There is evidence that low cardiorespiratory fitness (CRF) is associated with impaired metabolic risk profiles in children (5,7,13,18). The associations are particularly evident when comparing the lowest CRF category with moderate and high CRF categories (5,13,18). Large proportions of children have low CRF levels (25,29), and in the United States, it is estimated that 45% of boys and 36% of girls, age 12–13 yr, do not meet CRF standards (25). It is therefore important to identify approaches that can increase CRF levels in childhood.
Spending time in moderate-to-vigorous physical activity (MVPA) has been associated with increased CRF levels among children (25,30), and such findings contributed to the recommendation that children spend at least 60 min·d−1 in MVPA (26). However, even if children meet this recommendation, a large proportion of their time can be spent in sedentary behavior (24). There has been little research conducted to determine whether time spent in sedentary behavior is associated with CRF levels in children, adjusting for physical activity levels (11). This is notable because there is evidence that aging from childhood to adolescence is a key period during the life course when time spent in sedentary behavior increases (21). Longitudinal studies are needed to investigate if time spent in sedentary behavior is independently associated with changes in CRF levels during childhood.
Cross-sectional studies involving children have reported negative associations between screen-based sedentary behavior and several measures of CRF that include 1-mile run times, 20-m shuttle run laps, and peak oxygen uptakes estimated from submaximal tests (1,11,17,25). However, only the study by Hardy et al. (11) adjusted for physical activity levels, and in that study, the negative association between screen time and the number of shuttle run laps completed remained after the adjustment. That cross-sectional finding suggests that screen time is independently associated with CRF levels in children. To the best of our knowledge, no longitudinal studies have determined if screen-based sedentary behavior is associated with changes in CRF levels during childhood, adjusting for physical activity. The purpose of the present study is to determine whether time spent in screen-based sedentary behavior is associated with changes in CRF in a sample of children as they age from 11 to 13, adjusting for physical activity levels.
We used a longitudinal observational study design to address the aim of the present study. Participants completed 20-m shuttle run tests at ages 11 and 13, and the number of laps completed was used as a measure of CRF levels. At the same ages, the participants self-reported the time spent watching television, using a computer, and playing video games to provide a measure of screen-based sedentary behavior.
The children included in the current study were participants in the HEALTHY Study, which has been described in full detail elsewhere (12). The HEALTHY Study was designed as a cluster randomized controlled trial with middle schools as the unit of randomization. Middle schools were selected from seven field centers (six schools per field center): 1) Baylor College of Medicine, Houston, TX; 2) Oregon Health & Science University, Portland, OR; 3) University of California at Irvine, Irvine, CA; 4) Temple University, Philadelphia, PA; 5) University of North Carolina at Chapel Hill, Chapel Hill, NC; 6) University of Pittsburgh, Pittsburgh, PA; and 7) University of Texas Health Science Center at San Antonio, San Antonio, TX. To be included in the HEALTHY Study, the middle schools had to have a student body that was 50% minority (African American, Hispanic/Latino, and/or American Indian) or have 50% of students eligible for a free lunch (Fig. 1). Children attending the schools assigned to the control arm received no intervention materials during the study period, and on the basis of the purpose of the current study, only the children attending these schools were included in our analyses (Fig. 1). The screen time and CRF levels of the children attending the schools assigned to the intervention arm of the study may have been influenced by exposure to the intervention components (nutrition, physical education, behavior, and communication components) and so were excluded. Institutional review boards at each field center provided ethical approval for the study. The children provided written informed assent, and their parents provided written informed consent, for participation in the HEALTHY Study.
Participants performed 20-m shuttle run tests during physical education lessons. This field test required the participants to run back and forth between markers set 20 m apart, at an initial speed of 8.5 km·h−1. The pace of the test increased by 0.5 km·h−1 every minute, and audio signals determined if each lap was completed on time. Research assistants recorded the shuttle run laps completed, and if two laps were not completed within the required time, that specific participant’s test ended. The total number of laps completed was used as a measure of CRF in the current study. This field test has been validated against laboratory-measured maximal oxygen uptake in children (3,16) and has been shown to provide a reliable estimate of CRF among children (19,23).
