Effects of isotemporal substitution of sedentary behavior with light-intensity or moderate-to-vigorous physical activity on cardiometabolic markers in male adolescents

Autoři: Bruno P. Moura aff001;  Rogério L. Rufino aff001;  Ricardo C. Faria aff002;  Paulo Roberto S. Amorim aff002
Působiště autorů: Medical Science Graduate Program, Medical Sciences Faculty, Rio de Janeiro State University, Rio de Janeiro, Rio de Janeiro, Brazil aff001;  Department of Physical Education, Federal University of Viçosa, Viçosa, Minas Gerais, Brazil aff002
Vyšlo v časopise: PLoS ONE 14(11)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225856


Increasing prevalence of sedentary behavior (SB) combined with low levels of physical activity (PA) in children and adolescents has become a growing public health concern. Therefore, this study aimed to identify the daily behavioral pattern of adolescents and examine the isotemporal substitution effects of SB with light-intensity PA (LIPA) or moderate-to-vigorous PA (MVPA) on cardiometabolic markers. In this cross-sectional study, the daily behavioral pattern of Brazilian male adolescents was objectively measured for 7 days. Vector magnitude activity counts were used to estimate SB, LIPA, and MVPA with cut-points specifically validated for youth. The isotemporal substitution model was used to assess the effects of replacing different SB bouts (5, 10, 30, and 60 min) with LIPA or MVPA on cardiometabolic markers [body mass index, waist circumference, body fat percentage (BF%), total cholesterol, high-density lipoprotein cholesterol (HDL-C), non-HDL-C, low-density lipoprotein cholesterol, triglyceride (TG), glucose, insulin, homeostatic model assessment of insulin resistance (HOMA2-IR), insulin sensitivity (HOMA2-S), beta cell function (HOMA2-β), systolic-blood pressure (SBP), diastolic-blood pressure, and cardiometabolic risk score]. Male adolescents (n = 84; age, 16.7 ± 0.9 years) wore the GT3X+ for 6.7 ± 0.6 days, during 15.2 ± 2.3 h, and spent 72.9% of the time in SB, 17.3% in LIPA, and 9.8% in MVPA. SB replacement with LIPA was associated with increased HDL-C, TG, HOMA2-IR, and HOMA2-S and decreased SBP. In contrast, SB replacement with MVPA was associated with decreased BF%. Therefore, our findings suggest that replacing SB with LIPA showed positive results on HDL-C, HOMA2-S and SBP, while replacing SB with MVPA was associated with only one obesity indicator (BF%). Moreover, participants met the daily MVPA recommendations, but they still had a daily behavioral pattern with high SB. In this context, LIPAs can be considered an effective alternative to reduce SB and improve the health indicators of this population.

Klíčová slova:

Accelerometers – Adolescents – Behavior – Behavioral and social aspects of health – Exercise – Cholesterol – Insulin – Physical activity


1. Sherry AP, Pearson N, Clemes SA. The effects of standing desks within the school classroom: A systematic review. Prev Med Rep. 2016;3:338–47. doi: 10.1016/j.pmedr.2016.03.016 27419034

2. Silva DR, Minderico CS, Pinto F, Collings PJ, Cyrino ES, Sardinha LB. Impact of a classroom standing desk intervention on daily objectively measured sedentary behavior and physical activity in youth. J Sci Med Sport. 2018;21(9):919–24. doi: 10.1016/j.jsams.2018.01.007 29409737

3. Sudholz B, Timperio A, Ridgers ND, Dunstan DW, Baldock R, Holland B, et al. The Impact and Feasibility of Introducing Height-Adjustable Desks on Adolescents' Sitting in a Secondary School Classroom. AIMS Public Health. 2016;3(2):274–87. doi: 10.3934/publichealth.2016.2.274 29546162

