Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Epidemiology and Population Health

Timing of objectively-collected physical activity in relation to body weight and metabolic health in sedentary older people: a cross-sectional and prospective analysis

International Journal of Obesity volume 46, pages 515–522 (2022)Cite this article

Subjects

Abstract

Background

Little is known about the impact of timing as opposed to frequency and intensity of daily physical activity on metabolic health. Therefore, we assessed the association between accelerometery-based daily timing of physical activity and measures of metabolic health in sedentary older people.

Methods

Hourly mean physical activity derived from wrist-worn accelerometers over a 6-day period was collected at baseline and after 3 months in sedentary participants from the Active and Healthy Ageing study. A principal component analysis (PCA) was performed to reduce the number of dimensions (e.g. define periods instead of separate hours) of hourly physical activity at baseline and change during follow-up. Cross-sectionally, a multivariable-adjusted linear regression analysis was used to associate the principal components, particularly correlated with increased physical activity in data-driven periods during the day, with body mass index (BMI), fasting glucose and insulin, HbA1c and the homeostatic model assessment for insulin resistance (HOMA-IR). For the longitudinal analyses, we calculated the hourly changes in physical activity and change in metabolic health after follow-up.

Results

We included 207 individuals (61.4% male, mean age: 64.8 [SD 2.9], mean BMI: 28.9 [4.7]). Higher physical activity in the early morning was associated with lower fasting glucose (−2.22%, 95% CI: −4.19, −0.40), fasting insulin (−13.54%, 95%CI: −23.49, −4.39), and HOMA-IR (−16.07%, 95%CI: −27.63, −5.65). Higher physical activity in the late afternoon to evening was associated with lower BMI (−2.84%, 95% CI: −4.92, −0.70). Higher physical activity at night was associated with higher BMI (2.86%, 95% CI: 0.90, 4.78), fasting glucose (2.57%, 95% CI: 0.70, 4.30), and HbA1c (2.37%, 95% CI: 1.00, 3.82). Similar results were present in the prospective analysis.

Conclusion

Specific physical activity timing patterns were associated with more beneficial metabolic health, suggesting particular time-dependent physical activity interventions might maximise health benefits.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Characteristics in (changes in) physical activity in the study population.
Fig. 2: The associations between the individual variables of metabolic health and the principal components (PC) representing the baseline physical activity.
Fig. 3: The associations between the individual variables of metabolic health and the longitudinal principal components (PC) representing change in timing of physical activity.

Similar content being viewed by others

References

  1. Kohl HW 3rd, Craig CL, Lambert EV, Inoue S, Alkandari JR, Leetongin G, et al. The pandemic of physical inactivity: global action for public health. Lancet. 2012;380:294–305.

    Article  Google Scholar 

  2. WHO. Obesity: preventing and managing the global epidemic. Report of a WHO consultation. World Health Organ Tech Rep Ser. 2000;894:1–253. i-xii.

    Google Scholar 

  3. WHO. Global recommendations on physical activity for health. Switserland:WHO;2010.

  4. Sun F, Norman IJ, While AE. Physical activity in older people: a systematic review. BMC Public Health. 2013;13:449.

    Article  Google Scholar 

  5. Braskie MN, Boyle CP, Rajagopalan P, Gutman BA, Toga AW, Raji CA, et al. Physical activity, inflammation, and volume of the aging brain. Neuroscience. 2014;273:199–209.

    Article  CAS  Google Scholar 

  6. Notthoff N, Reisch P, Gerstorf D. Individual Characteristics and Physical Activity in Older Adults: a systematic review. Gerontology. 2017;63:443–59.

    Article  Google Scholar 

  7. Lewis P, Korf HW, Kuffer L, Gross JV, Erren TC. Exercise time cues (zeitgebers) for human circadian systems can foster health and improve performance: a systematic review. BMJ Open Sport Exerc Med. 2018;4:e000443.

    Article  Google Scholar 

  8. Marinac CR, Quante M, Mariani S, Weng J, Redline S, Cespedes Feliciano EM, et al. Associations Between Timing of Meals, Physical Activity, Light Exposure, and Sleep With Body Mass Index in Free-Living Adults. J Phys Act Health. 2019;16:214–21.

    Article  Google Scholar 

  9. Rijks Instituut voor Volksgezondheid en Milieu (RIVM): Volwassenen met overgewicht en obesitas 2020. https://www.volksgezondheidenzorg.info/onderwerp/overgewicht/cijfers-context/huidige-situatie#node-overgewicht-volwassenen. (last assessed November 12, 2021).

