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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Obesity (Silver Spring). Author manuscript; available in PMC 2010 February 1.
Published in final edited form as:
PMCID: PMC2746930

Sedentary Behaviour and Obesity in a Large Cohort of Children


The purpose of this study was to examine the association between sedentary behaviour and obesity among 12-year-old children, while adjusting for moderate-to-vigorous physical activity (MVPA) and other potential confounding variables.

Cross-sectional analyses were carried out with data from 5434 children who participated in the Avon Longitudinal Study of Parents and Children (ALSPAC). Fat mass was derived using dual x-ray emission absorptiometry (DXA), and height and weight measurements were used to calculate body mass index (BMI; kg/m2). The children wore an accelerometer for seven days. The cut points for sedentary behaviour and MVPA were ≤199 cpm and ≥3600 cpm, respectively. Logistic regression analyses were performed to estimate odds ratios (OR), adjusting for potential confounders of physical activity that included gender, social factors, early life factors and maturation.

The minimally adjusted association between sedentary behaviour and obesity was positive, OR=1.18 (1.08, 1.28). After adjusting for the series of potential confounders of physical activity the positive association remained, OR=1.32 (1.14, 1.53). The crude association between 15 minutes of MVPA per day and obesity was negative, OR=0.54 (0.48, 0.62). When 15 minutes of MVPA per day was additionally controlled for in the models, the positive associations between sedentary behaviour and obesity were negated.

Sedentary behaviour was positively associated with obesity in the 12-year-old children, but this association was not independent of MVPA; low levels of MVPA among the sedentary children increased the odds of obesity. These finding support the importance of specifically engaging in MVPA during childhood to reduce the prevalence of obesity.


The study of sedentary behaviour and its association with obesity during childhood and into adolescence is growing, with cross sectional and prospective studies having examined the association (1). The results from these studies are inconsistent, with some reporting strong positive associations (2) and others finding no associations (3).

When studying the association between sedentary behaviour and obesity it is important to distinguish sedentary behaviour from low levels of physical activity (4). Non-exercise activity thermogenesis (NEAT) encompasses low levels of physical activity (5), whereas sedentary behaviour refers to activity in which the work of the large skeletal muscles involved in habitual movement and postural control is very limited. Sitting is the most prevalent sedentary behaviour (6).

There is a tendency for time spent sedentary to increase from early to late adolescence and boys, despite engaging in moderate-to-vigorous physical activity (MVPA) for longer durations, are typically more sedentary than girls (7, 8). Furthermore, when maturing from adolescence into adulthood individuals tend to spend even more time engaged in sedentary behaviour (9). The importance of these patterns are of much concern, since high levels of sedentary behaviour have been shown to increase the risk of obesity-related health problems, including type II diabetes, cardiovascular disease, certain cancers, and various metabolic risk factors (1013).

Recommendations have been proposed for adults and children to engage in MVPA throughout the day, in part to address the obesity-related health problems cited above and to improve cardiorespiratory fitness (CRF) (14). Yet there is evidence to show that sedentary behaviour is a risk factor for obesity (15, 16) and an elevated metabolic risk profile, independent of MVPA (15, 17). It is even possible that sedentary behaviour may be independently associated with CRF, since MVPA does not explain all of the variance in CRF (18). These studies indicate that current physical activity recommendations may be incomplete and a specific recommendation for the time spent sedentary may be required.

In order to determine the independent effects of sedentary behaviour on health outcomes, sedentary activity must be measured with accuracy and precision. Television viewing time has been used as an indicator of sedentary behaviour in previous studies (2, 15) and although television viewing is the primary sedentary activity of adolescents, it is not the only sedentary activity in which they participate (19). Accelerometry can be used to measure the broad scope of sedentary behaviours in which individuals participate and may provide a more accurate and precise measure of sedentary behaviour (17, 20).

The purpose of this paper was to determine current levels of sedentary behaviour in a sample of children residing in the UK, using accelerometry, and to determine the association between sedentary behaviour and the odds of being obese, defined by fat mass ascertained through the use of dual energy x-ray absorptiometry (DXA).

Research Methods and Procedures

Study Participants

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort study. Baseline recruitment specified that pregnant women residing in the former County of Avon, in the United Kingdom, with an expected delivery between April 1991 and December 1992 were eligible for inclusion (21). A total of 14,541 pregnant women enrolled into the study at baseline. From the subsequent births, 11,952 children were invited to attend the 11-year research clinic for physiological and psychometric measurements. Only singleton children with complete accelerometer and body composition measures were included in the present study. Ethical approval for the study was obtained from the ALSPAC Law and Ethics Committee and the Local Research Ethics Committees.

