Open Access Highly Accessed Research

Screen time and physical activity during adolescence: longitudinal effects on obesity in young adulthood

Janne E Boone12, Penny Gordon-Larsen12*, Linda S Adair12 and Barry M Popkin12

Author Affiliations

1 Department of Nutrition, Schools of Public Health and Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

2 Carolina Population Center, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

For all author emails, please log on.

International Journal of Behavioral Nutrition and Physical Activity 2007, 4:26  doi:10.1186/1479-5868-4-26


The electronic version of this article is the complete one and can be found online at: http://www.ijbnpa.org/content/4/1/26


Received:30 October 2006
Accepted:8 June 2007
Published:8 June 2007

© 2007 Boone et al; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

The joint impact of sedentary behavior and physical activity on obesity has not been assessed in a large cohort followed from adolescence to adulthood.

Methods

Nationally representative longitudinal data from Waves II (1995; mean age: 15.9) and III (2001; mean age: 21.4) of the National Longitudinal Study of Adolescent Health (n = 9,155) were collected. Sex-stratified multivariate logistic regression analysis assessed the odds of obesity associated with Wave II MVPA and screen time, controlling for sociodemographic characteristics and change in MVPA and screen time from Wave II to III. Obesity was defined using body mass index (BMI, kg/m2) International Obesity Task Force cut-points at Wave II and adult cut-points at Wave III (BMI ≥ 30).

Results

In males, adjusted odds of prevalent obesity was strongly predicted by MVPA bouts [OR (95% CI): OR6 vs. 1 MVPA bouts = 0.50 (0.40, 0.62); OR4 vs. 40 hrs screen time = 0.83 (0.69, 1.00)]. In females, greater MVPA bouts and lower screen time correlated with lower prevalent obesity [OR (95% CI): OR6 vs. 1 MVPA bouts = 0.67 (0.49, 0.91); OR4 vs. 40 hrs screen time = 0.67 (0.53, 0.85)]. Longitudinally, adolescent screen time hours had a stronger influence on incident obesity in females [OR (95% CI): OR4 vs. 40 hrs = 0.58 (0.43, 0.80)] than males [OR (95% CI): OR4 vs. 40 hrs = 0.78 (0.61, 0.99)]. Longitudinal activity patterns were not predictive of incident obesity.

Conclusion

Reducing screen time during adolescence and into adulthood may be a promising strategy for reducing obesity incidence, especially in females.

Background

Obesity is a major and rapidly growing global public health concern and is associated with significant morbidity and mortality [1-3]. In the past three decades, obesity has tripled among adolescents [4] and more than doubled among 20–39 year olds in the U.S. [5]. A substantial amount of weight gain [6-9], concomitant with precipitous declines in physical activity [10-13], occurs in the transition from adolescence to young adulthood.

Physical activity and sedentary behavior have become major focal areas in obesity research, interventions, and policies. Both have been linked to obesity among adolescents [14-17] and adults [18-20] in observational studies and assessed as target behaviors in randomized controlled trials among young children and adolescents [21-25], but full understanding of optimal behavior patterns for obesity prevention is far from complete. Sedentary behaviors, such as television viewing and computer games, may influence energy balance through displacement of physical activity [26], increased energy intake [27,28], or reduced metabolic rate [29]. Sedentary behaviors are generally more strongly and consistently associated with obesity than physical activity [16], and are thus commonly targeted by interventionists working with young populations.

Physical activity and sedentary behavior often co-occur [30,31] despite being inversely correlated [26,32], but there are few studies that examine their combined effects on obesity development over time. Furthermore, longitudinal effects from adolescence to adulthood are particularly important due to high risk of obesity onset and the abundance of changes in lifestyle and environment during this transition period [33,34]. Unfortunately, longitudinal studies examining physical activity and sedentary activity in relation to obesity are sparse: the only American observational cohort that captures the adolescent to adulthood transition is limited to females in three cities [17]; international studies that capture this lifecycle phase assess sedentary activity [15,35] or physical activity [36,37], but not both; and randomized controlled trials are usually of short duration (less than one year) [21-24,38] and do not continue into young adulthood [25].

In this study, we assess the combined association of moderate to vigorous physical activity (MVPA) and TV/video viewing (screen time) on incident obesity in a large nationally representative survey of adolescents followed through young adulthood. We hypothesized that cross-sectional and longitudinal patterns of physical activity would mitigate the adverse association between sedentary behavior and both current and incident obesity.

