Abstract
BACKGROUND
To improve support and justification for health promotion efforts in schools, it is helpful to understand how students’ health behaviors affect academic performance.
METHODS
Fifth grade students completed an online school administered health survey with questions regarding their eating behavior, physical activity, academic performance, and sleep patterns. Differences in health behaviors were examined by sex, self-reported weight status, and sufficient (≥ 9 hours) versus insufficient sleep. Logistic regression was used to determine the relationship between academic performance and the health behaviors.
RESULTS
One-third of the sample did not get the recommended amount of physical activity and more than half of the students watched TV ≥ 2 hours/day. Self-reported overweight status was related to lower self-reported academic performance, fewer lunch and breakfast occasions, less physical activity, not meeting the recommendations for vegetable and soda consumption as well as hours of TV watching. Sufficient sleep (≥ 9 hours/night) was associated with better grades, meeting the recommended hours of daily TV watching and video game playing, being more physically active and increased breakfast and lunch frequency. Percentage of serving free/reduced lunch, soda consumption, breakfast frequency, amount of physical activity, and TV watching were associated with academic performance.
CONCLUSION
More positive health behaviors generally were associated with better academic performance. Promoting healthy behaviors in schools might improve not only students’ health academic performance as well.
Keywords: health behaviors, academic performance, sleep, nutrition, physical activity
The rise in children’s obesity rates suggests there is an urgent need to promote healthy behaviors among youth.1 Because obesity is associated with increased risk of many chronic diseases, addressing obesity in children can have a long-term positive impact on health. Rates of obesity and overweight status in US children are concerning, part due to their association with related health problems.2–5
Health Behaviors and Weight Status
Obese children differ from normal weight children in regards to their eating behavior, physical activity patterns and sleep patterns.6,7 In England, schoolchildren aged 10–16 years who only sometimes ate breakfast were more likely to be obese than those who always did.8 Other nutritional factors such as the consumption of sugar-sweetened beverages also have been identified as possible contributing factors to the increase in weight status among children.9 Some studies have shown a link between sleep patterns and risk of obesity in adolescents,10–12 and particularly in elementary school children.13 Other health related behaviors such as watching television also seem to affect children’s health. Results from the 2003–04 and 2005–06 National Health and Nutrition Examination Surveys suggest high TV viewing as a predictor for cardio-metabolic risk factors such as obesity or hypertension.14
Health Behaviors and Academic Performance
There is a great deal of interest in how health behaviors relate to academic performance. Sleep patterns, physical activity, eating behaviors, and body weight have all been suggested to influence academic performance.15–18 Edwards et al. 17 found that higher academic scores were associated with less soda consumption, more physical activity and fewer hours of watching television. A review investigating the relationship between obesity and academic performance revealed that overweight and obesity were associated with poorer academic performance.16 Whereas these data are promising, additional research is required to solidify the link between health behaviors and academic performance.
Interventions with Children
Much research in health behavior in schools has targeted 5th graders (age of 9–10 years).19–21 One reason is that persistent obesity is thought to develop before the age of 11 years.22 In addition, 5th grade students are already making decisions about food purchases and participation in physical activities; however, they also still depend on their families and the school when it comes to food and physical activity choices as well as other health behaviors such as sedentary behavior and sleep patterns. Lastly, because 5th graders are the oldest children in elementary schools they may be more willing to engage in interventions than children in middle schools where social pressures focus less on healthy behaviors, and physical activity declines.23
Colorado is the state with the lowest rates of adult obesity 24 but it is not the state with the lowest rates of childhood obesity.25 Data from the 2008 Colorado Child Health Survey showed that approximately 28.7 % of Colorado children ages 1–14 years were overweight or obese.26 Obesity rates vary as a function of ethnicity and socioeconomic status such that obesity is a particular problem in parts of Colorado with more minority and low income populations. The current study presented herein explored associations among various health behaviors, overweight, socioeconomic status and academic performance in 5th graders using secondary data from a school district in Denver, Colorado.
METHODS
Participants and Procedures
Data from 1095 students of 11 elementary schools enrolled in 5th grade in the school year 2009–10 and enrolled in the Cherry Creek School District in Denver, Colorado were used. The district has 41 elementary schools with a total of about 4000 enrolled 5th graders. The 11 elementary schools are part of an ongoing pilot study regarding health and wellness in this school district. The percentage of children across the elementary schools receiving free/reduced lunch ranged from 3.5% to 76.5%. About 65% of the school district had free/reduced lunch rates of 30% or below. About one fourth of the elementary schools in the district had free/reduced lunch rates of 50% and above. The selected schools do not differ from the remaining schools in terms of ethnicity, sex, or size.
All students in the district complete a biennial Climate, Safety, and Wellness Survey. At the 5th grade level, all students complete the survey on a voluntary basis. Letters are sent home to the students’ parents informing them of the survey and obtaining passive consent. Schools then have eligible students complete the survey, typically in a classroom in the school’s computer lab, during a one-month survey administration window. For schools that are on a year-round calendar, this window is typically extended for an additional 2 weeks to ensure that students on all tracks have the opportunity to complete the survey. To address potential difficulties some students may have with reading, a school staff member reads the questions aloud along with the students as they complete the survey. Completing the survey requires about 45 minutes.