Body Composition

Measures of height and weight were obtained at the 11-year clinic; the former was measured in bare feet with a Harpenden stadiometer (Holtain Ltd, Crosswell, UK) and the latter was measured using a Tanita body fat analyser and weighing scales (Model TBF 305, Tanita UK Ltd Middlesex, UK). Body mass index (BMI) was calculated by dividing weight (kg) by height (m2). Fat mass and lean mass were assessed using a Lunar Prodigy DXA scanner (GE Medical Systems). The top 10% of fat mass (adjusted for age, height and height2 to take into account differences in stature) defined obesity, and this was derived from the entire sample of children who attended the 11-year clinic. The top 10% for fat mass represents a clinically relevant level of excess fat mass, since the boys and girls had at least 34% and 39%, respectively, of their body weight as fat at this definition. Overweight was defined by the age-specific international cut points, which corresponded to a BMI of 25 kg/m2 at 18 years of age (22).

Physical Activity and Sedentary Behaviour

Physical activity and sedentary behaviour were assessed objectively. The children wore an accelerometer (MTI Actigraph AM7164 2.2, Fort Walton Beach, FL) around their waist, at the right hip, for seven days during waking hours, removing the accelerometer for bathing, swimming, or other water activities. Children recorded the times spent swimming or cycling on a time sheet, because the accelerometer does not provide an accurate account of non-weight bearing activities. However, no children were excluded who reported cycling or swimming, since previous sensitivity analyses revealed no differences in the results with and without their inclusion (23). The accelerometers were initialised to collect data via one-minute epochs, and total physical activity was expressed as the average counts per minute (cpm) over the period of valid recording (minimum of 3 days, 10 hours per day) (24). It was previously shown that there was no great variation in the mean times of accelerometer wear during the week and weekend (13.1±0.9 hours and 12.3±1.2 hours respectively) (24) and so reporting the absolute time spent sedentary and engaged in MVPA was deemed to be appropriate. A sub-group of 246 children participated in a calibration study to determine a cut point for MVPA, which was subsequently defined as ≥3600cpm. The protocol involved children wearing an accelerometer and an indirect calorimeter (Cosmed K4b2; Cosmed, Rome, Italy) while lying down, sitting, slow walking, fast walking, playing hopscotch and jogging; the MVPA cut point was based on an energy expenditure of 4 METs (25). The macro used to determine the physical activity intensities did so in blocks of 200cpm and so sedentary behaviour was defined as ≤199cpm (24). This cut point is comparable to other accelerometer definitions of sedentary behaviour in children (20).


Potential confounding variables, shown to be independently associated with obesity and physical activity in previous ALSPAC analyses (2629), were controlled for in our current analysis, Table 1 provides the time scale of when these data were collected. Social class was determined via the occupation of the mother or father (lowest class was used if the parents’ occupation status differed), which was recorded through the 32-week antenatal questionnaire. Five categories of class were identified using the UK 1991 Office for Population Censuses and Surveys criteria (30). Maternal education was also obtained from the 32-week antenatal questionnaire. Categories included none/Certificate of Secondary Education (CSE) (national school exam at 16); vocational, O level (national school exam at 16, beyond CSE); A level (national school exam at 18), or degree. Maternal smoking status during pregnancy was recorded on the 18-week and 32-week antenatal questionnaires. The 18-week antenatal questionnaire asked about the mothers’ smoking habits during the first trimester and in the previous two weeks. The 32-week antenatal questionnaire determined the mothers’ current number of cigarettes smoked per day. Maternal obesity (BMI ≥30kg/m2) was determined from the pre-pregnancy weight and height, recorded by the mother, when enrolled into the study. The birth weight of the offspring was obtained from obstetric records and/or birth notifications; gestational age was estimated from the last reported menstrual period to the time of birth. The total time of television viewing per week for each child was reported by the mother in the 38-month questionnaire. The mothers also noted their child’s sleeping patterns, when completing the earlier 30-month questionnaire. The pubertal status of the children was determined when they were approximately 11-years old. The child’s caregiver noted the development of pubic hair for boys and girls and breast development for girls (31) and only those completing this process within 16 weeks of receiving the questionnaire were included in our analyses.