The participants self-reported the time they spent watching television/videos, using a computer/the Internet, and playing video games during 2 d using the Self-Administered Physical Activity Checklist (SAPAC) (31). Children have a limited ability to recall behaviors, and so, a 2-d recall was used in the HEALTHY Study (12). The SAPAC was administered on a Wednesday, Thursday, or Friday to capture weekday screen time. Total screen time before and after school was determined, and screen times that exceeded 150 min before school and 450 min after school were considered excessive, and self-reported times at or below these values were considered plausible. These cutpoint values were close to the 90th screen time percentiles values. Participants who reported screen time in excess of these cutpoints had their times windsorized to the cutpoint values, and this approach has been used in past studies (32). The screen times before and after school were then summed, and the average hours per day spent engaged in screen time during 2 d was used a measure of overall screen time.
Spending time in physical activity and especially vigorous physical activity (VPA) is associated with higher CRF in children (30). For this reason, self-reported time spent in VPA was included in the current study as a covariate. The SAPAC included a range of activities, but only physical activities of 6 or more metabolic equivalents (METs) were used to estimate the time spent in VPA. The physical activity compendium was used to assign MET values to each activity (27). The children recalled the physical activities they performed on the previous 2 d. The SAPAC was administered on a Wednesday, Thursday, or Friday to provide a measure of weekday physical activity. The time spent in VPA before, during, and after school was determined for each participant, and self-reported times that exceeded 60 min before school, 80 min during school, and 240 min after school were considered excessive. These cutpoints were close to the 95th VPA percentiles and were thought to be the upper limit of plausible times during each period. Participants who reported VPA in excess of these cutpoints had their times windsorized to the cutpoint values, and this approach has been used in past studies (32). The times spent in each period were summed, and the average minutes per day of VPA during 2 d was used a measure of time spent in VPA.
Gender, socioeconomic status (SES), and body mass index (BMI) were also included in the current study as covariates. There is evidence that time spent in sedentary behavior varies between boys and girls, with evidence that screen time is higher among boys (10). Further, there are data showing that boys have higher CRF levels than girls (25). In addition, lower SES and higher BMI levels associate with more time spent in sedentary behavior (15,21) and lower CRF levels (25). The highest education level attained by the head of the household was reported, and household education level, high school level or lower or some college education or higher, was used as a measure of SES in the current study. The children were categorized into BMI percentile categories on the basis of Centers for Disease Control and Prevention growth chart reference values; the categories were ≤30th percentile, 31st to 84th percentile, 85th to 95th percentile, or ≥95th percentile.
For descriptive purposes, the means and SD for the continuous variables and frequencies and percents for categorical variables are presented. We used longitudinal quantile regression models to address the aims of the study. Traditional regression models focus on the mean and so are limited in that they do not extend to the noncentral locations of a distribution (i.e., upper and lower tails) (9). In the context of CRF, there is a particular interest in the effect of predictors at the lower tail of the CRF distribution (5,6). Quantile regression is an extension of ordinary least squares regression, and the coefficients from both statistical approaches are interpreted in the same manner (i.e., the coefficients in the present study represent the change in CRF for each unit change in the independent variable) (9). First, we created longitudinal quantile regression models to describe changes at the 10th, 25th, 50th, 75th, and 90th screen time percentiles from age 11 to 13. This was to determine whether changes in screen time with age were equal across the screen time distribution. Screen time was modeled as the dependent variable, and age (coded as 0 and 2) was included as the independent variable. Second, we created longitudinal quantile regression models to describe changes at the 10th, 25th, 50th, 75th, and 90th shuttle run lap percentiles from age 11 to 13. Shuttle run laps were modeled as the dependent variable, and age, gender, and age × gender were included as independent variables. This was to determine whether changes in shuttle run laps were equal across the shuttle run lap distribution and whether the changes were equal for boys and girls. Third, we created longitudinal quantile regression models to determine whether screen time was associated with changes at the 10th, 25th, 50th, 75th, and 90th shuttle run lap percentiles from age 11 to 13. In model 1, shuttle run laps were modeled as the dependent variable with age, screen time, BMI, and SES included as the independent variable. This was done to determine whether screen time was associated with changes in shuttle run laps and whether the changes were equal across the shuttle run lap distribution, adjusting for BMI and SES. In model 2, VPA was also included as an independent variable covariate to determine whether any associations between screen time and the shuttle run lap percentiles were independent of VPA. The correlation between repeated measures on individuals was accounted for by using a first-order autoregressive correlation structure (34). The 95% confidence intervals were estimated using 500 cluster bootstrap samples to account for the dependence between repeated measures (34). All analyses were conducted using Stata (SE, release 10.0, StataCorp, College Station, TX).
