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Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20
Health-Related Fitness of American Indian Youth a
b
c
Timothy A. Brusseau , Tom Finkelstein , Pamela H. Kulinna & Connie Pangrazi a
University of Utah
b
University of Arizona
c
c
Arizona State University Published online: 20 May 2014.
To cite this article: Timothy A. Brusseau, Tom Finkelstein, Pamela H. Kulinna & Connie Pangrazi (2014) Health-Related Fitness of American Indian Youth, Research Quarterly for Exercise and Sport, 85:2, 257-261, DOI: 10.1080/02701367.2014.893050 To link to this article: http://dx.doi.org/10.1080/02701367.2014.893050
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Research Quarterly for Exercise and Sport, 85, 257–261, 2014 Copyright q SHAPE America ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2014.893050
Health-Related Fitness of American Indian Youth Timothy A. Brusseau University of Utah
Tom Finkelstein Downloaded by [University of West Florida] at 13:03 06 October 2014
University of Arizona
Pamela H. Kulinna and Connie Pangrazi Arizona State University
A physically fit lifestyle is important for American Indian (AI) youth who are at risk for hypokinetic diseases, particularly type 2 diabetes. Some evidence exists on the physical activity patterns of AI youth, but there is little information on their health-related fitness. Purpose: The purpose of this study was to describe the health-related fitness levels of youth living in an AI community. Method: Participants included youth from 5th to 9th grade (N ¼ 85) in a Southwestern U.S. AI community. Youth were of AI descent and were 12.36 ^ 1.68 years of age. Participants completed 5 parts of the FITNESSGRAMw fitness test during physical education. The tests included the Progressive Aerobic Cardiorespiratory Endurance Run fitness test (cardiovascular fitness), curl-up (muscular endurance), pushup (muscular strength), sit-and-reach (flexibility), and body mass index (estimated body composition). Results: Results were similar to other youth studies with some of the students reaching the healthy fitness zone for muscular strength (28%), body composition (30%), flexibility (60%), aerobic fitness (63%), and muscular endurance (74%). Conclusions: Findings highlight the capacity for improvement for students across all of the components of health-related fitness. Keywords: FITNESSGRAMw, fitness levels, Native American, school health
Previous studies have highlighted the health disparity of American Indian (AI) youth. For example, AI youth have the highest rates of obesity and type 2 diabetes of youth in the United States (e.g., Castor et al., 2006; FagotCampagna, Burrows, & Williamson, 1999) and are at great risk (when compared with other youth) for other hypokinetic diseases (Kriska et al., 2003). When matched to an age and sex reference population at 5 and 10 years of age, AI youth were heavier and had a higher percentage of body fat (Salbe et al., 2002). AI youth are also 3 times more likely to be obese than other U.S. youth (Zephier, Himes, & Story, 1999). Eliminating these health disparities should be a priority. Submitted August 13, 2012; accepted October 7, 2013. Correspondence should be addressed to Timothy A. Brusseau, Department of Exercise and Sport Science, Sport Pedagogy and Physical Activity Assessment Laboratory, University of Utah, 250 S 1850 E Rm. 205, Salt Lake City, UT 84124. E-mail: [email protected]
An essential step in understanding chronic disease risk factors (including obesity) is to better understand the healthrelated fitness (Cooper et al., 2010) of individuals. The Institute of Medicine (2012) has suggested that collecting fitness data in youth will advance understanding of how fitness among young people translates into better health. The benefits associated with “health-enhancing” levels of fitness are numerous and well documented (e.g., Janssen & LeBlanc, 2010) and are linked to better health in adulthood. These advantages, however, are overshadowed by poor fitness (Freedson, Cureton, & Heath, 2000) among U.S. youth. Very little attention has been focused on understanding the health-related fitness of AI individuals. Previous work has primarily examined only the body composition of AI youth. For example, in 2002 –2003, large percentages of overweight AI youth ages 5 to 17 years old were reported to be 48.1% and 46.3% for boys and girls, respectively, above
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the 85th percentile of body mass index (BMI). AI children were consistently found to be over-represented when compared with national averages with respect to youthrelated BMI above the 95th percentile (all races; Caballero et al., 2003). More recently, Brusseau, Kulinna, TudorLocke, and Ferry (2013) determined that 67% of Southwestern U.S. AI children were overweight and 50% were obese. The high percentage of overweight and obese children in AI communities may lead to many health concerns, including type 2 diabetes and cardiovascular disease. Though findings suggest that as many as two in three AI students may be overweight/obese (e.g., Brusseau et al., 2013), investigations have not provided evidence related to youth health-related fitness levels. The examined fitness patterns can be used to create more successful interventions in AI communities. The testing process also benefits the students by allowing goal setting and tracking of results. Thus, the purpose of this study was to examine fitness levels (i.e., cardiovascular fitness, muscular strength, muscular endurance, flexibility, body composition) of youth in an AI community. A secondary purpose was to explore sex and BMI differences across fitness categories.
