Evidence map›Paper›PMID 41845049›Full record

ArticleNPJ digital medicine2026

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

Maddison L Mellow, Tyman E Stanford, Timothy Olds, Aaron Miatke, Ashleigh E Smith, Dorothea Dumuid

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Maddison L MellowAlliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia. maddison.mellow@adelaide.edu.au.
Tyman E StanfordAlliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia.
Timothy OldsAlliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia.
Aaron MiatkeAlliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia.
Ashleigh E Smith *Alliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia.
Dorothea Dumuid *Alliance for Research in Exercise, Nutrition and Activity (ARENA) Research Centre, Allied Health and Human Performance, College of Health, Adelaide University, Adelaide, Australia.

Funding

Australian Research Council Discovery Early Career Award (DECRA; DE230101174)Dementia Australia Research Foundation Henry Brodaty Mid-Career FellowshipHospital Research Foundation C-PJ-008-Transl-2020
6 · The paper itself

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.

Identifiers

PMID41845049
PMCPMC13139532

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.