Methods

Study Population

The study population includes more than 20,000 individuals enrolled in The National Longitudinal Study of Adolescent Health (Add Health), a prospective cohort study of adolescents, representative of the U.S. school-based population in grades 7 to 12 in 1994–95, and followed into adulthood. Add Health included a core sample plus subsamples of selected minority and other groupings collected under protocols approved by the Institutional Review Board at the University of North Carolina at Chapel Hill. The survey design and sampling frame have been discussed elsewhere [39]. Add Health includes three waves: Wave I (1995) did not collect measured height and weight, so we used Wave II (13,570 eligible adolescents, measured April to August, 1996) and Wave III (14,322 eligible young adults, measured August 2001 and April 2002) samples in the current analysis (10,828 total eligible longitudinal respondents). Wave II followed the school-based sample of adolescents who did not graduate prior to 1996; high school dropouts and older high school students were included. Eligible respondents included those with sample weights. Females reporting pregnancy for Wave II or Wave III (n = 378) and individuals who used a walking aid (e.g., cane, crutches, or wheelchair) (n = 57); were less than 13 or greater 20 years of age at Wave II, or less than 19 or greater than 26 years of age at Wave III (n = 115); or had incomplete physical activity, sedentary behavior, height, weight, or relevant demographic data (n = 1,231) were excluded from analysis. Less than 7% of Wave II respondents were between 18 and 20 years – these were typically older adolescents who were still enrolled in school; despite being older than adolescent chronological age, they were retained in the Wave II sample: (a) to maintain consistency with the sampling design for national representation and (b) based on their lifecycle stage "of school-age" in terms of social influences and life stage. Our final sample includes 9,155 adolescents, 13–20 years of age (mean = 15.9 ± 0.12) at Wave II and 19–26 years of age (mean = 21.4 ± 0.11) at Wave III for descriptive and logistic regression analysis.

Study Variables

Wave II and III in-home surveys of study participants provided physical activity, sedentary behavior, and body mass index [BMI: weight (kg)/height (m2)] data. Wave I in-home surveys of parents provided income and education data. Education was the highest level of education attained by either parent. Where missing (n = 1,502; 13.9%), income was imputed using a method similar to that used in other national surveys to deal with missing data [40,41]. Race and ethnicity were determined primarily from adolescent self-report; parent interviews were used as a secondary source.

Obesity

Body Mass Index (BMI) was computed from measured height and weight at Waves II and III. Adolescent respondents were classified as obese at Wave II using International Obesity Task Force (IOTF) BMI ≥ 30 kg/m2-equivalent age- and sex-specific BMI cut-points [42]. The IOTF cut-points provide comparability to otherwise discrepant obesity definitions for adolescents and adults [6,42], which is vital for longitudinal analysis spanning the adolescent and adult years. At Wave III, when respondents were 19 years and older, the adult obesity cut point (BMI ≥ 30 kg/m2) was used [43,44]. Incident obesity was defined as BMI ≥ 30 kg/m2 among Wave III respondents who were not obese (BMI below the age- and sex-specific IOTF obesity cutpoint) at Wave II.

Physical Activity and Screen Time

Described in detail elsewhere [45,46], Add Health surveys employed a standard activity recall. While the recall has not undergone validity or reliability testing, it was based on self-report questionnaires that have been validated in other large-scale epidemiologic studies with regard to physical activity [47]. However, validation of self-reported sedentary behavior is scant [48]. In this study, physical activity was defined as weekly frequency of moderate to vigorous physical activity (MVPA; includes skating & cycling, exercise, and active sports), hereafter referred to as "bouts". Screen time was defined as hours of television and video viewing per week. The screen time distribution included a very long tail; thus, screen time greater than 80 hours per week (Wave II: 81–120 hours, n = 131, 1.2%; Wave III: 81–198 hours, n = 119, 1.1%) was truncated at 80 hours in order to stabilize regression estimates. Longitudinal measures were created as change variables, representing the change in MVPA bouts and truncated screen time hours from Wave II to Wave III.

Statistical Analysis

Statistical analyses were performed using Stata, version 9.1 (StataCorp, College Station, TX) [49]. Descriptive analyses used post-stratification sample weights for national representation; adjusted Wald tests compared means and design-based F-tests compared distribution of categorical variables between obese and non-obese subgroups. All analyses used multiple stages of cluster sampling to adjust for survey design effects.

All analyses were stratified by sex due to known differences in physical activity and sedentary behavior patterns between males and females [12,34]. Logistic regression models were used to assess correlates of adolescent obesity status (Wave II) and predictors of obesity incidence from adolescence to young adulthood (Wave II to Wave III). Wave II obesity prevalence models included Wave II physical activity and Wave II screen time. Wave III obesity incidence models excluded respondents obese at Wave II and assessed Wave II physical activity, Wave II screen time, and physical activity & screen time change variables (Wave III-Wave II). All quadratic terms related to physical activity or screen time and all potential interactions between Wave II physical activity and screen time and change variables were assessed in each model; non-significant (p ≥ 0.10) terms were excluded. Age, race/ethnicity (White, Black, Native American, Asian, Hispanic), household income tertile, and highest parental education are established confounders in behavior-obesity relationships, so they were included in all models. Due to less consistent relationships involving season, smoking status, and geographic region, we tested these variables empirically for confounding and included them in the model as control variables if their inclusion resulted in at least a 10% change in estimate of the odds ratio for obesity related to physical activity and/or screen time.