Fitness Testing Instrument The FITNESSGRAMw (Meredith & Welk, 2010) physical fitness test battery was selected to assess students’ fitness levels. Tests were administered during physical education (PE) classes during 1 month in fall 2011. Participants completed five components of the Fitnessgram (Meredith & Welk, 2010) physical fitness test to measure five fitness components: (a) aerobic fitness (Progressive Aerobic Cardiorespiratory Endurance Run fitness test [PACER]), (b) muscular endurance (curl-up test), (c) muscular strength (pushup test), (d) flexibility (sit-and-reach test), and (e) estimated body composition (BMI). The individual results were then compared to criterion-referenced health standards established by the Cooper Institute Scientific Advisory Board, specific to age and sex. For each item, the Cooper Institute Scientific Advisory Board has defined a “healthy fitness zone” (HFZ) and a “needs improvement-high risk”/“low-risk” zone based on the minimum level of capacity related to each area of youth physical fitness needed for good health. The results from each component stand alone (e.g., a student may be in the HFZ for aerobic fitness but be in the needs improvement-high risk zone for flexibility). However, when the tests are taken together, the results are used to provide an overall assessment of a student’s physical fitness.
METHOD Data Collection Participants The participating school district enrolled approximately 360 students in Grades K –12, and schools were governed by the Bureau of Indian Affairs. The community is composed of families from several AI tribes. The school district serves a geographically dispersed community that is considered rural. All students shared the same school campus with an elementary building and a junior/senior high school building. There was no gymnasium available for elementary-aged (Grades 5 to 6) children. The older students (Grades 7 – 9) had updated teaching spaces including an aerobics room and weight room in addition to a gymnasium. The district suffered from high absenteeism rates (e.g., 14.5% per day for seventh grade), which influenced the number of student participants in this project. Approvals were gained prior to the beginning of the study from: (a) the university institutional review board, (b) the Educational Division of Tribal Council, and (c) the principal/administrators at the schools. Parental consent and student assent were also collected. All students in the fifth through ninth grades were invited to participate (n ¼ 150; schools had one class per grade level). Of the approximately 150 total students in Grades 5 through 9, 57% (or 85 students) had parental consent and student assent and completed all tests. Therefore, participants were 85 students in fifth through ninth grade (52% female, Mage ¼ 12.36 ^ 1.68 years old).
PE teachers were trained to use the Fitnessgram (Meredith & Welk, 2010) assessments during routine professional development sessions as part of a comprehensive healthy and active school initiative. Training sessions have been found to increase the reliability and validity of Fitnessgram testing (i.e., Morrow, Martin, & Jackson, 2010). The Fitnessgram training was performed by a university faculty member who held a Physical Best Health-Fitness Instructor certification (includes training in Fitnessgram protocol). All four PE teachers were given the needed Fitnessgram equipment and administration manuals. Prior to data collection, teachers had students practice the fitness tests at stations and practice the PACER test as a group two separate times so they were comfortable with the protocol and procedures for completing the fitness tests. Fitness testing was completed during four classes during a 2-week period. For elementary (Grades 5– 6) students, most fitness tests were administered on an outdoor playing field. Body composition assessments were conducted in a hallway away from other students. For secondary (Grades 7 – 9) students, testing was administered in the school gymnasium with body composition measured in the hallway just outside of the gymnasium. During the testing weeks, researchers were present to oversee the protocol and data-recording procedures to make sure the Fitnessgram administration protocol (Meredith &
Note. HFZ ¼ healthy fitness zone; aerobic fitness ¼ Progressive Aerobic Cardiorespiratory Endurance Run fitness test laps; body composition ¼ body mass index; muscular endurance ¼ curl-ups; muscular strength ¼ pushups; flexibility ¼ sit-and-reach; left ¼ left leg; right ¼ right leg. Overall, 7% of youth were in the HFZ for all five tests and 6% failed to be in the HFZ for any of the tests.
