An interactive tool to personalise 24-hour activity, sitting and sleep prescription for optimal health outcomes

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.

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Publication Details

Journal
npj Digital Medicine
Published
2026-03-17
DOI
https://doi.org/10.1038/s41746-026-02542-4
Primary Topic
Physical Activity and Health
Type
article
Field-Weighted Citation Impact
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article

An interactive tool to personalise 24-hour activity, sitting and sleep prescription for optimal health outcomes

Ty Stanford, Tim Olds, Maddison L. Mellow, Dorothea Dumuid et al.
npj Digital Medicine
Physical Activity and Health
article

An interactive tool to personalise 24-hour activity, sitting and sleep prescription for optimal health outcomes

Ty Stanford, Tim Olds, Maddison L. Mellow, Dorothea Dumuid, Aaron Miatke, Ashleigh E. Smith
article en

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.

npj Digital Medicine
The University of Adelaide (AU)
Openalex Percentile: Top 19%
Physical Activity and Health
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