  10. Piercy KL, Troiano RP, Ballard RM, Carlson SA, Fulton JE, Galuska DA, et al. The Physical Activity Guidelines for Americans. JAMA. 2018;320:2020–8.

    Article  Google Scholar 

  11. Chomistek AK, Shiroma EJ, Lee IM. The Relationship Between Time of Day of Physical Activity and Obesity in Older Women. J Phys Act Health. 2016;13:416–8.

    Article  Google Scholar 

  12. Reid KJ, Santostasi G, Baron KG, Wilson J, Kang J, Zee PC. Timing and intensity of light correlate with body weight in adults. PLoS ONE. 2014;9:e92251.

    Article  Google Scholar 

  13. Garaulet M, Gomez-Abellan P. Timing of food intake and obesity: a novel association. Physiol Behav. 2014;134:44–50.

    Article  CAS  Google Scholar 

  14. Garaulet M, Gomez-Abellan P, Alburquerque-Bejar JJ, Lee YC, Ordovas JM, Scheer FA. Timing of food intake predicts weight loss effectiveness. Int J Obes (Lond). 2013;37:604–11.

    Article  CAS  Google Scholar 

  15. Dashti HS, Scheer F, Saxena R, Garaulet M. Timing of Food Intake: Identifying Contributing Factors to Design Effective Interventions. Adv Nutr. 2019;10:606–20.

    Article  Google Scholar 

  16. de Cabo R, Mattson MP. Effects of Intermittent Fasting on Health, Aging, and Disease. N Engl J Med. 2019;381:2541–51.

    Article  Google Scholar 

  17. Lu TC, Fu CM, Ma MH, Fang CC, Turner AM. Healthcare Applications of Smart Watches. A Systematic Review. Appl Clin Inform. 2016;7:850–69.

    Article  Google Scholar 

  18. Piwek L, Ellis DA, Andrews S, Joinson A. The Rise of Consumer Health Wearables: Promises and Barriers. PLoS Med. 2016;13:e1001953.

    Article  Google Scholar 

  19. Jim HSL, Hoogland AI, Brownstein NC, Barata A, Dicker AP, Knoop H, et al. Innovations in Research and Clinical Care Using Patient-Generated Health Data. CA Cancer J Clin. 2020;70:182–99.

    Article  Google Scholar 

  20. Migueles JH, Rowlands A, Huber F, Sabia S, van Hees VT. GGIR: A Research Community–Driven Open Source R Package for Generating Physical Activity and Sleep Outcomes From Multi-Day Raw Accelerometer Data. J Measurement Phys Behav. 2019;2:188–96.

  21. van Kuppevelt D, Heywood J, Hamer M, Sabia S, Fitzsimons E, van Hees V. Segmenting accelerometer data from daily life with unsupervised machine learning. PLoS ONE. 2019;14:e0208692.

    Article  Google Scholar 

  22. Willis EA, Creasy SA, Honas JJ, Melanson EL, Donnelly JE. The effects of exercise session timing on weight loss and components of energy balance: midwest exercise trial 2. Int J Obes (Lond). 2020;44:114–24.

    Article  CAS  Google Scholar 

  23. Wijsman CA, Westendorp RG, Verhagen EA, Catt M, Slagboom PE, de Craen AJ, et al. Effects of a web-based intervention on physical activity and metabolism in older adults: randomized controlled trial. J Med Internet Res. 2013;15:e233.

    Article  Google Scholar 

  24. van Hees VT, Fang Z, Langford J, Assah F, Mohammad A, da Silva IC, et al. Autocalibration of accelerometer data for free-living physical activity assessment using local gravity and temperature: an evaluation on four continents. J Appl Physiol (1985). 2014;117:738–44.

    Article  Google Scholar 

  25. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia. 1985;28:412–9.

    Article  CAS  Google Scholar 

  26. Schumacher LM, Thomas JG, Raynor HA, Rhodes RE, Bond DS. Consistent Morning Exercise May Be Beneficial for Individuals With Obesity. Exerc Sport Sci Rev. 2020;48:201–8.

    Article  Google Scholar 

  27. Brooker PG, Gomersall SR, King NA, Leveritt MD. The feasibility and acceptability of morning versus evening exercise for overweight and obese adults: A randomized controlled trial. Contemp Clin Trials Commun. 2019;14:100320.

    Article  Google Scholar 

  28. Di Blasio A, Di Donato F, Mastrodicasa M, Fabrizio N, Di Renzo D, Napolitano G, et al. Effects of the time of day of walking on dietary behaviour, body composition and aerobic fitness in post-menopausal women. J Sports Med Phys Fitness. 2010;50:196–201.

