Longitudinal Associations between 24-h Movement Behaviors and Cardiometabolic Biomarkers: A Natural Experiment over Retirement.

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    • Source:
      Publisher: Lippincott Williams & Wilkins Country of Publication: United States NLM ID: 8005433 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1530-0315 (Electronic) Linking ISSN: 01959131 NLM ISO Abbreviation: Med Sci Sports Exerc Subsets: MEDLINE
    • Publication Information:
      Publication: Hagerstown, Md : Lippincott Williams & Wilkins
      Original Publication: Madison, Wis., American College of Sports Medicine.
    • Subject Terms:
    • Abstract:
      Introduction: Physical activity, sedentary behavior, and sleep, that is, 24-h movement behaviors, often change in the transition from work to retirement, which may affect cardiometabolic health. This study investigates the longitudinal associations between changes in 24-h movement behaviors and cardiometabolic biomarkers during the retirement transition.
      Methods: Retiring public sector workers ( n = 212; mean (SD) age, 63.5 (1.1) yr) from the Finnish Retirement and Aging study used a thigh-worn Axivity accelerometer and filled out a diary to obtain data on daily time spent in sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), and sleep before and after retirement (1 yr in-between). Cardiometabolic biomarkers, including LDL-cholesterol, HDL-cholesterol, total/HDL-cholesterol ratio, triglycerides, C-reactive protein, fasting glucose, and insulin, were measured. Associations between changes in 24-h movement behaviors and cardiometabolic biomarkers were analyzed using compositional robust regression and isotemporal substitution analysis.
      Results: Increasing LPA in relation to remaining behaviors was associated with an increase in HDL-cholesterol and decrease in total/HDL-cholesterol ratio ( P < 0.05 for both). For instance, reallocation of 30 min from sleep/SED to LPA was associated with an increase in HDL-cholesterol by 0.02 mmol·L -1 . Moreover, increasing MVPA in relation to remaining behaviors was associated with a decrease in triglycerides ( P = 0.02). Reallocation of 30 min from SED/sleep to MVPA was associated with 0.07-0.08 mmol·L -1 decrease in triglycerides. Findings related to LDL-cholesterol, C-reactive protein, fasting glucose, and insulin were less conclusive.
      Conclusions: During the transition from work to retirement, increasing physical activity at the expense of passive behaviors was associated with a better lipid profile. Our findings suggest that life transitions like retirement could be utilized more as an optimal time window for promoting physical activity and health.
      (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American College of Sports Medicine.)
    • References:
      Lear SA, Hu W, Rangarajan S, et al. The effect of physical activity on mortality and cardiovascular disease in 130 000 people from 17 high-income, middle-income, and low-income countries: the PURE study. Lancet . 2017;390(10113):2643–54.
      Kyu HH, Bachman VF, Alexander LT, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose–response meta-analysis for the Global Burden of Disease Study 2013. BMJ . 2016;354:i3857.
      World Health Organisation. World Health Organization (WHO): Cardiovascular diseases. [cited 2023 Oct 23]. Available from: https://www.who.int/health-topics/cardiovascular-diseases#tab=tab_1 .
      Chastin SFM, De Craemer M, De Cocker K, 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;53(6):370–6.
      Hadgraft NT, Winkler E, Climie RE, et al. Effects of sedentary behaviour interventions on biomarkers of cardiometabolic risk in adults: systematic review with meta-analyses. Br J Sports Med . 2021;55(3):144–54.
      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;39:25–36.
      Yin J, Jin X, Shan Z, et al. Relationship of sleep duration with all-cause mortality and cardiovascular events: a systematic review and dose–response meta-analysis of prospective cohort studies. J Am Heart Assoc . 2017;6(9):e005947.
      Kwok CS, Kontopantelis E, Kuligowski G, et al. Self-reported sleep duration and quality and cardiovascular disease and mortality: a dose–response meta-analysis. J Am Heart Assoc . 2018;7(15):e008552.
      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.
      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 Environ Res Public Health . 2020;17(7):2220.
      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):846–57.
      Grgic J, Dumuid D, Bengoechea EG, 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):69.
      Pedisic Z. 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.
      Aitchison J. The statistical analysis of compositional data. J R Stat Soc Ser B Methodol . 1982;44(2):139–77.
      Debache I, Bergouignan A, Chaix B, Sneekes EM, Thomas F, Sueur C. Associations of sensor-derived physical behavior with metabolic health: a compositional analysis in the record multisensor study. Int J Environ Res Public Health . 2019;16(5):741.
