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Dumuid, Dorothea, Olds, Timothy, Wake, Melissa, Lund Rasmussen, Charlotte, Pedišić, Željko, Hughes, Jim H., Foster, David J. R., Walmsley, Rosemary, Atkin, Andrew J. 
ORCID: https://orcid.org/0000-0002-3819-3448, Straker, Leon, Fraysse, Francois, Smith, Ross T., Neumann, Frank, Kenett, Ron S., Jarle Mork, Paul, Bennett, Derrick, Doherty, Aiden and Stanford, Ty
(2022)
Your best day: An interactive app to translate how time reallocations within a 24-hour day are associated with health measures.
PLoS One, 17 (9).
ISSN 1932-6203
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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.
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| Additional Information: | Funding Information: DD is supported by the Australian National Health and Medical Research Council (NHMRC) Early Career Fellowship APP1162166 and by the Centre of Research Excellence in Driving Global Investment in Adolescent Health funded by NHMRC APP1171981. MW is supported by NHMRC Principal Research Fellowship APP1160906. AD is supported by the Wellcome Trust [223100/Z/21/Z] and the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). RW is supported by a Medical Research Council Industrial trategy Studentship (grant number MR/S502509/1). This study was supported by NHMRC Ideas APP1186123. The CheckPoint study was supported by the NHMRC [APP1041352; APP1109355]; the National Heart Foundation of Australia [100660; The Royal Children’s Hospital Foundation [2014-241]; the Murdoch Children’s Research Institute (MCRI) [No award number available]; The University of Melbourne [No award number available]; the Financial Markets Foundation for Children [2014-055, 2016-310]; and the Australian Department of Social Services (DSS) [No award number available]. Research at the MCRI is supported by the Victorian Government’s Operational Infrastructure Support Program [No award number available]. The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. This paper uses unit record data from Growing Up in Australia, the Longitudinal Study of Australian Children. The study is conducted in partnership between DSS, the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). We thank the LSAC and CheckPoint study participants, staff and students for their contributions. The findings and views reported in this paper are solely those of the authors and should not be attributed to DSS, AIFS or the ABS. Dorothea Dumuid and Ty Stanford (University of South Australia) had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. REDCap (Research Electronic Data Capture) electronic data capture tools were used in this study. More information about this software can be found at: www.project-redcap.org. |
| Faculty \ School: | Faculty of Medicine and Health Sciences > Norwich Medical School Faculty of Medicine and Health Sciences > School of Health Sciences |
| UEA Research Groups: | Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Health Promotion Faculty of Medicine and Health Sciences > Research Centres > Population Health |
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| Depositing User: | LivePure Connector |
| Date Deposited: | 21 Oct 2022 14:41 |
| Last Modified: | 19 Oct 2023 03:27 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/89263 |
| DOI: | 10.1371/journal.pone.0272343 |
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