A total of 2097 children had baseline and follow-up shuttle run data, and of these, 988 were boys, and 1109 were girls. In terms of race/ethnicity, the sample was 54.3% Hispanic, 15.6% black, 21.2% white, and 9.0% other (Table 1). The sample was approximately split in terms of household education level (51.5% from households where the highest level of education attained was high school or lower and 48.5% from households where the highest education level attained was some college education or higher) (Table 1). The demographic characteristics of the sample reflect the selection protocol used for the HEALTHY Study (Fig. 1).
The mean screen times by gender and age are shown in Table 1. On average, the children spent 3.5 and 3.8 h·d−1 in screen time at ages 11 and 13, respectively. Using quantile regression, it was observed that increases in screen time from age 11 to 13 were different across the screen time distribution (Table 2). The greatest increase in screen time was observed at the 75th screen time percentile (5.00 to 5.50 h·d−1), and the smallest increase was observed at the 10th screen time percentile (0.75 to 1.01 h·d−1) (Table 2). In contrast, the 90th screen time percentile remained stable over time at 7.50 h·d−1 (Table 2). The quantile regression models were tested for visit × gender interaction terms, but there was no evidence that screen time changed differentially over time for boys or girls.
The mean number of shuttle run laps completed was greater among the boys compared with the girls in the sixth grade (Table 1). Further, there was an increase in the mean number of shuttle run laps from age 11 to 13 among the boys but not the girls (Table 1). Using quantile regression, changes in shuttle run laps were different for boys and girls (Table 3). For the boys, increases were observed at all shuttle run percentiles with the greatest increase at the 90th shuttle run lap percentile (+18 laps over time) and the smallest increase at the 10th shuttle run lap percentile (+3 laps over time). For the girls, there were no changes at the 10th and 25th shuttle run lap percentiles over time and small increases in shuttle run laps at the 50th, 75th, and 90th shuttle run lap percentiles (+2 laps, +1 lap, and +2 laps, respectively).
Because of the gender-specific changes in shuttle run laps over time, the associations between screen time and shuttle run lap percentiles are presented by gender in Table 4. In our sample of boys, more screen time was negatively associated with changes in shuttle run laps at the 25th, 50th, and 75th shuttle run percentiles (Table 4). The negative associations between screen time and number of shuttle run laps were progressively stronger from the 25th to the 75th shuttle run percentiles in the boys (Table 4). Negative associations were observed between screen time and changes in shuttle run laps at the 10th and 90th shuttle run percentiles, but these associations did not reach statistical significance. The observed associations were similar after adjusting for VPA levels (Table 4). The results were similar when adjusted for MVPA (4+ MET activities, data not shown).
For the girls, screen time was negatively associated with changes in shuttle run laps at the 50th, 75th, and 90th shuttle run percentiles (Table 4). The negative associations between screen time and shuttle run laps were progressively stronger from the 50th to the 90th shuttle run percentiles in the girls (Table 4). Negative associations were observed between screen time and changes in shuttle run laps at the 10th and 25th shuttle run percentiles, but these associations did not reach statistical significance (Table 4). The observed associations were similar after adjusting for VPA levels (Table 4). The results were similar when adjusted for MVPA (4+ MET activities, data not shown).
In Figure 2, shuttle run lap distributions at each grade are presented by high and low screen times. The 10th and 90th screen time percentiles described in Table 2 are used to define low and high screen time, respectively. It can be observed that the shuttle run distribution was predicted to shift to the right if all boys and girls in our sample had low screen time. The rightward shift was predicted to be most prominent at the upper tail in the girls and most prominent in the middle of the distribution for the boys.
In boys, we observed increases in CRF from age 11 to 13 with the greatest increase observed at the 90th CRF percentile. However, more screen time was associated with lower CRF from age 11 to 13 at the 25th, 50th, and 75th CRF percentiles. It was predicted that if all boys in our sample reduced their screen time, the CRF distribution would shift to the right, with the greatest rightward shift occurring at the 75th percentile. In girls, we observed that CRF remained relatively stable from age 11 to 13. However, more screen time was associated with lower CRF from age 11 to 13 at the 50th, 75th, and 90th CRF percentiles. It was predicted that if all the girls reduced their screen time, the shuttle run lap distribution would shift to the right, with the greatest shift occurring at the 90th percentile. In both boys and girls, borderline negative associations were observed between screen time and shuttle run laps at the 10th CRF percentile.
