Total SB and BMI/WC z-scores
More time spent in total SB was associated with higher BMI and WC z-scores, and these associations were strongest at the upper percentiles of the z-score distributions (Table 2). For example, at the 50th percentiles, each additional hour of SB was associated with a 0.03 (P < 0.001) higher BMI z-score and with a 0.04 (P < 0.001) higher WC z-score (Table 2, models 1b), whereas at the 90th percentiles, each additional hour of SB was associated with a 0.05 (P = 0.003) higher BMI z-score and with a 0.08 (P < 0.001) higher WC z-score (Table 2, models 1b). However, these associations did not hold after adjustment for MVPA (Table 2, models 2). Visual representation of the total SB association with WC z-score is presented in Figure 1, and the linear regression coefficients for total SB and the mean WC z-score (model 1b: β = 0.06, P < 0.001; model 2: beta = 0.01, P = 0.504) are plotted for comparison.
Television viewing and BMI/WC z-scores
In the subsample with complete television viewing data, more time spent watching television was associated with higher BMI and WC z-scores, and these associations were strongest at the upper percentiles of the z-score distributions and remained after adjustment for total SB and for MVPA (Fig. 2 and see Table, Supplemental Digital Content 1, http://links.lww.com/MSS/A786, which presents the specific beta coefficients, SE, and P values). For example, at the 10th percentiles, relative to <1 h·d–1 of television viewing, 3–4 h·d–1 was associated with a 0.08 (P = 0.005) higher BMI z-score and with a 0.08 (P = 0.022) higher WC z-score, whereas at the 90th percentiles, relative to <1 h·d–1 of television viewing, 3–4 h·d–1 was associated with a 0.34 (P < 0.001) higher BMI z-score and with a 0.35 (P < 0.001) higher WC z-score.
We repeated our analysis of accelerometry estimated MVPA and total SB using BMI z-scores calculated using reference population data from the UK and the US. The MVPA and total SB associations remained similar to our findings that used internal mean and SD values to calculate BMI z-scores (see Table, Supplemental Digital Content 2, http://links.lww.com/MSS/A787, which presents the results using BMI z-scores calculated using UK and US reference data). We also repeated our analysis by sex, race, and household income categories. The MVPA and the total SB associations with BMI and WC z-scores remained similar within each group (see Table, Supplemental Digital Content 3, http://links.lww.com/MSS/A788, which presents the results stratified by demographics). The WC z-score associations remained similar with the NHANES data removed (see Table, Supplemental Digital Content 4, http://links.lww.com/MSS/A789, which presents the WC z-score results with NHANES data removed).
This is the largest study to date to use standardized accelerometry methods and quantile regression to study the association between physical activity energy expenditure and childhood obesity. We found that more time spent in MVPA was independently associated with lower BMI and WC. By contrast, total SB was not independently associated with BMI or WC in our sample of children and adolescents. However, time spent in a specific leisure time SB, television viewing, was independently associated with higher BMI and WC. Interestingly, our quantile regression analysis revealed that the associations between MVPA, television viewing, and BMI/WC z-scores were nonlinear; these exposures were most influential at the upper percentiles of the BMI and WC frequency distributions. These nonlinear associations indicate that if more children were to meet the physical activity guidelines and limit their television viewing hours, then the prevalence of childhood obesity could be reduced by specifically shifting the upper percentiles of the BMI and WC frequency distributions to lower values.
We have shown in previous United States–based studies that lower accelerometry estimated MVPA and higher television viewing are associated with greater increases in BMI over time during childhood, and that these associations were strongest at the upper BMI percentiles (25,27). These findings are consistent with our present results and together support that increasing MVPA and lowering television viewing could help to prevent childhood obesity, potentially by increasing energy expenditure and correcting energy imbalance. However, there are contrasting data suggesting that increases in weight status lead to lower MVPA and higher SB (22). We cannot rule out reverse causality in our cross-sectional study. We also cannot rule out that a cyclical (bidirectional) relationship exists between physical activity energy expenditure and weight status. Indeed, the stronger association between MVPA and television viewing at the upper percentiles of the BMI/WC distributions could reflect such a cyclical relationship.
Alternatively, the stronger associations at the upper percentiles could be explained by gene–environment interactions. It is well known that BMI and WC are heritable traits and a large number of genetic loci are associated with higher BMI and WC (9). Specifically, individuals at the upper percentiles of the BMI and WC distributions are more likely to be genetically predisposed to obesity (27). If these individuals are exposed to lower MVPA or higher television viewing, then greater increases in their BMI/WC could be due to their genetic susceptibility to obesity, compared with those who are less likely to be genetically predisposed to higher BMI/WC at the lower percentiles of the distribution.