68 28 72 27 48 31.82 5.62 27.46 14.39 Right 9.30 SD.91 35.02 24.05 30.43 13.09 Left 9.36 SD 2.66 58 31 75 29 71 18.06 5.48 25.21 5.00 Right 9.43 SD 2.40 22.68 24.65 24.90 5.80 Left 9.37 SD 2.16 63 30 74 28 60 30.70 24.32 28.58 10.05 Left 9.41 SD 2.42 Aerobic Fitness Body Composition Muscular Endurance Muscular Strength Flexibility
27.83 5.50 26.31 11.78 Right 9.42 SD 2.69
% Met HFZ SD M
SD
% Met HFZ
M
Boys n ¼ 41
% Met HFZ SD
Descriptive results showed that a majority of participants were in the HFZ for aerobic fitness (63%), muscular endurance (74%), and flexibility (60%). Cumulatively, 30% of youth achieved the HFZ for estimated body composition. Twenty-eight percent of individuals met the HFZ for muscular strength. Table 1 provides the means, standard deviations, and percentage meeting the HFZ for the five Fitnessgram components. Independent t tests indicated that boys and girls scored statistically similarly for aerobic fitness, t(70) ¼ 1.87, p ¼ .07, r 2 ¼ .04; muscular endurance, t(67) ¼ 0.85, p ¼ .40, r 2 ¼ .01; flexibility, t(67) ¼ 2 0.12, p ¼ .91, r 2 , .01; and body composition, t(45) ¼ 2 0.37, p ¼ .71, r 2 , .01. Boys scored significantly, t(58) ¼ 2.43, p , .05, r 2 ¼ .09, higher for muscular strength. ANOVA results indicated that findings were similar for each Fitnessgram component for healthy-weight, overweight, and obese students: aerobic fitness, F(2, 36) ¼ 1.28, p ¼ .29, h2 ¼ .07, muscular endurance, F(2, 36) ¼ 0.50, p ¼ .61, h2 ¼ .03, muscular strength, F(2, 29) ¼ 0.32, p ¼ .73,
M
RESULTS
Fitness Components
Descriptive statistics were calculated (including means, standard deviations, and frequencies) for each fitness component. For BMI, students were classified as healthy weight, overweight, or obese (Ogden et al., 2002). The percentage of students who met the HFZ requirement for each component of physical fitness was also calculated. Independent t tests and analyses of variance (ANOVAs) were used to examine test differences by sex and weight status.
Girls n ¼ 44
Data Analysis
Overall n ¼ 85
Welk, 2010) and procedures were followed systematically and accurately. The teams measured the fitness variables together to ensure accuracy (agreement of the two researchers for the values) in the measurements and recordings. If different scores were determined by the two teachers (there was , 1% disagreement between reviewers), a mean of the two scores was used. The Fitnessgram data were provided to the researchers for aggregation and reporting. Fitness testing data were used by teachers and students only to inform student goal setting and track progress toward goals for physical activity participation and improving fitness components. Researchers assessed body composition measured using BMI. It was estimated by measuring height without shoes to the nearest 0.5 cm using a portable stadiometer (Seca, Hanover, MD) and by measuring weight to the nearest 0.1 kg on a digital scale (Seca 882, Hanover, MD). BMI was calculated using kilograms per meter-squared.
TABLE 1 Mean, Standard Deviation, and Percent Meeting the Healthy Fitness Zone for Five FITNESSGRAMw Components by Sex Among 85 AI Youth
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h 2 ¼ .02, and flexibility, F(2, 34) ¼ 2.17, p ¼ .13, h2 ¼ .11.
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DISCUSSION Depending on the test, between 28% and 74% of participants were in the HFZ. A majority of youth in the current study performed in the HFZ for muscular endurance, aerobic fitness, and flexibility. However, a majority of AI youth failed to meet the HFZ for muscular strength and estimated body composition. Overall, 7% of the students were in the HFZ for all five components and 6% failed to reach the HFZ for any of the tests. In general, AI youth performed similarly to previous studies (e.g., Welk, Meredith, Ihmels, & Seeger, 2010) examining youth health-related fitness. The Texas schools data reported the percentage of youth reaching the HFZ for cardiovascular fitness at approximately 58% (Welk et al., 2010), similar to findings from the current study (i.e., 63%). These results are also similar to previous studies examining the physical activity patterns of AI youth (Brusseau, Kulinna, Kloeppel, & Ferry, 2012; Brusseau et al., 2013). It may be reasonable to conclude that AI youth are generally as active as and have similar cardiovascular fitness and similar levels of muscular endurance to individuals with other ethnic backgrounds in the United States (e.g., Welk et al., 2010). One area of fitness in which this sample of AI children excelled was for the component of flexibility. The current sample of students met the HFZ 60% of the time compared with the Texas sample, who were reportedly less flexible (48% met the HFZ; Welk et al., 2010). Flexibility has many benefits including increased range of motion, decreased risk for injury, better postural alignment, improved circulation, and prevention of low back pain or other spinal problems (Alter, 2004). The AI youth participating in this study were generally less fit than other samples when examining the fitness component of body composition. For example, BMI results indicated that 70% of the boys and girls were overweight or obese in the current sample while the reverse findings were reported by Welk and his colleagues (2010), with only 30% of the youth being categorized into the overweight or obese categories. Similarly, the current sample also performed poorly compared with other samples in the area of muscular strength. For example, only a small percentage (28%) of youth achieved the HFZ for the pushups. Again, for this second component of fitness, the results were reversed for the larger, more diverse Texas study participants (Welk et al., 2010), with about 70% of students reaching the HFZ for muscular strength. Our findings were consistent with other studies examining health-related fitness in youth with few sex