      McGregor DE, Carson V, Palarea-Albaladejo J, Dall PM, Tremblay MS, Chastin SFM. Compositional analysis of the associations between 24-h movement behaviours and health indicators among adults and older adults from the Canadian health measure survey. Int J Environ Res Public Health . 2018;15(8):1779.
      Farrahi V, Kangas M, Walmsley R, et al. Compositional associations of sleep and activities within the 24-h cycle with cardiometabolic health markers in adults. Med Sci Sports Exerc . 2021;53(2):324–32.
      McGregor DE, Palarea-Albaladejo J, Dall PM, Stamatakis E, Chastin SFM. Differences in physical activity time-use composition associated with cardiometabolic risks. Prev Med Rep . 2018;13:23–9.
      Biddle GJH, Edwardson C, Henson J, et al. Associations of physical behaviours and behavioural reallocations with markers of metabolic health: a compositional data analysis. Int J Environ Res Public Health . 2018;15(10):2280.
      Gropper H, John JM, Sudeck G, Thiel A. The impact of life events and transitions on physical activity: a scoping review. PLoS One . 2020;15(6):e0234794.
      Suorsa K, Leskinen T, Pasanen J, et al. Changes in the 24-h movement behaviors during the transition to retirement: compositional data analysis. Int J Behav Nutr Phys Act . 2022;19(1):121.
      Suorsa K, Gupta N, Leskinen T, et al. Modifications of 24-h movement behaviors to prevent obesity in retirement: a natural experiment using compositional data analysis. Int J Obes (Lond) . 2023;47(10):922–30.
      Pedron S, Maier W, Peters A, et al. The effect of retirement on biomedical and behavioral risk factors for cardiovascular and metabolic disease. Econ Hum Biol . 2020;38:100893.
      Xue B, Head J, McMunn A. The impact of retirement on cardiovascular disease and its risk factors: a systematic review of longitudinal studies. Gerontologist . 2020;60(5):e367–77.
      Leskinen T, Pulakka A, Heinonen O, et al. Changes in non-occupational sedentary behaviours across the retirement transition: the Finnish Retirement and Aging (FIREA) study. J Epidemiol Community Health . 2018;72(8):695–701.
      Suorsa K, Pulakka A, Leskinen T, et al. Comparison of sedentary time between thigh-worn and wrist-worn accelerometers. J Meas Phys Behav . 2020;3(3):234–43.
      Hettiarachchi P, Johansson P. ActiPASS (Version 0.80) [Computer software]. 2023 [cited 2023 Oct 23]. Available from: https://doi.org/10.5281/zenodo.7701098 . (PMID: 10.5281/zenodo.7701098)
      Skotte J, Korshøj M, Kristiansen J, Hanisch C, Holtermann A. Detection of physical activity types using triaxial accelerometers. J Phys Act Health . 2014;11(1):76–84.
      Stemland I, Ingebrigtsen J, Christiansen CS, et al. Validity of the Acti4 method for detection of physical activity types in free-living settings: comparison with video analysis. Ergonomics . 2015;58(6):953–65.
      Hettiarachchi P, Aili K, Holtermann A, Stamatakis E, Svartengren M, Palm P. Validity of a non-proprietary algorithm for identifying lying down using raw data from thigh-worn triaxial accelerometers. Sensors (Basel) . 2021;21(3):904.
      Tudor-Locke C, Aguiar EJ, Han H, et al. Walking cadence (steps/min) and intensity in 21–40 year olds: CADENCE-adults. Int J Behav Nutr Phys Act . 2019;16(1):8.
      Statistics of Finland Web site [Internet]. Classification of occupations 2010. [cited 2023 Oct 23]. Available from: http://www.stat.fi/meta/luokitukset/ammatti/001-2010/index_en.html .
      Hays RD, Sherbourne CD, Mazel RM. The RAND 36-Item Health Survey 1.0. Health Econ . 1993;2(3):217–27.
      Aalto AM, Aro A, Teperi J. RAND-36 as measure of health-related quality of life. Reliability and construct validity and reference values in the Finnish general population. 1999. Available from: https://www.julkari.fi/bitstream/handle/10024/76006/Tu101.pdf .
      Pelclová J, Štefelová N, Dumuid D, et al. Are longitudinal reallocations of time between movement behaviours associated with adiposity among elderly women? A compositional isotemporal substitution analysis. Int J Obes (Lond) . 2020;44(4):857–64.