To illustrate the effects of shifts in continuous physical activity and screen time variables on incident obesity, model estimates were used to (1) calculate odds ratios for incident obesity and (2) predict obesity incidence based on assigned physical activity and sedentary behavior profiles. Odds ratios compare selected physical activity and screen time levels to an "undesirable" referent profile of low physical activity (1 bout per week) and high screen time (40 hours per week). These values correspond to the 10th and 90th percentiles, respectively, at Wave II for the total sample; there was no reason to suspect differential reporting or differential associations with obesity by sex, so combined values for males and females were used to provide consistent comparisons.

Given the complexity of our models, involving quadratic terms and change variables, we used coefficients from the models along with experimentally assigned physical activity and screen time values to predict obesity incidence. We (a) fit logistic models of obesity incidence from adolescence to young adulthood (Wave II to Wave III) including Wave II physical activity, Wave II screen time, and physical activity and screen time change variables (Wave III-Wave II), (b) assigned specified physical activity-screen time combinations to all members of the cohort, then (c) applied model coefficients from (a) to the experimentally assigned physical activity and screen time values. These results provide predicted obesity incidence at specified physical activity and screen time levels, providing the opportunity for "natural" experiments involving simulated manipulations of the activity and screen time profiles and, as such, aiding in the interpretation of model results.

Results

Sample characteristics

A total of 4,879 males and 4,276 females met study criteria. Obesity prevalence by sex and wave of survey are presented in Table 1. A considerable proportion of males (11.5%) and (10.9%) of females were obese at Wave II, and rates approximately doubled by Wave III to 21.1% for males and 23.9% for females. Obesity incidence from Wave II to III was 13.2% (SE = 0.76) among males and 15.9% (SE = 0.96) among females.

Table 1. Weighted obesity prevalencea [% (SE)] (Waves II and III)b

At Waves II and III, obesity prevalence was similar by smoking status and, except for males at Wave II, season. Obesity prevalence was significantly higher among those with lower household income and, except for males at Wave III, lower parental education. Among females, obesity prevalence also varied by race at both waves and region at Wave III (Table 1). Non-obese females were slightly younger than obese females at both waves (Table 2).

Table 2. Descriptive statistics for age, physical activity, and screen time [mean (SE)] (Waves II and III)a

Descriptive statistics for physical activity and screen time by sex, wave of survey, and obesity status are presented in Table 2. At Wave II, non-obese males and females reported more physical activity and less screen time than obese males and females. Males not obese at Wave III experienced a slightly greater decline in physical activity from Wave II to Wave III than those who were obese. Females not obese at Wave III reported less screen time at Wave III and nearly no change in screen time from Wave II to Wave III as opposed to an increase in screen time among females obese at Wave III.

Cross-Sectional association between physical activity & sedentary behavior and obesity during adolescence

Among males, odds of prevalent obesity during adolescence were lower with more bouts of physical activity and higher, although to a lesser degree, with more hours of screen time (Table 3). Among females, both physical activity and screen time were strong correlates of odds of obesity, although physical activity suggested a slight curvilinear relationship in which odds of obesity peaked at two to three physical activity bouts per week.

Table 3. Cross-sectional physical activity and screen time as predictors of obesity status of participants (Wave II)a

Longitudinal association between adolescent physical activity & sedentary behavior and incident obesity between adolescence and early adulthood Coefficients for incident obesity from adolescence to adulthood

For both males and females, significantly higher odds for incident obesity were observed with greater hours of screen time during adolescence as well as an increase in screen time from adolescence to early adulthood (Table 4). Neither physical activity during adolescence nor change in physical activity significantly predicted incident obesity in males. Similar to the cross-sectional analysis, there was a slight curvilinear relationship for physical activity among females.

Table 4. Physical activity and screen time as predictors of 5-year incident obesity (Wave II to III)a

Relative odds of incident obesity from adolescence to young adulthood

Based on coefficients from the model displayed in Table 4, Table 5 presents odds ratios for Wave III incident obesity at selected levels (10th, 25th, 50th, 75th, 90th percentiles) of Wave II physical activity & screen time for males and females, respectively (referent profile: 1 bout of physical activity and 40 hours screen time; all other variables were held constant). Consistent with the model estimates, relative odds of incident obesity become more protective with lower adolescent screen time regardless of physical activity. This association was more dramatic in females than in males: for example, very low screen time (4 hours per week) reduced relative odds of incident obesity by over 40% (OR4 hours, 1 bout = 0.58) for females and over 20% for males (OR4 hours, 1 bouts = 0.78) with very low physical activity (1 bout MVPA per week). These patterns were similar with negative, constant, and positive temporal shifts in screen time. Furthermore, this pattern is nearly identical at each level of physical activity, despite statistical significance in the logistic regression model for females.

