Skip to main content

Use of Compositional Data Analysis to Show Estimated Changes in Cardiometabolic Health by Reallocating Time to Light-Intensity Physical Activity in Older Adults

Sports Medicine volume 50, pages 205–217 (2020)Cite this article

Abstract

Background

All physical activity (PA) behaviours undertaken over the day, including sleep, sedentary time, standing time, light-intensity PA (LIPA) and moderate-to-vigorous PA (MVPA) have the potential to influence cardiometabolic health. Since these behaviours are mutually exclusive, standard statistical approaches are unable to account for the impact on time spent in other behaviours.

Objective

By employing a compositional data analysis (CoDA) approach, this study examined the associations of objectively measured time spent in sleep, sedentary time, standing time, LIPA and MVPA over a 24-h day on markers of cardiometabolic health in older adults.

Methods

Participants (n =366; 64.6 years [5.3]; 46% female) from the Mitchelstown Cohort Rescreen Study provided measures of body composition, blood lipid and markers of glucose control. An activPAL3 Micro was used to obtain objective measures of sleep, sedentary time, standing time, LIPA and MVPA, using a 7-day continuous wear protocol. Regression analysis, using geometric means derived from CoDA (based on isometric log-ratio transformed data), was used to examine the relationship between the aforementioned behaviours and markers of cardiometabolic health.

Results

Standing time and LIPA showed diverging associations with markers of body composition. Body mass index (BMI), body mass and fat mass were negatively associated with LIPA (all p <0.05) and positively associated with standing time (all p <0.05). Sedentary time was also associated with higher BMI (p <0.05). No associations between blood markers and any PA behaviours were observed, except for triglycerides, which were negatively associated with standing time (p < 0.05). Reallocating 30 min from sleep, sedentary time or standing time, to LIPA, was associated with significant decreases in BMI, body fat and fat mass.

Conclusion

This is the first study to employ CoDA in older adults that has accounted for sleep, sedentary time, standing time, LIPA and MVPA in a 24-h cycle. The findings support engagement in LIPA to improve body composition in older adults. Increased standing time was associated with higher levels of adiposity, with increased LIPA associated with reduced adiposity; therefore, these findings indicate that replacing standing time with LIPA is a strategy to lower adiposity.

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

Access options

Buy single article

Instant access to the full article PDF.

USD 49.95

Price includes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1

Data Availability Statement

The data that support the findings of this study are available from the lead author (Cormac Powell; cormacpowell@swimireland.i.e.) upon reasonable request and with permission from all involved institutions. All analyses were conducted by Leonard D. Browne in R (version 3.4.2) with the ‘Compositions’, ‘robCompositions’ and ‘lmtest’ R packages, as well as the R code developed by Dumuid et al. [68].

Notes

  1. Supplementary file available at https://figshare.com/s/dc9ec1e2cd202e6afc8c.

References

  1. Tremblay MS, et al. Sedentary Behavior Research Network (SBRN)—terminology consensus project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75.

    PubMed  PubMed Central  Google Scholar 

  2. Tremblay MS, et al. Physiological and health implications of a sedentary lifestyle. Appl Physiol Nutr Metab. 2010;35(6):725–40.

    PubMed  Google Scholar 

  3. Kanagasabai T, Chaput JP. Sleep duration and the associated cardiometabolic risk scores in adults. Sleep Health. 2017;3(3):195–203.

    PubMed  Google Scholar 

  4. Celis-Morales CA, et al. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PLoS One. 2012;7(5):e36345.

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Swartz AM, et al. Prediction of body fat in older adults by time spent in sedentary behavior. J Aging Phys Act. 2012;20(3):332–44.

    PubMed  Google Scholar 

  6. Keating SE, et al. Objectively quantified physical activity and sedentary behavior in predicting visceral adiposity and liver fat. J Obes. 2016;2016:2719014.

    PubMed  PubMed Central  Google Scholar 

  7. Celis-Morales CA, et al. Objective vs. self-reported physical activity and sedentary time: effects of measurement method on relationships with risk biomarkers. PloS One. 2012;7(5):e36345.

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Gennuso KP, et al. Dose-response relationships between sedentary behaviour and the metabolic syndrome and its components. Diabetologia. 2015;58(3):485–92.

    CAS  PubMed  Google Scholar 

  9. Tigbe WW, et al. Time spent in sedentary posture is associated with waist circumference and cardiovascular risk. Int J Obes (Lond). 2017;41(5):689–96.

    CAS  Google Scholar 

  10. Powell C, et al. The cross-sectional associations between objectively measured sedentary time and cardiometabolic health markers in adults: a systematic review with meta-analysis component. Obes Rev. 2018;19(3):381–95.