For this analysis, answers from the Climate, Safety and Wellness Survey regarding the 5th grader’ health behaviors (hours of sleep, TV and video console usage, fruit and vegetable consumption, soda consumption, physical activity behavior, breakfast and lunch frequency, weight status) were selected. Questions about TV and computer usage, soda consumption, physical activity, and self-perceived weight status were taken from the Youth Risk Behavior Survey (YRBS). 27 Although the YRBS was originally designed for use in high school aged youth, researchers have used it with younger children; 28 students as young as 5th grade have been noted to understand questions from the YRBS. 29
Data Analysis
Because the YRBS uses categorical scales for the assessment of TV and computer/video usage (eg, On a 7-point Ordinal scale with 1 indicating “no TV watching on an average school day” and 7 indicating “5 or more hours of TV watching”) and the consumption of soda (On a 7-point Ordinal scale with 1 indicating “no soda in the last 7 days” and 7 indicating “4 or more times per day”) these variables were re-coded based on the recommendations for healthy behaviors from international health agencies (eg, the Australian Government, the Centers for Disease Control) into two categories (eg, < 2 hours vs. > 2 hours TV or video usage; less than one soda per week vs. one soda or more per week). 30–32
Independent samples t-tests (for continuous variables) and Pearson Chi-square tests (for categorical variables) were used to compare health behaviors between boys and girls, normal versus overweight students, and students reporting adequate (≥ 9 hours) versus inadequate (<9 hours) of sleep. Equal variance t-tests were used. The relationship between academic performance (the dependent variable) and health behaviors (the independent variables) were determined by logistic regression analysis. The percentage of students qualifying for free/reduced lunch (a surrogate for socio-economic status) and sex were included as covariates. All significance levels were set at α = 0.05. SPSS Version PASW 18 software was used for all analysis.
RESULTS
Data from 1095 5th grade students were analyzed with an even distribution among boys and girls (Table 1). The self-reported rate of overweight in the total sample was 11.6% which is lower than the Colorado average (27.2% for the age range of 10–17 years). Almost 25% of the sample described themselves as underweight.
Table 1.
Sex Differences in Sample Characteristics and Health Behaviors
| Total | Boy | Girl | p-value | |
|---|---|---|---|---|
| N (%) | 1095 (100) | 567 (51.8) | 516 (47.1) | |
| Ethnicity | ||||
| Non-Hispanic | 910 (83.1) | 477 (84.1) | 423 (82.0) | |
| Hispanic | 185 (16.9) | 90 (15.9) | 93 (18.0) | |
| Race | ||||
| White | 640 (58.4) | 326 (57.5) | 310 (60.1) | |
| African American | 183 (16.7) | 109 (19.2) | 73 (14.1) | p = .026 |
| American Indian/Alaskan Native | 53 (4.8) | 29 (5.1) | 24 (4.7) | |
| Asian | 134 (12.2) | 59 (10.4) | 74 (14.3) | p = .049 |
| Native Hawaiian/Pacific Islander | 24 (2.2) | 10 (1.8) | 14 (2.7) | |
| Other | 61 (5.7) | 34 (6.0) | 21 (4.1) | |
| Grade level | ||||
| A’s & B’s | 857 (70.3) | 375 (66.7) | 377 (74.4) | p = .020 |
| B’s & C’s | 255 (23.7) | 147 (26.1) | 106 (20.9) | |
| C’s, D’s & F’s | 64 (6.0) | 30 (7.2) | 24 (4.7) | |
|
| ||||
| Weight status | ||||
| Underweight | 266 (24.3) | 153 (27.0) | 110 (21.3) | p =.049 |
| Normal Weight | 658 (60.1) | 323 (57.0) | 330 (64.0) | |
| Overweight | 127 (11.6) | 69 (12.2) | 57 (11.0) | |
| % free/reduced lunch* | ||||
| Low (<50%) | 761 (74.1) | 386 (74.2) | 373 (75.1) | |
| High (>50%) | 266 (25.9) | 134 (25.8) | 124 (24.9) | |
| Physical Activity | ||||
| < 5 days/week | 326(30.6) | 146 (26.4) | 178 (35.2) | p = .002 |
| ≥ 5 days/week | 740 (69.4) | 406 (73.6) | 327 (64.8) | |
| TV hours/day | ||||
| < 2 hours/day | 611 (57.3) | 304 (54.7) | 303(60.5) | |
| ≥ 2 hours/day | 455 (42.7) | 252 (45.3) | 198 (39.5) | |
| Video games | ||||
| < 2 hours/day | 754 (70.3) | 338 (60.7) | 412 (81.4) | p < .001 |
| ≥2 hours/day | 318 (29.7) | 219 (39.3) | 94 (18.6) | |
| Sleep hours | ||||
| < 9 hours/night | 345 (32.3) | 198 (35.4) | 146 (29.1) | p = .029 |
| ≥ 9 hours/night | 724 (67.7) | 361 (64.6) | 355 (70.9) | |
| Daily Fruit Consumption | ||||
| 0 times/day | 37 (3.5) | 23 (4.1) | 14 (2.8) | |
| 1 time or more/day | 1031 (96.5) | 535 (95.9) | 487 (97.2) | |
| Daily Vegetable Consumption | ||||
| 0 times/day | 104 (10.2) | 62 (11.9) | 41 (8.3) | |
| 1 time or more/day | 919 (89.8) | 461 (88.1) | 451 (91.7) | |
| Soda consumption | ||||
| < once per week | 895 (84.4) | 450 (81.2) | 437 (87.8) | p = .004 |
| ≥ once per week | 166 (15.6) | 104 (18.8) | 61 (12.2) | |
|
| ||||
| Mean± SD | Mean± SD | Mean±SD | ||
|
| ||||
| Days being physically active | 4.61±2.1 | 4.9 ± 2.1 | 4.3 ± 2.0 | |
| Days of lunch/week | 6.49±1.3 | 6.44 ±1.3 | 6.55±1.2 | p = .011 |
| Days of breakfast/week | 6.15±1.7 | 6.14±1.7 | 6.16±1.7 | |
The free/reduced lunch variable is based on the school district’s information for each of the 41 elementary schools and is not based on the individual child.