It has been proposed that SB, independent of time spent in MVPA, is an obesity risk factor. Studies that have estimated total SB using accelerometry have observed associations with measures of childhood obesity (10,17,20,24,25,37), but such associations have tended not to hold after adjustment for MVPA (17,20,24,37). We observed the same pattern in our study, suggesting that total SB is not an independent risk factor for childhood obesity. By contrast, there is convincing evidence that television viewing is associated with childhood obesity, independent of MVPA (13,27,36). Television viewing is the most common leisure time SB, but it is also a behavior associated with snacking and exposure to food advertisements (35). Therefore, the television viewing associations may not be fully explained by the SB–energy expenditure paradigm and could be partly explained by increases in energy intake. In addition, television viewing is the most common activity before going to bed (7), and our television viewing associations could be partly explained by reductions in total sleep time (27).
The strengths of our study include the large sample size and the standardized methods to estimate MVPA and total SB using accelerometry. Given the large sample size, we were able to perform sensitivity analyses and replicated our findings in key subgroups. We used quantile regression, and the advantages of this analytical approach are increasingly being recognized for investigating continuous variables in epidemiological studies (4). By using this method, we observed stronger associations between MVPA, total SB (not independent of MVPA), and television viewing at the upper percentiles of the BMI and WC z-score distributions; these patterns of association across the percentiles would have been missed had we modeled the mean z-scores using linear regression. We used both BMI and WC as primary outcomes to make inferences on overall fat mass and visceral fat mass, and the latter is of particular clinical importance (2). However, it should be noted that studies have shown stronger correlations between WC and total fat mass compared with visceral fat mass (18); therefore, WC may not be a specific marker for visceral adiposity and follow-up studies using more direct estimates of visceral fat mass are needed. Our study does have other limitations. We adjusted for key confounders, but the ICAD database does not have dietary or sleep data to include as covariates. Also, given the populations of origin for this study, a dichotomous race variable was used; it will be important to replicate our findings in more diverse populations. The majority (91%) of participants provide three or more days of valid accelerometry data. We included participants who provided one and two valid days accelerometry data and the MVPA and total SB estimates may not be representative of habitual patterns among these participants. We used a cross-sectional design and so were not able to establish the temporality between our exposures and z-score outcomes.
Preventing childhood obesity has the greatest potential to counter the short and long-term health problems associated with obesity. We conclude that increasing MVPA and decreasing television viewing in childhood could help to prevent obesity in early life. By using quantile regression, we showed that increasing the time children spend in MVPA and decreasing the time they spend watching television could help to lower BMI and WC, especially for children in the population with the higher BMI and WC for their age.
The study results are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The ICAD Collaborators include the following: Prof. L. B. Andersen, University of Southern Denmark, Odense, Denmark (Copenhagen School Child Intervention Study); Prof. S. Anderssen, Norwegian School for Sport Science, Oslo, Norway (EYHS, Norway); Prof. G. Cardon, Department of Movement and Sports Sciences, Ghent University, Belgium (Belgium Pre-School Study); Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Hyattsville, MD (NHANES); Prof. A. Cooper, Centre for Exercise, Nutrition and Health Sciences, University of Bristol, UK (PEACH); Dr. R. Davey, Centre for Research and Action in Public Health, University of Canberra, Australia (Children's Health and Activity Monitoring for Schools [CHAMPS]); Prof. U. Ekelund, Norwegian School of Sport Sciences, Oslo, Norway, and MRC Epidemiology Unit, University of Cambridge, UK; Dr. D. W. Esliger, School of Sports, Exercise and Health Sciences, Loughborough University, UK; Dr. K. Froberg, University of Southern