Middle School Students' Body Mass Index and ...

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Sep 6, 2008 - Sport Science at Texas Tech University. Hyunju Oh is with the Department of Recreation and Sport Pedagogy at Ohio. University. Huiping ...
ResearchGao, Note—Physiology Oh, and Sheng Research Quarterly for Exercise and Sport ©2011 by the American Alliance for Health, Physical Education, Recreation and Dance Vol. 82, No. 1, pp. 145–150

Middle School Students’ Body Mass Index and Physical Activity Levels in Physical Education Zan Gao, Hyunju Oh, and Huiping Sheng

Key words: accelerometer, moderate to vigorous physical activity, sedentary


ne of the most critical public concerns in the United States is the rapid increase in childhood obesity, partly due to the social and environmental changes (e.g., excessive TV and computer use, pressures of standardized testing, etc.) in the past few decades, which has resulted in less physical activity in school children’s daily routines (Zhu, 2008). In particular, youth obesity has increased from 7% to 19% in children (ages 6–11 years) and from 5% to 17% in adolescents (ages 12–19 years) in the past three decades (Hedley et al., 2004; Ogden, Flegal, Carroll, & Johnson, 2002; Ogden et al., 2006). Childhood obesity often tracks into adulthood (Guo & Chumlea, 1999) and generally leads to numerous physical and mental health problems, such as type 2 diabetes, cardiovascular disease, and low self-confidence (U.S. Department of Health and Human Services [USDHHS], 2001). Regular physical activity participation has been identified as an indispensible strategy to prevent childhood obesity (Ogden et al., 2006). However, it is posited that children and adolescents’ physical activity levels tend to decline during the developmental years (Parish & Treasure, 2003). It has been well documented (Kulinna, Martin, Lai, Kliber, & Reed, 2003;

Submitted: September 6, 2008 Accepted: November 3, 2009 Zan Gao is with the Department of Health, Exercise, and Sport Science at Texas Tech University. Hyunju Oh is with the Department of Recreation and Sport Pedagogy at Ohio University. Huiping Sheng is with the Department of Family and Community Medicine at the University of New Mexico.

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Parish & Treasure, 2003) that this activity declines with age during adolescence, but carefully designed research can help educators develop effective strategies that can control and reverse this trend. Schools can play a critical role in preventing obesity in children and adolescents by increasing physical activity levels and health-related physical fitness regimens among this population (Pate et al., 2006; Wallhead & Buckworth, 2004). More than 95% of children and adolescents are enrolled in schools where they can participate in structured physical education programs (Levin, McKenzie, Hussey, Kelder, & Lytle, 2001; Simons-Morton, O’Hara, Parcel, Baranowski, & Wilson, 1990; USDHHS, 1996). Healthy People 2010 includes an objective to promote health and reduce chronic disease through school children’s daily physical activity levels (USDHHS, 2000). Specifically, it has been recommended that students’ active participation in physical education classes should comprise at least 50% of the class time (Pate et al., 2006). Physical education programs are a potentially important channel for promoting physical activity and fitness among youth and are an avenue for fighting childhood obesity (Simons-Morton et al., 1990). Therefore, increasing students’ activity levels in physical education classes has become imperative for physical educators and health promoters. Obesity is defined as an excess of body fat mass (Bellizzi & Dietz, 1990). Measuring students’ body mass index (BMI; kg/m2) in school is one approach to assess obesity (Nihiser et al., 2007). BMI and skinfold measurements have been used widely in field settings due to their convenience and low cost. Researchers have suggested that BMI has a strong correlation to skinfold thickness in children (e.g., Rowland, 1990). BMI testing has advantages over skinfold measurement in that it is not intrusive and it is easy to calculate with acceptable accuracy (Raustorp,