There were no sex differences in ethnicity but Pearson chi-square tests revealed a significant higher percentage of boys reporting to be African-American, c2 (1, N=1083) = 4.980, p = .026, and to underweight or slightly underweight than girls, c3 (2, N=1042) = 6.050, p = .049. A higher percentage of girls reported to be Asian, c2 (1, N=1083) = 3.884, p = .049, and having better grades than boys, c3 (2, N=1069) = 7841, p = .020.
Sex Differences in Various Health Behaviors
No sex differences in days where they reported ≥ 60 minutes of physical activity were found, but a Pearson chi-square test revealed a significantly higher percentage of boys being physically active for the recommended 5 days or more per week than girls, c2 (1, N=1057) = 9.604, p = .002 (Table 1).
Chi-square tests showed no significant gender differences in fruit and vegetable category but significant differences for soda consumption. Significantly more boys drank more than one soda per day compared to girls, c2 (1, N=1052) = 8.440, p = .004. Also, significantly more boys compared to girls played video games for 2 hours or more per day in the last 7 days, c2 (1, N=1063) = 54.901, p <.001. Furthermore, significantly more boys failed to receive the sufficient amount of 9 hours or more of sleep per night, c2 (1, N=1060) = 4.751, p = .029.
Weight Status and Various Health Behaviors
A significantly larger proportion of overweight children reported lower grades than normal weight children, c3 (2, N=776) = 6.897, p = .032. The TV category (c2 (1, N=774) = 7.706, p = .006), soda (c2 (1, N=773) = 3.894, p = .048) and vegetable consumption categories (c2 (1, N=749) = 4.211, p = .040) all significantly differed by weight status. The group of overweight children was more likely not to eat vegetables daily and overweight children were more likely to watch 2 hours or more of television daily and to drink soda once per week or more. Overweight and normal weight children also differed significantly in their days of eating lunch, p = .005, and breakfast, p < .001. The difference in days of physical activity for a least 60 minutes between overweight and normal weight children approached significance with, p = .071 (Table 2).
Table 2.
Association of Weight Status, Academic Performance and Various Health Behaviors
| Normal weight | Overweight | p-value | |
|---|---|---|---|
| N (%) | 650 | 126 | |
| Grade level | |||
| A’s & B’s | 462 (71.1) | 80 (74.4) | p = .020 |
| B’s & C’s | 154 (23.7) | 32 (20.9) | |
| C’s, D’s & F’s | 34 (5.2) | 14 (4.7) | |
| Daily TV hours | |||
| < 2 hours/day | 375 (57.9) | 56 (44.4) | p =.006 |
| ≥ 2 hours/day | 273 (42.1) | 70 (55.6) | |
| Daily video game hours | |||
| < 2 hours/day | 468 (71.1) | 85 (68.0) | |
| ≥ 2 hours/day | 186 (28.3) | 40 (32.0) | |
| Daily fruit consumption | |||
| 0 times/day | 20 (3.1) | 3 (2.4) | |
| 1 time or more/day | 631 (96.9) | 122 (97.6) | |
| Daily vegetable consumption | |||
| 0 times/day | 49 (7.8) | 16 (13.6) | p =.040 |
| 1 time or more/day | 582 (92.2) | 102 (86.4) | |
| Physical activity recommendation | |||
| ≥ 5 days/week | 446 (68.6) | 80 (64.0) | p =.071 |
| < 5 days/week | 204 (31.4) | 45 (36.0) | |
| Soda consumption | |||
| < once per week | 558 (85.7) | 96 (78.7) | p =.048 |
| ≥ once per week | 93 (14.3) | 26 (21.3) | |
|
| |||
| Mean±SD | Mean±SD | ||
|
| |||
| Days being physically active | 4.62± 2.1 | 4.25±2.1 | p =.071 |
| Days of lunch/week | 6.60±1.1 | 6.27±1.5 | p =.005 |
| Days of breakfast/week | 6.22±1.6 | 5.65±2.2 | p <.001 |
Relationship of Sleep with Other Health Behaviors
Experts recommend 9 hours/night of sleep for children of this age.33 Therefore, independent sample t-tests were conducted comparing children with and without the recommended hours of sleep for health behaviors and academic performance (Table 3).