The negative associations we observed between screen time and changes in CRF were adjusted for self-reported VPA. Previous cross-sectional studies have investigated the association between screen time and measures of CRF (1,11,17,25), but only the study by Hardy et al. (11) adjusted for physical activity levels. In that study, median shuttle run laps were higher among children in the lowest screen time quintile, compared with children in the highest screen time quintile (11). To the best of our knowledge, no longitudinal studies have investigated the relationship between sedentary behavior and changes in CRF in children, adjusting for time spent in physical activity. We identified one longitudinal study that specifically assessed changes in mean CRF from age 7 to 9 in relation to television viewing, and there was evidence that more television viewing was associated with reduced CRF levels over time (22). However, that study did not adjust for physical activity levels (22). Additional longitudinal studies are needed to confirm if screen time is independently associated with changes in measures of CRF during childhood. The findings in the current longitudinal study and those reported cross-sectionally by Hardy et al. (11) suggest that more screen time may lead to lower CRF levels during childhood.
There is also a need to replicate our study using objective measures of sedentary behavior. The relationship between accelerometry-measured sedentary behavior and CRF has been investigated in a cross-sectional study of European children (20). The investigators categorized children into low- and high-sedentary behavior groups, on the basis of percent time spent in sedentary behavior (<69% and ≥69% of time, respectively) (20). There was no difference in the mean number of shuttle run stages completed between low- and high-sedentary behavior groups in the boys (20). The mean number of shuttle run stages completed was lower for girls in the high-sedentary behavior category. However, there was no association between sedentary behavior and mean shuttle run stages if the girls spent 60 min or more per day in MVPA (20). These data suggest that the association between sedentary behavior and CRF in girls is not independent of MVPA. Direct comparisons between this cross-sectional study and our longitudinal study are limited by the different study designs and methodologies used to measure and report time spent in sedentary behavior (20). Furthermore, the different populations under observation (European children age 12 to 17 vs US children age 11 to 13) also need to be taken into consideration when drawing comparisons (20).
We used longitudinal quantile regression models to analyze our data, which allowed for the investigation of screen time on changes across the CRF distribution (9). We found that the association between screen time and CRF was not uniform across the CRF distribution. This finding is of interest in the context of the population-based approach to preventive medicine (28). On the basis of our results, if all children reduced their screen time, the CRF distribution would shift to the right; however, the rightward shift would not be uniform and would be minimal at the lower tail of the CRF distribution. There is evidence that the largest gains in health associated with higher CRF levels are observed between the lowest and middle CRF categories in children (5,13,18). The same findings were first observed in adults (2,6), indicating that a rightward shift at the lower tail of the CRF distribution is of most clinical importance. Further research is needed to replicate our findings and to identify if there are preventive measures that associate with a rightward shift at the lower tail of the CRF distribution.
Interestingly, age was a strong predictor of CRF increases among the boys, especially at the upper tail of the CRF distribution, but not among the girls. In addition, the age association remained after including the amount of screen time and the other covariates, in our models. This indicates that biological maturation is a driver of increases in CRF in the boys but not in the girls. Hormonal and body composition changes (increase in skeletal muscle) in boys, compared with the hormonal and body composition changes (increase in adipose tissue) in girls, may explain why we observed CRF increases in the boys from age 11 to 13 (14).
One explanation as to why we observed a nonuniform association between screen time and age-related changes in CRF is that CRF is a partly heritable trait (4). It has been shown in a study of family members that aerobic exercise training does not uniformly increase CRF levels, with some families being nonresponders (4). It is likely that children genetically predisposed to lower CRF levels are at the lower tail of the CRF distribution, and this may explain the minimal leftward shift at the lower tail of the CRF distribution (i.e., there may be nonresponders at the lower tail of the distribution, resistant to age- and screen time–associated changes in CRF due to genetic predispositions) (4).
