The Association Between Time-Use Behaviors and Physical and Mental Well-Being in Adults: A Compositional Isotemporal Substitution Analysis

in Journal of Physical Activity and Health
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Background: Substantial evidence links activity domains with health and well-being; however, research has typically examined time-use behaviors independently, rather than considering daily activity as a 24-hour time-use composition. This study used compositional data analysis to estimate the difference in physical and mental well-being associated with reallocating time between behaviors. Methods: Participants (n = 430; 74% female; 41 [12] y) wore an accelerometer for 7 days and reported their body mass index; health-related quality of life (QoL); and symptoms of depression, anxiety, and stress. Regression models determined whether time-use composition, comprising sleep, sedentary behavior, light physical activity (LPA), and moderate to vigorous physical activity (MVPA), was associated with well-being. Compositional isotemporal substitution models estimated the difference in well-being associated with reallocating time between behaviors. Results: Time-use composition was associated with body mass index and physical health-related QoL. Reallocating time to MVPA from sleep, sedentary behavior, and LPA showed favorable associations with body mass index and physical health-related QoL, whereas reallocations from MVPA to other behaviors showed unfavorable associations. Reallocations from LPA to sedentary behavior were associated with better physical health–related QoL and vice versa. Conclusion: Results reinforce the importance of MVPA for physical health but do not suggest that replacing sedentary behavior with LPA is beneficial for health and well-being.

Curtis, Dumuid, Olds, Ryan, Edney, and Maher are with the Alliance for Research in Exercise, Nutrition, and Activity, University of South Australia, Adelaide, SA, Australia. Plotnikoff is with the Priority Research Centre for Physical Activity and Nutrition, The University of Newcastle, Newcastle, NSW, Australia. Vandelanotte is with the Physical Activity Research Group, School of Health, Medical and Applied Sciences, Central Queensland University, Rockhampton, QLD, Australia.

Curtis (Rachel.Curtis@unisa.edu.au) is corresponding author.
  • 1.

    Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report. Washington, US: U.S. Department of Health and Human Services; 2008.

    • Search Google Scholar
    • Export Citation
  • 2.

    Zhai L, Zhang Y, Zhang D. Sedentary behaviour and the risk of depression: a meta-analysis. Br J Sports Med. 2015;49(11):705709. PubMed ID: 25183627 doi:10.1136/bjsports-2014-093613

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 3.

    Biswas A, Oh PI, Faulkner GE, et al. Sedentary time and its association with risk for disease incidence, mortality, and hospitalization in adults: a systematic review and meta-analysis. Ann Intern Med. 2015;162(2):123132. PubMed ID: 25599350 doi:10.7326/M14-1651

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 4.

    Itani O, Jike M, Watanabe N, Kaneita Y. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 2017;32:246256. PubMed ID: 27743803 doi:10.1016/j.sleep.2016.08.006

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 5.

    Jike M, Itani O, Watanabe N, Buysse DJ, Kaneita Y. Long sleep duration and health outcomes: a systematic review, meta-analysis and meta-regression. Sleep Med Rev. 2018;39:2536. PubMed ID: 28890167 doi:10.1016/j.smrv.2017.06.011

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 6.

    Zhai L, Zhang H, Zhang D. Sleep duration and depression among adults: a meta-analysis of prospective studies. Depress Anxiety. 2015;32(9):664670. PubMed ID: 26047492 doi:10.1002/da.22386

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Chastin SFM, 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. PubMed ID: 26461112 doi:10.1371/journal.pone.0139984

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519527. PubMed ID: 19584129 doi:10.1093/aje/kwp163

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    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):135146.

    • Search Google Scholar
    • Export Citation
  • 10.

    Dumuid D, Stanford TE, Martin-Fernández J-A, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):37263738. PubMed ID: 28555522 doi:10.1177/0962280217710835

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 11.

    Pedišić Ž, Dumuid D, Olds TS. Integrating sleep, sedentary behaviour, and physical activity research in the emerging field of time-use epidemiology: definitions, concepts, statistical methods, theoretical framework, and future directions. Kinesiology. 2017;49(2):252269.

    • Search Google Scholar
    • Export Citation
  • 12.

    Dumuid D, Pedišić Ž, Stanford TE, 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 Methods Med Res. 2019;28(3):846857. PubMed ID: 29157152 doi:10.1177/0962280217737805

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    Dumuid D, Stanford TE, Pedišić Ž, 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(1):311. PubMed ID: 29499689 doi:10.1186/s12889-018-5207-1

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 14.

    Fairclough SJ, Dumuid D, Taylor S, et al. Fitness, fatness and the reallocation of time between children’s daily movement behaviours: an analysis of compositional data. Int J Behav Nutr Phys Act. 2017;14(1):64. PubMed ID: 28486972 doi:10.1186/s12966-017-0521-z

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 15.