Table 3.
Association of Recommended Hours of Sleep for Children (≥ 9 hours), Academic Performance and Various Health Behaviors
| ≥ 9 hours of sleep/night | < 9 hours of sleep/night | p-value | |
|---|---|---|---|
| N (%) | 724 (67.7) | 345 (32.3) | |
| Grade level | |||
| A’s & B’s | 531 (74.0) | 210 (61.9) | p <.001 |
| B’s & C’s | 150 (20.9) | 102 (30.1) | |
| C’s, D’s & F’s | 37 (5.2) | 27 (8.0) | |
| Weight status | |||
| Underweight | 178 (25.2) | 85 (25.4) | |
| Normal Weight | 449 (63.7) | 203 (60.8) | |
| Overweight | 78 (11.1) | 46 (13.8) | |
| Daily TV hours | |||
| < 2 hours/day | 438 (61.1) | 168 (49.9) | p =.001 |
| ≥ 2 hours/day | 279 (38.9) | 169 (50.1) | |
| Daily video game hours | |||
| < 2 hours/day | 528 (73.5) | 215 (62.9) | p <.001 |
| ≥ 2 hours/day | 190 (26.5) | 127 (37.1) | |
| Daily fruit consumption | |||
| 0 times/day | 23 (3.2) | 12 (3.5) | |
| 1 time or more/day | 693 (96.8) | 328 (96.5) | |
| Daily vegetable consumption | |||
| 0 times/day | 64 (9.2) | 40 (12.5) | |
| 1 time or more/day | 628 (90.8) | 280 (87.5) | |
| Physical activity recommendation | |||
| ≥ 5 days/week | 510 (71.5) | 121 (35.5) | p =.021 |
| < 5 days/week | 203 (28.5) | 220 (64.5) | |
| Soda consumption | |||
| < once per week | 605 (85.5) | 280 (82.1) | |
| ≥ once per week | 103 (14.5) | 61 (17.9) | |
|
| |||
| Mean±SD | Mean±SD | ||
|
| |||
| Days being physically active | 4.68±2.0 | 4.44±2.2 | p =.003 |
| Days of lunch/week | 6.61±1.1 | 6.25±1.5 | p <.001 |
| Days of breakfast/week | 6.26±1.6 | 5.90±1.9 | p <.001 |
The Chi-square test revealed that school grades significantly differed by sleep category, c3 (2, N=1057) = 15.913, p < .001 (Table 3). Significant differences were also observed for the recommended days of physical activity, c2 (1, N = 1054) = 5.328, p = .021, and for the recommended hours of television viewing, c2 (1, N= 1054) = 11.844, p = .001, and computer/video usage, c2 (1, N= 1060) = 12.587, p < .001. Independent samples t-tests revealed significant differences for number of days eating lunch, p < .000, and breakfast, p < .001 as well as number of days being physically active, p = .003. The group of 5th grade students with the recommended hours or more of sleep was more likely to have better grades, watch fewer hours of television, play less video games, eat breakfast and lunch more often and to be more physically active than the group of 5th grade students with less than the recommended hour of sleep.
Academic Performance and Health Behaviors
For the analysis of the relationship between academic performance and health behavior variables, self-reported academic performance, an 8-item categorical ordinal scale, was dichotomized into “Students with A’s through B’s” and “Students with some B’s or lower grades” (B’s and C’s, Mostly C’s through F’s) performance because the data was highly skewed. Stepwise logistic regression analysis was used to predict self-reported academic performance from health behaviors, sex, and socio-economic status as covariates (Table 4). The variables significantly associated with academic performance in the regression model include soda consumption (>1 soda/day), breakfast frequency (<5 days/week), physical activity (< 5 days/week), and socio-economic status measured in percentage of free/reduce lunch participation (<50% free/reduced lunch). The overall model was significant with Chi-square = 92.784, p < .001 with df 4.
Table 4.
Binary Stepwise Logistic Regression Comparing Academic Performance with Socio-Economic Status, Nutrition and Physical Activity Behaviors.
| Dependent Variable | Independent Variables | beta | S.E. | p-value | Exp(B) |
|---|---|---|---|---|---|
| Academic performance | < 50% free/reduced lunch | −1.296 | .229 | p <.001 | .274 |
| <5 days of physical activity | .542 | .228 | p = .018 | 1.719 | |
| > one soda/day | −1.318 | .260 | p <.001 | .268 | |
| Breakfast/week | −.154 | .055 | p =.005 | .857 |
DISCUSSION
Overall, 11.6% of the students considered themselves as slightly overweight or very overweight. Although this is lower than the Colorado average, it is based on self-report and therefore lacks objective verification. Research on accuracy of weight status perception in children has shown that especially children in the overweight category tend to underestimate their weight status.34,35 Nevertheless, some behaviors considered as less favorable for health are apparent not only in overweight students. Less than 30% of the overall sample reported to be physically active for a total of ≥ 60 minutes on < 5 days per week as recommended by the Centers for Disease Control and Prevention.36 Only about 42% of the sample reported to watch fewer than two hours of television per day as recommended by various experts such as the Australian Government or the American Academy of Pediatrics.30,31 On the other hand, fruit and vegetable consumption was high with only 37 students indicating that they do not eat fruits daily and with only 9.5% of the students indicating that they do not eat vegetables on a daily basis. This is a substantially lower percentage than reported in US children, 37,38 as well as children in other countries. 39, 40 In addition, soda consumption was fairly low across all students with only 15.2% indicating they drink soda once or more per day which is also lower than among US children in general. 41, 42 Similar to the results of Edwards et al.17 examining health behaviors among 6th graders in Minnesota, we found an association between higher academic performance and more days of having breakfast and physical activity and less soda consumption. A positive relationship between physical fitness and academic performance in 4th through 9th grade students was also found by London and Castrechini.43 Also, higher socio-economic status (measured by percentage of free/reduced lunch students in a school) and viewing television less than 2 hours per day were variables associated with higher academic performance.
