Physical Activity - Digital Phenotyping - Activity Tracking
Conditions
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.
Interventions
BEHAVIORALobservation 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
Hasselt University
PXL University College
Study design
Observational model
COHORT
Time perspective
OTHER
Eligibility
Sex/Gender
ALL
Age
65 Years to No maximum
Inclusion criteria
* Participants are 65 years of older * Participants are competent to give informed consent * Participants are able to actively participate in the study * Participants are community-dwelling (living independent at home or in a service apartment) * Without a severe illness * Dutch language proficiency as native speaker
Exclusion criteria
* Current neurological disorder such as Parkinson's disease, multiple sclerosis, cerebrovascular accident, … * Current cardiovascular disorder such as stroke, acute myocardial infarct, coronary artery bypass grafting, percutaneous coronary intervention less than 5 years ago * Current respiratory disorder, such as chronic obstructive pulmonary disease, pneumonia, pulmonary fibrosis, asthma, … * Current severe metabolic disorder, such as diabetes type 1 and 2, severe osteoporosis, … * Current severe cognitive disorders, such as Alzheimer's disease, vascular dementia, Lewy Body dementia, frontotemporal dementia,
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Digital phenotypes of PA | 14 days | Patterns of physical activity behavior |
Contacts
Primary ContactKim Daniels, MS
Outcome results
None listed