Cross-Sectional Associations of Objectively-Measured Physical Activity and Sedentary Time with Body Composition and Cardiorespiratory Fitness in Mid-Childhood: The PANIC Study

Paul J Collings, Kate Westgate, Juuso Väistö, Katrien Wijndaele, Andrew J Atkin, Eero A Haapala, Niina Lintu, Tomi Laitinen, Ulf Ekelund, Soren Brage, Timo A Lakka, Paul J Collings, Kate Westgate, Juuso Väistö, Katrien Wijndaele, Andrew J Atkin, Eero A Haapala, Niina Lintu, Tomi Laitinen, Ulf Ekelund, Soren Brage, Timo A Lakka

Abstract

Background: The minimum intensity of physical activity (PA) that is associated with favourable body composition and cardiorespiratory fitness (CRF) remains unknown.

Objective: To investigate cross-sectional associations of PA and sedentary time (ST) with body composition and CRF in mid-childhood.

Methods: PA, ST, body composition and CRF were measured in a population-based sample of 410 children (aged 7.6 ± 0.4 years). Combined heart-rate and movement sensing provided estimates of PA energy expenditure (PAEE, kJ/kg/day) and time (min/day) at multiple fine-grained metabolic equivalent (MET) levels, which were also collapsed to ST and light PA (LPA), moderate PA (MPA) and vigorous PA (VPA). Fat mass index (FMI, kg/m2), trunk fat mass index (TFMI, kg/m2) and fat-free mass index (FFMI, kg/m2.5) were derived from dual-energy X-ray absorptiometry. Maximal workload from a cycle ergometer test provided a measure of CRF (W/kg FFM). Linear regression and isotemporal substitution models were used to investigate associations.

Results: The cumulative time above 2 METs (221 J/min/kg) was inversely associated with FMI and TFMI in both sexes (p < 0.001) whereas time spent above 3 METs was positively associated with CRF (p ≤ 0.002); CRF increased and adiposity decreased dose-dependently with increasing MET levels. ST was positively associated with FMI and TFMI (p < 0.001) but there were inverse associations between all PA categories (including LPA) and adiposity (p ≤ 0.002); the magnitude of these associations depended on the activity being displaced in isotemporal substitution models but were consistently stronger for VPA. PAEE, MPA and to a greater extent VPA, were all positively related to CRF (p ≤ 0.001).

Conclusions: PA exceeding 2 METs is associated with lower adiposity in mid-childhood, whereas PA of 3 METs is required to benefit CRF. VPA was most beneficial for fitness and fatness, from a time-for-time perspective, but displacing any lower-for-higher intensity may be an important first-order public health strategy. Clinical trial registry number (website): NCT01803776 ( https://ichgcp.net/clinical-trials-registry/NCT01803776 ).

Conflict of interest statement

Ethical approval

All procedures were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Written informed consent was acquired from the parent/caregiver of each child and every child provided assent to participation.

Figures

Fig. 1
Fig. 1
Daily cumulative awake time spent above single-MET categories. Data are mean values and error bars represent ± standard deviation. Sedentary time corresponds to the region: ≤1.5 METs; light physical activity: 1.5–3 METs; moderate physical activity: >3–6 METs; vigorous physical activity: >6 METs. Inset shows magnified plot for >4 METs. METs metabolic equivalents
Fig. 2
Fig. 2
Associations between the cumulative awake time above single MET categories and a FMI, b TFMI, c FFMI and d CRF. Statistical analyses performed using multiple imputed datasets and linear regression adjusted for physical activity monitor wear characteristics (proportion of weekend data and season of measurement), demographics (age, sex, household income), behaviours (sleep duration, energy intake, frequency of breakfast consumption, number of meals per day, snacking), birth weight, maternal and paternal BMI. Adjustment for CRF was further made when FMI, TFMI and FFMI were outcomes and CRF was adjusted for FMI. School clustering was accounted for by using robust standard errors. FMI and TFMI were skewed and natural log-transformed prior to analyses, their data have been back-transformed to represent the percentage difference (95 % CI) in variables per 10 min spent above a MET level. All results are scaled to represent the association between exposures and outcomes per 10 min difference in exposures. METs metabolic equivalents, FMI fat mass index, TFMI trunk fat mass index, FFMI fat-free mass index, CRF cardiorespiratory fitness

