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.

Alliance for Research in Exercise Nutrition and Activity Allied Health and Human Performance University of South Australia Adelaide South Australia Australia

Australian Centre for Interactive and Virtual Environments Wearable Computer Lab University of South Australia Adelaide South Australia Australia

Big Data Institute Li Ka Shing Centre for Health Information and Discovery University of Oxford Oxford United Kingdom

Clinical and Health Sciences Australian Centre for Precision Health University of South Australia Adelaide South Australia Australia

Department of Paediatrics The University of Melbourne Parkville Victoria Australia

Department of Public Health and Nursing Norwegian University of Science and Technology Trondheim Norway

Faculty of Physical Culture Palacký University Olomouc Czech Republic

Institute for Health and Sport Victoria University Melbourne Victoria Australia

Liggins Institute The University of Auckland Grafton New Zealand

Medical Research Council Population Health Research Unit University of Oxford Oxford United Kingdom

Murdoch Children's Research Institute Parkville Victoria Australia

National Institute of Health Research Oxford Biomedical Research Centre Oxford University Hospitals NHS Foundation Trust John Radcliffe Hospital Oxford United Kingdom

Nuffield Department of Population Health University of Oxford Oxford United Kingdom

Optimisation and Logistics School of Computer Science The University of Adelaide Adelaide South Australia Australia

Pfizer Inc Groton CT United States of America

School of Allied Health Faculty of Health Science Curtin University Perth Australia

School of Health Sciences University of East Anglia Norwich United Kingdom

School of Pharmacy and Medical Sciences University of South Australia Adelaide South Australia Australia

The KPA Group The Samuel Neaman Institute Technion Haifa Israel

University of Turin Turin Italy

Zobrazit více v PubMed

Janssen I, Clarke A E., Carson V, Chaput JP, Giangregorio LM, Kho M.E, et al.. A systematic review of compositional data analysis studies examining associations between sleep, sedentary behaviour, and physical activity with health outcomes in adults. Appl Physiol Nutr Metab. 2020;45(10 (Suppl. 2)):S248–s257. PubMed

Cuartis RG, Dumuid D, Olds T, Plotnikoff R, Vandelanotte C, Ryan J, et al.. The association between time-use behaviors and physical and mental well-being in adults: a compositional isotemporal substitution analysis. J Phys Act Health. 2020;17(2);197–203. doi: 10.1123/jpah.2018-0687 PubMed DOI

Smith AE, Dumuid D, Goldsworthy MR, Graetz L, Hodyl, N, Thronton, NL, et al.. Daily activities are associated with non-invasive measures of neuroplasticity in older adults. Clinical Neurophysiology. 2020;132(4):984–992. PubMed

Carson V, Tremblay MS, & Chastin SF. Cross-sectional associations between sleep duration, sedentary time, physical activity, and adiposity indicators among Canadian preschool-aged children using compositional analyses. BMC Public Health. 2017;17(5):848. doi: 10.1186/s12889-017-4852-0 PubMed DOI PMC

Watson A, Dumuid D, & Olds T. Associations Between 24-Hour Time Use and Academic Achievement in Australian Primary School–Aged Children. Health Ed Behav. 2020;47(6):905–913. doi: 10.1177/1090198120952041 PubMed DOI

Chong KH, Parrish AM, Cliff DP, Dumuid D, & Okely AD. Cross-Sectional and Longitudinal Associations between 24-Hour Movement Behaviours, Recreational Screen Use and Psychosocial Health Outcomes in Children: A Compositional Data Analysis Approach. Int J Envir Res Pub Health. 2021;18(11):5995. doi: 10.3390/ijerph18115995 PubMed DOI PMC

Pedišić Ž. Measurement issues and poor adjustments for physical activity and sleep undermine sedentary behaviour research—the focus should shift to the balance between sleep, sedentary behaviour, standing and activity. Kinesiology. 2014;46(1);135–146.

Dumuid D, Stanford TE, Martin-Fernández J-A, Pedišić Ž, Maher CA, Lewis LK, et al.. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–3738. doi: 10.1177/0962280217710835 PubMed DOI

Chastin SF, Palarea-Albaladejo J, Dontje ML, & Skelton DA. Combined effects of time spent in physical activity, sedentary behaviors and sleep on obesity and cardio-metabolic health markers: a novel compositional data analysis approach. PLoS One. 2015;10(10):e0139984. doi: 10.1371/journal.pone.0139984 PubMed DOI PMC

Aitchison J. The statistical analysis of compositional data. J R Stat Soc Series B Stat Methodol. 1982;44(2):139–177.