      Štefelová N, Dygrýn J, Hron K, Gába A, Rubín L, Palarea-Albaladejo J. Robust compositional analysis of physical activity and sedentary behaviour data. Int J Environ Res Public Health . 2018;15(10):2248.
      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.
      Sampasa-Kanyinga H, Colman I, Dumuid D, et al. Longitudinal association between movement behaviours and depressive symptoms among adolescents using compositional data analysis. PLoS One . 2021;16(9):e0256867.
      Mann S, Beedie C, Jimenez A. Differential effects of aerobic exercise, resistance training and combined exercise modalities on cholesterol and the lipid profile: review, synthesis and recommendations. Sports Med . 2014;44(2):211–21.
      Palazón-Bru A, Hernández-Lozano D, Gil-Guillén VF. Which physical exercise interventions increase HDL-cholesterol levels? A systematic review of meta-analyses of randomized controlled trials. Sports Med . 2021;51(2):243–53.
      Press V, Freestone I, George CF. Physical activity: the evidence of benefit in the prevention of coronary heart disease. QJM . 2003;96(4):245–51.
      Bakrania K, Yates T, Rowlands AV, et al. Intensity thresholds on raw acceleration data: euclidean norm minus one (ENMO) and mean amplitude deviation (MAD) approaches. PLoS One . 2016;11(10):e0164045.
      Flórez-Pregonero A, Buman MS, Ainsworth BE. The accuracy of the placement of wearable monitors to classify sedentary and stationary time under free-living conditions. J Meas Phys Behav . 2018;1(4):165–73.
      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.
      Carvalho MJ, Marques E, Mota J. Training and detraining effects on functional fitness after a multicomponent training in older women. Gerontology . 2009;55(1):41–8.
      Ávila-Gandía V, Ramos-Campo DJ, García-Sánchez E, Luque-Rubia AJ, López A, López-Román FJ. Training, detraining and retraining effects of moderate vs. high intensity exercise training programme on cardiovascular risk factors. J Hypertens . 2023;41(3):411–9.
      Lin X, Zhang X, Guo J, et al. Effects of exercise training on cardiorespiratory fitness and biomarkers of cardiometabolic health: a systematic review and meta-analysis of randomized controlled trials. J Am Heart Assoc . 2015;4(7):e002014.
      Kelley GA, Kelley KS, Roberts S, Haskell W. Comparison of aerobic exercise, diet or both on lipids and lipoproteins in adults: a meta-analysis of randomized controlled trials. Clin Nutr . 2012;31(2):156–67.
      Hespanhol Junior LC, Pillay JD, van Mechelen W, Verhagen E. Meta-analyses of the effects of habitual running on indices of health in physically inactive adults. Sports Med . 2015;45(10):1455–68.
      Booker R, Holmes ME, Newton RL Jr., Norris KC, Thorpe RJ Jr., Carnethon MR. Compositional analysis of movement behaviors’ association on high-sensitivity C-reactive protein: the Jackson Heart Study. Ann Epidemiol . 2022;76:7–12.
      Edwards JJ, Griffiths M, Deenmamode AHP, O’Driscoll JM. High-intensity interval training and cardiometabolic health in the general population: a systematic review and meta-analysis of randomised controlled trials. Sports Med . 2023;53(9):1753–63.
      Wang Y, Li H, Yang D, Wang M, Han Y, Wang H. Effects of aerobic exercises in prediabetes patients: a systematic review and meta-analysis. Front Endocrinol (Lausanne) . 2023;14:1227489.
      Hou L, Wang Q, Pan B, et al. Exercise modalities for type 2 diabetes: a systematic review and network meta-analysis of randomized trials. Diabetes Metab Res Rev . 2023;39(1):e3591.
      Khalafi M, Symonds ME, Ghasemi F, Rosenkranz SK, Rohani H, Sakhaei MH. The effects of exercise training on postprandial glycemia and insulinemia in adults with overweight or obesity and with cardiometabolic disorders: a systematic review and meta-analysis. Diabetes Res Clin Pract . 2023;201:110741.
      Dunstan DW, Kingwell BA, Larsen R, et al. Breaking up prolonged sitting reduces postprandial glucose and insulin responses. Diabetes Care . 2012;35(5):976–83.
      Peddie MC, Bone JL, Rehrer NJ, Skeaff CM, Gray AR, Perry TL. Breaking prolonged sitting reduces postprandial glycemia in healthy, normal-weight adults: a randomized crossover trial. Am J Clin Nutr . 2013;98(2):358–66.