References

    1. Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes. 2011;35(7):891–898. doi: 10.1038/ijo.2010.222.
    1. Dietz WH. Critical periods in childhood for the development of obesity. Am J Clin Nutr. 1994;59(5):955–959.
    1. Hughes AR, Sherriff A, Lawlor DA, Ness AR, Reilly JJ. Incidence of obesity during childhood and adolescence in a large contemporary cohort. Prevent Med. 2011;52(5):300–304. doi: 10.1016/j.ypmed.2011.02.014.
    1. Armstrong N. Aerobic fitness and physical activity in children. Pediatr Exerc Sci. 2013;25(4):548–560. doi: 10.1123/pes.25.4.548.
    1. Jimenez-Pavon D, Kelly J, Reilly JJ. Associations between objectively measured habitual physical activity and adiposity in children and adolescents: systematic review. Int J Pediatr Obes. 2010;5(1):3–18. doi: 10.3109/17477160903067601.
    1. Graf C. Preventing and treating obesity in pediatrics through physical activity. EPMA J. 2011;2(3):261–270. doi: 10.1007/s13167-011-0091-0.
    1. Department of Health, Physical Activity, Health Improvement and Protection. Start active, stay active: a report on physical activity from the four home countries’ Chief Medical Officers. London: Department of Health; 2011.
    1. World Health Organization (WHO) Global recommendations on physical activity for health. Geneva: WHO Press; 2010.
    1. Armstrong N, Tomkinson GR, Ekelund U. Aerobic fitness and its relationship to sport, exercise training and habitual physical activity during youth. Br J Sport Med. 2011;45(11):849–858. doi: 10.1136/bjsports-2011-090200.
    1. Ortega FB, Ruiz JR, Castillo MJ, Sjostrom M. Physical fitness in childhood and adolescence: a powerful marker of health. Int J Obes. 2008;32(1):1–11. doi: 10.1038/sj.ijo.0803774.
    1. DuBose KD, Eisenmann JC, Donnelly JE. Aerobic fitness attenuates the metabolic syndrome score in normal-weight, at-risk-for-overweight, and overweight children. Pediatrics. 2007;120(5):e1262–e1268. doi: 10.1542/peds.2007-0443.
    1. Griffiths LJ, Cortina-Borja M, Sera F, Pouliou T, Geraci M, Rich C, et al. How active are our children? Findings from the Millennium Cohort Study. BMJ Open. 2013;3(8):e002893. doi: 10.1136/bmjopen-2013-002893.
    1. Hallal PC, Andersen LB, Bull FC, Guthold R, Haskell W, Ekelund U, et al. Global physical activity levels: surveillance progress, pitfalls, and prospects. Lancet. 2012;380(9838):247–257. doi: 10.1016/S0140-6736(12)60646-1.
    1. Dumith SC, Gigante DP, Domingues MR, Kohl HW., 3rd Physical activity change during adolescence: a systematic review and a pooled analysis. Int J Epidemiol. 2011;40(3):685–698. doi: 10.1093/ije/dyq272.
    1. Collings PJ, Wijndaele K, Corder K, Westgate K, Ridgway CL, Sharp SJ, et al. Magnitude and determinants of change in objectively-measured physical activity, sedentary time and sleep duration from ages 15 to 17.5 y in UK adolescents: the ROOTS study. Int J Behav Nutr Phys Act. 2015;12(1):61. doi: 10.1186/s12966-015-0222-4.
    1. Ekelund U, Hildebrand M, Collings PJ. Physical activity, sedentary time and adiposity during the first two decades of life. Proc Nutr Soc. 2014;73(2):319–329. doi: 10.1017/S0029665114000019.
    1. Ruiz JR, Rizzo NS, Hurtig-Wennlof A, Ortega FB, Warnberg J, Sjostrom M. Relations of total physical activity and intensity to fitness and fatness in children: the European Youth Heart Study. Am J Clin Nutr. 2006;84(2):299–303.
    1. Gutin B, Yin Z, Humphries MC, Barbeau P. Relations of moderate and vigorous physical activity to fitness and fatness in adolescents. Am J Clin Nutr. 2005;81(4):746–750.
    1. Hay J, Maximova K, Durksen A, Carson V, Rinaldi RL, Torrance B, et al. Physical activity intensity and cardiometabolic risk in youth. Arch Pediatr Adolesc Med. 2012;166(11):1022–1029. doi: 10.1001/archpediatrics.2012.1028.
    1. Aires L, Silva P, Silva G, Santos MP, Ribeiro JC, Mota J. Intensity of physical activity, cardiorespiratory fitness, and body mass index in youth. J Phys Act Health. 2010;7(1):54–59. doi: 10.1123/jpah.7.1.54.