4. Tremblay MS, Aubert S, Barnes JD, Saunders TJ, Carson V, Latimer-Cheung AE, et al. Sedentary Behavior Research Network (SBRN)—Terminology Consensus Project process and outcome. Int J Behav Nutr Phys Act. 2017;14(1):75. doi: 10.1186/s12966-017-0525-8 28599680

5. Caspersen CJ, Powell KE, Christenson GM. Physical activity, exercise, and physical fitness: definitions and distinctions for health-related research. Public Health Rep. 1985;100(2):126–31. 3920711

6. U.S. Department of Health and Human Services. Physical Activity Guidelines for Americans. 2nd ed. Washington, DC: U.S. Department of Health and Human Services; 2018.

7. Whitaker KM, Buman MP, Odegaard AO, Carpenter KC, Jacobs DR Jr., Sidney S, et al. Sedentary Behaviors and Cardiometabolic Risk: An Isotemporal Substitution Analysis. Am J Epidemiol. 2018;187(2):181–9. doi: 10.1093/aje/kwx209 28595346

8. Australia’s Physical Activity & Sedentary Behaviour Guidelines for Young People (13–17 years) [Internet]. Department of Health. 2017 [cited 13 January 2019]. Available from: http://www.health.gov.au/internet/main/publishing.nsf/Content/health-pubhlth-strateg-phys-act-guidelines#apa1317.

9. 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(1):e010212. doi: 10.1161/JAHA.118.010212 30616480

10. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A, et al. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA. 2012;307(7):704–12. doi: 10.1001/jama.2012.156 22337681

11. Baranowski T. Increasing physical activity among children and adolescents: Innovative ideas needed. J Sport Health Sci. 2019;8(1):1–5. doi: 10.1016/j.jshs.2018.09.011 30719376

12. Grgic J, Dumuid D, Bengoechea EG, Shrestha N, Bauman A, Olds T, 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. doi: 10.1186/s12966-018-0691-3 30001713

13. World Health Organization. Global recommendations on physical activity for health. Geneva: World Health Organization; 2010.

14. 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(1):65. doi: 10.1186/s12966-018-0695-z 29986718

15. Howard B, Winkler EA, Sethi P, Carson V, Ridgers ND, Salmon JO, et al. Associations of Low- and High-Intensity Light Activity with Cardiometabolic Biomarkers. Med Sci Sports Exerc. 2015;47(10):2093–101. doi: 10.1249/MSS.0000000000000631 25668400

16. Knaeps S, De Baere S, Bourgois J, Mertens E, Charlier R, Lefevre J. Substituting Sedentary Time With Light and Moderate to Vigorous Physical Activity is Associated With Better Cardiometabolic Health. J Phys Act Health. 2018;15(3):197–203. doi: 10.1123/jpah.2017-0102 28872401

17. UK Department of Health and Social Care. Start Active, Stay Active: A report on physical activity from the four home countries’ Chief Medical Officers. London: Department of Health, Physical Activity, Health Improvement and Protection; 2011.

18. Tremblay MS, Carson V, Chaput JP, Connor Gorber S, Dinh T, Duggan M, et al. Canadian 24-Hour Movement Guidelines for Children and Youth: An Integration of Physical Activity, Sedentary Behaviour, and Sleep. Appl Physiol Nutr Metab. 2016;41(6 Suppl 3):S311-27. doi: 10.1139/apnm-2016-0151 27306437

19. Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–27. doi: 10.1093/aje/kwp163 19584129

20. Moore JB, Beets MW, Brazendale K, Blair SN, Pate RR, Andersen LB, et al. Associations of Vigorous-Intensity Physical Activity with Biomarkers in Youth. Med Sci Sports Exerc. 2017;49(7):1366–74. doi: 10.1249/MSS.0000000000001249 28277404