Table 1
time scale of data collection

Statistical Analyses

Descriptive statistics for the study variables were calculated for the total group and by gender. To determine if sedentary behaviour was related to obesity, we ran a series of logistic regression models, with social factors, early life factors and biological maturation incrementally adjusted for in chronological order. This allowed for the impact of each factor on the association between sedentary behaviour and obesity to be documented. The first model included sedentary hours per day and gender (there was no evidence of a gender interaction). Independent variables in the second model included all of the variables from the first model plus social factors (2729): social class, mother’s education, whether the mother smoked during pregnancy, whether the mother was obese, birth weight, and gestational age. The third model included all of the variables from the second model and whether the child watched more than 8 hours of TV per week at 38 months, and if he or she slept 10.5 or more hours per night at 30 months (26). The fourth model included all of the variables in the third model and pubertal status. A second series of multiple logistic regression models were analyzed with the addition of 15 minutes of MVPA per day to each of the four models to fully determine the independent association between sedentary behaviour and obesity. The 15 minutes of MVPA per day variable was calculated by multiplying the one minute of MVPA β coefficient by fifteen. The sample sizes vary between the models, due to missing questionnaire items, so the analyses were repeated using only participants with complete data and t-test were performed to compare those with complete data and those with missing data. All analyses were conducted using SAS (version 9.1).


A total of 11,952 children were invited to the 11-yr clinic; 7,159 attended and 6,622 agreed to wear an accelerometer for one week. A total of 5,595 children provided valid accelerometry data. The 1,564 children with invalid accelerometry data were more likely to have a higher BMI and to be male, although these differences were small (24). A total of 5504 children (males n=2624) provided both body composition measures and valid accelerometry data. After excluding multiple births, our final sample consisted 5434 singleton children (males n=2590).

The characteristics of the children are detailed in Table 2. The mean age of the boys and girls attending the 11-year clinic was 11.8 (±0.2) years; the children hereafter will be referred to as 12-year-olds. There was a higher prevalence of overweight, defined by BMI, among the girls compared to the boys. The prevalence of obesity was slightly lower than the top 10% for fat mass definition that was determined from all the children providing body composition measures, indicating that obese children were less likely to adhere to the accelerometry protocol. Nonetheless the difference in the prevalence of obesity between genders was minimal and was very close to our definition. Data concerning pubertal status revealed a greater proportion of girls to be in later stages of development compared to the boys. The television viewing hours per week, at 38 months, were similar between genders. The majority of the children accrued at least 10.5 hours of sleep per night at 30 months; however, a greater proportion of boys than girls slept for less than 10.5 hours per night.

Table 2
characteristics of the ALSPAC participants attending the 11-year clinic with valid accelerometry and body composition data

The accelerometry data describing time spent sedentary are presented in Table 3. Girls spent more time than boys engaged in sedentary behaviour, but only on weekdays. Time spent engaged in MVPA was greater among boys than girls, as was the total volume of physical activity expressed as total counts per minute (Table 3).

Table 3
time spent sedentary and engaged in moderate-to-vigorous physical activity (MVPA) as assessed by accelerometry

The results in Figure 1 show that the minimally adjusted odds of being obese were 1.18 (1.08, 1.28) times more likely for every hour spent sedentary per day among the 12-year-old children (model 1). This positive association was strengthened (OR=1.30 (1.16, 1.44), OR=1.35 (1.20, 1.51) and OR=1.32 (1.14, 1.53)) after adjusting for the social factors, early life factors and maturation in models 2, 3 and 4 respectively.

Figure 1
Logistic Regression results for the association between obesity and sedentary hours per day, (+MVPA: additionally adjusting for 15 minutes of MVPA per day). Model 1 (n=5434) adjusting for gender; model 2 (n=4102) additionally adjusted for social factors; ...

The crude association between 15 minutes of MVPA per day and obesity among the 12-year-olds yielded an OR of 0.54 (0.48, 0.62). When all the models were additionally adjusted for 15 minutes of MVPA per day, the previous positive associations between an hour spent sedentary per day and obesity at 12 years were negated; OR=0.99 (0.91, 1.09), OR=1.09 (0.97, 1.22), OR=1.12 (0.99, 1.27) and OR=1.09 (0.93, 1.28) for models 1 through 4 respectively (Figure 1).

