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

Prospective association of daily ambulatory activity with metabolic syndrome in middle-aged and older Japanese adults: the Toon Health Study

Abstract

Background

This cohort study aimed to examine the relationship between objectively measured daily ambulatory activity (AA) variables and the onset of metabolic syndrome (MetS) in middle-aged and older Japanese individuals.

Methods

A total of 1,034 participants (women, 76.8%; mean age, 56.9 years) who were initially free from MetS, underwent objective assessment of daily AA using a uniaxial accelerometer at baseline. The number of steps, time accumulated in light-intensity AA (LIAA), moderate-to-vigorous intensity AA (MVAA), and total AA (LIAA + MVAA) were calculated. The diagnostic criteria outlined by the Japanese standards were employed to define the presence of MetS. To explore the association between AA variables and MetS onset, both multivariate logistic regression and a restricted cubic spline model were used while controlling for variables such as age, sex, education, alcohol habit, smoking habit, energy intake, and the number of MetS components present at baseline.

Results

Over the course of the 5-year follow-up period, 116 participants (11.2%) developed MetS. In terms of the number of steps, LIAA, and total AA, the third quartile had significantly lower multivariate adjusted odds ratios for MetS onset than the first quartile. The odds ratios (95% confidence intervals) were 0.386 (0.197–0.755), 0.527 (0.285–0.975), and 0.392 (0.206–0.745), respectively. In the spline model, an L-shaped association with MetS was observed for the number of steps (p for nonlinearity = 0.066), LIAA (p for nonlinearity = 0.034), and total AA (p for nonlinearity = 0.040).

Conclusions

Among the variables related to AA, the index of daily amount AA, in particular, may be linked to the onset of MetS.

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

Access options

Buy this article

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

Fig. 1
Fig. 2: Restricted cubic spline regression is employed to analyze the relationship between ambulatory activity (AA) variables and the onset of metabolic syndrome (MetS).

Similar content being viewed by others

Data availability

Data are available upon a reasonable request.

References

  1. Cornier MA, Dabelea D, Hernandez TL, Lindstrom RC, Steig AJ, Stob NR, et al. The metabolic syndrome. Endocr Rev. 2008;29:777–822.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Saklayen MG. The Global Epidemic of the Metabolic Syndrome. Curr Hypertens Rep. 2018;20:12.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Ranasinghe P, Mathangasinghe Y, Jayawardena R, Hills AP, Misra A. Prevalence and trends of metabolic syndrome among adults in the Asia-Pacific region: a systematic review. BMC Public Health. 2017;17:101.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zhang D, Liu X, Liu Y, Sun X, Wang B, Ren Y, et al. Leisure-time physical activity and incident metabolic syndrome: a systematic review and dose-response meta-analysis of cohort studies. Metabolism. 2017;75:36–44.

    Article  CAS  PubMed  Google Scholar 

  5. Amirfaiz S, Shahril MR. Objectively measured physical activity, sedentary behavior, and metabolic syndrome in adults: systematic review of observational evidence. Metab Syndr Relat Disord. 2019;17:1–21.

    Article  PubMed  Google Scholar 

  6. Wu J, Zhang H, Yang L, Shao J, Chen D, Cui N, et al. Sedentary time and the risk of metabolic syndrome: a systematic review and dose-response meta-analysis. Obes Rev. 2022;23:e13510.

    Article  PubMed  Google Scholar 

  7. Bijnen FC, Feskens EJ, Caspersen CJ, Mosterd WL, Kromhout D. Age, period, and cohort effects on physical activity among elderly men during 10 years of follow-up: the Zutphen Elderly Study. J Gerontol A Biol Sci Med Sci. 1998;53:M235–241.

    Article  CAS  PubMed  Google Scholar 

  8. Tanaka C, Fujiwara Y, Sakurai R, Fukaya T, Yasunaga M, Tanaka S. Locomotive and non-locomotive activities evaluated with a triaxial accelerometer in adults and elderly individuals. Aging Clin Exp Res. 2013;25:637–43.

    Article  PubMed  Google Scholar 

  9. National Health Service. Walking for health. https://www.nhs.uk/Live-well/exercise/running-and-aerobic-exercises/walking-for-health/. Accessed 18 Jan 2024.

  10. Ara I, Aparicio-Ugarriza R, Morales-Barco D, Nascimento de Souza W, Mata E, González-Gross M. Physical activity assessment in the general population; validated self-report methods. Nutr Hosp. 2015;31:211–218.