Table 5. Physical activity and screen time odds ratios for 5-year incident obesity (Wave II to III)a

Adjusted obesity incidence from adolescence to young adulthood

Also based on model coefficients displayed in Table 4, Figure 1 shows predicted obesity incidence at experimentally assigned combinations of physical activity and screen time profiles (25th and 75th percentiles of MVPA and screen time). Change in MVPA from Wave II to III was held constant due to lack of statistical significance as shown in Table 4, and change in screen time was not displayed because patterns were similar across the relatively narrow range of observed values.

thumbnailFigure 1. Predicted obesity incidence (%) based on obesity incidence models (Wave II to III)a. a National Longitudinal Study of Adolescent Health. Predicted obesity incidence (%) based on multivariate sex-specific logistic regression models (shown in Table 3) predicting incident obesity from Wave II to Wave III, and accounting for sampling strategy and including baseline MVPA (moderate to vigorous physical activity, bouts/week, Wave II) and screen time (television and video viewing, hours/week, Wave II), as well as age at wave III, household income, highest parental education, race, and season at Wave III. Selected MVPA and Screen Time levels correspond to the 25th and 75thpercentiles for males and females combined during Wave II. Assumes no change in screen time.

For both males and females, predicted obesity incidence was highest for high screen time profiles during adolescence and the effects of experimental changes in physical activity were minimal, although the effects of experimental changes in screen time were more pronounced for females than males. Given constant screen time, predicted obesity incidence for males with low screen time (7 hours/week) paired with high MVPA (5 bouts/week) during adolescence was 12.4%, versus 14.1% for a less desirable profile of high screen time (40 hours/week) and low MVPA (1 bout/week). In comparison, analogous predicted obesity incidences for females were 13.7% and 17.6% (Figure 1). Experimental profiles involving changes in screen time produced similar patterns: reductions in screen time from adolescence to adulthood predicted lower obesity incidence, but not enough to overcome the obesity-promoting associations of screen time during adolescence.

Discussion

In this large, nationally representative, longitudinal cohort, weekly hours of screen time during adolescence independently and significantly predicted incident obesity in early adulthood. Fewer weekly hours of screen time during adolescence reduced the relative odds of incident obesity by over 40% among females and over 20% among males. Contrary to our hypothesis, longitudinal patterns of physical activity were less important predictors of incident obesity. Our results suggest that physical activity, if not maintained, may not be protective in the long-term and may not counteract the negative effects of screen time during adolescence and over time. Declines in screen time during the transition from adolescence to young adulthood were associated with lower obesity incidence, particularly for females, but not enough to overcome the obesity-promoting effects of screen time during adolescence. Our longitudinal results, including stronger results for females [35], are consistent with other studies summarized in a recent literature review [16] and in other recent studies showing a positive relationship between sedentary behavior and body weight [15,16,50] and no longitudinal relationship between physical activity and obesity [36,37], although existing literature is inconsistent.

In contrast, our cross sectional analysis suggests a strong protective association between physical activity and prevalent obesity for adolescent males, while both physical activity and screen time were important factors for adolescent females. This protective cross-sectional association between physical activity and obesity is not observed longitudinally, perhaps because such associations are diminished by other important lifestyle changes occurring during this lifecycle period [51]. Gordon-Larsen et al. showed a positive relationship between physical activity and incident obesity among adolescents between Waves I and II of Add Health [52], which is consistent with this explanation because it studied a one year follow-up period in contrast with the five year period assessed in the current study. Additionally, as noted in the Must and Tybor review [16], cross-sectional associations may reflect causal effects of physical activity patterns on obesity, but they could also reflect limited capacity to engage in exercise or sports due to extreme obesity, lack of social support for overweight or obese adolescents, or weight loss efforts.

In females, the likelihood of obesity peaked at two to three MVPA bouts per week, perhaps reflecting reverse causality as described above or inaccurate reporting of MVPA. Those engaging in two or three bouts of MVPA may have been more likely to exercise for weight loss or maintenance and hence at greater risk of obesity. Alternatively, reporting two or three bouts may reflect over-reporting to a greater extent than those with high MVPA frequency; that is, due to social desirability, those with no MVPA may over-report within a reasonable range, resulting in high obesity prevalence and incidence in this MVPA range.

For females, reduced screen time was associated with a greater relative reduction of a larger absolute obesity incidence than for males (13% for males versus 16% for females), resulting in a greater potential impact of screen time on obesity incidence. Assuming no change in screen time, experimentally assigning low versus high screen time led to predicted obesity incidence of approximately 14% and 18%, respectively (a 3 to 4 percentage point difference) for females and 12% and 14% (a 1 to 2 percentage point difference) for males. These results underscore the potential public health impact of reducing screen time during adolescence on controlling obesity incidence in females in particular.