Strengths of the current study include the repeated measures of CRF at ages 11 and 13. We adjusted for self-reported VPA, and only a single cross-sectional study has controlled for this key covariate in the past (11). Further, we used longitudinal quantile regression to study the association between screen time and changes in CRF. This approach allowed for the study of screen time at the tails of the CRF distribution, and past studies have only investigated the median or mean CRF levels (9). There are limitations in the current study. Self-reported time spent in screen time was used as a measure of sedentary behavior, and there is evidence that screen time may not provide a good representation of total sedentary behavior (8). It is therefore possible that those with low screen time spent time in sedentary behavior through other behaviors (e.g., sitting talking to friends) (8). It is also a limitation that screen time was assessed during 2 d because of the recall abilities of children. Studies assessing screen time during a longer period, perhaps through momentary recall diaries, could advance this area of research; this would also allow for the investigation of other sedentary behaviors in relation to CRF (8). Time spent in VPA was self-reported, and the children may not have accurately recalled the specific time spent in each activity (33). Using an objective measure of sedentary behavior and physical activity would overcome many of the limitations of self-reported screen time and VPA (20,30). The 20-m shuttle run test is a desirable method to assess CRF levels in a large number of children in the field setting; maximal aerobic exercise tests provide better measures of CRF but are impractical in such a large study (23). We were only able to measure the linear change in CRF levels between ages 11 and 13, and additional studies following children for longer periods are needed to better determine changes in CRF levels during childhood. More than half of our sample was Hispanic, and all children were in the United States. Further research is needed to determine whether similar associations between screen time and changes in CRF are observed in other populations of children.
In conclusion, screen time was negatively associated with changes in CRF from age 11 to 13 in boys at the 25th, 50th, and 75th CRF percentiles, independent of VPA. For girls, more screen time was negatively associated with changes in CRF from age 11 to 13 at the 50th, 75th, and 90th CRF percentiles, independent of VPA. Borderline negative associations were observed between screen time and changes in CRF from age 11 to 13 at the 10th CRF percentile in the boys and girls.
The authors thank Dr. Matteo Bottai for helping with the quantile regression analyses. The HEALTHY Study was conducted by the HEALTHY Study investigators and supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). The data from the HEALTHY Study reported here were supplied by the NIDDK Central Repositories. This article was not prepared in collaboration with the investigators of the HEALTHY Study and does not necessarily reflect the opinions or views of the HEALTHY Study, the NIDDK Central Repositories, or the NIDDK. The study reported here was part of a dissertation project that was supported by a graduate assistantship.
The authors have no conflicts of interest to declare.
The results of the present study do not constitute endorsement by the American College of Sports Medicine.
1. Armstrong CA, Sallis JF, Alcaraz JE, Kolody B, McKenzie TL, Hovell MF. Children’s television
viewing, body fat, and physical fitness. Am J Health Promot. 1998; 12 (6): 363–8.
2. Blair SN, Kohl HW 3rd, Paffenbarger RS Jr, Clark DG, Cooper KH, Gibbons LW. Physical fitness and all-cause mortality. A prospective study of healthy men and women. JAMA. 1989; 262 (17): 2395–401.
3. Boreham CA, Paliczka VJ, Nichols AK. A comparison of the PWC170 and 20-MST tests of aerobic
fitness in adolescent schoolchildren. J Sports Med Phys Fitness. 1990; 30 (1): 19–23.
4. Bouchard C, An P, Rice T, et al.. Familial aggregation of VO(2max) response to exercise training: results from the HERITAGE Family Study. J Appl Physiol. 1999; 87 (3): 1003–8.
5. Carnethon MR, Gulati M, Greenland P. Prevalence and cardiovascular disease correlates of low cardiorespiratory fitness in adolescents and adults. JAMA. 2005; 294 (23): 2981–8.
6. Church T. The low-fitness phenotype as a risk factor: more than just being sedentary? Obesity (Silver Spring). 2009; 17 (Suppl 3): S39–42.
7. Ekelund U, Anderssen S, Andersen LB, et al.. Prevalence and correlates of the metabolic syndrome in a population-based sample of European youth. Am J Clin Nutr. 2009; 89 (1): 90–6.
8. 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.
9. Hao L, Naimen DQ. Quantile Regression. Thousand Oaks, CA: SAGE Publications; 2007. p. 136.
10. 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.
11. Hardy LL, Dobbins TA, Denney-Wilson EA, Okely AD, Booth ML. Sedentariness, small-screen recreation, and fitness in youth. Am J Prev Med. 2009; 36 (2): 120–5.