Denmark, Odense, Denmark (EYHS, Denmark); Dr. P. Hallal, Postgraduate Program in Epidemiology, Federal University of Pelotas, Brazil (1993 Pelotas Birth Cohort); Prof. K. F. Janz, Department of Health and Human Physiology, Department of Epidemiology, University of Iowa, Iowa City (Iowa Bone Development Study); Dr. K. Kordas, School of Social and Community Medicine, University of Bristol, UK (ALSPAC); Dr. S. Kriemler, Institute of Social and Preventive Medicine, University of Zürich, Switzerland (Kinder-Sportstudie); Dr. A. Page, Centre for Exercise, Nutrition and Health Sciences, University of Bristol, UK; Prof. R. Pate, Department of Exercise Science, University of South Carolina, Columbia (Physical Activity in Pre-school Children [CHAMPS-US] and Project Trial of Activity for Adolescent Girls); Dr. J. J. Puder, Service of Endocrinology, Diabetes and Metabolism, Centre Hospitalier Universitaire Vaudois, University of Lausanne, Switzerland (Ballabeina Study); Prof. J. Reilly, Physical Activity for Health Group, School of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK (Movement and Activity Glasgow Intervention in Children); Prof. J. Salmon, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Australia (Children Living in Active Neigbourhoods); Prof. L. B. Sardinha, Exercise and Health Laboratory, Faculty of Human Movement, Technical University of Lisbon, Portugal (EYHS, Portugal); Dr. L. B. Sherar, School of Sports, Exercise and Health Sciences, Loughborough University, UK; Dr. A. Timperio, Centre for Physical Activity and Nutrition Research, Deakin University Melbourne, Australia (Healthy Eating and Play Study); and Dr. E. M. F. van Sluijs, MRC Epidemiology Unit, University of Cambridge, UK (Sport, Physical activity and Eating Behaviour: Environmental Determinants in Young people). The authors thank all participants and funders of the original studies that contributed data to ICAD. The pooling of the data was funded through a grant from the National Prevention Research Initiative (grant no. G0701877) (http://www.mrc.ac.uk/research/initiatives/national-prevention-research-initiative-npri/).
The funding partners relevant to this award are the following: British Heart Foundation, Cancer Research UK, Department of Health, Diabetes UK, Economic and Social Research Council, Medical Research Council, Research and Development Office for the Northern Ireland Health and Social Services, Chief Scientist Office, Scottish Executive Health Department, The Stroke Association, Welsh Assembly Government, and World Cancer Research Fund. This work was additionally supported by the Medical Research Council (grant nos. MC_UU_12015/3 and MC_UU_12015/7), Bristol University, Loughborough University, and Norwegian School of Sport Sciences. The authors also gratefully acknowledge the contribution of Prof. Chris Riddoch, Prof. Ken Judge, and Dr. Pippa Griew to the development of ICAD.
The UK Medical Research Council and the Wellcome Trust (grant no. 102215/2/13/2) and the University of Bristol provide core support for ALSPAC.
Jonathan Mitchell was supported by the National Heart, Lung, And Blood Institute of the National Institutes of Health under award no. K01HL123612. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Altman DG, Royston P. The cost of dichotomising continuous variables. BMJ
2. Barreira TV, Broyles ST, Gupta AK, Katzmarzyk PT. Relationship of anthropometric indices to abdominal and total body fat in youth: sex and race differences. Obesity (Silver Spring)
3. Bassett DR, John D, Conger SA, Fitzhugh EC, Coe DP. Trends in physical activity and sedentary behaviors of United States youth. J Phys Act Health
4. Beyerlein A. Quantile regression—opportunities and challenges from a user's perspective. Am J Epidemiol
5. Beyerlein A, Toschke AM, von Kries R. Risk factors for childhood overweight: shift of the mean body mass index and shift of the upper percentiles: results from a cross-sectional study. Int J Obes (Lond)
6. Biddle SJ, Gorely T, Marshall SJ. Is television
viewing a suitable marker of sedentary behavior in young people? Ann Behav Med
7. Biddle SJ, Marshall SJ, Gorely T, Cameron N. Temporal and environmental patterns of sedentary and active behaviors during adolescents' leisure time. Int J Behav Med
8. Boyd A, Golding J, Macleod J, et al. Cohort profile: the ‘children of the 90s’—the index offspring of the Avon Longitudinal Study of Parents and Children. Int J Epidemiol
9. Bradfield JP, Taal HR, Timpson NJ, et al. A genome-wide association meta-analysis identifies new childhood obesity loci. Nat Genet