    CAS  PubMed  Google Scholar 

  11. Lee IM, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet. 2012;380(9838):219–29.

    PubMed  PubMed Central  Google Scholar 

  12. Trejo-Gutierrez JF, Fletcher G. Impact of exercise on blood lipids and lipoproteins. J Clin Lipidol. 2007;1(3):175–81.

    PubMed  Google Scholar 

  13. Pedersen BK, Saltin B. Exercise as medicine: evidence for prescribing exercise as therapy in 26 different chronic diseases. Scand J Med Sci Sports. 2015;25(Suppl 3):1–72.

    PubMed  Google Scholar 

  14. Howard B, et al. Associations of low- and high-intensity light activity with cardiometabolic biomarkers. Med Sci Sports Exerc. 2015;47(10):2093–101.

    CAS  PubMed  Google Scholar 

  15. Chastin SFM, et al. How does light-intensity physical activity associate with adult cardiometabolic health and mortality? Systematic review with meta-analysis of experimental and observational studies. Br J Sports Med. 2019;56(6):370–6.

    Google Scholar 

  16. Amagasa S, 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(1):65.

    PubMed  PubMed Central  Google Scholar 

  17. Loprinzi PD. Light-intensity physical activity and all-cause mortality. Am J Health Promot. 2017;31(4):340–2.

    PubMed  Google Scholar 

  18. Buckley JP, et al. The sedentary office: an expert statement on the growing case for change towards better health and productivity. Br J Sports Med. 2015;49(21):1357–62.

    PubMed  Google Scholar 

  19. Duvivier B, et al. Benefits of substituting sitting with standing and walking in free-living conditions for cardiometabolic risk markers, cognition and mood in overweight adults. Front Physiol. 2017;8:353.

    PubMed  PubMed Central  Google Scholar 

  20. 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):135–46.

    Google Scholar 

  21. Leite MLC, Prinelli F. A compositional data perspective on studying the associations between macronutrient balances and diseases. Eur J Clin Nutr. 2017;71:1365.

    CAS  PubMed  Google Scholar 

  22. Leite MLC. Applying compositional data methodology to nutritional epidemiology. Stat Methods Med Res. 2014;25(6):3057–65.

    PubMed  Google Scholar 

  23. Mert MC, et al. Compositional data analysis in epidemiology. Stat Methods Med Res. 2016;27(6):1878–91.

    PubMed  Google Scholar 

  24. Tsilimigras MCB, Fodor AA. Compositional data analysis of the microbiome: fundamentals, tools, and challenges. Ann Epidemiol. 2016;26(5):330–5.

    PubMed  Google Scholar 

  25. Leite ML. Applying compositional data methodology to nutritional epidemiology. Stat Methods Med Res. 2016;25(6):3057–65.

    PubMed  Google Scholar 

  26. Chastin SF, et al. 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  PubMed Central  Google Scholar 

  27. Dumuid D, et al. Compositional data analysis for physical activity, sedentary time and sleep research. Stat Methods Med Res. 2018;27(12):3726–38.

    PubMed  Google Scholar 

  28. Foley L, et al. Patterns of health behaviour associated with active travel: a compositional data analysis. Int J Behav Nutr Phys Act. 2018;15(1):26.

    PubMed  PubMed Central  Google Scholar 

  29. Gupta N, et al. A comparison of standard and compositional data analysis in studies addressing group differences in sedentary behavior and physical activity. Int J Behav Nutr Phys Act. 2018;15(1):53.

    PubMed  PubMed Central  Google Scholar 

  30. 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):252–69.

    Google Scholar 

  31. Carson V, et al. Associations between sleep duration, sedentary time, physical activity, and health indicators among Canadian children and youth using compositional analyses. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S294–302.

    PubMed  Google Scholar 

  32. Katzmarzyk PT, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health. 2013;13:900.

    PubMed  PubMed Central  Google Scholar 

  33. Kearney PM, et al. Cohort profile: the Cork and Kerry Diabetes and Heart Disease Study. Int J Epidemiol. 2013;42(5):1253–62.

    PubMed  Google Scholar 

  34. Kyle UG, et al. Bioelectrical impedance analysis—part I: review of principles and methods. Clin Nutr. 2004;23(5):1226–43.

    PubMed  Google Scholar 

  35. Sergi G, et al. Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons. Aging Clin Exp Res. 2017;29(4):591–7.

    PubMed  Google Scholar 

  36. Dowd KP, et al. The measurement of sedentary patterns and behaviors using the activPAL (TM) Professional physical activity monitor. Physiol Meas. 2012;33(11):1887–99.

    PubMed  Google Scholar 

  37. Powell C, et al. Simultaneous validation of five activity monitors for use in adult populations. Scand J Med Sci Sports. 2017;27(12):1881–92.

    CAS  PubMed  Google Scholar 

  38. Matthews CE, et al. Amount of time spent in sedentary behaviors in the United States, 2003-2004. Am J Epidemiol. 2008;167(7):875–81.