    Dumuid D, Lewis LK, Olds TS, Maher C, Bondarenko C, Norton L. Relationships between older adults’ use of time and cardio-respiratory fitness, obesity and cardio-metabolic risk: a compositional isotemporal substitution analysis. Maturitas. 2018;110:104110. PubMed ID: 29563028 doi:10.1016/j.maturitas.2018.02.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Olds T, Burton NW, Sprod J, 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. PubMed ID: 29953472 doi:10.1371/journal.pone.0199605

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 17.

    Edney S, Plotnikoff R, Vandelanotte C, et al. “Active Team” a social and gamified app-based physical activity intervention: randomised controlled trial study protocol. BMC Public Health. 2017;17(1):859. PubMed ID: 29096614 doi:10.1186/s12889-017-4882-7

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Esliger DW, Rowlands AV, Hurst TL, Catt M, Murray P, Eston RG. Validation of the GENEA accelerometer. Med Sci Sports Exerc. 2011;43(6):10851093. PubMed ID: 21088628 doi:10.1249/MSS.0b013e31820513be

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Trost SG, McIver KL, Pate RR. Conducting accelerometer-based activity assessments in field-based research. Med Sci Sports Exerc. 2005;37(suppl 11):S531S543. doi:10.1249/01.mss.0000185657.86065.98.

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    van Hees VT, Renström F, Wright A, et al. Estimation of daily energy expenditure in pregnant and non-pregnant women using a wrist-worn tri-axial accelerometer. PloS One. 2011;6(7):e22922. PubMed ID: 21829556 doi:10.1371/journal.pone.0022922

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 21.

    Ware JE, Kosinski M, Keller SD. A 12-Item Short-Form Health Survey: construction of scales and preliminary tests of reliability and validity. Med Care. 1996;34(3):220233. PubMed ID: 8628042 doi:10.1097/00005650-199603000-00003

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Ware JE, Kosinski M, Keller SD. SF-12: How to Score the SF-12 Physical and Mental Health Summary Scales. Boston, MA: The Health Institute, New England Medical Center; 1998.

    • Search Google Scholar
    • Export Citation
  • 23.

    Lovibond PF, Lovibond SH. The structure of negative emotional states: comparison of the Depression Anxiety Stress Scales (DASS) with the beck depression and anxiety inventories. Behav Res Ther. 1995;33(3):335343. PubMed ID: 7726811 doi:10.1016/0005-7967(94)00075-U

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Asghari A, Saed F, Dibajnia P. Psychometric properties of the Depression Anxiety Stress Scales-21 (DASS-21) in a non-clinical Iranian sample. Int J Psychol. 2008;2(2):82102.

    • Search Google Scholar
    • Export Citation
  • 25.

    van den Boogaart KG, Tolosana-Delgado R. “compositions”: a unified R package to analyze compositional data. Comput Geosci. 2008;34(4):320338. doi:10.1016/j.cageo.2006.11.017

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 26.

    Templ M, Hron K, Filzmoser P. robCompositions: an R-package for robust statistical analysis of compositional data. In: Pawlowsky-Glahn V, Buccianti A, eds. Compositional Data Analysis. Chichester, UK: John Wiley & Sons; 2011:341355.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 27.

    Sattelmair J, Pertman J, Ding EL, Kohl HW, Haskell W, Lee IM. Dose–response between physical activity and risk of coronary heart disease: a meta-analysis. Circulation. 2011;124(7):789795. PubMed ID: 21810663 doi:10.1161/CIRCULATIONAHA.110.010710

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 28.

    Australian Government Department of Health. Make Your Move—Sit Less—Be Active for Life! Australia’s Physical Activity and Sedentary Behaviour Guidelines for Adults. Canberra, Australia: Australian Government; 2014.

    • Search Google Scholar
    • Export Citation
  • 29.

    Stamatakis E, Ulf E, Ding D, Hamer M, Bauman AE, Lee IM. Is the time right for quantitative public health guidelines on sitting? A narrative review of sedentary behaviour research paradigms and findings. Br J Sports Med. 2019;53(6):377382. PubMed ID: 29891615 doi:10.1136/bjsports-2018-099131

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 30.

    Hirshkowitz M, Whiton K, Albert SM, et al. National Sleep Foundation’s sleep time duration recommendations: methodology and results summary. Sleep Health. 2015;1(1):4043. PubMed ID: 29073412 doi:10.1016/j.sleh.2014.12.010

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 31.

    Kawachi I, Berkman LF. Social ties and mental health. J Urban Health. 2001;78(3):458467. PubMed ID: 11564849 doi:10.1093/jurban/78.3.458

  • 32.

    Plasqui G, Bonomi AG, Westerterp KR. Daily physical activity assessment with accelerometers: new insights and validation studies. Obes Rev. 2013;14(6):451462. PubMed ID: 23398786 doi:10.1111/obr.12021

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
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