Furthermore, sleep has been identified as a contributing factor to the development of obesity and health in general as well as academic performance. 44 Although the results did not show a significant relationship between weight status and adequate hours of sleep (≥ 9 hours per night for 5th graders), they showed that adequate hours of sleep were associated with better school grades, fewer hours of watching TV and playing video games, and more days of eating breakfast and lunch. Previous research results have suggested similar associations regarding academic achievement and TV watching 45,46 but the idea of an association between adequate hours of sleep and eating breakfast and lunch regularly in children appears to not have been investigated yet.
Looking at specific health behaviors and their relationship with overweight status, it was revealed that when comparing overweight with normal weight 5th grade students, the group of overweight students were more likely to watch 2 or more hours of television daily, to consume soda on a daily basis, to eat breakfast and lunch as well as vegetables less frequently and were less physically active than the group of normal weight students. Similar associated patterns between health behaviors and weight status can be found elsewhere. 47–50 Also, the group of overweight students were more likely to have lower school grades although overweight was not an independent predictor of academic performance in the regression model. The regression model showed that children with good academic performance were less likely to be participating in the free/reduced school lunch program, but more likely to be physically active for ≥ 5 days/week. In addition, the odds for children with good academic performance to have daily breakfast and to drink fewer sodas per week were higher than for children with poor academic performance.
Limitations
A major limitation of this study was the reliance on self-reports of all the variables. In general, self-reported questionnaires have limitations and can lead to biased results (eg, by being influenced by social desirability). One indicator for possible biased results is the lower rates of soda consumption and the relatively high rates of fruit and vegetable consumption in our sample. Even so, given the practical difficulties created by gathering objective data on the broad array of variables assessed in the current study, it is thought the self-report data provide an important preliminary step in the process of understanding the relationship among these variables.
In addition, it was decided to dichotomize some variables either because the data were highly skewed or because standard cut points such as recommendations regarding physical activity and food intake were a better fit for the hypothesis. Also, the possible response choices for the academic performance variable were not ideal to measure good compared to poor academic performance. Given the large sample size, the clustering of the data (eg, the free/reduced lunch percentage was only available on school level not individual level) was overlooked which could have caused residual confounding by free/reduced lunch percentage in the regression model.
Finally, the data are cross-sectional and do not allow for determining whether health behaviors and weight status are casually related to academic performance. Nevertheless, for the purpose of policy and intervention recommendations, this study is an important step in the process of convincing policy makers and schools of the importance of promoting healthy behaviors in regards to academic performance and children’s overall health.
Conclusion
We found that health behaviors and body weight status were related to academic performance. Those students who reported being at a healthy weight and engaging in healthier behaviors were more likely to be in the “better grade” category. By providing further scientific support for the association of health behaviors with academic performance, school districts could more effectively justify their emphasis on health promoting activities throughout the school day and obtain (financial) support for related policy changes. In addition, the results could provide information not only for teachers and policy makers regarding changes to the school environment through nutrition education on soda consumption or the importance of sleep, but also for educating parents regarding the relationships between sleep, weight status, academic performance and other health behaviors of their children.
IMPLICATIONS FOR SCHOOL HEALTH
Some school districts may view the time and resources needed to promote health behaviors such as adequate sleep, enough fruits and vegetables, less soda consumption, more physical activity, less media time and frequent breakfast and lunch as being in direct conflict to meeting specific academic goals mandated by the No Child Left Behind Act of 2001 (Public Law 107-110), time constraints and budget cuts. The current assessment suggests that health promotion should not be a competing priority but should be part of academic achievement support. Informing school authorities of the associations between health behaviors and academic performance, as well as incorporating such results into the school curriculum to inform teachers, students, and parents, could be strategies that encourage pro-health behaviors. Offering time and opportunities to be physically active and providing favorable fruit and vegetable choices as well as attractive alternatives for sugar-sweetened carbonated beverages could be another path to effective health promotion in schools.
Human Subjects Approval Statement
This study was approved by the University of Colorado at Denver Institutional Review Board as well as the Cherry Creek School District Review Board.
Acknowledgments
The study was funded by the NIH grants HD055036, DK048520 and DK42549.
