorithm is used due to the complexity of the data If no suitable model is found a neural network will be used to determine digital phenotypes related to PA behavior in older adults
The measurements are based on the Behavioral Change Wheel and include self-reporting assessments clinical assessments for cross-sectional data collection and ecological momentary assessment EMA as well as time series data collection for longitudinal data The statistical analysis will involve multivariate regression analysis and time series analysis with a Bonferroni correction to account for multiple comparisons A machine learning algorithm is used due to the complexity of the data If no suitable model is found a neural network will be used to determine digital phenotypes related to PA behavior in older adults
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None