Evidence map›Paper›PMID 36070284›Full record

ArticlePloS one2022

Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.

Dorothea Dumuid, Timothy Olds, Melissa Wake, Charlotte Lund Rasmussen, Željko Pedišić, Jim H Hughes, David Jr Foster, Rosemary Walmsley, Andrew J Atkin, Leon Straker and 8 more

Abstract read
In one paragraph

Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Twenty Four-Hour Movement Behaviours Research Among Australian Children and Adolescents: A Scoping Review.Health promotion journal of Australia : official journal of Australian Association of Health Promotion Professionals · 2025
    Article
  6. Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
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

18 authors.

Dorothea DumuidAlliance for Research in Exercise, Nutrition and Activity, Allied Health & Human Performance, University of South Australia, Adelaide, South Australia, Australia.ORCID 0000-0003-3057-0963
Timothy OldsAlliance for Research in Exercise, Nutrition and Activity, Allied Health & Human Performance, University of South Australia, Adelaide, South Australia, Australia.
Melissa WakeMurdoch Children's Research Institute, Parkville, Victoria, Australia.
Charlotte Lund RasmussenDepartment of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
Željko PedišićInstitute for Health and Sport, Victoria University, Melbourne, Victoria, Australia.
Jim H HughesSchool of Pharmacy and Medical Sciences, University of South Australia, Adelaide, South Australia, Australia.ORCID 0000-0002-1862-5805
David Jr FosterClinical and Health Sciences, Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia.
Rosemary WalmsleyBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
Andrew J AtkinSchool of Health Sciences, University of East Anglia, Norwich, United Kingdom.
Leon StrakerSchool of Allied Health, Faculty of Health Science, Curtin University, Perth, Australia.
Francois FraysseAlliance for Research in Exercise, Nutrition and Activity, Allied Health & Human Performance, University of South Australia, Adelaide, South Australia, Australia.
Ross T SmithAustralian Centre for Interactive and Virtual Environments, Wearable Computer Lab, University of South Australia, Adelaide, South Australia, Australia.
Frank NeumannOptimisation and Logistics, School of Computer Science, The University of Adelaide, Adelaide, South Australia, Australia.
Ron S KenettThe KPA Group, The Samuel Neaman Institute, Technion, Haifa, Israel.
Paul Jarle MorkDepartment of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway.
Derrick BennettNuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
Aiden DohertyBig Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, United Kingdom.
Ty StanfordAlliance for Research in Exercise, Nutrition and Activity, Allied Health & Human Performance, University of South Australia, Adelaide, South Australia, Australia.

Funding

Department of HealthMedical Research Council MR/S502509/1Wellcome TrustWellcome Trust 223100/Z/21/Z
6 · The paper itself

Abstract

Reallocations of time between daily activities such as sleep, sedentary behavior and physical activity are differentially associated with markers of physical, mental and social health. An individual's most desirable allocation of time may differ depending on which outcomes they value most, with these outcomes potentially competing with each other for reallocations. We aimed to develop an interactive app that translates how self-selected time reallocations are associated with multiple health measures. We used data from the Australian Child Health CheckPoint study (n = 1685, 48% female, 11-12 y), with time spent in daily activities derived from a validated 24-h recall instrument, %body fat from bioelectric impedance, psychosocial health from the Pediatric Quality of Life Inventory and academic performance (writing) from national standardized tests. We created a user-interface to the compositional isotemporal substitution model with interactive sliders that can be manipulated to self-select time reallocations between activities. The time-use composition was significantly associated with body fat percentage (F = 2.66, P < .001), psychosocial health (F = 4.02, P < .001), and academic performance (F = 2.76, P < .001). Dragging the sliders on the app shows how self-selected time reallocations are associated with the health measures. For example, reallocating 60 minutes from screen time to physical activity was associated with -0.8 [95% CI -1.0 to -0.5] %body fat, +1.9 [1.4 to 2.5] psychosocial score and +4.5 [1.8 to 7.2] academic performance. Our app allows the health associations of time reallocations to be compared against each other. Interactive interfaces provide flexibility in selecting which time reallocations to investigate, and may transform how research findings are disseminated.

Indexed as

Mobile ApplicationsQuality of LifeAustraliaChildExerciseFemaleHumansMaleSedentary Behavior

Identifiers

PMID36070284
PMCPMC9451088

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.