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Pangrazi, & Stahle, 2004). Additionally, BMI testing is accepted as a national standard for assessing obesity (Nihiser et al., 2007). The Centers for Disease Control and Prevention (CDC) BMI cut-off points have been extrapolated to provide standards for overweight and obese children 2–20 years of age (Kuczmarski et al., 2000; Nihiser et al., 2007). Empirical studies have consistently shown a graded and negative relationship between BMI and physical activity among youth and adults (Davy, Harrell, Stewart, & King, 2004; Grundy et al., 1999; National Heart Lung and Blood Institute [NHLBI], 1998; Sharpe, Granner, Hutto, Ainsworth, & Cook, 2004). Increasing individuals’ physical activity levels is associated with preventing or attenuating weight gain over time (DiPietro, 1999; NHLBI, 1998; Welk & Blair, 2000) and better maintenance of weight loss (Grundy et al., 1999). For example, Datar and Sturm (2004) indicated that increased physical education instruction time decreased overweight or obese school girls’ BMI, although there was no significant difference among overweight boys or students with a normal BMI. There are no known studies that investigated the effect of BMI on students’ physical activity levels in physical education class. In response, we designed this study to determine whether there were differences in students’ physical activity levels in physical education class across different BMI groups, with the goal of designing and implementing appropriate intervention strategies to promote youth physical activity levels and reduce obesity. Currently, various techniques have been used to measure children’s and adolescents’ physical activity. According to Sirard and Pate (2001), direct observation is cumbersome, because it is costly and requires high expertise and long measurement time periods; self-report measures are unreliable due to children’s low cognitive function and the sporadic nature of their physical activity. Pedometers only provide limited information, such as steps; other factors can affect heart rate monitors, such as temperature, humidity, and emotional stress. However, accelerometers have been found to be valid and reliable for objectively measuring ambulatory physical activity intensity among school children in field settings (Pfeiffer, McIver, Dowda, Almeida, & Pate, 2006; Sirard & Pate, 2001; Zhu, 2008). Specifically, accelerometers can be used to assess total ambulatory activity levels (activity frequency, intensity, and duration). They provide an estimate of energy expenditure, including detailed information regarding physical activity intensity (sedentary, light, moderate, and vigorous) and time spent at different intensity levels (Ainsworth, 2000; Bassett, 2000). Because no studies have been conducted in the United States to analyze the relationship between BMI levels and accelerometer-based physical activity among children and adolescents in physical education, we examined the effect of BMI on students’ physical activity level in a middle school physical education class. We


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proposed three research questions: (a) what percentages of students are overweight and obese based on the BMI; (b) what is students’ physical activity level in physical education as measured by accelerometers; and (c) are there significant differences in students’ physical activity levels across different BMI groups (normal weight vs. overweight and obese).

Method Participants and Research Setting The participants were 149 students (75 boys, 74 girls) enrolled in one suburban public school in the southern United States. They consisted of sixth- (28.9%), seventh(36.2%), and eighth- (34.9%) grade students ranging in age from 10 to 14 years (M age = 12.48 years, SD = 1.02). Their racial characteristics were: 89.3% Caucasian, 7.4% African American, 2% Asian American, and 1.3% Hispanic American. Most participants were from middle to high socioeconomic status families. Prior to the study, we obtained permission from the university Institutional Review Board, the school district, the school principal, the physical education teachers, and all participants and their parents/guardians. Participants attended two or three 90-min physical education classes per week taught by three physical education teachers on alternate days. After students changed clothes, the physical education teachers took attendance in the gym. Students then participated in warm-up routines, activities, and games. In general, the teachers would introduce the skills to be learned, organize a practice schedule, and provide feedback when necessary. All classes ended with a lesson assessment. Learning activities during the data collection period included playing catch-ball, walking/jogging, line dancing, soccer, and table tennis. Procedures We measured participants’ height and weight individually during the first week of the school year and assured them of confidentiality. In the following 2 weeks, we measured their physical activity levels in a physical education class, which represented a typical instructional day for one of the learning activities. Actical activity monitors (Mini-Mitter Co., Inc., Bend, OR) were distributed at the beginning of the class; each student’s identification number corresponded to the number on his or her accelerometer. The researchers showed the students how to wear the monitors properly (waistband-secured at their left hip). At the end of each class, the students returned the accelerometers, and the researchers immediately recorded the class physical activity time. We also obtained students’ self-reported information on age, sex, ethnicity, and grade for sample composition.