    PubMed  Google Scholar 

  29. Henson J, Rowlands AV, Baldry E, Brady EM, Davies MJ, Edwardson CL, et al. Physical behaviors and chronotype in people with type 2 diabetes. BMJ Open Diabetes Res Care. 2020;8:e001375.

  30. Facer-Childs ER, Boiling S, Balanos GM. The effects of time of day and chronotype on cognitive and physical performance in healthy volunteers. Sports Med Open. 2018;4:47.

    Article  Google Scholar 

  31. Vera B, Dashti HS, Gomez-Abellan P, Hernandez-Martinez AM, Esteban A, Scheer F, et al. Modifiable lifestyle behaviors, but not a genetic risk score, associate with metabolic syndrome in evening chronotypes. Sci Rep. 2018;8:945.

    Article  Google Scholar 

  32. Dekker SA, Noordam R, Biermasz NR, de Roos A, Lamb HJ, Rosendaal FR, et al. Habitual Sleep Measures are Associated with Overall Body Fat, and not Specifically with Visceral Fat, in Men and Women. Obesity (Silver Spring). 2018;26:1651–8.

    Article  Google Scholar 

  33. Hirotsu C, Tufik S, Andersen ML. Interactions between sleep, stress, and metabolism: from physiological to pathological conditions. Sleep Sci. 2015;8:143–52.

    Article  Google Scholar 

  34. Hower IM, Harper SA, Buford TW. Circadian Rhythms, Exercise, and Cardiovascular Health. J Circadian Rhythms. 2018;16:7.

    Article  Google Scholar 

  35. Stenvers DJ, Scheer F, Schrauwen P, la Fleur SE, Kalsbeek A. Circadian clocks and insulin resistance. Nat Rev Endocrinol. 2019;15:75–89.

    Article  Google Scholar 

  36. Akintola AA, Noordam R, Jansen SW, de Craen AJ, Ballieux BE, Cobbaert CM, et al. Accuracy of Continuous Glucose Monitoring Measurements in Normo-Glycemic Individuals. PLoS ONE. 2015;10:e0139973.

    Article  Google Scholar 

  37. Park S, Jastremski CA, Wallace JP. Time of day for exercise on blood pressure reduction in dipping and nondipping hypertension. J Hum Hypertens. 2005;19:597–605.

    Article  CAS  Google Scholar 

Download references

Funding

The AGO study was financially supported by Philips Consumer Lifestyle, and the Netherlands Genomics Initiative/Netherlands Organisation for Scientific Research (NGI/NOW) (grant numbers: 05040202 and 050–060–810). DvH and RN were supported by the VELUX Stiftung (grant number 1156). RN was supported by an innovation grant from the Dutch Heart Foundation [grant number 2019T103]. The funders had no role in the design, analyses and interpretation of the results of the study.

Author information

Authors and Affiliations

  1. Department of Internal Medicine, Section of Gerontology and Geriatrics, Leiden University Medical Center, Leiden, the Netherlands

    Gali Albalak, Carolien A. Wijsman, Simon P. Mooijaart, Diana van Heemst & Raymond Noordam

  2. Department of Rehabilitation Medicine, Leiden University Medical Center, Leiden, the Netherlands

    Marjon Stijntjes

  3. BioMechanical Engineering, Delft University of Technology, Delft, the Netherlands

    Marjon Stijntjes

  4. Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands

    P. Eline Slagboom & Frans J. van der Ouderaa

  5. Max Planck Institute for Biology and Ageing, Cologne, Germany

    P. Eline Slagboom

Authors
  1. Gali Albalak
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marjon Stijntjes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carolien A. Wijsman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. P. Eline Slagboom
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frans J. van der Ouderaa
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Simon P. Mooijaart
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Diana van Heemst
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Raymond Noordam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

GA, MS, DvH, and RN conceptualised the study. GA performed the data analysis. GA, MS, DvH, and RN contributed to the interpretation of the data. CAW, SPM, FvdO, DvH, and PES contributed to the study design and CAW and SPM performed the data collection. PES, FvdO, SPM, DvH, and RN contributed to the funding acquisition. The drafting of the initial version of the paper was done by GA and RN. All authors critically revised the paper for important intellectual content. All authors read and approved the final paper.

Corresponding author

Correspondence to Gali Albalak.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Albalak, G., Stijntjes, M., Wijsman, C.A. et al. Timing of objectively-collected physical activity in relation to body weight and metabolic health in sedentary older people: a cross-sectional and prospective analysis. Int J Obes 46, 515–522 (2022). https://doi.org/10.1038/s41366-021-01018-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-021-01018-7

This article is cited by

Search

Advanced search

Quick links