    PubMed  PubMed Central  Google Scholar 

  39. Healy GN, et al. Sedentary time and cardio-metabolic biomarkers in US adults: NHANES 2003-06. Eur Heart J. 2011;32(5):590–7.

    PubMed  PubMed Central  Google Scholar 

  40. Harrington J, et al. Sociodemographic, health and lifestyle predictors of poor diets. Public Health Nutr. 2011;14(12):2166–75.

    PubMed  Google Scholar 

  41. Amagasa S, 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(1):65.

    PubMed  PubMed Central  Google Scholar 

  42. Fuzeki E, Engeroff T, Banzer W. Health benefits of light-intensity physical activity: a systematic review of accelerometer data of the National Health and Nutrition Examination Survey (NHANES). Sports Med. 2017;47(9):1769–93.

    PubMed  Google Scholar 

  43. Bann D, et al. Light Intensity physical activity and sedentary behavior in relation to body mass index and grip strength in older adults: cross-sectional findings from the Lifestyle Interventions and Independence for Elders (LIFE) study. PLoS One. 2015;10(2):e0116058.

    PubMed  PubMed Central  Google Scholar 

  44. Loprinzi PD, Lee H, Cardinal BJ. Evidence to support including lifestyle light-intensity recommendations in physical activity guidelines for older adults. Am J Health Promot. 2015;29(5):277–84.

    PubMed  Google Scholar 

  45. Loprinzi PD, Lee H, Cardinal BJ. Dose response association between physical activity and biological, demographic, and perceptions of health variables. Obes Facts. 2013;6(4):380–92.

    PubMed  PubMed Central  Google Scholar 

  46. Lohne-Seiler H, et al. Accelerometer-determined physical activity and self-reported health in a population of older adults (65–85 years): a cross-sectional study. BMC Public Health. 2014;14:284.

    PubMed  PubMed Central  Google Scholar 

  47. Taylor D. Physical activity is medicine for older adults. Postgrad Med J. 2014;90(1059):26.

    PubMed  Google Scholar 

  48. Berkemeyer K, et al. The descriptive epidemiology of accelerometer-measured physical activity in older adults. Int J Behav Nutr Phys Act. 2016;13:2.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Paoli A, et al. Effects of high-intensity circuit training, low-intensity circuit training and endurance training on blood pressure and lipoproteins in middle-aged overweight men. Lipids Health Dis. 2013;12:131.

    PubMed  PubMed Central  Google Scholar 

  50. van der Ploeg HP, et al. Standing time and all-cause mortality in a large cohort of Australian adults. Prev Med. 2014;69:187–91.

    PubMed  Google Scholar 

  51. Healy GN, et al. Replacing sitting time with standing or stepping: associations with cardio-metabolic risk biomarkers. Eur Heart J. 2015;36(39):2643–9.

    PubMed  Google Scholar 

  52. Van Der Berg JD, et al. Replacement effects of sedentary time on metabolic outcomes: the maastricht study. Med Sci Sports Exerc. 2017;49(7):1351–8.

    Google Scholar 

  53. Danquah IH, et al. Estimated impact of replacing sitting with standing at work on indicators of body composition: cross-sectional and longitudinal findings using isotemporal substitution analysis on data from the Take a Stand! study. PLoS One. 2018;13(6):e0198000.

    PubMed  PubMed Central  Google Scholar 

  54. Carr LJ, et al. Cross-sectional examination of long-term access to sit-stand desks in a professional office setting. Am J Prev Med. 2016;50(1):96–100.

    PubMed  Google Scholar 

  55. Chaput JP, et al. Workplace standing time and the incidence of obesity and type 2 diabetes: a longitudinal study in adults. BMC Public Health. 2015;15:111.

    PubMed  PubMed Central  Google Scholar 

  56. Bailey DP, Locke CD. Breaking up prolonged sitting with light-intensity walking improves postprandial glycemia, but breaking up sitting with standing does not. J Sci Med Sport. 2015;18(3):294–8.

    PubMed  Google Scholar 

  57. Pulsford RM, et al. Intermittent walking, but not standing, improves postprandial insulin and glucose relative to sustained sitting: a randomised cross-over study in inactive middle-aged men. J Sci Med Sport. 2017;20(3):278–83.

    PubMed  Google Scholar 

  58. Mekary RA, et al. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–27.

    PubMed  PubMed Central  Google Scholar 

  59. Bucksch J. Physical activity of moderate intensity in leisure time and the risk of all cause mortality. Br J Sports Med. 2005;39(9):632–8.

    CAS  PubMed  PubMed Central  Google Scholar 

  60. Powell KE, Paluch AE, Blair SN. Physical activity for health: what kind? How much? How intense? On top of what? Annu Rev Public Health. 2011;32:349–65.