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Measures Body Mass Index. We used a Seca stadiometer (Seca Corporation, Hanover, MD) to record participants’ height, rounded in centimeters, and measured their weight using a digital weight scale (Detecto, Web City, MO), rounded up in kilograms. We calculated each child’s BMI (kg/cm2) then converted BMI to normative percentiles for their sex and age. For example, a 12-year-old boy at the 85th percentile has a BMI higher than 85 out of every 100 boys of the same age in the reference population (Kuczmarski et al., 2002). We determined BMI percentiles using the CDC’s BMI-for-age growth charts for girls and boys (Kuczmarski et al., 2000), and identified their weight status from the BMI-for-age percentile. Participants were classified as obese if their BMI was at or above the 95th percentile for age and overweight if their BMI was > 85th percentile and < 95th percentile. They were classified as normal weight if their BMI was > 5th percentile and < 85th percentile. Participants’ were classified as underweight if their BMI was < 5th percentile (Nihiser et al., 2007). Table 1 shows the cut-off points for overweight and obesity, on the CDC standards (Kuczmarski et al., 2000). Accelerometer. The Actical accelerometer is one of the smallest available (28 x 27 x 10 mm, 17 g with a watch battery). To better capture the short activity bursts typical of school children (Pfeiffer et al., 2006), we set the monitors to record physical counts using 15-s epochs, because longer epochs are unable to detect bursts of high activity (Nilsson, Ekelund, Yngve, & Sjöstrom, 2002). The monitors’ sampling frequency was 32 Hz, and sensitivity was 0.01 g. The devices collected motions in the 0.5–3.0 Hz frequency range. In other words, voltage generated by the sensor was amplified and filtered by analog circuitry and passed to a digital converter; this process was repeated 32 times per second. The resulting 1-s value was divided by 4 then added to an accumulated activity value for the epoch. The devices were programmed and downloaded by connecting the monitor to a serial port computer interface using ActiReaders. Once data were downloaded to the corresponding software, data files were exported into a Microsoft Excel format (Microsoft Corporation, Redmond, WA). Table 1. Centers for Disease Control and Prevention cut-off points for body mass index for overweight and obesity by gender between 10 and 14 years Age (years)

Underweight Overweight Obesity Boys Girls Boys Girls Boys Girls

10 11 12 13 14

14.2 14 19.4 20 22.2 14.5 14.3 20.2 20.8 23.2 15 14.8 21 21.7 24.2 15.4 15.3 21.8 22.5 25.2 16 15.8 22.6 23.3 26

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23 24.2 25.2 26.2 27.2

We used the activity cut-off points established by Puyau and colleagues (2004) to classify the children’s data: (a) 0–99 counts/min = sedentary; (b) 100–1,499 counts/min = light; and (c) ≥ 1,500 counts/min = moderate to vigorous physical activity (MVPA). According to Puyau et al. (2004), optimal physical activity thresholds have been determined by regression and receiver operating characteristic analysis. We categorized the data into sedentary, light, and moderate and vigorous levels based on the following thresholds: (a) sedentary level was set as activity energy expenditure (AEE) < .01 kcal·kg-1·min-1, encompassing physical activities of minimal body movements in sitting or reclining positions; (b) light level was an AEE > .01 kcal·kg-1·min-1 and < .04 kcal·kg-1·min-1, reflecting a low level of exertion in the standing position; and (c) moderate and vigorous levels were set at an AEE > .04 kcal·kg-1·min-1, involving medium and high levels of exertion in the standing position. We then calculated cut points based on the AEE cutoffs and prediction equations. Recent studies with children and adolescents have substantiated the validity and reliability of Actical activity monitors (Pfeiffer et al., 2006; Puyau, Adolph, Vohra, Zakeri, & Butte, 2004). For example, Puyau et al. (2004) validated the monitors using energy expenditure as the criterion measure of children’s (ages 7–18 years) physical activity. They found that activity counts explained most of the variability in activity energy expenditure (energy expenditure - basal metabolic rate) and physical activity ratio (energy expenditure/basal metabolic rate), with small contributions (r2 = .044) of age, sex, weight, and height. Pfeiffer et al. (2006) also provided robust support for monitor validation by showing a high correlation (r = .89) between maximal oxygen uptake and activity counts. We used a student’s percentage of time engaged in sedentary physical activity, light intensity physical activity, and MVPA as the outcome measures, quantified as the time the students spent in different intensity levels divided by the physical education class duration (Arnett & Lutz, 2003). We retrieved outcome measures directly from the activity monitor outputs. Data Analyses Prior to the data analysis, we ran a power analysis, using G*Power 3 software, to determine the appropriate sample size (Faul, Erdfelder, Lang, & Buchner, 2007). A minimum sample size of 110 was sufficient for power at .80, two-tailed, at α = .05, effect size = .30. We performed all other statistical analyses using SPSS software, version 13.0 (SPSS, Inc., Chicago, IL). According to Fairclough and Stratton (2006), the available curricular activities may influence students’ activity levels in physical education class. Thus, we conducted a preliminary multivariate analysis of variance (MANOVA) to determine whether students’ activity levels


