Figure 1 presents the combined associations of MVPA and sedentary time with BMI z-scores. In both boys and girls, there was a significant decreasing trend for BMI z-scores across increasing tertiles of MVPA (P < 0.001), but the trend across tertiles of sedentary behavior was not significant. Similar trends in BMI z-score were observed across tertiles of VPA and sedentary time (results not shown).
Figure 2 shows odds ratios for assessing the associations of MVPA and sedentary behavior with obesity across the study sites. The odds ratios for obesity were significant for MVPA (0.49; 95% CI, 0.44–0.55), VPA (0.41; 95% CI, 0.37–0.46), and sedentary time (1.19; 95% CI, 1.08–1.30) in the overall sample. The negative associations of MVPA and VPA with obesity were significant in all of the 12 study sites, whereas the positive association between sedentary time and obesity was significant in only five (Australia, Canada, Colombia, South Africa, and the United States) of the 12 study sites.
The results of the ROC curve analyses are presented in Table 3. In the total sample and in both boys and girls, the AUC for sedentary behavior was significantly lower than the AUC for both MVPA and VPA (P < 0.001), and the AUC for MVPA was significantly lower than the AUC for VPA (P < 0.001). The overall threshold for MVPA was 55 min·d−1 (95% CI, 50–64), and the threshold was higher in the boys (65 min·d−1; 95% CI, 55–75) compared to the girls (49 min·d−1; 95% CI, 43–62). The overall threshold for VPA was 14 min·d−1 (95% CI, 11–16), and the threshold was higher in the boys (20 min·d−1; 95% CI, 16–22) compared to the girls (11 min·d−1; 95% CI, 9–13). The overall threshold for sedentary time was 482 min·d−1 (95% CI, 455–535) and was similar in the girls (497 min·d−1; 95% CI, 449–534) and boys (482 min·d−1; 95% CI, 455–554). In country-specific analyses (results not shown), the thresholds ranged from 39 to 72 min·d−1 for MVPA in 10 sites where the AUC was significant; from 8 to 22 min·d−1 for VPA in the 12 sites where the AUC was significant; and from 475 to 609 min·d−1 for sedentary behavior in 8 sites where the AUC was significant.
In sensitivity analyses conducted using the Treuth et al. (27) thresholds for MVPA, the associations between MVPA and obesity were similar. The odds ratio for obesity was similar (0.27; 95% CI, 0.23–0.33), and the association between MVPA and obesity remained significant in all 12 study sites. The AUC for the ROC curve analysis was also significant when using the Treuth et al. (27) MVPA threshold (AUC, 0.66; 95% CI, 0.65–0.67). The associations observed when using the WHO BMI thresholds (4) were also maintained when using the CDC BMI thresholds (14). For example, the odds ratios (per SD) for obesity were similar for MVPA (0.47; 95% CI, 0.42–0.53), VPA (0.38; 95% CI, 0.34–0.43), and sedentary time (1.18; 95% CI, 1.07–1.32) in the overall sample when applying the CDC thresholds. Furthermore, the associations of MVPA and VPA with obesity remained significant in all 12 sites, and the association between sedentary time and obesity remained significant in four of the 12 sites, respectively.
Time spent in MVPA and VPA was consistently associated with obesity in this multinational sample of children, and the optimal threshold of 55 min·d−1 of MVPA supports global recommendations, which call for 60 min·d−1 of physical activity for children and youth (33). The robust, standardized data collection procedures used across all world regions in this study and the consistency of the relationship across countries at different stages of economic and epidemiologic transition strongly indicate that physical activity is a robust correlate of obesity across different cultures, races, and geographical settings. The relationship between sedentary time and obesity is weaker, not independent of MVPA, and less consistent across the study sites.
The results support the conclusion of the 2008 Physical Activity Recommendations for Americans that “Regular physical activity in children and adolescents promotes a healthy body weight and body composition” (32). Indeed, results from the National Institute of Child Health and Human Development Study of Early Child Care and Youth Development demonstrated that time spent in MVPA was negatively associated with changes in BMI from 9 to 15 yr of age (19). The results of the present study confirm the association between MVPA and obesity in US children and extend the results to other world regions. Accelerometers are now ubiquitous in physical activity research as well as in “real-world” settings, as smart phones and other commercial devices such as wrist band physical activity monitors have proliferated in the marketplace (15). Whereas current physical activity recommendations were largely informed by studies relying on self-reported measures of physical activity, objective data such as those reported in the present study have the potential to inform future physical activity guidelines with real-world significance. However, there is a need for independent validation of new devices as they come into the market to ensure they are measuring what they purport to measure.
The results of the present study also support data from regional multinational studies that have objectively measured physical activity by accelerometry. Although limited to European children, time spent in MVPA was significantly inversely associated with body fatness (sum of skinfolds) in 1292 nine- to 10-yr-old children from four countries in the European Youth Heart Study.(7) Results of cluster analyses among 766 10- to 12-yr-old European children participating in the ENERGY study indicated that both higher MVPA and lower sedentary time in girls was associated with lower BMI; however, MVPA was the dominant correlate in boys (3). Furthermore, ROC curve analyses among 2094 adolescents from the HELENA study in Europe identified an optimal threshold of 55 min·d−1 for differentiating obese from normal weight youth, and their thresholds were similarly higher in the boys (56 min·d−1) compared to the girls (49 min·d−1) (17). This sex difference in physical activity levels associated with obesity has also been reported when using steps per day as a metric (31). The underlying mechanism for this sex difference is not understood; however, girls tend to have higher levels of percent body fat for a given BMI percentile (10), which could influence the associations with physical activity. The results of the present multinational study extend those of these smaller studies that were limited to European context and confirm that MVPA is a robust correlate of obesity in multiple economic and geographic settings.
The finding in the present study that time spent in MVPA was more consistently related to obesity than time spent in sedentary behavior supports results from the International Children’s Accelerometry Database (ICAD; n = 20,871), which indicated that MVPA, but not sedentary time, was associated with cardiometabolic risk factors, including waist circumference (6). The ICAD analysis provides a wealth of important information, although it is limited to data from studies conducted in the United States, Europe, and Brazil using different protocols (6). The present study expands upon these data by the use of a standardized protocol across the 12 sites from five different world regions and confirms that MVPA is more strongly related to obesity than sedentary behavior among 9- to 11-yr-old children. The reason for the lack of association between sedentary time and obesity in some countries is not known. Television viewing is one type of sedentary behavior that has been more strongly associated with obesity in children than global measures of overall sedentary time (26). Television viewing may be a more prominent activity in the developed countries, where an association was found (Australia, Canada, Colombia, South Africa, and the United States), which may partially explain the greater associations observed in these countries. However, more research is required to better understand associations among indicators of specific sedentary behaviors and health outcomes such as obesity.
The results of the sensitivity analyses indicate that similar associations with obesity are obtained when using different MVPA thresholds or different definitions of obesity. We chose to use the Evenson et al. (8) MVPA threshold for our primary analysis, as a 15-s epoch may be more appropriate than a 60-s epoch to capture the sporadic nature of children’s activity, and it provides the best classification accuracy among the currently available thresholds for sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents (28). Furthermore, we chose to use the WHO BMI thresholds given that this is an international study of children from five world regions. However, the consistency of the results when using the CDC BMI thresholds is encouraging and suggests that our results may be comparable to other studies that have used the CDC thresholds.
This study has several strengths and limitations that warrant discussion. A marked strength is the large diverse sample of children from study sites from every world region, which varied in their prevalence of obesity, MVPA, VPA, and sedentary behavior. This served to increase the variability among the dependent and independent variables and increased the generalizability of the results. Hence, the results strongly indicate that the associations seen between physical activity and obesity are robust and not related to certain types of cultures, races, or geographical settings. An additional strength is the rigorous standardization of the protocol across all study sites, which included the use of the same anthropometric equipment and the same model of accelerometer to objectively monitor physical activity and sedentary behavior at all study sites. Furthermore, all data management and analysis were centrally conducted at the coordinating center, which assured consistency in data handling and interpretation.
The cross-sectional design of this study limits conclusions regarding cause-and-effect relationships and the direction of the associations observed. Furthermore, we cannot exclude the possibility that unmeasured confounding variables may explain some of the observed relationships. The results for MVPA and VPA were very consistent across the study sites, but it is not known whether lower levels of physical activity are the cause or the consequence of obesity in this sample of children. We objectively assessed physical activity and sedentary behavior using accelerometry; however, a limitation of this approach is the inability to quantify some physical activities such as cycling and swimming. It should be noted that the optimal threshold (identified as 55 min·d−1 MVPA in this study) may vary according to the method and algorithm used in classifying physical activity intensity and hence the threshold should be regarded as showing the approximate magnitude rather than an exact value. Finally, we used a threshold of 25 or less counts per 15 s to quantify time spent in sedentary behavior. Although this is commonly used threshold that is related to sitting in children during school hours (23), it does not measure “sitting” per se nor time spent in specific sedentary behaviors such as TV viewing; rather, it captures nonmovement and time spent in low-intensity movements. Further research using devices that capture posture in addition to intensity is required to better quantify sedentary behavior in children.
In conclusion, higher levels of MVPA and VPA were associated with lower odds of obesity in this multinational sample of children, independent of sedentary behavior. Attaining at least 55 min·d−1 of MVPA seems to be associated with lower obesity, which supports guidelines that call for 60 min·d−1 of MVPA in childhood. The implementation of a standardized protocol across countries that vary widely in economic and epidemiologic transition highlights the robustness of the relationship between physical activity and obesity in children.
Funding/Support: ISCOLE was funded by The Coca-Cola Company. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript.
The authors thank the ISCOLE External Advisory Board and the ISCOLE participants and their families who made this study possible. The ISCOLE Research Group includes the following: Coordinating Center, Pennington Biomedical Research Center: Peter T. Katzmarzyk, PhD (co-PI), Timothy S. Church, MD, PhD (co-PI), Denise G. Lambert, RN (Project Manager), Tiago Barreira, PhD, Stephanie Broyles, PhD, Ben Butitta, BS, Catherine Champagne, PhD, RD, Shannon Cocreham, MBA, Kara Dentro, MPH, Katy Drazba, MPH, Deirdre Harrington, PhD, William Johnson, PhD, Dione Milauskas, MS, Emily Mire, MS, Allison Tohme, MPH, Ruben Rodarte MS, MBA; Data Management Center, Wake Forest University: Bobby Amoroso, BS, John Luopa, BS, Rebecca Neiberg, MS, Scott Rushing, BS; Australia, University of South Australia: Timothy Olds, PhD (site co-PI), Carol Maher, PhD (site co-PI), Lucy Lewis, PhD, Katia Ferrar, B Physiol (Hon), Effie Georgiadis, BPsych, Rebecca Stanley, BAppSc (OT) Hon; Brazil, Centro de Estudos do Laboratório de Aptidão Física de São Caetano do Sul (CELAFISCS): Victor Keihan Rodrigues Matsudo, MD, PhD (site PI), Sandra Matsudo, MD, PhD, Timoteo Araujo, MSc, Luis Carlos de Oliveira, MSc, Leandro Rezende, BSc, Luis Fabiano, BSc, Diogo Bezerra, BSc, Gerson Ferrari, MSc; Canada, Children’s Hospital of Eastern Ontario Research Institute: Mark S. Tremblay, PhD (site co-PI), Jean-Philippe Chaput, PhD (site co-PI), Priscilla Bélanger, MA, Mike Borghese, MSc, Charles Boyer, MA, Allana LeBlanc, MSc, Claire Francis, MSc, Geneviève Leduc, PhD; China, Tianjin Women’s and Children’s Health Center: Pei Zhao, MD (site co-PI), Gang Hu, MD, PhD (site co-PI), Chengming Diao, MD, Wei Li, MD, Weiqin Li, MSc, Enqing Liu, MD, Gongshu Liu, MD, Hongyan Liu, MSc, Jian Ma, MD, Yijuan Qiao, MSc, Huiguang Tian, PhD, Yue Wang, MD, Tao Zhang, MSc, Fuxia Zhang, MD; Colombia, Universidad de los Andes: Olga Sarmiento, MD, PhD (site PI), Julio Acosta, Yalta Alvira, BS, Maria Paula Diaz, Rocio Gamez, BS, Maria Paula Garcia, Luis Guillermo Gómez, Lisseth Gonzalez, Silvia Gonzalez, RD, Carlos Grijalba, MD, Leidys Gutierrez, David Leal, Nicolas Lemus, Etelvina Mahecha, BS, Maria Paula Mahecha, Rosalba Mahecha, BS, Andrea Ramirez, MD, Paola Rios, MD, Andres Suarez, Camilo Triana; Finland, University of Helsinki: Mikael Fogelholm, ScD (site PI), Elli Hovi, BS, Jemina Kivelä, Sari Räsänen, BS, Sanna Roito, BS, Taru Saloheimo, MS, Leena Valta; India, St. Johns Research Institute: Anura Kurpad, MD, PhD (site co-PI), Rebecca Kuriyan, PhD (site co-PI), Deepa P. Lokesh, BSc, Michelle Stephanie D’Almeida, BSc, Annie Mattilda R, MSc, Lygia Correa, BSc, Vijay D, BSc; Kenya, Kenyatta University: Vincent Onywera, PhD (site co-PI), Mark S. Tremblay, PhD (site Co-PI), Lucy-Joy Wachira, PhD, Stella Muthuri, PhD; Portugal, University of Porto: Jose Maia, PhD (site PI), Alessandra da Silva Borges, BA, Sofia Oliveira Sá Cachada, Msc, Raquel Nichele de Chaves, MSc, Thayse Natacha Queiroz Ferreira Gomes, MSc, Sara Isabel Sampaio Pereira, BA, Daniel Monteiro de Vilhena e Santos, PhD, Fernanda Karina dos Santos, MSc, Pedro Gil Rodrigues da Silva, BA, Michele Caroline de Souza, MSc; South Africa, University of Cape Town: Vicki Lambert, PhD (site PI), Matthew April, BSc (Hons), Monika Uys, BSc (Hons), Nirmala Naidoo, MSc, Nandi Synyanya, Madelaine Carstens, BSc(Hons); United Kingdom, University of Bath: Martyn Standage, PhD (site PI), Sean Cumming, PhD, Clemens Drenowatz, PhD, Lydia Emm, MSc, Fiona Gillison, PhD, Julia Zakrzewski, PhD; United States, Pennington Biomedical Research Center: Catrine Tudor-Locke, PhD (site PI), Ashley Braud, Sheletta Donatto, MS, LDN, RD, Corbin Lemon, BS, Ana Jackson, BA, Ashunti Pearson, MS, Gina Pennington, BS, LDN, RD, Daniel Ragus, BS, Ryan Roubion, John Schuna, Jr., PhD; Derek Wiltz. The ISCOLE External Advisory Board includes Alan Batterham, PhD, Teesside University, Jacqueline Kerr, PhD, University of California, San Diego; Michael Pratt, MD, Centers for Disease Control and Prevention; Angelo Pietrobelli, MD, Verona University Medical School.
Dr. Church receives honoraria for lectures from scientific, educational, and lay groups. Dr. Church has a book entitled Move Yourself: The Cooper Clinic Medical Director’s Guide to All the Healing Benefits of Exercise. Dr. Church is a member of the Jenny Craig/Curves Medical Advisory Board as well as the Utilifit/FIX Corporate advisory board. Dr. Church has consulted for Medscape, Vivus, and Takada, and he serves as the Senior Medical Advisor for Catapult Health and the Chief Medical Officer at ACAP health. Dr. Fogelholm has received a research grant from Fazer Finland. Dr. Kurpad is a member of the Advisory Board of McCain Foods. Dr. Matsudo is a member of the Scientific Advisory Board of Actigraph. The authors report no other potential conflicts of interest. The results of the present study do not constitute endorsement by the American College of Sports Medicine.
The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE) is registered at ClinicalTrials.gov (Identifier NCT01722500).
1. Andersen LB, Harro M, Sardinha LB, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet
. 2006; 368 (9532): 299–304.
2. Barreira TV, Schuna JM Jr, Mire EF, et al. Distinguishing children’s nocturnal sleep using 24-hour waist accelerometry. Med Sci Sports Exerc
. 2015; 47 (5): 937–43.
3. De Bourdeaudhuij I, Verloigne M, Maes L, et al. Associations of physical activity and sedentary time with weight and weight status among 10- to 12-year-old boys and girls in Europe: a cluster analysis within the ENERGY project. Pediatr Obes
. 2013; 8 (5): 367–75.
4. De Onis M, Onyanga AW, Borghi E, Siyam A, Nishida C, Siekmann J. Development of a WHO growth reference for school-aged children and adolescents. Bull WHO
. 2007; 85: 660–7.
5. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the area under two or more correlated receiver operating characteristic curves: A nonparametric approach. Biometrics
. 1988; 44: 837–45.
6. Ekelund U, Luan J, Sherar LB, Esliger DW, Griew P, Cooper A. Moderate to vigorous physical activity and sedentary time and cardiometabolic risk factors in children and adolescents. JAMA
. 2012; 307 (7): 704–12.
7. Ekelund U, Sardinha LB, Anderssen SA, et al. Associations between objectively assessed physical activity and indicators of body fatness in 9- to 10-y-old European children: a population-based study from 4 distinct regions in Europe (the European Youth Heart Study). Am J Clin Nutr
. 2004; 80 (3): 584–90.
8. 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): 1557–65.
9. Grontved A, Hu FB. Television viewing and risk of type 2 diabetes, cardiovascular disease, and all-cause mortality: a meta-analysis. JAMA
. 2011; 305 (23): 2448–55.
10. Heo M, Wylie-Rosett J, Pietrobelli A, Kabat GC, Rohan TE, Faith MS. US pediatric population-level associations of DXA-measured percentage of body fat with four BMI metrics with cutoffs. Int J Obes
. 2014; 38 (1): 60–8.
11. 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 Act
. 2010; 7: 40.
12. Katzmarzyk PT, Barreira TV, Broyles ST, et al. The International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE): design and methods. BMC Public Health
. 2013; 13: 900.
13. Kenward MG, Roger JH. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics
. 1997; 53: 983–97.
14. Kuczmarski RJ, Ogden CL, Guo SS, et al. 2000 CDC growth charts for the United States: methods and development. Vital Health Stat 11
. 2002; 246: 1–190.
15. Lee JM, Kim Y, Welk GJ. Validity of consumer-based physical activity monitors. Med Sci Sports Exerc
. 2014; 46 (9): 1840–8.
16. Mark AE, Janssen I. Dose–response relation between physical activity and blood pressure in youth. Med Sci Sports Exerc
. 2008; 40 (6): 1007–12.
17. Martinez-Gomez D, Ruiz JR, Ortega FB, et al. Recommended levels of physical activity to avoid an excess of body fat in European adolescents: the HELENA Study. Am J Prev Med
. 2010; 39 (3): 203–11.
18. Mitchell JA, Byun W. Sedentary behavior and health outcomes in children and adolescents. Am J Life Med
. 2014; 8: 173–99.
19. Mitchell JA, Pate RR, Espana-Romero V, O’Neill JR, Dowda M, Nader PR. Moderate-to-vigorous physical activity is associated with decreases in body mass index from ages 9 to 15 years. Obesity
. 2013; 21 (3): E280–93.
20. Pearson N, Braithwaite RE, Biddle SJ, van Sluijs EM, Atkin AJ. Associations between sedentary behaviour and physical activity in children and adolescents: a meta-analysis. Obes Rev
. 2014; 15 (8): 666–75.
21. Perkins NJ, Schisterman EF. The inconsistency of “optimal” cutpoints obtained using two criteria based on the receiver operating characteristic curve. Am J Epidemiol
. 2006; 163 (7): 670–5.
22. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report
. Washington, DC: U.S. Department of Health and Human Services; 2008. pp. 683.
23. Ridgers ND, Salmon J, Ridley K, O’Connell E, Arundell L, Timperio A. Agreement between activPAL and ActiGraph for assessing children’s sedentary time. Int J Behav Nutr Phys Act
. 2012; 9: 15.
24. Shields M, Tremblay MS. Canadian childhood obesity estimates based on WHO, IOTF and CDC cut-points. Int J Pediatr Obes
. 2010; 5 (3): 265–73.
25. Thorp AA, Owen N, Neuhaus M, Dunstan DW. Sedentary behaviors and subsequent health outcomes in adults: a systematic review of longitudinal studies, 1996–2011. Am J Prev Med
. 2011; 41 (2): 207–15.
26. Tremblay MS, LeBlanc AG, Kho ME, et al. Systematic review of sedentary behaviour and health indicators in school-aged children and youth. Int J Behav Nutr Phys Act
. 2011; 8: 98.
27. Treuth MS, Schmitz K, Catellier DJ, et al. Defining accelerometer thresholds for activity intensities in adolescent girls. Med Sci Sports Exerc
. 2004; 36 (7): 1259–66.
28. Trost SG, Loprinzi PD, Moore R, Pfeiffer KA. Comparison of accelerometer cut points for predicting activity intensity in youth. Med Sci Sports Exerc
. 2011; 43 (7): 1360–8.
29. Tudor-Locke C, Barreira TV, Schuna JM Jr, Mire EF, Katzmarzyk PT. Fully automated waist-worn accelerometer algorithm for detecting children’s sleep-period time separate from 24-h physical activity or sedentary behaviors. Appl Physiol Nutr Metab
. 2014; 39 (1): 53–7.
30. Tudor-Locke C, Barreira TV, Schuna JM, et al. Improving wear time compliance with a 24-hour waist-worn accelerometer protocol in the International Study of Childhood Obesity, Lifestyle and the Environment (ISCOLE). Int J Behav Nutr Phys Act
. 2015; 12 (1): 172.
31. Tudor-Locke C, Pangrazi RP, Corbin CB, et al. BMI-referenced standards for recommended pedometer-determined steps/day in children. Prev Med
. 2004; 38 (6): 857–64.
32. US Department of Health and Human Services. 2008 Physical Activity Guidelines for Americans
. Washington, DC: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2008. p. 61.
33. World Health Organization. Global Recommendations on Physical Activity for Health
. Geneva, Switzerland: World Health Organization; 2010. p. 58.
Keywords:© 2015 American College of Sports Medicine
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