    PubMed  Google Scholar 

  61. Lee IM, Skerrett PJ. Physical activity and all-cause mortality: what is the dose-response relation? Med Sci Sports Exerc. 2001;33(6 Suppl):S459–71 (discussion S493-4).

    CAS  PubMed  Google Scholar 

  62. Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: US Department of Health and Human Services.

  63. Jefferis BJ, et al. Adherence to physical activity guidelines in older adults, using objectively measured physical activity in a population-based study. BMC Public Health. 2014;14:382.

    PubMed  PubMed Central  Google Scholar 

  64. Rangaraj VR, Knutson KL. Association between sleep deficiency and cardiometabolic disease: implications for health disparities. Sleep Med. 2016;18:19–35.

    PubMed  Google Scholar 

  65. Knutson KL. Does inadequate sleep play a role in vulnerability to obesity? Am J Hum Biol. 2012;24(3):361–71.

    PubMed  PubMed Central  Google Scholar 

  66. Knutson KL. Sleep duration and cardiometabolic risk: a review of the epidemiologic evidence. Best Pract Res Clin Endocrinol Metab. 2010;24(5):731–43.

    PubMed  PubMed Central  Google Scholar 

  67. Tremblay MS, et al. Incidental movement, lifestyle-embedded activity and sleep: new frontiers in physical activity assessment. Can J Public Health. 2007;98(Suppl 2):S208–17.

    PubMed  Google Scholar 

  68. Dumuid D, 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):846–57.

    PubMed  Google Scholar 

  69. Saeidifard F, et al. Differences of energy expenditure while sitting versus standing: a systematic review and meta-analysis. Eur J Prev Cardiol. 2018;25(5):522–38.

    PubMed  Google Scholar 

  70. Kozey-Keadle S, et al. Validation of wearable monitors for assessing sedentary behavior. Med Sci Sports Exerc. 2011;43(8):1561–7.

    PubMed  Google Scholar 

  71. Matthew CE. Calibration of accelerometer output for adults. Med Sci Sports Exerc. 2005;37(11 Suppl):S512–22.

    PubMed  Google Scholar 

  72. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14(5):411–6.

    PubMed  Google Scholar 

  73. Lee IM, Shiroma EJ. Using accelerometers to measure physical activity in large-scale epidemiological studies: issues and challenges. Br J Sports Med. 2014;48(3):197–201.

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the MCR Study participants, the LivingHealth Clinic staff and the Mitchelstown research team involved in the data collection.

Author information

Authors and Affiliations

  1. Department of Physical Education and Sport Sciences, University of Limerick, Limerick, Ireland

    Brian P. Carson & Alan E. Donnelly

  2. Performance Department, Swim Ireland, Sport HQ, Dublin, Ireland

    Cormac Powell

  3. Graduate Entry Medical School, University of Limerick, Limerick, Ireland

    Leonard D. Browne

  4. Physical Activity for Health Research Cluster, University of Limerick, Limerick, Ireland

    Cormac Powell, Brian P. Carson & Alan E. Donnelly

  5. Health Research Institute, University of Limerick, Limerick, Ireland

    Leonard D. Browne, Brian P. Carson & Alan E. Donnelly

  6. Department of Sport and Health, Athlone Institute of Technology, Westmeath, Ireland

    Kieran P. Dowd

  7. HRB Centre for Health and Diet Research School of Public Health, University College Cork, Cork, Ireland

    Ivan J. Perry, Patricia M. Kearney & Janas M. Harrington

Authors
  1. Cormac Powell
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leonard D. Browne
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Brian P. Carson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kieran P. Dowd
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ivan J. Perry
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Patricia M. Kearney
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Janas M. Harrington
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Alan E. Donnelly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Cormac Powell or Alan E. Donnelly.

Ethics declarations

Funding

Funding was provided by a University of Limerick Department of Physical Education and Sport Sciences Postgraduate Scholarship Programme (2013–2017) and the Health Research Board Centre for Health and Diet Research (HRB 2007/2013).

Conflict of interest

As per the signed conflicts of interest forms, Cormac Powell, Leonard D. Browne, Brian P. Carson, Kieran P. Dowd, Ivan J. Perry, Patricia M. Kearney, Janas M. Harrington and Alan E. Donnelly have no conflicts of interest to declare.

Ethical approval

All participants gave signed consent to participate in the research study, in addition to having their anonymised data being used for publication. Ethics Committee approval conforming to the Declaration of Helsinki was obtained from the Clinical Research Ethics Committee of University College Cork.

Electronic Supplementary Material

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Powell, C., Browne, L.D., Carson, B.P. et al. Use of Compositional Data Analysis to Show Estimated Changes in Cardiometabolic Health by Reallocating Time to Light-Intensity Physical Activity in Older Adults. Sports Med 50, 205–217 (2020). https://doi.org/10.1007/s40279-019-01153-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40279-019-01153-2