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differed across curriculum activities. Second, we calculated means and standard deviations for BMI, percentage of time in sedentary physical activity, light intensity physical activity, and MVPA. Third, we performed a one-way (normal weight versus overweight/obese) MANOVA with curriculum activity as covariate to determine whether BMI differences existed in each of these percentages.

Results The results of the preliminary analysis revealed significant differences in students’ physical activity levels across different curriculum activities, Wilks’ Λ = .71, F(12, 376) = 4.35, p < .001). In general, team-based invasion games (i.e., soccer and catch-ball) promoted relatively higher MVPA than individual activities (e.g., line dancing). Given that the effect of curriculum activity existed in this study, it served as the covariate for further analyses. To examine the prevalence of overweight and obese school children, we compared participants’ BMI values with the CDC BMI cut-off points (Kuczmarski et al., 2000). The average BMI was 19.81 kg·m-2, with a range of 14.01–32.11 kg·m-2. In particular, the average BMI for the normal weight group (n = 116; 77.85%) was 18.57 (SD = 2.02), whereas the average BMI for the overweight and obese group (n = 33; 22.15%) was 24.14 (SD = 2.19). Among the overweight and obese group, 12.08% were overweight students while 10.07% were obese students. Table 2 shows descriptive statistics for the entire sample. On average, students spent 66.62% of the class time in MVPA as measured by accelerometers. In other words, the physical education classes provided a mean MVPA for more than 50% of the class time. In terms of other accelerometer outcomes, students spent 24.43% and 8.95% of the class time in light intensity physical activity or being sedentary, respectively.

The MANOVA yielded a significant main effect for BMI, Wilks’ Λ = .91, F(3, 144) = 3.30, p = .005; η2 = .09. However, there was no significant effect for the covariate, Wilks’ Λ = .99, F(3, 144) =.53, p = .66; η2 = .01. These results indicated that curriculum activities did not moderate the effect of BMI on students’ physical activity levels. Follow-up post hoc pairwise comparison with Bonferroni adjustment revealed the overweight and obese students spent a significantly higher percentage of their time being sedentary than students with normal weight, F(1, 146) = 10.04, p = .002, η2 = .06. By contrast, students with normal weight spent a higher percentage of time in MVPA than overweight and obese students, F(1, 146) = 4.89, p = .029, η2 = .03. There were no further BMI differences in other outcome measures (see Table 3).

Discussion Our first research question was to identify the percentage of overweight and obese students. We used BMI to classify different weight groups (i.e., normal or overweight and obese group), because it is convenient and cost-effective for a large-scale study. We used the CDC’s BMI-for-age growth charts for girls and boys as cut-off points. In this study, approximately 10% of the students were obese, which indicated that obesity in this sample was somewhat less prevalent than the 17% national average. One plausible explanation is that this population came from middle and upper middle class families. It has been reported that a lower socioeconomic status might be a potential contributing factor to poor dietary habits and low physical activity and, thus, a higher prevalence of obesity (Davy, Harrell, Stewart, & King, 2004). Therefore, it is not surprising that the obesity rate of youth from relatively high socioeconomic status families would be a bit low. In response to the second research question, we used accelerometers to measure students’ activity levels in physical education classes. In general, the students spent more

Table 2. Descriptive statistics among variables (N = 149) Age (years) Body mass index (kg/m2) Percent time in sedentary (accelerometer; %) Percent time in light intensity (accelerometer; %) Percent time in MVPA (accelerometer; %)

Minimum Maximum 10


M SD 12.48


14.01 32.11 19.81 3.10 .00






24.43 11.99



66.62 17.23

Note. M = mean; SD = standard deviation; MVPA = moderateto-vigorous physical activity.