12. Hirst K, Baranowski T, DeBar L, et al.. HEALTHY study rationale, design and methods: moderating risk of type 2 diabetes in multi-ethnic middle school students. Int J Obes (Lond). 2009; 33 (Suppl 4): S4–20.
13. Janssen I, Cramp WC. Cardiorespiratory fitness is strongly related to the metabolic syndrome in adolescents. Diabetes Care. 2007; 30 (8): 2143–4.
14. Janz KF, Mahoney LT. Three-year follow-up of changes in aerobic
fitness during puberty: the Muscatine Study. Res Q Exerc Sport. 1997; 68 (1): 1–9.
15. King AC, Parkinson KN, Adamson AJ, et al.. Correlates of objectively measured physical activity and sedentary behaviour in English children. Eur J Public Health. 2011; 21 (4): 424–31.
16. Leger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run
test for aerobic
fitness. J Sports Sci. 1988; 6 (2): 93–101.
17. Lobelo F, Dowda M, Pfeiffer KA, Pate RR. Electronic media exposure and its association with activity-related outcomes in female adolescents: cross-sectional and longitudinal
analyses. J Phys Act Health. 2009; 6 (2): 137–43.
18. Lobelo F, Pate RR, Dowda M, Liese AD, Daniels SR. Cardiorespiratory fitness and clustered cardiovascular disease risk in U.S. adolescents. J Adolesc Health. 2010; 47 (4): 352–9.
19. Mahoney C. 20-MST and PWC170 validity in non-Caucasian children in the UK. Br J Sports Med. 1992; 26 (1): 45–7.
20. Martinez-Gomez D, Ortega FB, Ruiz JR, et al.. Excessive sedentary time and low cardiorespiratory fitness in European adolescents: the HELENA study. Arch Dis Child. 2011; 96 (3): 240–6.
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. 2008; 167 (7): 875–81.
22. Mota J, Ribeiro JC, Carvalho J, Santos MP, Martins J. Television
viewing and changes in body mass index and cardiorespiratory fitness over a two-year period in schoolchildren. Pediatr Exerc Sci. 2010; 22 (2): 245–53.
23. Ortega FB, Artero EG, Ruiz JR, et al.. Reliability of health-related physical fitness tests in European adolescents. The HELENA Study. Int J Obes (Lond). 2008; 32 (Suppl 5): S49–57.
24. Pate RR, O’Neill JR, Lobelo F. The evolving definition of “sedentary.” Exerc Sport Sci Rev. 2008; 36 (4): 173–8.
25. Pate RR, Wang CY, Dowda M, Farrell SW, O’Neill JR. Cardiorespiratory fitness levels among US youth 12 to 19 years of age: findings from the 1999–2002 National Health and Nutrition Examination Survey. Arch Pediatr Adolesc Med. 2006; 160 (10): 1005–12.
26. Physical Activity Guidelines Advisory Committee. Physical Activity Guidelines Advisory Committee Report. Washington (DC): US Department of Health and Human Services; 2008. p. 683. Available from: US Department of Health and Human Services.
27. Ridley K, Ainsworth BE, Olds TS. Development of a compendium of energy expenditures for youth. Int J Behav Nutr Phys Act. 2008; 5: 45.
28. Rose G. Sick individuals and sick populations. Int J Epidemiol. 1985; 14 (1): 32–8.
29. Ruiz JR, Ortega FB, Martinez-Gomez D, et al.. Objectively measured physical activity and sedentary time in European adolescents: the HELENA study. Am J Epidemiol 2011; 174: 173–84.
30. 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.
31. Sallis JF, Buono MJ, Roby JJ, Micale FG, Nelson JA. Seven-day recall and other physical activity self-reports in children and adolescents. Med Sci Sports Exerc. 1993; 25 (1): 99–108.
32. Sallis JF, Taylor WC, Dowda M, Freedson PS, Pate RR. Correlates of vigorous physical activity for children in grades 1 through 12: comparing parent-reported and objectively measured physical activity. Pediatr Exerc Sci. 2002; 14 (1): 30–44.
33. Sirard JR, Pate RR. Physical activity assessment in children and adolescents. Sports Med. 2001; 31 (6): 439–54.
34. Wei Y, Pere A, Koenker R, He X. Quantile regression methods for reference growth charts. Stat Med. 2006; 25 (8): 1369–82.