Contributor Information
Nanette Stroebele, Email: Nanette.Stroebele@ucdenver.edu, University of Colorado Anschutz Medical Campus1, Mailstop C263, 13001 E 17thPlace, Aurora, CO 80045, Phone: +1 303 724 9046, Fax: +1 303 724 9976.
Janise McNally, Email: jmcnally@cherrycreekschools.org, Educational Services Center2, Cherry Creek School District, 4700 South Yosemite Street Greenwood Village, CO 80111, Phone: +1 720 554 4234, Fax: +1 720 554 4477.
Amy Plog, Email: aplog@cherrycreekschools.org, Educational Services Center2, Cherry Creek School District, 4700 South Yosemite Street, Greenwood Village, CO 80111.
Scott Siegfried, Email: ssiegfried@cherrycreekschools.org, Educational Services Center2, Cherry Creek School District, 4700 South Yosemite Street, Greenwood Village, CO 80111.
James O. Hill, Email: James.Hill@ucdenver.edu, Anschutz Health & Wellness Center, University of Colorado Anschutz Medical Campus1, Mailstop C263, 13001 E 17th Place, Aurora, CO 80045, Phone: +1 303 724 9011, Fax: +1 303 724 9976.
References
- 1.Ogden CL, Carroll MD, Flegal KM. High body mass index for age among US children and adolescents, 2003–2006. JAMA. 2008;299(20):2401–2405. doi: 10.1001/jama.299.20.2401. [DOI] [PubMed] [Google Scholar]
- 2.Dabelea D, Pettitt DJ, Jones KL, Arslanian SA. Type II diabetes mellitus in minority children and adolescents. An emerging problem. Endocrinol Metab Clin North Am. 1999;28(4):709–729. doi: 10.1016/s0889-8529(05)70098-0. [DOI] [PubMed] [Google Scholar]
- 3.Berenson GS, Agirbasli M, Nguyen QM, Chen W, Srinivasan SR. Glycemic status, metabolic syndrome, and cardiovascular risk in children. Med Clin North Am. 2011;95(2):409–417. doi: 10.1016/j.mcna.2010.11.011. [DOI] [PubMed] [Google Scholar]
- 4.Weiss R, Dziura J, Burgert TS, et al. Obesity and the metabolic syndrome in children and adolescents. N Engl J Med. 2004;350(23):2362–2374. doi: 10.1056/NEJMoa031049. [DOI] [PubMed] [Google Scholar]
- 5.Rodriguez BL, Fujimoto WY, Mayer-Davis EJ, et al. Prevalence of cardiovascular disease risk factors in U.S. children and adolescents with diabetes: the SEARCH for Diabetes in Youth Study. Diabetes Care. 2006;29(8):1891–1896. doi: 10.2337/dc06-0310. [DOI] [PubMed] [Google Scholar]
- 6.Eagle TF, Gurm R, Goldberg CS, et al. Health status and behavior among middle-school children in a midwest community: what are the underpinnings of childhood obesity? Am Heart J. 2010;160(6):1185–1189. doi: 10.1016/j.ahj.2010.09.019. [DOI] [PubMed] [Google Scholar]
- 7.Lytle LA, Pasch KE, Farbakhsh K. The relationship between sleep and weight in a sample of adolescents. Obesity (Silver Spring) 2011;19(2):324–331. doi: 10.1038/oby.2010.242. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Sanderock GR, Voss C, Dye L. Associations between habitual school-day breakfast consumption, body mass index, physical activity and cardiorespiratory fitness in English schoolchildren. Eur J Clin Nutr. 2010;64(10):1086–1092. doi: 10.1038/ejcn.2010.145. [DOI] [PubMed] [Google Scholar]
- 9.Must A, Barish EE, Bandini LG. Modifiable risk factors in relation to changes in BMI and fatness: what have we learned from prospective studies of school-aged children? Int J Obes. 2009;33(7):705–715. doi: 10.1038/ijo.2009.60. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Must A, Parisi SM. Sedentary behavior and sleep: paradoxical effects in association with childhood obesity. Int J Obes. 2009;33(Suppl 1):S82–S86. doi: 10.1038/ijo.2009.23. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Bawazeer NM, Al-Daghri NM, Valsamakis G, et al. Sleep duration and quality associated with obesity among Arab children. Obesity (Silver Spring) 2009;17(12):2251–2253. doi: 10.1038/oby.2009.169. [DOI] [PubMed] [Google Scholar]
- 12.Nixon GM, Thompson JM, Han DY, et al. Short sleep duration in middle childhood: risk factors and consequences. Sleep. 2008;31(7):71–78. doi: 10.1093/sleep/31.1.71. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Bebee DW, Lewin D, Zeller M, et al. Sleep in overweight adolescents: shorter sleep, poorer sleep quality, sleepiness, and sleepdisordered breathing. J Pediatr Psychol. 2007;32(1):69–79. doi: 10.1093/jpepsy/jsj104. [DOI] [PubMed] [Google Scholar]
- 14.Carson V, Janssen I. Volume, patterns, and types of sedentary behavior and cardio-metabolic health in children and adolescents: a cross-sectional study. BMC Public Health. 2011;11:274. doi: 10.1186/1471-2458-11-274. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Curcio G, Ferrera M, DeGennaro L. Sleep loss, learning capacity and academic performance. Sleep Med Rev. 2006;10(5):323–337. doi: 10.1016/j.smrv.2005.11.001. [DOI] [PubMed] [Google Scholar]