Dumuid D, Pedišić Ž, Palarea-Albaladejo J, Martín-Fernández JA, Hron K, & Olds T. Compositional data analysis in time-use epidemiology: what, why, how. Int J Envir Res Pub Health. 2020;17(7):2220. PubMed PMC

Dumuid D, Pedišić Ž, Stanford TE, Martín-Fernández JA, Hron K, Maher CA, et al.. The compositional isotemporal substitution model: a method for estimating changes in a health outcome for reallocation of time between sleep, physical activity and sedentary behaviour. Stat Meth Med Res. 2019;28(3):846–857. PubMed

Gomersall S, Maher C, English C, Rowlands A, Dollman J, Norton K, et al.. Testing the activitystat hypothesis: a randomised controlled trial. BMC Public Health. 2016;16(1):1–14. doi: 10.1186/s12889-016-3568-x PubMed DOI PMC

Olds T, Burton NW, Sprod J, Maher CA, Ferrar K, Brown WJ, et al.. One day you’ll wake up and won’t have to go to work: The impact of changes in time use on mental health following retirement. PLoS One. 2018;13(6):e0199605. doi: 10.1371/journal.pone.0199605 PubMed DOI PMC

Sprod J, Olds T, Brown W, Burton N, van Uffelen J, Ferrar K, et al.. Changes in use of time across retirement: A longitudinal study. Maturitas. 2017;100:70–76. doi: 10.1016/j.maturitas.2017.02.018 PubMed DOI

Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, et al.. Health outcomes associated with reallocations of time between sleep, sedentary behaviour, and physical activity: a systematic scoping review of isotemporal substitution studies. Int J Behav Nutr Phys Act. 2018;15(1):1–68. PubMed PMC

Dumuid D, Stanford TE, Pedišić Ž, Maher CA, Lewis LK, Martín-Fernández J.A, et al.. Adiposity and the isotemporal substitution of physical activity, sedentary time and sleep among school-aged children: a compositional data analysis approach. BMC Public Health. 2018;18(311):1–10. doi: 10.1186/s12889-018-5207-1 PubMed DOI PMC

Clifford SA, Davies S, & Wake M. Child Health CheckPoint: Cohort summary and methodology of a physical health and biospecimen module for the Longitudinal Study of Australian Children. BMJ Open. 2019;9(Suppl 3):3–22. doi: 10.1136/bmjopen-2017-020261 PubMed DOI PMC

Gray M, & Sanson A. Growing Up in Australia: The Longitudinal Study of Australian Children [online]. Family Matters. 2005;72:4–9.

ADA Dataverse Accessing LSAC data [cited August 23, 2021]. Available from: https://growingupinaustralia.gov.au/data-and-documentation/accessing-lsac-data

Ridley K, Olds TS, & Hill A. The multimedia activity recall for children and adolescents (MARCA): development and evaluation. Int J Behav Nutr Phys Act. 2006;3(1):1–11. doi: 10.1186/1479-5868-3-10 PubMed DOI PMC

Clifford SA, Gillespie AN, Olds T, Grobler AC, & Wake M. Body composition: population epidemiology and concordance in Australian children aged 11–12 years and their parents. BMJ Open. 2019;9(Suppl 3):95–105. doi: 10.1136/bmjopen-2018-023698 PubMed DOI PMC

Varni JW, Burwinkle TM, Seid M, & Skarr D. The PedsQL™* 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3(6):329–341. doi: 10.1367/1539-4409(2003)003&lt;0329:tpaapp&gt;2.0.co;2 PubMed DOI

National Assessment Program NAPLAN [cited December 15, 2020]. Available from: https://nap.edu.au/naplan

Bond L, Clements J, Bertalli N, Evans-Whipp T, McMorris BJ, Patton GC, et al.. A comparison of self-reported puberty using the Pubertal Development Scale and the Sexual Maturation Scale in a school-based epidemiologic survey. J Adolesc. 2006;29(5):709–720. doi: 10.1016/j.adolescence.2005.10.001 PubMed DOI

Blakemore T, Strazdins L, & Gibbings J. Measuring family socioeconomic position. Aust Soc Pol. 2009;8:121–168.