21. Hansen BH, Anderssen SA, Andersen LB, Hildebrand M, Kolle E, Steene-Johannessen J, et al. Cross-Sectional Associations of Reallocating Time Between Sedentary and Active Behaviours on Cardiometabolic Risk Factors in Young People: An International Children's Accelerometry Database (ICAD) Analysis. Sports Med. 2018;48(10):2401–12. doi: 10.1007/s40279-018-0909-1 29626333

22. Contardo Ayala AM, Salmon J, Timperio A, Sudholz B, Ridgers ND, Sethi P, et al. Impact of an 8-Month Trial Using Height-Adjustable Desks on Children's Classroom Sitting Patterns and Markers of Cardio-Metabolic and Musculoskeletal Health. Int J Environ Res Public Health. 2016;13(12). doi: 10.3390/ijerph13121227 27973414

23. Hinckson E, Salmon J, Benden M, Clemes SA, Sudholz B, Barber SE, et al. Standing Classrooms: Research and Lessons Learned from Around the World. Sports Med. 2016;46(7):977–87. doi: 10.1007/s40279-015-0436-2 26626071

24. Cardon G, De Clercq D, De Bourdeaudhuij I, Breithecker D. Sitting habits in elementary schoolchildren: a traditional versus a "Moving school". Patient Educ Couns. 2004;54(2):133–42. doi: 10.1016/S0738-3991(03)00215-5 15288906

25. Benzo RM, Gremaud AL, Jerome M, Carr LJ. Learning to Stand: The Acceptability and Feasibility of Introducing Standing Desks into College Classrooms. Int J Environ Res Public Health. 2016;13(8). doi: 10.3390/ijerph13080823 27537901

26. Moura BP, Rufino RL, Faria RC, Amorim PRS. Accelerometry database of Brazilian adolescents; 2019 [cited 30 March 2019]. Dataset: Mendeley Data [Internet]. Available from: https://data.mendeley.com/datasets/cpy375t3cp/1. doi: 10.17632/cpy375t3cp.1

27. Lohman TG, Roche AF, Martorell R. Anthropometric Standardization Reference Manual. Champaign, IL: Human Kinects; 1988.

28. Growth reference 5–19 years. BMI-for-age (5–19 years) [Internet]. World Health Organization. 2007 [cited 13 January 2019]. Available from: https://www.who.int/growthref/who2007_bmi_for_age/en/.

29. Hoffman DJ, Toro-Ramos T, Sawaya AL, Roberts SB, Rondo P. Estimating total body fat using a skinfold prediction equation in Brazilian children. Ann Hum Biol. 2012;39(2):156–60. doi: 10.3109/03014460.2012.660989 22324842

30. Malachias M, Plavnik FL, Machado CA, Malta D, Scala LCN, Fuchs S. 7th Brazilian Guideline of Arterial Hypertension: Chapter 1—Concept, Epidemiology and Primary Prevention. Arq Bras Cardiol. 2016;107(3 Suppl 3):1–6. doi: 10.5935/abc.20160151 27819380

31. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18(6):499–502. 4337382

32. Bays HE, Jones PH, Orringer CE, Brown WV, Jacobson TA. National Lipid Association Annual Summary of Clinical Lipidology 2016. J Clin Lipidol. 2016;10(1 Suppl):S1–43. doi: 10.1016/j.jacl.2015.08.002 26891998

33. The HOMA2 Calculator [Internet]. Oxford Centre for Diabetes, Endocrinology and Metabolism. 2004 [cited 10 December 2018]. Available from: https://www.dtu.ox.ac.uk/homacalculator/.