The association between screen time and incident obesity may differ by sex due to biologic differences in changes in energy expenditure in response to screen time, in the effects of other lifestyle factors, misclassification biases by sex, or, more likely, a combination of all three. Biologic differences such as differential reductions in metabolic rate while engaging in sedentary activity are not evident in clinical research, but these studies are often limited to young children or include only one sex [29,53]. Other lifestyle factors such as changes in energy intake related to television viewing [27,28] or concurrent activities while watching television or videos could also differ between males and females. Similarly, as hypothesized by Dunstan et al. [54], screen time may be an indicator of sedentary behaviors in general, including activities such as computer use or reading, to a greater extent in females than males. Unfortunately, we are not able to quantify energy intake or overall energy expenditure in Add Health, so we were unable to investigate this issue. Recent studies in adult populations have demonstrated stronger associations between television viewing and metabolic risk factors in women than men [54-56], suggesting that this difference is not spurious and reflects important sex differences in the influence of screen time on obesity and obesity-related conditions. In addition, findings point to questions regarding assessment of screen time.

Regardless of the mechanism or whether it is a direct relationship – increased "junk food" consumption in response to advertisements, deflated metabolic rate, or some other factor – these results suggest that adolescent females who watch less television and videos during adolescence and/or reduce their viewing time from adolescence to young adulthood are less likely to develop obesity as they become young adults, regardless of their physical activity level. Thus, reducing screen time during adolescence is likely an essential component of obesity prevention, particularly for females. Furthermore, the level of screen time at which obesity odds were reduced in this study are consistent with the American Academy of Pediatrics recommendation [57] of 14 or fewer hours of television viewing per week in pediatric populations.

There are some limitations of the current study. As noted above, we could not assess energy intake, and activity data are subject to self-report bias. Misclassification is probably most dramatic among obese respondents reporting more socially desirable behavior, which would have attenuated the association between screen time and physical activity with obesity. For the same reason, those becoming obese between Waves II and III may have underreported screen time and/or over-reported physical activity to a greater extent at Wave III; this differential misclassification would have also attenuated the observed association between changes in these behaviors and Wave III incident obesity.

Second, the activity recall may not have captured all MVPA performed by respondents, and the "bouts" of activity classification is imprecise because each bout could be of any duration. Both of these factors would be expected to reduce precision and further dilute the apparent influence of physical activity. Furthermore, MVPA bouts may more accurately reflect physical activity level during adolescence, when more easily recalled organized sports practices and competitions are more common; this bias could potentially explain the lack of association between physical activity in young adulthood as well as stronger association with screen time, which may be more accurately reported, than physical activity. Finally, unmeasured factors related to both screen time and obesity such as an awareness of healthful behaviors could have contributed to these findings.

Conclusion

Physical activity and sedentary behavior are common targets for obesity prevention and treatment, and this study assesses the extent to which they predict obesity during a critical period in which risk for obesity onset is high and adult lifestyle takes shape. Our findings support public health strategies for reducing obesity incidence in this period that focus on reducing screen time during adolescence and maintaining these screen time reductions into young adulthood. Our research further suggests that physical activity during adolescence remains important for the establishment of lifelong habits as well as short-term weight maintenance, but that physical activity levels would need to be increased substantially and sustained into adulthood in order to counteract the negative effects of screen time. Findings were complex for males and suggest that identification of possible intervention points for males is an important next step. Further investigation of the mechanism through which screen time might influence development of obesity will help to guide potential intervention strategies. Finally, additional randomized trials that assess the combined effects of sedentary behavior and physical activity on obesity over a long follow-up period that captures the transition from adolescence to adulthood might help to confirm or refute the causal nature of these relationships.

Competing interests

There were no potential or real conflicts of financial or personal interest with the financial sponsors of the scientific project.

Authors' contributions

JB, PG-L, LSA, and BMP contributed to the study design, JB and PG-L contributed to the data analysis. All four authors contributed to the writing and approval of the manuscript.

Acknowledgements

The major funding of this project comes from the National Institutes of Health (R01-HD041375, R01-HD041375, K01-HD044263, and R01-HD39183-01. The authors would like to thank Ms. Frances Dancy for her helpful administrative assistance. This research uses data from Add Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from Add Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524[58] There were no potential or real conflicts of financial or personal interest with the financial sponsors of the scientific project.

References

  1. Must A, Jacques PF, Dallal GE, Bajema CJ, Dietz WH: Long-term morbidity and mortality of overweight adolescents. A follow-up of the Harvard Growth Study of 1922 to 1935.

    N Engl J Med 1992, 327(19):1350-1355. PubMed Abstract OpenURL

  2. Berenson GS, Srinivasan SR, Bao W, Newman WP 3rd, Tracy RE, Wattigney WA: Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study.

    N Engl J Med 1998, 338(23):1650-1656. PubMed Abstract | Publisher Full Text OpenURL

  3. Mahoney LT, Burns TL, Stanford W, Thompson BH, Witt JD, Rost CA, Lauer RM: Coronary risk factors measured in childhood and young adult life are associated with coronary artery calcification in young adults: the Muscatine Study.

    J Am Coll Cardiol 1996, 27(2):277-284. PubMed Abstract | Publisher Full Text OpenURL

  4. Ogden CL, Flegal KM, Carroll MD, Johnson CL: Prevalence and trends in overweight among US children and adolescents, 1999-2000.

