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.
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Data availability
The data that support the findings of this study are available from the UK Biobank but restrictions apply to the availability of these data, which were used under licence for the current study, and so are not publicly available.
Code availability
The underlying code for the data preparation, cleaning, analysis and diagnostics for this study is freely accessible on GitHub and can be accessed via this link (https://github.com/tystan/ukbb-cog-lasso) in addition to the ‘ideal day’ interactive personalisation tool hosted at https://arena2024.shinyapps.io/ideal-day/ with the associated underlying code also freely accessed on GitHub too (https://github.com/tystan/ideal-day).
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Acknowledgements
This research has been conducted using the UK Biobank Resource under Application Number 62254. The study was funded by a National Health and Medical Research Council 2022 Medical Research Future Fund Effective Treatments and Therapies grant (application ID: 2022954). A.S. was supported by a Dementia Australia Research Foundation Henry Brodaty Mid-Career Fellowship. D.D. was supported by an Australian Research Council (ARC) Discovery Early Career Award (DECRA; DE230101174). T.S. was supported by a Hospital Research Foundation grant (C-PJ-008-Transl-2020) awarded to A.S. and D.D. The funders played no role in study design, data collection, analysis or interpretation of data, or the writing of this manuscript. We wish to acknowledge the wider Small Steps team for their contribution to the co-design of the digital interface, detailed elsewhere.
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M.M. conceptualized the study, supported data analysis, and prepared the manuscript. T.S. conceptualized the study, conducted data analysis and contributed to manuscript development. T.O. and A.M. contributed to manuscript development. A.S. led the co-design of the app interface, conceptualized the study, and contributed to manuscript development. D.D. conceptualized the study, supported data analysis, and prepared the manuscript. All authors read and approved the final manuscript.
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Mellow, M.L., Stanford, T.E., Olds, T. et al. An interactive tool to personalise 24-hour activity, sitting and sleep prescription for optimal health outcomes. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02542-4
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DOI: https://doi.org/10.1038/s41746-026-02542-4