    1. Collings PJ, Wijndaele K, Corder K, Westgate K, Ridgway CL, Dunn V, et al. Levels and patterns of objectively-measured physical activity volume and intensity distribution in UK adolescents: the ROOTS study. Int J Behav Nutr Phys Act. 2014;11:23. doi: 10.1186/1479-5868-11-23.
    1. Hesketh KR, McMinn AM, Ekelund U, Sharp SJ, Collings PJ, Harvey NC, et al. Objectively measured physical activity in four-year-old British children: a cross-sectional analysis of activity patterns segmented across the day. Int J Behav Nutr Phys Act. 2014;11:1. doi: 10.1186/1479-5868-11-1.
    1. Katzmarzyk PT. Physical activity, sedentary behavior, and health: paradigm paralysis or paradigm shift? Diabetes. 2010;59(11):2717–2725. doi: 10.2337/db10-0822.
    1. Janssen I, LeBlanc AG. Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. Int J Behav Nutr Phys. 2010;7:40. doi: 10.1186/1479-5868-7-40.
    1. Mekary RA, Willett WC, Hu FB, Ding EL. Isotemporal substitution paradigm for physical activity epidemiology and weight change. Am J Epidemiol. 2009;170(4):519–527. doi: 10.1093/aje/kwp163.
    1. Loprinzi PD, Cardinal BJ, Lee H, Tudor-Locke C. Markers of adiposity among children and adolescents: implications of the isotemporal substitution paradigm with sedentary behavior and physical activity patterns. J Diabetes Metabol Disord. 2015;14:46. doi: 10.1186/s40200-015-0175-9.
    1. Aggio D, Smith L, Hamer M. Effects of reallocating time in different activity intensities on health and fitness: a cross sectional study. Int J Behav Nutr Phy. 2015;12:83. doi: 10.1186/s12966-015-0249-6.
    1. Eloranta AM, Lindi V, Schwab U, Tompuri T, Kiiskinen S, Lakka HM, et al. Dietary factors associated with overweight and body adiposity in Finnish children aged 6-8 years: the PANIC Study. Int J Obes. 2012;36(7):950–955. doi: 10.1038/ijo.2012.89.
    1. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child. 1995;73(1):25–29. doi: 10.1136/adc.73.1.25.
    1. Tompuri TT, Lakka TA, Hakulinen M, Lindi V, Laaksonen DE, Kilpelainen TO, et al. Assessment of body composition by dual-energy X-ray absorptiometry, bioimpedance analysis and anthropometrics in children: the Physical Activity and Nutrition in Children study. Clin Physiol Funct Imaging. 2015;35(1):21–33. doi: 10.1111/cpf.12118.
    1. Wells JC. A critique of the expression of paediatric body composition data. Arch Dis Child. 2001;85(1):67–72. doi: 10.1136/adc.85.1.67.
    1. Lintu N, Tompuri T, Viitasalo A, Soininen S, Laitinen T, Savonen K, et al. Cardiovascular fitness and haemodynamic responses to maximal cycle ergometer exercise test in children 6–8 years of age. J Sport Sci. 2014;32(7):652–659. doi: 10.1080/02640414.2013.845681.
    1. Brage S, Ekelund U, Brage N, Hennings MA, Froberg K, Franks PW, et al. Hierarchy of individual calibration levels for heart rate and accelerometry to measure physical activity. J Appl Physiol. 2007;103(2):682–692. doi: 10.1152/japplphysiol.00092.2006.
    1. Brage S, Brage N, Franks PW, Ekelund U, Wareham NJ. Reliability and validity of the combined heart rate and movement sensor Actiheart. Eur J Clin Nutr. 2005;59(4):561–570. doi: 10.1038/sj.ejcn.1602118.
    1. Brooke HL, Corder K, Atkin AJ, van Sluijs EMF. A systematic literature review with meta-analyses of within- and between-day differences in objectively measured physical activity in school-aged children. Sports Med. 2014;44(10):1427–1438. doi: 10.1007/s40279-014-0215-5.
    1. Stegle O, Fallert SV, MacKay DJC, Brage S. Gaussian process robust regression for noisy heart rate data. IEEE Trans Bio Med Eng. 2008;55(9):2143–2151. doi: 10.1109/TBME.2008.923118.
    1. Brage S, Westgate K, Franks PW, Stegle O, Wright A, Ekelund U, et al. Estimation of free-living energy expenditure by heart rate and movement sensing: a doubly-labelled water study. PloS One. 2015;10(9):e0137206. doi: 10.1371/journal.pone.0137206.
    1. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005;8(7A):1133–1152. doi: 10.1079/PHN2005801.
    1. Evenson KR, Catellier DJ, Gill K, Ondrak KS, McMurray RG. Calibration of two objective measures of physical activity for children. J Sports Sci. 2008;26(14):1557–1565. doi: 10.1080/02640410802334196.