A higher proportion of males, obese children and those born to mothers with the lowest education level had missing data (data not shown). Additional logistic regression analyses were performed using subjects who had complete data for all the confounding variables (n=2421), and the same patterns of association were found for all the models, with and without MVPA (data not shown). The patterns of association were also similar when BMI derived overweight and obesity were used as dependent variables (data not shown). Model 4 was repeated with the inclusion of lean mass as a covariate, and this had no effect on our results (data not shown).


The odds of obesity in our sample of children increased per hour of sedentary behaviour. This positive association persisted after controlling for potential confounding variables, including age, sex, height, height2, parental sociodemographic characteristics, maternal smoking, maternal obesity status, early life sleep and television viewing patterns, and pubertal development. However, when the four models that adjusted for these confounders were further adjusted for MVPA, the association between sedentary behaviour and obesity disappeared in each - indicating that the time spent engaged in MVPA is lower among sedentary children. This interpretation is supported by the negative correlation that exists between sedentary behaviour and MVPA (r = −0.39, p<0.001) in our sample.

Our findings are in contrast to a recent study involving a sample of European children, which reported that the positive association between sedentary behaviour (television viewing) and adiposity was independent of physical activity and other confounding variables (16). The difference in findings between the two studies may be due to self-reported television viewing hours being used as a proxy measure of sedentary behaviour, the use of BMI as a measure of adiposity or the adjustment for total physical activity and not specifically MVPA (16). These results support the need to specifically limit time spent engaged in sedentary behaviour in order to avoid excess adiposity. Whereas, our data support the need to engage in MVPA, as recommended (14), in order to reduce the prevalence of obesity and do not indicate that an additional recommendation for time spent sedentary is needed.

The accumulation of physical activity is widely believed to exert health benefits. More specifically, it is the intensity of physical activity accumulated that is of most importance. In a sample of Swedish adolescents, a positive association between television viewing and obesity was subsequently nullified upon adjusting for vigorous physical activity (VPA) in addition to other confounding variables (32). In a sample of Australian adults, breaks in sedentary time to engage in light intensity physical activity were shown to be beneficial in terms of waist circumference, BMI and metabolic risk factors, independent of total sedentary time and MVPA (33). These findings along with our results show that health outcomes are potentially improved by the whole spectrum of physical activity intensities, when physical activity is incorporated into sedentary lifestyles.

There is strong evidence that MVPA, and especially VPA, is positively associated with CRF in children (18). Perhaps the impact of MVPA on obesity in our results may be explained by an increased CRF and subsequent preferential fat utilization as a fuel through β oxidation. However, MVPA does not explain all the variance in CRF (18), and excessive sedentary behaviour, regardless of MVPA accumulated, may have been detrimental to the 12 year olds’ CRF.

It is also possible that the independent effects of sedentary behaviour might well have remained, after adjustment for MVPA, for outcomes concerning metabolic risk. A recent review suggests that a loss of the thousands of intermittent muscle contractions (NEAT) throughout the day that occurs with sedentary lifestyles could invoke adverse metabolic consequences (6), and in adults there is evidence that sedentary behaviour is associated with metabolic risk factors independent of MVPA (17). Sardinha et al. reported that the time spent sedentary, measured through accelerometry, in 10-year-olds, was positively associated with insulin resistance (34), however MVPA was not adjusted for in their models. It has also been suggested that the independent positive association between sedentary behaviour (television viewing) and adiposity leads to adverse metabolic profiles in children as a consequence of the increased adiposity (16). Additional large population-based studies are needed to enhance our understanding of the associations between sedentary behaviour and metabolic risk factors in children.

A second theme of our results regards the time boys and girls spent engaged in sedentary behaviour. Our data are in contrast to previous studies that have noted that boys spend more time sedentary than girls (7, 8). However, these studies used self-reported small screen recreation (SSR) as a proxy measure of sedentary behaviour. Recent data from the U.S. and Portugal that used accelerometry to assess sedentary behaviour among children and adolescents mirrored our results (34, 35). Accelerometry data has consistently found girls to be more sedentary than boys.