- 16.Taras H, Potts-Datema W. Sleep and student performance at school. J Sch Health. 2005;75(5):248–254. doi: 10.1111/j.1746-1561.2005.00033.x. [DOI] [PubMed] [Google Scholar]
- 17.Edwards JU, Mauch L, Winkelman MR. Relationship of nutrition and physical activity behaviors and fitness measures to academic performance for sixth graders in a Midwest city school district. J Sch Health. 2011;81(2):65–73. doi: 10.1111/j.1746-1561.2010.00562.x. [DOI] [PubMed] [Google Scholar]
- 18.Kantomaa MT, Tammelin TH, Demakakos P, Ebeling HE, Taanila AM. Physical activity, emotional and behavioural problems, maternal education and self-reported educational performance of adolescents. Health Educ Res. 2010;25(2):368–379. doi: 10.1093/her/cyp048. [DOI] [PubMed] [Google Scholar]
- 19.Gortmaker SL, Peterson K, Wiecha J, et al. Reducing obesity via a school-based interdisciplinary intervention among youth: Planet Health. Arch Pediatr Adolesc Med. 1999;153(4):409–418. doi: 10.1001/archpedi.153.4.409. [DOI] [PubMed] [Google Scholar]
- 20.Kipping RR, Payne C, Lawlor DA. Randomised controlled trial adopting US school obesity prevention to England. Arch Disease Child. 2008;93(6):469–473. doi: 10.1136/adc.2007.116970. [DOI] [PubMed] [Google Scholar]
- 21.Klesges RC, Obarzanek E, Kumanyika S, et al. The Memphis Girls’ health Enrichment Multi-site Studies (GEMS): an evaluation of the efficacy of a 2-year obesity prevention program in African American girls. Arch Pediatr Adolesc Med. 2010;164(11):1107–1014. doi: 10.1001/archpediatrics.2010.196. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Wardle J, Henning Broderson N, Cole TJ, et al. Development of adiposity in adolescence: five year longitudinal study of an ethnically and socioeconomically diverse sample of young people in Britain. BMJ. 2006;332(7559):1130–1135. doi: 10.1136/bmj.38807.594792.AE. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Caspersen CJ, Pereira MA, Curran KM. Changes in physical activity patterns in the United States, by sex and cross-sectional age. Med Sci Sports Exerc. 2000;32(9):1601–1609. doi: 10.1097/00005768-200009000-00013. [DOI] [PubMed] [Google Scholar]
- 24.Centers for Disease Control and Prevention. [Accessed July 28, 2011];US Obesity Trends, 1985–2008. Available at: http://www.cdc.gov/obesity/data/trends.html#State.
- 25.State health facts.org. Colorado: Percent of Children who are Overweight or Obese. The Henry J. Kaiser Family Foundation; 2007. [Accessed July 28, 2011]. Available at: http://www.statehealthfacts.org/profileind.jsp?ind=51&cat=2&rgn=7. [Google Scholar]
- 26.Center for Environmental Health and Information and Statistics, Health Statistics Section. 2008 Child Health Survey. Colorado Department of Public Health and Environment; [Accessed July 28, 2011]. Available at: http://www.cdphe.state.co.us/hs/mchdata/chs2008PUB.pdf. [Google Scholar]
- 27.Centers for Disease Control and Prevention. [Accessed September 2, 2008];Youth Risk Behavior Survey. 1990 Available at: www.cdc.gov/yrbs.
- 28.Burgess Dowdell E. Alcohol use, smoking, and feeling unsafe: health risk behaviors of two urban seventh grade classes. Issues Compr Pediatr Nurs. 2006;29(3):157–171. doi: 10.1080/01460860600846925. [DOI] [PubMed] [Google Scholar]
- 29.Riesch SK, Anderson LS, Angresano N, Canty-Mitchell J, Johnson DL, Krainuwat K. Evaluating content validity and test-retest reliability of the children’s health risk behavior scale. Public Health Nurs. 2006;23(4):366–272. doi: 10.1111/j.1525-1446.2006.00574.x. [DOI] [PubMed] [Google Scholar]
- 30.Australian Government. Department of Health and Aging. [Accessed July 27, 2011];Physical Activity Recommendations. Available at: http://www.health.gov.au/internet/main/publishing.nsf/content/health-pubhlth-strateg-phys-act-guidelines.
- 31.American Academy of Pediatrics, Committee on Public Education. Children, adolescents, and television. Pediatrics. 2001;107(2):423–426. doi: 10.1542/peds.107.2.423. [DOI] [PubMed] [Google Scholar]
- 32.Centers for Disease Control and Prevention. [Accessed August 8, 2011];The CDC Guide to Strategies for Reducing the Consumption of Sugar-Sweetened Beverages. Available at: http://www.cdph.ca.gov/SiteCollectionDocuments/StratstoReduce_Sugar_Sweetened_Bevs.pdf.