    JAMA 2002, 288(14):1728-1732. PubMed Abstract | Publisher Full Text OpenURL

  5. Flegal KM, Carroll MD, Ogden CL, Johnson CL: Prevalence and trends in obesity among US adults, 1999-2000.

    JAMA 2002, 288(14):1723-1727. PubMed Abstract | Publisher Full Text OpenURL

  6. Gordon-Larsen P, Adair LS, Nelson MC, Popkin BM: Five-Year obesity incidence in the transition period between adolescence and adulthood: the National Longitudinal Study of Adolescent Health.

    Am J Clin Nutr 2004, 80:569-575. PubMed Abstract | Publisher Full Text OpenURL

  7. McTigue KM, Garrett JM, Popkin BM: The natural history of the development of obesity in a cohort of young U.S. adults between 1981 and 1998.

    Ann Intern Med 2002, 136(12):857-864. PubMed Abstract | Publisher Full Text OpenURL

  8. Lewis CE, Jacobs DR Jr., McCreath H, Kiefe CI, Schreiner PJ, Smith DE, Williams OD: Weight gain continues in the 1990s: 10-year trends in weight and overweight from the CARDIA study. Coronary Artery Risk Development in Young Adults.

    Am J Epidemiol 2000, 151(12):1172-1181. PubMed Abstract | Publisher Full Text OpenURL

  9. Ravussin E, Swinburn B: Energy Metabolism. In Obesity: theory and therapy. 2nd edition. Edited by Stunkard AJ, Wadden TA. New York , Raven Press; 1993:97-123. OpenURL

  10. Kimm SY, Glynn NW, Kriska AM, Barton BA, Kronsberg SS, Daniels SR, Crawford PB, Sabry ZI, Liu K: Decline in physical activity in black girls and white girls during adolescence.

    N Engl J Med 2002, 347(10):709-715. PubMed Abstract | Publisher Full Text OpenURL

  11. Sallis JF: Age-related decline in physical activity: a synthesis of human and animal studies.

    Med Sci Sports Exerc 2000, 32(9):1598-1600. PubMed Abstract | Publisher Full Text OpenURL

  12. Gordon-Larsen P, Nelson MC, Popkin BM: Longitudinal physical activity and sedentary behavior trends: adolescence to adulthood.

    Am J Prev Med 2004, 27(4):277-283. PubMed Abstract | Publisher Full Text OpenURL

  13. van Mechelen W, Twisk JW, Post GB, Snel J, Kemper HC: Physical activity of young people: the Amsterdam Longitudinal Growth and Health Study.

    Med Sci Sports Exerc 2000, 32(9):1610-1616. PubMed Abstract | Publisher Full Text OpenURL

  14. Monda KL, Popkin BM: Cluster analysis methods help to clarify the activity-BMI relationship of Chinese youth.

    Obes Res 2005, 13(6):1042-1051. PubMed Abstract | Publisher Full Text OpenURL

  15. Hancox RJ, Milne BJ, Poulton R: Association between child and adolescent television viewing and adult health: a longitudinal birth cohort study.

    Lancet 2004, 364(9430):257-262. PubMed Abstract | Publisher Full Text OpenURL

  16. 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:S84-96. PubMed Abstract | Publisher Full Text OpenURL

  17. Kimm SY, Glynn NW, Obarzanek E, Kriska AM, Daniels SR, Barton BA, Liu K: Relation between the changes in physical activity and body-mass index during adolescence: a multicentre longitudinal study.

    Lancet 2005, 366(9482):301-307. PubMed Abstract | Publisher Full Text OpenURL

  18. Hu FB, Li TY, Colditz GA, Willett WC, Manson JE: Television watching and other sedentary behaviors in relation to risk of obesity and type 2 diabetes mellitus in women.

    JAMA 2003, 289(14):1785-1791. PubMed Abstract | Publisher Full Text OpenURL

  19. Brown WJ, Miller YD, Miller R: Sitting time and work patterns as indicators of overweight and obesity in Australian adults.

    Int J Obes Relat Metab Disord 2003, 27(11):1340-1346. PubMed Abstract | Publisher Full Text OpenURL

  20. Fung TT, Hu FB, Yu J, Chu NF, Spiegelman D, Tofler GH, Willett WC, Rimm EB: Leisure-time physical activity, television watching, and plasma biomarkers of obesity and cardiovascular disease risk.

    Am J Epidemiol 2000, 152(12):1171-1178. PubMed Abstract | Publisher Full Text OpenURL

  21. Goldfield GS, Mallory R, Parker T, Cunningham T, Legg C, Lumb A, Parker K, Prud'homme D, Gaboury I, Adamo KB: Effects of open-loop feedback on physical activity and television viewing in overweight and obese children: a randomized, controlled trial.