34. Franks PW, Ekelund U, Brage S, Wong MY, Wareham NJ. Does the association of habitual physical activity with the metabolic syndrome differ by level of cardiorespiratory fitness? Diabetes Care. 2004;27(5):1187–93. doi: 10.2337/diacare.27.5.1187 15111543

35. Brage S, Wedderkopp N, Ekelund U, Franks PW, Wareham NJ, Andersen LB, et al. Features of the metabolic syndrome are associated with objectively measured physical activity and fitness in Danish children: the European Youth Heart Study (EYHS). Diabetes Care. 2004;27(9):2141–8. doi: 10.2337/diacare.27.9.2141 15333475

36. Macgregor AP, Borghese MM, Janssen I. Is replacing time spent in 1 type of physical activity with another associated with health in children? Appl Physiol Nutr Metab. 2019:1–7. doi: 10.1139/apnm-2018-0323 30653335

37. Santos-Lozano A, Marin PJ, Torres-Luque G, Ruiz JR, Lucia A, Garatachea N. Technical variability of the GT3X accelerometer. Med Eng Phys. 2012;34(6):787–90. doi: 10.1016/j.medengphy.2012.02.005 22417978

38. Romanzini M, Petroski EL, Ohara D, Dourado AC, Reichert FF. Calibration of ActiGraph GT3X, Actical and RT3 accelerometers in adolescents. Eur J Sport Sci. 2014;14(1):91–9. doi: 10.1080/17461391.2012.732614 24533499

39. Choi L, Liu Z, Matthews CE, Buchowski MS. Validation of accelerometer wear and nonwear time classification algorithm. Med Sci Sports Exerc. 2011;43(2):357–64. doi: 10.1249/MSS.0b013e3181ed61a3 20581716

40. Migueles JH, Cadenas-Sanchez C, Ekelund U, Delisle Nystrom C, Mora-Gonzalez J, Lof M, et al. Accelerometer Data Collection and Processing Criteria to Assess Physical Activity and Other Outcomes: A Systematic Review and Practical Considerations. Sports Med. 2017;47(9):1821–45. doi: 10.1007/s40279-017-0716-0 28303543

41. Sasaki JE, John D, Freedson PS. Validation and comparison of ActiGraph activity monitors. J Sci Med Sport. 2011;14(5):411–6. doi: 10.1016/j.jsams.2011.04.003 21616714

42. Faul F, Erdfelder E, Buchner A, Lang AG. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav Res Methods. 2009;41(4):1149–60. doi: 10.3758/BRM.41.4.1149 19897823

43. Sherar LB, Griew P, Esliger DW, Cooper AR, Ekelund U, Judge K, et al. International children's accelerometry database (ICAD): design and methods. BMC Public Health. 2011;11:485. doi: 10.1186/1471-2458-11-485 21693008

44. 2018 Physical Activity Guidelines Advisory Committee. 2018 Physical Activity Guidelines Advisory Committee Scientific Report. Washington, DC: U.S. Department of Health and Human Services, 2018.

45. Klenk J, Kerse N. Every step you take. BMJ. 2019;366:l5051. doi: 10.1136/bmj.l5051

46. Ekelund U, Tarp J, Steene-Johannessen J, Hansen BH, Jefferis B, Fagerland MW, et al. Dose-response associations between accelerometry measured physical activity and sedentary time and all cause mortality: systematic review and harmonised meta-analysis. BMJ. 2019;366:l4570. doi: 10.1136/bmj.l4570 31434697

47. Sartini C, Wannamethee SG, Iliffe S, Morris RW, Ash S, Lennon L, et al. Diurnal patterns of objectively measured physical activity and sedentary behaviour in older men. BMC Public Health. 2015;15:609. doi: 10.1186/s12889-015-1976-y 26141209

48. Tremblay MS, Colley RC, Saunders TJ, Healy GN, Owen N. Physiological and health implications of a sedentary lifestyle. Appl Physiol Nutr Metab. 2010;35(6):725–40. doi: 10.1139/H10-079 21164543

49. Mekary RA, Ding EL. Isotemporal Substitution as the Gold Standard Model for Physical Activity Epidemiology: Why It Is the Most Appropriate for Activity Time Research. Int J Environ Res Public Health. 2019;16(5). doi: 10.3390/ijerph16050797 30841555

Článek vyšel v časopise


2019 Číslo 11