      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.
      Buffey AJ, Herring MP, Langley CK, Donnelly AE, Carson BP. The acute effects of interrupting prolonged sitting time in adults with standing and light-intensity walking on biomarkers of cardiometabolic health in adults: a systematic review and meta-analysis. Sports Med . 2022;52(8):1765–87.
      Gale JT, Wei DL, Haszard JJ, Brown RC, Taylor RW, Peddie MC. Breaking up evening sitting with resistance activity improves postprandial glycemic response: a randomized crossover study. Med Sci Sports Exerc . 2023;55(8):1471–80.
      Loh R, Stamatakis E, Folkerts D, Allgrove JE, Moir HJ. Effects of interrupting prolonged sitting with physical activity breaks on blood glucose, insulin and triacylglycerol measures: a systematic review and meta-analysis. Sports Med . 2020;50(2):295–330.
      MacLeod SF, Terada T, Chahal BS, Boulé NG. Exercise lowers postprandial glucose but not fasting glucose in type 2 diabetes: a meta-analysis of studies using continuous glucose monitoring. Diabetes Metab Res Rev . 2013;29(8):593–603.
      Henst RHP, Pienaar PR, Roden LC, Rae DE. The effects of sleep extension on cardiometabolic risk factors: a systematic review. J Sleep Res . 2019;28(6):e12865.
      Strath SJ, Kaminsky LA, Ainsworth BE, et al. Guide to the assessment of physical activity: clinical and research applications: a scientific statement from the American Heart Association. Circulation . 2013;128(20):2259–79.
      Columb M, Atkinson M. Statistical analysis: sample size and power estimations. BJA Educ . 2016;16(5):159–61.
      Ali-Kovero K, Pietilainen O, Mauramo E, et al. Changes in fruit, vegetable and fish consumption after statutory retirement: a prospective cohort study. Br J Nutr . 2020;123(12):1390–5.
    • Contributed Indexing:
      Local Abstract: [Publisher, Finnish] Physical activity, sedentary behavior, and sleep, that is, 24-h movement behaviors, often change in the transition from work to retirement, which may affect cardiometabolic health. This study investigates the longitudinal associations between changes in 24-h movement behaviors and cardiometabolic biomarkers during the retirement transition. [Publisher, Finnish] Retiring public sector workers ( n = 212; mean (SD) age, 63.5 (1.1) yr) from the Finnish Retirement and Aging study used a thigh-worn Axivity accelerometer and filled out a diary to obtain data on daily time spent in sedentary behavior (SED), light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA), and sleep before and after retirement (1 yr in-between). Cardiometabolic biomarkers, including LDL-cholesterol, HDL-cholesterol, total/HDL-cholesterol ratio, triglycerides, C-reactive protein, fasting glucose, and insulin, were measured. Associations between changes in 24-h movement behaviors and cardiometabolic biomarkers were analyzed using compositional robust regression and isotemporal substitution analysis. [Publisher, Finnish] Increasing LPA in relation to remaining behaviors was associated with an increase in HDL-cholesterol and decrease in total/HDL-cholesterol ratio ( P < 0.05 for both). For instance, reallocation of 30 min from sleep/SED to LPA was associated with an increase in HDL-cholesterol by 0.02 mmol·L −1 . Moreover, increasing MVPA in relation to remaining behaviors was associated with a decrease in triglycerides ( P = 0.02). Reallocation of 30 min from SED/sleep to MVPA was associated with 0.07–0.08 mmol·L −1 decrease in triglycerides. Findings related to LDL-cholesterol, C-reactive protein, fasting glucose, and insulin were less conclusive. [Publisher, Finnish] During the transition from work to retirement, increasing physical activity at the expense of passive behaviors was associated with a better lipid profile. Our findings suggest that life transitions like retirement could be utilized more as an optimal time window for promoting physical activity and health.
    • Accession Number:
      0 (Biomarkers)
      0 (Cholesterol, HDL)
      0 (Insulin)
      0 (Blood Glucose)
      9007-41-4 (C-Reactive Protein)
      0 (Triglycerides)
      0 (Cholesterol, LDL)
    • Publication Date:
      Date Created: 20240228 Date Completed: 20240614 Latest Revision: 20240813
    • Publication Date:
      20240814
    • Accession Number:
      10.1249/MSS.0000000000003415
    • Accession Number:
      38415991