    Pediatrics 2006, 118(1):e157-66. PubMed Abstract | Publisher Full Text OpenURL

  22. Patrick K, Norman GJ, Calfas KJ, Sallis JF, Zabinski MF, Rupp J, Cella J: Diet, physical activity, and sedentary behaviors as risk factors for overweight in adolescence.

    Arch Pediatr Adolesc Med 2004, 158(4):385-390. PubMed Abstract | Publisher Full Text OpenURL

  23. Robinson TN, Killen JD, Kraemer HC, Wilson DM, Matheson DM, Haskell WL, Pruitt LA, Powell TM, Owens AS, Thompson NS, Flint-Moore NM, Davis GJ, Emig KA, Brown RT, Rochon J, Green S, Varady A: Dance and reducing television viewing to prevent weight gain in African-American girls: the Stanford GEMS pilot study.

    Ethn Dis 2003, 13(1 Suppl 1):S65-77. PubMed Abstract OpenURL

  24. Robinson TN: Reducing children's television viewing to prevent obesity: a randomized controlled trial.

    JAMA 1999, 282(16):1561-1567. PubMed Abstract | Publisher Full Text OpenURL

  25. Gortmaker SL, Peterson K, Wiecha J, Sobol AM, Dixit S, Fox MK, Laird N: Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health.

    Arch Pediatr Adolesc Med 1999, 153(4):409-418. PubMed Abstract | Publisher Full Text OpenURL

  26. Epstein LH, Roemmich JN, Paluch RA, Raynor HA: Physical activity as a substitute for sedentary behavior in youth.

    Ann Behav Med 2005, 29(3):200-209. PubMed Abstract | Publisher Full Text OpenURL

  27. Matheson DM, Killen JD, Wang Y, Varady A, Robinson TN: Children's food consumption during television viewing.

    Am J Clin Nutr 2004, 79:1088-1094. PubMed Abstract | Publisher Full Text OpenURL

  28. Giammattei J, Blix G, Marshak HH, Wollitzer AO, Pettitt DJ: Television watching and soft drink consumption: associations with obesity in 11- to 13-year-old schoolchildren.

    Arch Pediatr Adolesc Med 2003, 157(9):882-886. PubMed Abstract | Publisher Full Text OpenURL

  29. Klesges RC, Shelton ML, Klesges LM: Effects of television on metabolic rate: potential implications for childhood obesity.

    Pediatrics 1993, 91(2):281-286. PubMed Abstract OpenURL

  30. Nelson MC, Gordon-Larsen P, Adair LS, Popkin BM: Adolescent physical activity and sedentary behavior patterning and long-term maintenance.

    Am J Prev Med 2005, 28(3):259-266. PubMed Abstract | Publisher Full Text OpenURL

  31. Owen N, Leslie E, Salmon J, Fotheringham MJ: Environmental determinants of physical activity and sedentary behavior.

    Exerc Sport Sci Rev 2000, 28(4):153-158. PubMed Abstract OpenURL

  32. Marshall SJ, Biddle SJ, Gorely T, Cameron N, Murdey I: Relationships between media use, body fatness and physical activity in children and youth: a meta-analysis.

    Int J Obes Relat Metab Disord 2004, 28(10):1238-1246. PubMed Abstract | Publisher Full Text OpenURL

  33. Boreham C, Robson PJ, Gallagher AM, Cran GW, Savage JM, Murray LJ: Tracking of physical activity, fitness, body composition and diet from adolescence to young adulthood: The Young Hearts Project, Northern Ireland.

    Int J Behav Nutr Phys Act 2004, 1(1):14. PubMed Abstract | BioMed Central Full Text | PubMed Central Full Text OpenURL

  34. Telama R, Yang X, Viikari J, Valimaki I, Wanne O, Raitakari O: Physical activity from childhood to adulthood: a 21-year tracking study.

    Am J Prev Med 2005, 28(3):267-273. PubMed Abstract | Publisher Full Text OpenURL

  35. Hancox RJ, Poulton R: Watching television is associated with childhood obesity: but is it clinically important?

    Int J Obes (Lond) 2006, 30(1):171-175. PubMed Abstract | Publisher Full Text OpenURL

  36. Twisk JW, Kemper HC, van Mechelen W: The relationship between physical fitness and physical activity during adolescence and cardiovascular disease risk factors at adult age. The Amsterdam Growth and Health Longitudinal Study.

    Int J Sports Med 2002, 23 Suppl 1:S8-14. PubMed Abstract | Publisher Full Text OpenURL

  37. Boreham C, Twisk J, Neville C, Savage M, Murray L, Gallagher A: Associations between physical fitness and activity patterns during adolescence and cardiovascular risk factors in young adulthood: the Northern Ireland Young Hearts Project.

    Int J Sports Med 2002, 23 Suppl 1:S22-6. PubMed Abstract | Publisher Full Text OpenURL

  38. Sahota P, Rudolf MC, Dixey R, Hill AJ, Barth JH, Cade J: Randomised controlled trial of primary school based intervention to reduce risk factors for obesity.