10. Carson V, Stone M, Faulkner G. Patterns of sedentary behavior and weight status among children. Pediatr Exerc Sci
11. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ
12. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child
13. Ekelund U, Brage S, Froberg K, et al. TV viewing and physical activity are independently associated with metabolic risk in children: the European Youth Heart Study. PLoS Med
14. 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
15. Ekelund U, Tomkinson G, Armstrong N. What proportion of youth are physically active? Measurement issues, levels and recent time trends. Br J Sports Med
16. Flegal KM, Carroll MD, Kuczmarski RJ, Johnson CL. Overweight and obesity in the United States: prevalence and trends, 1960–1994. Int J Obes Relat Metab Disord
17. Katzmarzyk PT, Barreira TV, Broyles ST, et al. Physical activity, sedentary time, and obesity in an international sample of children. Med Sci Sports Exerc
18. Katzmarzyk PT, Bouchard C. Where is the beef? Waist circumference is more highly correlated with BMI
and total body fat than with abdominal visceral fat in children. Int J Obes (Lond)
19. Kuczmarski RJ, Ogden CL, Grummer-Strawn LM, et al. CDC growth charts: United States. Adv Data
20. Kwon S, Burns TL, Levy SM, Janz KF. Which contributes more to childhood adiposity-high levels of sedentarism or low levels of moderate-through-vigorous physical activity? The Iowa Bone Development Study. J Pediatr
21. Matthews CE, Chen KY, Freedson PS, et al. Amount of time spent in sedentary behaviors in the United States, 2003–2004. Am J Epidemiol
22. Metcalf BS, Hosking J, Jeffery AN, Voss LD, Henley W, Wilkin TJ. Fatness leads to inactivity, but inactivity does not lead to fatness: a longitudinal study in children (EarlyBird 45). Arch Dis Child
23. Mitchell JA, Byun W. Sedentary behavior and health outcomes in children and adolescents. Am J Lifestyle Med
24. Mitchell JA, Mattocks C, Ness AR, et al. Sedentary behavior and obesity in a large cohort of children. Obesity (Silver Spring)
25. Mitchell JA, Pate RR, Beets MW, Nader PR. Time spent in sedentary behavior and changes in childhood BMI
: a longitudinal study from ages 9 to 15 years. Int J Obes (Lond)
26. Mitchell JA, Pate RR, Dowda M, et al. A prospective study of sedentary behavior in a large cohort of youth. Med Sci Sports Exerc
27. Mitchell JA, Rodriguez D, Schmitz KH, Audrain-McGovern J. Greater screen time is associated with adolescent
obesity: a longitudinal study of the BMI
distribution from Ages 14 to 18. Obesity (Silver Spring)
28. Nader PR, Bradley RH, Houts RM, McRitchie SL, O'Brien M. Moderate-to-vigorous physical activity from ages 9 to 15 years. JAMA
29. Ng M, Fleming T, Robinson M, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet
30. Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of childhood and adult obesity in the United States, 2011–2012. JAMA
31. Pettitt DJ, Talton J, Dabelea D, et al. Prevalence of diabetes in U.S. youth in 2009: the SEARCH for diabetes in youth study. Diabetes Care
32. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report
. Washington (DC): U.S. Department of Health and Human Services, 2008. Available from: U.S. Department of Health and Human Services.
33. Sedentary Behaviour Research Network. Letter to the editor: standardized use of the terms “sedentary” and “sedentary behaviours.” Appl Physiol Nutr Metab
34. Sherar LB, Griew P, Esliger DW, et al. International children's accelerometry
): design and methods. BMC Public Health
35. Sisson SB, Shay CM, Broyles ST, Leyva M. Television
-viewing time and dietary quality among U.S. children and adults. Am J Prev Med
36. Staiano AE, Harrington DM, Broyles ST, Gupta AK, Katzmarzyk PT. Television
, adiposity, and cardiometabolic risk in children and adolescents. Am J Prev Med
37. Steele RM, van Sluijs EM, Cassidy A, Griffin SJ, Ekelund U. Targeting sedentary time or moderate- and vigorous-intensity activity: independent relations with adiposity in a population-based sample of 10-y-old British children. Am J Clin Nutr
38. The NS, Suchindran C, North KE, Popkin BM, Gordon-Larsen P. Association of adolescent
obesity with risk of severe obesity in adulthood. JAMA
39. Tirosh A, Shai I, Afek A, et al. Adolescent BMI
trajectory and risk of diabetes versus coronary disease. N Engl J Med
40. Troiano RP, Berrigan D, Dodd KW, Mâsse LC, Tilert T, McDowell M. Physical activity in the United States measured by accelerometer. Med Sci Sports Exerc
41. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc
Keywords:© 2017 American College of Sports Medicine
ADOLESCENT; ACCELEROMETRY; ICAD; BMI; TELEVISION