- 33.Centers for Disease Control and Prevention. [Accessed July 29, 2011];Sleep and sleep disorders. Available at: http://www.cdc.gov/features/sleep/
- 34.Saxton J, Hill C, Chadwick P, Wardle J. Weight status and perceived body size in children. Arch Dis Child. 2009;94(12):944–949. doi: 10.1136/adc.2009.162578. [DOI] [PubMed] [Google Scholar]
- 35.Abbott RA, Lee AJ, Stubbs CO, Davies PSW. Accuracy of weight status perception in contemporary Australian children and adolescents. J Paediatr Child Health. 2010;46(6):343–348. doi: 10.1111/j.1440-1754.2010.01719.x. [DOI] [PubMed] [Google Scholar]
- 36.Centers for Disease Control and Prevention. [Accessed July 27, 2011];How much physical activity do children need? Available at: http://www.cdc.gov/physicalactivity/everyone/guidelines/children.html.
- 37.Sandeno C, Wolf G, Drake T, Reicks M. Behavioral strategies to increase fruit and vegetable intake by fourth- through sixth-grade students. J Am Diet Assoc. 2000;100(7):828–830. doi: 10.1016/S0002-8223(00)00239-X. [DOI] [PubMed] [Google Scholar]
- 38.US Department of Agriculture. [Accessed November 24, 2011];Pyramid servings intakes in the US: 1999–2002. Available at: http://www.ars.usda.gov/sp2UserFiles/Place/12355000/foodlink/ts_3-0.pdf.
- 39.Melbye EL, Overby NC, Ogaard T. Child consumption of fruit and vegetables: the role of child cognitions and parental feeding practices. Public Health Nutr. 2011 doi: 10.1017/S1368980011002679. Epub ahead of print. [DOI] [PubMed] [Google Scholar]
- 40.Diethelm K, Jankovic N, Moreno LA, Huybrechts I, et al. Food intake of European adolescents in the light of different food-based dietary guidelines: results of the HELENA (Healthy Lifestyle in Europe by Nutrition Adolescence) Study. Public Health Nutr. 2012;15(3):386–398. doi: 10.1017/S1368980011001935. [DOI] [PubMed] [Google Scholar]
- 41.Harnack L, Stang J, Story M. Soft drink consumption among US children and adolescents: nutritional consequences. J Am Diet Assoc. 1999;99(4):436–441. doi: 10.1016/S0002-8223(99)00106-6. [DOI] [PubMed] [Google Scholar]
- 42.Foltz JL, Cook SR, Szilagyi PG, Auinger P, et al. US adolescent nutrition, exercise, and screen time baseline levels prior to national recommendations. Clin Pediatr. 2011;50(5):424–433. doi: 10.1177/0009922810393499. [DOI] [PubMed] [Google Scholar]
- 43.London RA, Castrechini S. A longitudinal examination of the link between youth physical fitness and academic achievement. J Sch Health. 2011;81(7):400–408. doi: 10.1111/j.1746-1561.2011.00608.x. [DOI] [PubMed] [Google Scholar]
- 44.Noland H, Price JH, Dake J, Telljohan SK. Adolescents’ sleep behaviors and perceptions of sleep. J Sch Health. 2009;79(5):224–230. doi: 10.1111/j.1746-1561.2009.00402.x. [DOI] [PubMed] [Google Scholar]
- 45.Gruber R, Laviolette R, Deluca P, Monson E, Cornish K, Carrier J. Short sleep duration is associated with poor performance on IQ measures in healthy school-age children. Sleep Med. 2010;11(3):289–294. doi: 10.1016/j.sleep.2009.09.007. [DOI] [PubMed] [Google Scholar]
- 46.Chaput JP, Brunet M, Tremblay A. Relationship between short sleeping hours and childhood overweight/obesity: results from the ‘Québec en Forme’ Project. Int J Obes. 2006;30(7):1080–1085. doi: 10.1038/sj.ijo.0803291. [DOI] [PubMed] [Google Scholar]
- 47.Tudor-Locke C, Craig CL, Cameron C, Griffiths JM. Canadian children’s and youth’s pedometer-determined steps/day, parent-reported TV watching time, and overweight/obesity: the CANPLAY Surveillance Study. Int J Behav Nutr Phys Act. 2011;8:66. doi: 10.1186/1479-5868-8-66. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Danner FW. A national longitudinal study of the association between hours of TV viewing and the trajectory of BMI growth among US children. J Pediatr Psychol. 2008;33(19):1100–1107. doi: 10.1093/jpepsy/jsn034. [DOI] [PubMed] [Google Scholar]
- 49.Deshmusk-Taskar PR, Nicklas TA, O’Neil CE, Keast DR, Radcliffe JD, Cho S. The relationship of breakfast skipping and type of breakfast consumption with nutrient intake and weight status in children and adolescents: the National Health and Nutrition Examination Study 1999–2006. J Am Diet Assoc. 2010;110(6):869–878. doi: 10.1016/j.jada.2010.03.023. [DOI] [PubMed] [Google Scholar]
- 50.Mello MM, Studdert DM, Brennan TA. Obesity – the new frontier of public health law. N Engl J Med. 2006;354(24):2601–2010. doi: 10.1056/NEJMhpr060227. [DOI] [PubMed] [Google Scholar]