    BMJ 2001, 323(7320):1029-1032. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  39. Gordon-Larsen P, McMurray RG, Popkin BM: Adolescent physical activity and inactivity vary by ethnicity: The National Longitudinal Study of Adolescent Health.

    J Pediatr 1999, 135(3):301-306. PubMed Abstract | Publisher Full Text OpenURL

  40. Winkleby MA, Kraemer HC, Ahn DK, Varady AN: Ethnic and socioeconomic differences in cardiovascular disease risk factors: findings for women from the Third National Health and Nutrition Examination Survey, 1988-1994.

    JAMA 1998, 280(4):356-362. PubMed Abstract | Publisher Full Text OpenURL

  41. Winkleby MA, Robinson TN, Sundquist J, Kraemer HC: Ethnic variation in cardiovascular disease risk factors among children and young adults: findings from the Third National Health and Nutrition Examination Survey, 1988-1994.

    JAMA 1999, 281(11):1006-1013. PubMed Abstract | Publisher Full Text OpenURL

  42. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH: Establishing a standard definition for child overweight and obesity worldwide: international survey.

    BMJ 2000, 320(7244):1240-1243. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL

  43. NHLBI Expert Panel on the Identification, Evaluation, and Treatment of Overweight and Obesity in Adults. Clinical Guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: the evidence report

    Obes Res 1998, 6(Suppl 2):51S-209S. PubMed Abstract OpenURL

  44. Obesity: preventing and managing the global epidemic. Report of a WHO consultation

    World Health Organ Tech Rep Ser 2000, 894:i-xii, 1-253. PubMed Abstract OpenURL

  45. Gordon-Larsen P, McMurray RG, Popkin BM: Determinants of adolescent physical activity and inactivity patterns.

    Pediatrics 2000, 105(6):E83. PubMed Abstract | Publisher Full Text OpenURL

  46. Add Health Codebooks [http://www.cpc.unc.edu/projects/addhealth/codebooks] webcite

  47. Sallis JF, McKenzie TL, Kolody B, Lewis M, Marshall S, Rosengard P: Effects of health-related physical education on academic achievement: project SPARK.

    Res Q Exerc Sport 1999, 70(2):127-134. PubMed Abstract OpenURL

  48. Dietz WH: The role of lifestyle in health: the epidemiology and consequences of inactivity.

    Proc Nutr Soc 1996, 55(3):829-840. PubMed Abstract | Publisher Full Text OpenURL

  49. StataCorp: Stata statistical software: STATA/SE for Unix. 9.1st edition. College Station, TX , Stata Corporation; 2006.

  50. Elgar FJ, Roberts C, Moore L, Tudor-Smith C: Sedentary behaviour, physical activity and weight problems in adolescents in Wales.

    Public Health 2005, 119(6):518-524. PubMed Abstract | Publisher Full Text OpenURL

  51. Harris KM, Gordon-Larsen P, Chantala K, Udry JR: Longitudinal trends in race/ethnic disparities in leading health indicators from adolescence to young adulthood.

    Arch Pediatr Adolesc Med 2006, 160(1):74-81. PubMed Abstract | Publisher Full Text OpenURL

  52. Gordon-Larsen P, Adair LS, Popkin BM: Ethnic differences in physical activity and inactivity patterns and overweight status.

    Obes Res 2002, 10(3):141-149. PubMed Abstract | Publisher Full Text OpenURL

  53. Dietz WH, Bandini LG, Morelli JA, Peers KF, Ching PL: Effect of sedentary activities on resting metabolic rate.

    Am J Clin Nutr 1994, 59(3):556-559. PubMed Abstract OpenURL

  54. Dunstan DW, Salmon J, Healy GN, Shaw JE, Jolley D, Zimmet PZ, Owen N: Association of television viewing with fasting and 2-h postchallenge plasma glucose levels in adults without diagnosed diabetes.

    Diabetes Care 2007, 30(3):516-522. PubMed Abstract | Publisher Full Text OpenURL

  55. Dunstan DW, Salmon J, Owen N, Armstrong T, Zimmet PZ, Welborn TA, Cameron AJ, Dwyer T, Jolley D, Shaw JE: Associations of TV viewing and physical activity with the metabolic syndrome in Australian adults.

    Diabetologia 2005, 48(11):2254-2261. PubMed Abstract | Publisher Full Text OpenURL

  56. Bertrais S, Beyeme-Ondoua JP, Czernichow S, Galan P, Hercberg S, Oppert JM: Sedentary behaviors, physical activity, and metabolic syndrome in middle-aged French subjects.

    Obes Res 2005, 13(5):936-944. PubMed Abstract | Publisher Full Text OpenURL

  57. American Academy of Pediatrics: Children, adolescents, and television

    Pediatrics 2001, 107(2):423-426. PubMed Abstract | Publisher Full Text OpenURL

  58. Add Health Data [http://www.cpc.unc.edu/projects/addhealth/data] webcite