    PubMed  Google Scholar 

  11. Kraus WE, Janz KF, Powell KE, Campbell WW, Jakicic JM, Troiano RP, et al. 2018 Physical Activity Guidelines Advisory Committee. Daily step counts for measuring physical activity exposure and its relation to health. Med Sci Sports Exerc. 2019;51:1206–12.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Hall KS, Hyde ET, Bassett DR, Carlson SA, Carnethon MR, Ekelund U, et al. Systematic review of the prospective association of daily step counts with risk of mortality, cardiovascular disease, and dysglycemia. Int J Behav Nutr Phys Act. 2020;17:78.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Paluch AE, Bajpai S, Bassett DR, Carnethon MR, Ekelund U, Evenson KR, et al. Steps for health collaborative. Daily steps and all-cause mortality: a meta-analysis of 15 international cohorts. Lancet Public Health. 2022;7:e219–e228.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Paluch AE, Bajpai S, Ballin M, Bassett DR, Buford TW, Carnethon MR, et al. Steps for health collaborative. Prospective association of daily steps with cardiovascular disease: a harmonized meta-analysis. Circulation. 2023;147:122–31.

    Article  PubMed  Google Scholar 

  15. Saito I, Maruyama K, Kato T, Takata Y, Tomooka K, Kawamura R, et al. Role of insulin resistance in the association between resting heart rate and type 2 diabetes: a prospective study. J Diabetes Complic. 2022;36:108319.

    Article  CAS  Google Scholar 

  16. Kumahara H, Schutz Y, Ayabe M, Yoshioka M, Yoshitake Y, Shindo M, et al. The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: a validation study against whole-body indirect calorimetry. Br J Nutr. 2004;91:235–43.

    Article  CAS  PubMed  Google Scholar 

  17. Crouter SE, Schneider PL, Karabulut M, Bassett DR Jr. Validity of 10 electronic pedometers for measuring steps, distance, and energy cost. Med Sci Sports Exerc. 2003;35:1455–60.

    Article  PubMed  Google Scholar 

  18. Schneider PL, Crouter S, Bassett DR. Pedometer measures of free-living physical activity: comparison of 13 models. Med Sci Sports Exerc. 2004;36:331–5.

    Article  PubMed  Google Scholar 

  19. Nishida Y, Higaki Y, Taguchi N, Hara M, Nakamura K, Nanri H, et al. Intensity-specific and modified effects of physical activity on serum adiponectin in a middle-aged population. J Endocr Soc. 2018;3:13–26.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Yamamoto N, Maruyama K, Saito I, Tomooka K, Tanigawa T, Kawamura K, et al. Latent profile analysis approach to the relationship between daily ambulatory activity patterns and metabolic syndrome in middle-aged and elderly Japanese individuals: the Toon Health Study. Environ Health Prev Med. 2023;28:57.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Ishikawa-Takata K, Naito Y, Tanaka S, Ebine N, Tabata I. Use of doubly labeled water to validate a physical activity questionnaire developed for the Japanese population. J Epidemiol. 2011;21:114–21.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Honda T, Chen S, Kishimoto H, Narazaki K, Kumagai S. Identifying associations between sedentary time and cardio-metabolic risk factors in working adults using objective and subjective measures: a cross-sectional analysis. BMC Public Health. 2014;14:1307.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Matsuzawa Y. Committee to evaluate diagnostic standards for metabolic syndrome definition and the diagnostic standard for metabolic syndrome. Nihon Naika Gakkai Zasshi. 2005;94:794–809.

  24. Yamagishi K, Iso H. The criteria for metabolic syndrome and the national health screening and education system in Japan. Epidemiol Health. 2017;39:e2017003.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Kim J, Tanabe K, Yokoyama N, Zempo H, Kuno S. Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study. BMC Public Health. 2011;11:624.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Kudo N, Nishide R, Mizutani M, Ogawa S, Tanimura S. Association between the type of physical activity and metabolic syndrome in middle-aged and older adult residents of a semi-mountainous area in Japan. Environ Health Prev Med. 2021;26:46.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Takahashi K, Yoshimura Y, Amimoto T, Kunii D, Komatsu T, Yamamoto S. Validation of food frequency questionnaire based on food groups for estimating individual nutrient intake. Jpn J Nutr. 2001;59:221–32. (in Japanese)

    Article  Google Scholar 

  28. Report of the Subdivision on Resources the Council for Science and Technology Ministry of Education, Culture, Sports, Science and Technology, Japan. Standard Tables of Food Composition in Japan 2010. http://www.mext.go.jp/b_menu/shingi/gijyutu/gijyutu3/houkoku/1298713.htm. Accessed 18 Jan 2024.

  29. Lee IM, Shiroma EJ, Kamada M, Bassett DR, Matthews CE, Buring JE. Association of step volume and intensity with all-cause mortality in older women. JAMA Intern Med. 2019;179:1105–12.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Amagasa S, Machida M, Fukushima N, Kikuchi H, Takamiya T, Odagiri Y, et al. Is objectively measured light-intensity physical activity associated with health outcomes after adjustment for moderate-to-vigorous physical activity in adults? A systematic review. Int J Behav Nutr Phys Act. 2018;15:65.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Lindsay T, Wijndaele K, Westgate K, Dempsey P, Strain T, De Lucia Rolfe E, et al. Joint associations between objectively measured physical activity volume and intensity with body fatness: the Fenland study. Int J Obes. 2022;46:169–77.