There are limitations to our study. Our cohort represents a group of children of similar age in one place in one time, so our results may not be accurate for all populations. Also, the sample of children who provided valid accelerometry and body composition data had a slightly lower prevalence of obesity (below the top 10% for fat mass) compared to the entire sample who attended the 11-year clinic and this was more pronounced among the boys (Table 1). The definition of obesity used was thought to represent a clinically relevant level of excess fat mass, but levels below the top 10% for fat mass could also be associated with additional health risks. Similar results were found when BMI defined overweight was used as the dependent variable. More males, obese children and those born to mothers with the lowest education level had missing data and this should be taken into consideration when generalizing these results. However, when all the models were repeated for those with complete data, the patterns of association were almost identical. In terms of the minimal requirements for accelerometry wear – Fairclough et al. reported that the number of days required to obtain an accurate account of girls’ physical activity was greater than what was needed for boys (36). However, we have shown previously that the minimal requirement of 3 days of wear, for boys and girls, provided an acceptable reliability coefficient, while ensuring good power (24). The use of a one-minute epoch is perhaps a limitation of our accelerometry data, since MVPA can potentially be underestimated. Our cut point for sedentary behaviour was similar to the Treuth et al. cut point (20). Although not an exact match, due to our macro determining the cut points in blocks of 200cpm, we feel our cut point is very representative of sedentary behaviour. It should be taken into consideration that the Treuth et al. cut points were determined from a sample of adolescent girls in the U.S. (20) and regardless of a calibration protocol, differences in cut points shall exist between populations.

In conclusion, the major finding of our study was that sedentary behaviour was positively associated with obesity, but this association disappeared when MVPA was taken into account. As we have shown previously, a lack of MVPA is associated with obesity among the 12-year-olds in our cohort (23). The current physical activity guidelines advocate MVPA for improving numerous health outcomes and do not specifically recommend limiting the time spent sedentary. Our data supports the need to engage in MVPA in order to reduce the prevalence of obesity during childhood.


We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The UK Medical Research Council, the Wellcome Trust and the University of Bristol provide core support for ALSPAC. This publication is the work of the authors and S.N. Blair will serve as guarantor for the contents of this paper. This research was specifically funded by a grant from National Heart, Lung and Blood Institute (R01 HL071248-01A) and a grant from the Wellcome Trust GR068049MA.


Disclosure Statement

The authors have no conflicts of interest to declare.