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Table 3. Percentage of time spent in physical acivity (N = 149) Normal weight OW/obese (n = 116) (n = 33) M SD M SD Percent time in sedentary (accelerometer; %) 7.64* 7.76 Percent time in light in tensity (accelerometer; %) 24.18 11.74 Percent time in MVPA (accelerometer; %) 68.17* 15.56

13.54* 13.82 25.31 12.99 61.14* 21.54

Note. OW = overweight; M = mean; SD = standard deviation; MVPA = moderate-to-vigorous physical activity. *Significant difference between the groups, p < .05.

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than 50% of the class time in MVPA as measured by the accelerometers. This result is consistent with recent studies (e.g., Arnett & Lutz, 2003). It has been recommended that students (a) have daily physical education classes, (b) receive a substantial percentage of in-school physical activity in physical education classes, and (c) be physically active for at least half the physical education class time (Datar & Sturm, 2004). Apparently, the results of this study meet the recommendations (Pate et al., 2006; USDHHS, 2000). Our third and primary research question was to investigate the effect of BMI on students’ activity levels in physical education class. As expected, BMI affected students’ percentage of time spent in sedentary activity and MVPA. The students with normal weight spent significantly more time in MVPA and less time in sedentary activities than their overweight and obese classmates. This is in line with previous studies (Grundy et al., 1999; NHLBI, 1998), indicating the graded and negative relationship between BMI and physical activity levels. The uniqueness of this study includes using accelerometers to measure students’ different physical activity intensity levels. The findings of this study provide empirical evidence in this new area of inquiry. In summary, the findings of this study provide baseline information on school children’s BMI levels, their activity levels in physical education class, and the differences in physical activity levels across BMI levels. In general, the students in this study were physically active for at least 50% of physical education class time, although normal weight students were more physically active than overweight and obese students. Therefore, physical educators and health promoters should use strategies to motivate these students to be more physically active in and outside of physical education class. Several effective strategies might include helping students understand their BMI and set appropriate weight control goals, as well as providing a caring and safe learning environment, decreasing peer pressure, and assisting students to successfully complete their tasks. It is imperative to implement the Active and Healthy Schools initiative (Horne, 2010) and motivate students to develop and maintain a physically active lifestyle. To achieve this, it takes a community to promote wellness and physical activity in addition to a physical education curriculum. Specifically, the initiative should also include enhancing general health and well being through physical activity and nutritional awareness; incorporating student, staff, and community involvement; promoting activity throughout the school day and beyond; and teaching good nutrition and eating habits. Therefore, physical educators would need the help of others (parents, administrators, other educators, community organizers, etc.) to deliver a consistent message to children. There are several research limitations. First, teachers’ attitudes and behaviors in class could influence students’ physical activity levels; however, we did not investigate the

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potential effect on students’ physical activity levels in this study. Future researchers should take these conditions into account, particularly when conducting studies that involve multiple instructors. Second, one should interpret BMI results in children and adolescents with caution, as an individual’s height, weight, bone mass, and percentage of body fat change at different times and rates during child development, particularly during puberty (Nihiser et al., 2007). Additionally, our sample was predominantly Caucasian American at one school site, which may limit the generalizability of findings. Continued research, therefore, is needed to (a) examine, identify, and investigate the effects of the teachers’ attitudes and behaviors on students’ physical activity levels, (b) determine students’ body composition using multiple indexes such as percentage of body fat and waist circumference, and (c) target a large number of diverse students from multiple schools to increase the accuracy and generalizability of the findings.

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Authors’ Note Please address correspondence concerning this article to Zan Gao, Department of Health, Exercise,) and Sport Science, Box 43011, 109 Exercise Science Center, Texas Tech University, Lubbock, TX 79409-3011. E-mail: [email protected]

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