R Core Team R version 4.1.0: A Language and Environment for Statistical Computing [cited June 18, 2021]. Available from: https://www.R-project.org/

Van den Boogaart KG, & Tolosana-Delgado R. “compositions”: A unified R package to analyze compositional data. Comput Geosci. 2008;34(4):320–338.

Palarea-Albaladejo J, & Martín-Fernández JA. zCompositions—R package for multivariate imputation of left-censored data under a compositional approach. Chemometr Intell Lab Syst. 2015;143:85–96.

Palarea-Albaladejo J, Martín-Fernández JA, & Gómez-García J. A parametric approach for dealing with compositional rounded zeros. Mathematical Geology. 2007;39(7):625–645.

Van den Boogaart KG, & Tolosana-Delgado R. Analyzing compositional data with R, vol. 122: Springer, 2013

Boisbunon A, Canu S, Fourdrinier D, Strawderman W, & Wells MT. Akaike’s information criterion, Cp and estimators of loss for elliptically symmetric distributions. International Statistical Review. 2014;82(3):422–439.

Chang W, Cheng J, Allaire J. et al. shiny: Web Application Framework for R. R package version 1.6.0 [cited February 22, 2021]. Available from: https://CRAN.R-project.org/package=shiny

Dumuid D, & Stanford T. GitHub: Reallocation Adventure [cited August 28, 2021]. Available from: https://github.com/dotdum/realloc_adventure

Martín-Fernández J.A, Barceló-Vidal C, & Pawlowsky-Glahn V Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Mathematical Geology. 2003;35:253–278.

Rencher AC. & Schaalje GB Multiple Regression: Tests of Hypotheses and Confidence Intervals. In: Linear models in statistics. edn. Hoboken, New Jersey: John Wiley & Sons, pp. 185–225, 2008.

Zou GY, Taleban J, & Huo CY. Confidence interval estimation for lognormal data with application to health economics. Computational Statistics & Data Analysis. 2009;53(11):3755–3764.

Dumuid D, Olds T, Wake M, Lund Rasmussen C, Pedišić Ž, Hughes J, et al Design Your Best Day [cited August 28, 2021]. Available from: https://dorotheadumuid.shinyapps.io/Design_Your_Best_Day/

Poitras VJ, Gray CE, Borghese MM. et al.. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6):S197–S239. doi: 10.1139/apnm-2015-0663 PubMed DOI

Saunders TJ, Gray CE, Poitras VJ, Carson V, Chaput J-P, Janssen I, et al.. Combinations of physical activity, sedentary behaviour and sleep: relationships with health indicators in school-aged children and youth. Appl Physiol Nutr Metab. 2016;41(6):S283–S293. doi: 10.1139/apnm-2015-0626 PubMed DOI

Carson V, Hunter S, Kuzik N, Gray CE, Poitras VJ, Chaput JP, et al.. Systematic review of sedentary behaviour and health indicators in school-aged children and youth: an update. Appl Physiol Nutr Metab. 2016;41(6):S240–S265. doi: 10.1139/apnm-2015-0630 PubMed DOI

Australian Bureau of Statistics Census of Population and Housing: Socio-Economic Indexes for Areas (SEIFA), Australia, 2016. [cited December 20, 2020]. Available from: https://www.abs.gov.au/ausstats/abs@.nsf/mf/2033.0.55.001

Yeager DS, Dahl RE and Dweck CS. Why interventions to influence adolescent behavior often fail but could succeed. Perspect Pych 2018;13:101–122. doi: 10.1177/1745691617722620 PubMed DOI PMC

Gillison FB, Rouse P, Standage M, Sebire SJ, Ryan RM. A meta-analysis of techniques to promote motivation for health behaviour change from a self-determination theory perspective. Health Psych Rev. 2019;13(1):110–30. doi: 10.1080/17437199.2018.1534071 PubMed DOI

Wake M, Hu YJ, Warren H, Danchin M, Fahey M, Orsini F, et al.. Integrating trials into a whole-population cohort of children and parents: statement of intent (trials) for the Generation Victoria (GenV) cohort. BMC Med Res Methodol. 2020;20(1):238. doi: 10.1186/s12874-020-01111-x PubMed DOI PMC