Reference List

1. Must A, Tybor DJ. Physical activity and sedentary behavior: a review of longitudinal studies of weight and adiposity in youth. Int J Obes (Lond) 2005;29 Suppl 2:S84–S96. [PubMed]
2. Gortmaker SL, Must A, Sobol AM, Peterson K, Colditz GA, Dietz WH. Television viewing as a cause of increasing obesity among children in the United States, 1986–1990. Arch Pediatr Adolesc Med. 1996;150(4):356–62. [PubMed]
3. Wake M, Hesketh K, Waters E. Television, computer use and body mass index in Australian primary school children. J Paediatr Child Health. 2003;39(2):130–4. [PubMed]
4. Pate RR, O’Neill JR, Lobelo F. The evolving definition of “sedentary” Exerc Sport Sci Rev. 2008;36(4):173–8. [PubMed]
5. Levine JA, Vander Weg MW, Hill JO, Klesges RC. Non-exercise activity thermogenesis: the crouching tiger hidden dragon of societal weight gain. Arterioscler Thromb Vasc Biol. 2006;26(4):729–36. [PubMed]
6. Hamilton MT, Hamilton DG, Zderic TW. Role of low energy expenditure and sitting in obesity, metabolic syndrome, type 2 diabetes, and cardiovascular disease. Diabetes. 2007;56(11):2655–67. [PubMed]
7. Nelson MC, Neumark-Stzainer D, Hannan PJ, Sirard JR, Story M. Longitudinal and secular trends in physical activity and sedentary behavior during adolescence. Pediatrics. 2006;118(6):e1627–e1634. [PubMed]
8. Hardy LL, Dobbins TA, Denney-Wilson EA, Okely AD, Booth ML. Descriptive epidemiology of small screen recreation among Australian adolescents. J Paediatr Child Health. 2006;42(11):709–14. [PubMed]
9. Gordon-Larsen P, Nelson MC, Popkin BM. Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood. Am J Prev Med. 2004;27(4):277–83. [PubMed]
10. Elley CR, Kerse NM, Arroll B. Why target sedentary adults in primary health care? Baseline results from the Waikato Heart, Health, and Activity Study. Prev Med. 2003;37(4):342–8. [PubMed]
11. Cook S, Auinger P, Li C, Ford ES. Metabolic syndrome rates in United States adolescents, from the National Health and Nutrition Examination Survey, 1999–2002. J Pediatr. 2008;152(2):165–70. [PubMed]
12. Hu FB. Sedentary lifestyle and risk of obesity and type 2 diabetes. Lipids. 2003;38(2):103–8. [PubMed]
13. Healy GN, Dunstan DW, Salmon J, et al. Objectively measured light-intensity physical activity is independently associated with 2-h plasma glucose. Diabetes Care. 2007;30(6):1384–9. [PubMed]
14. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report, 2008. Washington, DC: U.S. Department of Health and Human Services; 2008.
15. Jakes RW, Day NE, Khaw KT, et al. Television viewing and low participation in vigorous recreation are independently associated with obesity and markers of cardiovascular disease risk: EPIC-Norfolk population-based study. Eur J Clin Nutr. 2003;57(9):1089–96. [PubMed]
16. 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. 2006;3(12):e488. [PubMed]
17. Healy GN, Wijndaele K, Dunstan DW, et al. Objectively measured sedentary time, physical activity, and metabolic risk: the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) Diabetes Care. 2008;31(2):369–71. [PubMed]
18. 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. [PubMed]
19. Gorely T, Marshall SJ, Biddle SJ, Cameron N. Patterns of Sedentary Behaviour and Physical Activity Among Adolescents in the United Kingdom: Project STIL. J Behav Med. 2007;30(6):521–31. [PubMed]
20. 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. [PMC free article] [PubMed]
21. Golding J, Pembrey M, Jones R. ALSPAC--the Avon Longitudinal Study of Parents and Children. I. Study methodology. Paediatr Perinat Epidemiol. 2001;15(1):74–87. [PubMed]
22. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ. 2000;320(7244):1240–3. [PMC free article] [PubMed]
23. Ness AR, Leary SD, Mattocks C, et al. Objectively measured physical activity and fat mass in a large cohort of children. PLoS Med. 2007;4(3):e97. [PMC free article] [PubMed]
24. Mattocks C, Ness A, Leary S, et al. Use of accelerometers in a large field-based study of children: protocols, design issues, and effects on precision. Journal of Physical Activity & Health. 2008;5(Supp 1):S94–S107. [PubMed]
25. Mattocks C, Leary S, Ness A, et al. Calibration of an accelerometer during free-living activities in children. Int J Pediatr Obes. 2007:1–9. [PubMed]
26. Reilly JJ, Armstrong J, Dorosty AR, et al. Early life risk factors for obesity in childhood: cohort study. BMJ. 2005;330(7504):1357. [PMC free article] [PubMed]
27. Ness AR, Leary S, Reilly J, et al. The social patterning of fat and lean mass in a contemporary cohort of children. Int J Pediatr Obes. 2006;1(1):59–61. [PubMed]
28. Riddoch CJ, Mattocks C, Deere K, et al. Objective measurement of levels and patterns of physical activity. Arch Dis Child. 2007;92(11):963–9. [PMC free article] [PubMed]
29. Mattocks C, Ness A, Deere K, et al. Early life determinants of physical activity in 11 to 12 year olds: cohort study. BMJ. 2008;336(7634):26–29. [PMC free article] [PubMed]
30. Her Majesty’s Stationary Office. Standard Occupational Classification. London: Office of Population and Census Surveys; 1991.
31. Tanner JM. Normal growth and techniques of growth assessment. Clin Endocrinol Metab. 1986;15(3):411–51. [PubMed]
32. Ortega FB, Ruiz JR, Sjostrom M. Physical activity, overweight and central adiposity in Swedish children and adolescents: the European Youth Heart Study. Int J Behav Nutr Phys Act. 2007;4:61. [PMC free article] [PubMed]
33. Healy GN, Dunstan DW, Salmon J, et al. Breaks in sedentary time: beneficial associations with metabolic risk. Diabetes Care. 2008;31(4):661–6. [PubMed]
34. Sardinha LB, Andersen LB, Anderssen SA, et al. Objectively measured time spent sedentary is associated with insulin resistance independent of overall and central body fat in 9- to 10-year-old Portuguese children. Diabetes Care. 2008;31(3):569–75. [PubMed]
35. Matthews CE, Chen KY, Freedson PS, et al. Amount of Time Spent in Sedentary Behaviors in the United States, 2003–2004. Am J Epidemiol. 2008 [PMC free article] [PubMed]
36. Fairclough SJ, Butcher ZH, Stratton G. Whole-day and segmented-day physical activity variability of northwest England school children. Prev Med. 2007;44(5):421–5. [PubMed]