An interactive tool to personalise 24-hour activity, sitting and sleep prescription for optimal health outcomes.
cross_sectional · Level IV
Where this comes from
- Record sourced from PubMed, PMID 41845049.
- Also identified by DOI 10.1038/s41746-026-02542-4.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
Personalised interventions which optimise the balance of physical activity (PA), sleep and sedentary behaviour (i.e., time use) in the 24-h day may be more effective than one-size-fits-all approaches. We present an interactive app to personalise 24-h time use based on individuals' health and sociodemographic characteristics. Analyses used cross-sectional data from 53,057 UK Biobank participants. Average daily time use was measured using 7-day accelerometry data and expressed as a 24-h composition using isometric log-ratio transformation. Five cognitive composites were derived from web-based tests. Regularized linear regression examined the relationship between 24-h time-use composition and cognition, with sociodemographic and health characteristics as additional predictors. Model estimates were used to estimate optimized cognition based on the interaction of 24-h time-use composition and personal characteristics. Our 'ideal day' app delivers personalised 24-h time-use recommendations tailored to individual characteristics. We demonstrate that personalisation of time-use interventions can be achieved in real time using open-source software.