    Article  Google Scholar 

  32. Whitaker KM, Pettee Gabriel K, Buman MP, Pereira MA, Jacobs DR Jr, Reis JP, et al. Associations of accelerometer-measured sedentary time and physical activity with prospectively assessed cardiometabolic risk factors: the CARDIA study. J Am Heart Assoc. 2019;8:e010212.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Amagasa S, Fukushima N, Kikuchi H, Oka K, Chastin S, Tudor-Locke C, et al. Older adults daily step counts and time in sedentary behavior and different intensities of physical activity. J Epidemiol. 2021;31:350–5.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Master H, Annis J, Huang S, Beckman JA, Ratsimbazafy F, Marginean K, et al. Association of step counts over time with the risk of chronic disease in the All of US Research Program. Nat Med. 2022;28:2301–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Ponsonby AL, Sun C, Ukoumunne OC, Pezic A, Venn A, Shaw JE, et al. Objectively measured physical activity and the subsequent risk of incident dysglycemia: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Diabetes Care. 2011;34:1497–502.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Kraus WE, Yates T, Tuomilehto J, Sun JL, Thomas L, McMurray JJV, et al. Relationship between baseline physical activity assessed by pedometer count and new-onset diabetes in the NAVIGATOR trial. BMJ Open Diabetes Res Care. 2018;6:e000523.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Ballin M, Nordström P, Niklasson J, Alamäki A, Condell J, Tedesco S, et al. Daily step count and incident diabetes in community-dwelling 70-year-olds: a prospective cohort study. BMC Public Health. 2020;20:1830.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Garduno AC, LaCroix AZ, LaMonte MJ, Dunstan DW, Evenson KR, Wang G, et al. Associations of daily steps and step intensity with incident diabetes in a prospective cohort study of older women: The OPACH Study. Diabetes Care. 2022;45:339–47.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Cuthbertson CC, Moore CC, Sotres-Alvarez D, Heiss G, Isasi CR, Mossavar-Rahmani Y, et al. Associations of steps per day and step intensity with the risk of diabetes: the Hispanic Community Health Study / Study of Latinos (HCHS/SOL). Int J Behav Nutr Phys Act. 2022;19:46.

    Article  PubMed  PubMed Central  Google Scholar 

  40. Rothman KJ. Epidemiology: an intruduction (2nd edition), Oxford University Press, New York, NY, 2012.

  41. Sylvia LG, Bernstein EE, Hubbard JL, Keating L, Anderson EJ. Practical guide to measuring physical activity. J Acad Nutr Diet. 2014;114:199–208.

    Article  PubMed  Google Scholar 

  42. Bull FC, Al-Ansari SS, Biddle S, Borodulin K, Buman MP, Cardon G, et al. World Health Organization 2020 guidelines on physical activity and sedentary behaviour. Br J Sports Med. 2020;54:1451–62.

    Article  PubMed  Google Scholar 

  43. Ministry of Health, Labour and Welfare of Japan. The National Health and Nutrition Survey in Japan, 2019. https://www.mhlw.go.jp/content/001066903.pdf. Accessed 18 Jan 2024.

  44. Hattori T, Konno S, Munakata M. Gender differences in lifestyle factors associated with metabolic syndrome and preliminary metabolic syndrome in the general population: the Watari study. Intern Med. 2017;56:2253–9.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Helmerhorst HJ, Brage S, Warren J, Besson H, Ekelund U. A systematic review of reliability and objective criterion-related validity of physical activity questionnaires. Int J Behav Nutr Phys Act. 2012;9:103.

    Article  PubMed  PubMed Central  Google Scholar 

  46. Yamamoto N, Shimada M, Nakagawa N, Sawada SS, Nishimuta M, Kimura Y, et al. Tracking of pedometer-determined physical activity in healthy elderly Japanese people. J Phys Act Health. 2015;12:1421–9.

    Article  PubMed  Google Scholar 

  47. Jebb SA, Moore MS. Contribution of a sedentary lifestyle and inactivity to the etiology of overweight and obesity: current evidence and research issues. Med Sci Sports Exerc. 1999;31:S534–541.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We wish to thank the staff and participants of the Toon Health Study and the municipal authorities, officers, and health professionals of Toon City for their valuable contributions. This research was funded by JSPS KAKENHI, grant number 21H03202, 20H01617, 20590647, and 22390134.

Author information

Authors and Affiliations

Authors

Contributions

IS, TT, and HO designed the study. KM, KT, RK, and YT collected the data. NY analyzed data and wrote the manuscript. KM and IS contributed to the discussion and reviewed/edited the manuscript. All authors have provided the final approval.

Corresponding author

Correspondence to Naofumi Yamamoto.

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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yamamoto, N., Maruyama, K., Saito, I. et al. Prospective association of daily ambulatory activity with metabolic syndrome in middle-aged and older Japanese adults: the Toon Health Study. Int J Obes 48, 733–740 (2024). https://doi.org/10.1038/s41366-024-01483-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41366-024-01483-w

Search

Quick links