    1. Collings PJ, Wijndaele K, Corder K, Westgate K, Ridgway CL, Sharp SJ, et al. Prospective associations between sedentary time, sleep duration and adiposity in adolescents. Sleep Med. 2015;16(6):717–722. doi: 10.1016/j.sleep.2015.02.532.
    1. Brage S, Westgate K, Wijndaele K, Godinho J, Griffin S, Wareham N. Evaluation of a method for minimising diurnal information bias in objective sensor data. Int Conf Amb Mon Phys Act Mov. 2013(Conference Proceeding).
    1. Carnell S, Wardle J. Measuring behavioural susceptibility to obesity: validation of the child eating behaviour questionnaire. Appetite. 2007;48(1):104–113. doi: 10.1016/j.appet.2006.07.075.
    1. Viana V, Sinde S, Saxton JC. Children’s Eating Behaviour Questionnaire: associations with BMI in Portuguese children. Br J Nutr. 2008;100(2):445–450. doi: 10.1017/S0007114508894391.
    1. White IR, Royston P, Wood AM. Multiple imputation using chained equations: issues and guidance for practice. Stat Med. 2011;30(4):377–399. doi: 10.1002/sim.4067.
    1. Treuth MS, Hou N, Young DR, Maynard LM. Accelerometry-measured activity or sedentary time and overweight in rural boys and girls. Obes Res. 2005;13(9):1606–1614. doi: 10.1038/oby.2005.197.
    1. Butte NF, Puyau MR, Adolph AL, Vohra FA, Zakeri I. Physical activity in nonoverweight and overweight Hispanic children and adolescents. Med Sci Sports Exerc. 2007;39(8):1257–1266. doi: 10.1249/mss.0b013e3180621fb6.
    1. Kwon S, Janz KF, Burns TL, Levy SM. Association between light-intensity physical activity and adiposity in childhood. Pediatr Exerc Sci. 2011;23(2):218–229. doi: 10.1123/pes.23.2.218.
    1. Hughes AR, Henderson A, Ortiz-Rodriguez V, Artinou ML, Reilly JJ. Habitual physical activity and sedentary behaviour in a clinical sample of obese children. Int J Obes. 2006;30(10):1494–1500. doi: 10.1038/sj.ijo.0803334.
    1. Metallinos-Katsaras ES, Freedson PS, Fulton JE, Sherry B. The association between an objective measure of physical activity and weight status in preschoolers. Obesity. 2007;15(3):686–694. doi: 10.1038/oby.2007.571.
    1. Carson V, Ridgers ND, Howard BJ, Winkler EA, Healy GN, Owen N, et al. Light-intensity physical activity and cardiometabolic biomarkers in US adolescents. PloS One. 2013;8(8):e71417. doi: 10.1371/journal.pone.0071417.
    1. Collings PJ, Brage S, Ridgway CL, Harvey NC, Godfrey KM, Inskip HM, et al. Physical activity intensity, sedentary time, and body composition in preschoolers. Am J Clin Nutr. 2013;97(5):1020–1028. doi: 10.3945/ajcn.112.045088.
    1. Basterfield L, Pearce MS, Adamson AJ, Reilly JK, Parkinson KN, Reilly JJ, et al. Effect of choice of outcome measure on studies of the etiology of obesity in children. Ann Epidemiol. 2012;22(12):888–891. doi: 10.1016/j.annepidem.2012.09.007.
    1. Biddle S, Mutrie N. Psychology of physical activity: determinants, well-being and interventions. 2. London: Routledge; 2008.
    1. Talma H, Chinapaw MJ, Bakker B, HiraSing RA, Terwee CB, Altenburg TM. Bioelectrical impedance analysis to estimate body composition in children and adolescents: a systematic review and evidence appraisal of validity, responsiveness, reliability and measurement error. Obes Rev Off J Int Assoc Study Obes. 2013;14(11):895–905. doi: 10.1111/obr.12061.
    1. Corder K, Brage S, Wareham NJ, Ekelund U. Comparison of PAEE from combined and separate heart rate and movement models in children. Med Sci Sports Exerc. 2005;37(10):1761–1767. doi: 10.1249/.
    1. Corder K, Brage S, Mattocks C, Ness A, Riddoch C, Wareham NJ, et al. Comparison of two methods to assess PAEE during six activities in children. Med Sci Sports Exerc. 2007;39(12):2180–2188. doi: 10.1249/mss.0b013e318150dff8.
    1. Thompson D, Batterham AM, Bock S, Robson C, Stokes K. Assessment of low-to-moderate intensity physical activity thermogenesis in young adults using synchronized heart rate and accelerometry with branched-equation modeling. J Nutr. 2006;136(4):1037–1042.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir