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ORIGINAL RESEARCH COMMUNICATION |
1 From the MRC Epidemiology Unit, Cambridge, United Kingdom (UE and SB); the Department of Physical Education and Health, Örebro University, Örebro, Sweden (UE); the Faculty of Human Movement, Technical University of Lisbon, Portugal (LBS); the Department of Sports Medicine, The Norwegian University of Sport and Physical Education, Norway (SAA and LBA); the Department of Public Health, University of Tartu, Estonia (MH); the Diabetes and Arthritis Epidemiology Section, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ (PWF); the Department of Exercise and Health Sciences, University of Bristol, United Kingdom (ARC); the London Sport Institute, Middlesex University, United Kingdom (CR); the Institute of Sport Science & Clinical Biomechanics, University of Southern Denmark, Odense, Denmark (SB and KF)
2 Supported by grants from the Danish Heart Foundation, the Danish Medical Research Council Health Foundation, the Danish Council for Sports Research, the Foundation in Memory of Asta Florida Bolding Renée Andersen, the Faculty of Health Sciences, the University of Southern Denmark, the Estonian Science Foundation (grant nos. 3277 and 5209), the Norwegian Council of Cardiovascular Diseases, the Eckbo Legacy, and the European Social Fund. 3 Reprints not available. Address correspondence to U Ekelund, MRC Epidemiology Unit, Strangeways Research Laboratories, Wort’s Causeway, Cambridge CB1 8RN, United Kingdom. E-mail: ue202{at}medschl.cam.ac.uk.
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Objective: Our aim was to study the associations of objectively measured PA volume and its subcomponents with indicators of body fatness.
Design: A cross-sectional study of 1292 children aged 9–10 y from 4 distinct regions in Europe (Odense, Denmark; the island of Madeira, Portugal; Oslo; and Tartu, Estonia) was conducted. PA was measured by accelerometry, and indicators of body fatness were the sum of 5 skinfold thicknesses and body mass index (BMI; in kg/m2). We examined the associations between PA and body fatness by using general linear models adjusted for potential confounding variables.
Results: After adjustment for sex, study location, sexual maturity, birth weight, and parental BMI, time (min/d) spent at moderate and vigorous PA (P = 0.032) and time (min/d) spent at vigorous PA were significantly (P = 0.015) and independently associated with body fatness. Sex, study location, sexual maturity, birth weight, and parental BMI explained 29% (adjusted R2 = 0.29) of the variation in body fatness. Time spent at vigorous PA explained an additional 0.5%. Children who accumulated <1 h of moderate PA/d were significantly fatter than were those who accumulated >2 h/d.
Conclusions: The accumulated amount of time spent at moderate and vigorous PA is related to body fatness in children, but this relation is weak; the explained variance was <1%.
Key Words: Accelerometry • adiposity • children • energy expenditure • physical activity • skinfold thickness • European Youth Heart Study
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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Identifying the components of PA (ie, sedentary behavior; time spent at light, moderate, and vigorous PA; and the total amount of PA) that are related to overweight and obesity in children may be essential for the primary and secondary prevention of all diseases that result from sedentary behavior, not the least of which is the prevention of obesity and its comorbidities. However, interventions designed to reduce sedentary behavior, to increase PA, or both may not be efficient if they focus on inappropriate subcomponents of PA predicted from observational studies. Unfortunately, measuring the subcomponents of PA in epidemiologic studies is difficult because of the complex and latent nature of the exposure (10).
As is true for other behaviors, the assessment of PA in children is more challenging than is that in adults. Although some previous population-based epidemiologic studies that assessed the relation between PA and obesity in children indicated that sedentary behavior is related to overweight and obesity (11, 12), these studies assessed PA using self-report methods. However, self-report data may at best give a crude estimate of the amount of time spent at various types of PA and inactivity. Furthermore, these methods lack validity in young children and have led to the recommendation that self-reports should not be used in children younger than 10 y (13). In contrast, motion sensors based on accelerometry provide detailed information of the intensity and duration of PA on a minute-by-minute basis, have good validity compared with gold standard methods (14), are feasible to use in large-scale studies (15), and provide information about the total amount of PA and its subcomponents. We recently described the patterns of PA in 9–10-y-old children from 4 distinct regions of Europe, with the impression of consistency across different geographic and socioeconomic regions (15). Therefore, the purpose of the present study was to examine the cross-sectional associations between PA patterns and indicators of obesity, after adjustment for putative confounders, in 9–10-y-old children to improve the understanding of these complex relations.
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SUBJECTS AND METHODS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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The study procedures were explained verbally to all children. The local research ethics committee of the 4 study regions approved the study protocol, which was uniform across study locations.
Anthropometric measures
Weight was measured to the nearest 0.1 kg with a calibrated beam balance scale while the children were wearing light clothing. Height was measured to the nearest 0.5 cm with a transportable Harpenden stadiometer while the children were shoeless. The beam balance was calibrated by using a weight of known mass, and the stadiometer was calibrated with a metal measuring tape. Body mass index (BMI) was calculated as weight (kg)/height squared(m). On the basis of BMI, the children were grouped in normal-weight (boys: <19.1–19.8; girls: <19.1–19.9), overweight (boys: 
19.1–19.8 and <22.8–24.0; girls: 19.1–19.9 and <22.8–24.1), and obese (boys: 22.8–24.0; girls: 22.8–24.0) groups according to the age-adjusted cutoffs described by Cole et al (16). Five skinfold-thickness measurements (triceps, biceps, subscapula, suprailiac, and calf) were taken on the left side of the body according to the criteria described by Lohman et al (17). All measurements were taken twice and in rotation. If the difference between the 2 measurements differed by >2 mm, a third measurement was taken, and the 2 closest measurements were averaged. All data collectors were centrally trained, and inter- and intracollector reliabilities were assessed at each study location on a regular basis. The sum of 5 skinfold thicknesses was used as an indicator of body fat percentage. Sexual maturity was assessed by the data collectors, using Tanner’s (18) 5-stage scale for breast development in girls and pubic hair in boys. Few children (n = 9 girls) were classified as Tanner stage 3; therefore, we combined these children with those in Tanner stage 2. The children’s’ birth weights and their parents’ BMIs were obtained from the parents.
Assessment of physical activity
Free-living PA was assessed with the MTI (formerly known as the CSA activity monitor) model WAM 7164 (Manufacturing Technology Inc, Fort Walton Beach, FL) accelerometer over 2 weekdays and 2 weekend days, as previously described (15). Briefly, the children wore the accelerometer in an elastic waistband on the right hip during the daytime, except while bathing and during other aquatic activities. Children who did not manage to record 600 min/d of activity for 3 d were excluded from further analyses. Activity data were stored on a minute-by-minute basis and were downloaded to a computer before analysis. A Microsoft Excel-based macro was used for data reduction and further analyses. The outcome variables were daily activity counts (counts · min–1 · d–1), which is an indicator of the total volume of PA, and time (min/d) spent at different PA-intensity categories. These were calculated to determine which subcomponents of PA, if any, are associated with overweight and obesity and to establish whether these subcomponents were related in a dose-response manner. Because published cutoffs for different intensity levels in children vary substantially between studies [eg, between 906 (19) and 3200 counts/min for moderate intensity (20)], 10 intensity categories were established in multiples of 500 counts/min from 0 to >4500 counts/min. Sedentary behavior was defined as <500 counts/min, light PA as 500–1999 counts/min, moderate and vigorous PA as 2000 counts/min, and vigorous PA as 3000 counts/min. The lower cutoff for moderate activity (ie, 2000 counts/min) is broadly equivalent to walking at 3 km/h (21).
PA-related energy expenditure (PAEE) was predicted on the basis of activity counts (counts/min) and sex by using an equation derived from the simultaneous measurements of PA by the accelerometer and from free-living total energy expenditure (TEE) measured by the doubly labeled water method (14). Resting energy expenditure (REE) was estimated from prediction equations (22), and TEE was calculated by summing PAEE and REE.
Statistical methods
The data are presented as means ± SDs unless otherwise stated. All variables were normally distributed except for the sum of 5 skinfold thicknesses, which was log10 transformed before analyses. Differences in age, anthropometric variables, energy expenditure, components of PA between sexes, and study locations were tested by analysis of variance. Differences in the fraction of time spent at moderate and vigorous PA between different BMI groups were tested by using general linear models (analysis of covariance) with Bonferroni’s adjustments for multiple comparisons. This analysis was also adjusted for study location, sex, and sexual maturity. Associations between variables were assessed by using Pearson’s correlation coefficients. The independent associations between PA (total volume of activity and the fraction of time spent at sedentary, light, moderate and vigorous, and vigorous PA) with body fat (sum of skinfold thicknesses) and BMI were tested by using general linear models (analysis of covariance). The PA variables were entered separately into the models. All analyses were adjusted for sex, sexual maturity, and study location. The interaction term sex by sexual maturity was also introduced into the models. Data were also reanalyzed by using the statistical methods described above with additional adjustment for birth weight and maternal and paternal BMI. Because birth weight and parental BMI were only available for 533 girls and 514 boys, this analysis included 1047 children. As above, the PA variables were entered separately into the models. All analyses were performed by using the Statistical Package for Social Sciences (SPSS, version 10.0 for WINDOWS; SPSS Inc, Chicago), and the level of significance was set at P < 0.05.
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RESULTS |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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. Significant differences were observed between study locations for all of the anthropometric variables (P < 0.001). The distribution of overweight and obese children differed significantly between countries. Twenty-seven percent of Portuguese children, 13.4% of Norwegian children, 12.0% of Danish children, and 9.8% of Estonian children were classified as overweight or obese. In total, 191 (14.8%) children were classified as overweight or obese, of whom 39 (3%) were obese.
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, the results of the analysis of variance showed that TEE, PAEE, the total volume of PA, and the fraction of time spent at moderate and vigorous PA were significantly higher in boys than in girls (P < 0.001). Girls spent significantly more time in sedentary activities (P = 0.002). We also observed significant differences between study locations (P < 0.001), although the main impression was consistency across study locations. No significant effect of sexual maturity was observed for any of the PA components.
The normal-weight children, on the basis of BMI, were significantly (P = 0.022) more active (ie, greater fraction of time spent at moderate and vigorous PA) than were the obese children, after adjustment for sex, study location, and sexual maturity (Figure 1).
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The results of the general linear models are presented in Table 2. The sum of 5 skinfold thicknesses (log10) was significantly influenced by sex (P < 0.0001), study location (P < 0.0001), and sexual maturity (P < 0.0001). A significant interaction effect (sex x sexual maturity) was also observed. After adjustment for sex, study location, and sexual maturity, time spent at moderate and vigorous PA (P = 0.04) and time spent at vigorous PA (P = 0.02) were independent predictors of body fatness. Sex, study location, and sexual maturity explained 24% (adjusted R2 = 0.24) of the variation in body fatness. PA (ie, time spent at vigorous PA) explained an additional 0.5% of the variance.
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). In these models, sex, study location, sexual maturity, birth weight, and parental BMI explained 29% (adjusted R2 = 0.29) of the variation in body fatness. As above, vigorous PA explained an additional 0.5% of the variance. Adjustment for age at examination did not affect our results.
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The children were then divided into 4 groups on the basis of their time spent at moderate and vigorous PA (ie, >2000 counts/min). After adjustment for sex, study location, and sexual maturity, we observed a significant association between activity group and body fatness derived from skinfold-thickness measurement (P = 0.01). This relation persisted after adjustment for birth weight and parental BMI (P = 0.01). Children who accumulated 2 h of moderate PA had significantly lower skinfold-thickness measurements than did those who spent <1 h/d at this level of intensity (P = 0.006) (Figure 2). We repeated our analyses by substituting BMI as the outcome variable and found that activity group did not significantly contribute to the explained variance in BMI.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONSUBJECTS AND METHODSRESULTSDISCUSSIONREFERENCES |
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It is generally believed that the current obesity epidemic is mainly due to environmental changes, affecting the energy balance equation (23). However, the relative and absolute contributions of sedentary behavior and excess food intake on obesity are unknown. Results from studies that examined the relation between energy expenditure and body fatness in children, in which energy expenditure was measured by using the doubly labeled water method, are equivocal (24-29). This has led some to suggest that time devoted to PA is a more important factor than is energy expenditure in energy regulation (30). Our data indicated that <1% of the variation in body fatness was explained by time devoted to level of activity.
Prospective studies using the doubly labeled water method have indicated that PAEE does not predict future weight gain in children (26). This finding may suggest that food intake, especially the consumption of energy-dense food, plays a more important role in the development of overweight and obesity in children than does the amount of PA (31). Data suggest that the consumption of sugar-sweetened soft drinks among children has more than doubled during recent decades (32). This is important because excessive sweetened drink consumption is associated with greater weight gain (33), and the odds of becoming obese may increase by as much as 60% with each additional serving of sugar-sweetened drinks that a child consumes (34). One serving (250 mL) of a soft drink is broadly equal to 130 kcal. A child can consume this amount of energy within minutes, whereas >30 min of moderate PA (equal to brisk walking) is required to maintain energy balance. However, aside from the data published by Ludwig et al (34), there is little additional evidence that food intake predicts obesity in childhood (35, 36), although a mechanism for the relation between soft drink consumption and body fat has been suggested (37).
There are several limitations that should be considered when interpreting the findings from the present study. First, it is not possible to infer a causal relation from cross-sectional data such as that in the present study. Although we controlled for several potential confounders such as sex, study location, sexual maturity, birth weight, and parental BMI, we cannot be certain that other unmeasured confounders, such as dietary composition and volume, genetic variation, and sociocultural factors, do not explain our observations.
We reduced the potential for recall bias, which is an unavoidable component of self-report assessment of PA in children and adults, by directly assessing PA with accelerometers. Despite the advantages associated with the use of direct assessment when exploring associations between the components of PA and body fatness in children, these methods also have limitations, which should be considered when interpreting our findings. First, accelerometers do not accurately reflect the energy expenditure associated with bicycling, upper body movement, walking uphill, and stair climbing and cannot be used to any practical extent during aquatic activities. Furthermore, the cutoffs used to define intensity categories are arbitrary because they depend on the type of activities performed when establishing the relation between activity counts and energy expenditure (38). Nonetheless, accelerometry accurately reflects energy expenditure during walking and jogging, within the approximate range of 3 to 9 km/h. We reduced our data into multiples of 500 counts/min and defined moderate PA as >2000 counts/min, because this value is broadly equivalent to walking at 3–4 km/h in 9–10-y-old children (21, 39). Furthermore, this value approximates the mean for previously published cutoffs for moderate-intensity activity in children (19, 20). Ideally, one should individually calibrate the relation between movement counts and energy expenditure during locomotion in each child (40). However, this is often unfeasible in large epidemiologic studies such as ours. Furthermore, 4–5 d of activity monitoring have been proposed as a suitable duration for accurately and reliably assessing the usual activity behavior in children (41), whereas only 3 d of data were available in some of our participants. It is possible that this slightly shorter monitoring period may be less reflective of the usual PA pattern in children, which may mask the true association between PA and body fatness.
The relations that we observed between PA and body fatness were small. Nonetheless, these findings may have important public health implications because sedentary behavior is common in most industrialized societies. The recent consensus statement put forward by the International Association for the Study of Obesity relating to PA level in adults suggests that 
45–60 min of moderate PA or more is required to prevent unhealthy weight gain. The prevention of weight regain may require an even greater volume of PA (42). Moreover, according to the same statement, the volume of PA required for successful primary and secondary prevention of obesity in children may well exceed that required in adults, although no specific recommendation was put forward. Given the uncertainty in defining cutoffs for different intensity levels by means of accelerometry, our data suggest that children who are physically active for >2 h/d at an intensity level broadly equivalent to that associated with walking were significantly leaner than those who accumulated <1 h PA/d at the same intensity level. This may imply that 60 min of moderate PA each day, similar to the current recommendation for health-related PA in young people (43), is the minimal amount of PA that needs to be undertaken if the aim is to curb the increasing prevalence of obesity in children. However, the data in the present study are sufficient only for the generation of such hypotheses, because to address these questions properly requires data from longitudinal prospective studies.
The environmental factors that promote overweight and obesity are multilevel and multifactorial (44). Thus, effective preventive strategies need to address the underlying social, cultural, physical, and economical determinants of childhood obesity and are likely to include interventions designed to decrease sedentary behavior in children. Nonetheless, it is probably wise to consider strategies that promote integrated approaches to the prevention of obesity, because obesity etiology is evidentially complex and multifactorial. Furthermore, the beneficial effects on obesity of combining preventive strategies may at least be additive and quite possibly synergistic.
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ACKNOWLEDGMENTS |
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None of the authors had any financial interest in this study.
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REFERENCES |
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| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