differences found (Welk et al., 2010). The present study also reinforces previous studies’ findings that have shown high percentages of overweight/obese AI youth, surpassing the national U.S. reference standards for any age (Brusseau et al., 2012, 2013; Caballero et al., 2003; Styne, 2010; Zephier, Himes, Story, & Zhou, 2006). In fact, Styne (2010) found that 40% to 50% of AI youth younger than 10 years old from many communities are classified as either overweight or obese, and Brusseau et al. (2013) also reported those numbers can reach as high as 70% in an AI community. Physical fitness helps to predict an individual’s future health status (Artero et al., 2012). Specifically, higher levels of aerobic fitness and muscular strength are associated with better health (Artero et al., 2012). The low muscular strength levels of the current sample, along with 37% of youth not meeting the HFZ for aerobic fitness and 70% failing to meet the HFZ for estimated body composition, highlight the public health concerns for this population. AI youth are already at increased risk for many hypokinetic diseases (Kriska et al., 2003), and the health-related fitness scores from the current study highlight the risks for premature mortality and chronic disease. Limitations There are a few limitations to note. First, the sample size was not representative of national AI youth fitness levels; the data were from one Southwestern U.S. AI community. Second, the small sample size limited additional statistical analyses. Lastly, the large standard deviations make comparisons with additional studies difficult and do not allow for meaningful between-group comparisons. Future Research The current sample included 70% of participants outside of the HFZ for BMI. Although a majority of youth were overweight or obese, many youth were still able to reach the HFZ for cardiovascular health. Fitness testing across other AI communities is needed to make findings more generalizable. Furthermore, only a few studies have been published exploring the health-related fitness of youth using the Fitnessgram. A nationally representative sample of youth fitness testing may be needed to make generalizations about fitness. Additional studies examining AI youth’s healthrelated fitness trends would also benefit from random selection of participants from multiple AI communities.
WHAT DOES THIS ARTICLE ADD? The current study examined fitness levels of youth in an AI community and thus provided baseline data of AI youth fitness levels for future investigation and for designing
AMERICAN INDIAN HEALTH-RELATED FITNESS
interventions. Results suggested a capacity for improvement for all of the components of health-related fitness. Seventy percent of youth failed to reach the HFZ for body composition and muscular strength, and these areas may need to be targeted. Due to the association between fitness and better health, improvements in health-related fitness are needed (Artero et al., 2012).
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REFERENCES Alter, M. J. (2004). Science of flexibility (3rd ed.). Champaign, IL: Human Kinetics. Artero, E. G., Lee, D. C., Lavie, C. J., Espan˜a-Romero, V., Sui, X., Church, T. S., & Blair, S. N. (2012). Effects of muscular strength on cardiovascular risk factors and prognosis. Journal of Cardiopulmonary Rehabilitation and Prevention, 32, 351– 358. Brusseau, T. A., Kulinna, P. H., Kloeppel, T., & Ferry, M. (2012). Seasonal variation of American Indian children’s school-day physical activity. Biomedical Human Kinetics, 4, 76–81. Brusseau, T. A., Kulinna, P. H., Tudor-Locke, C., & Ferry, M. (2013). Daily physical activity patterns of children living in an American Indian community. Journal of Physical Activity and Health, 10, 48–53. Caballero, B., Himes, J. H., Lohman, T., Davis, S. M., Stevens, J., Evans, M., . . . Pablo, J. (2003). Body composition and overweight prevalence in 1704 schoolchildren from 7 American Indian communities. American Journal of Clinical Nutrition, 78, 308–312. Castor, M. L., Smyser, M. S., Taualii, M. M., Park, A. N., Lawson, S. A., & Forquera, R. A. (2006). A nationwide population-based study identifying health disparities between American Indian/Alaska Natives and the general populations living in select urban counties. American Journal of Public Health, 96, 1478– 1484. Cooper, K. H., Everett, D., Meredith, M., Kloster, J., Rathbone, M., & Read, K. (2010). Preface: Texas statewide assessment of youth fitness. Research Quarterly for Exercise and Sport, 81(Suppl. 3), Sii –Siv. Fagot-Campagna, A., Burrows, N. R., & Williamson, D. F. (1999). The public health epidemiology of type 2 diabetes in children and adolescents: A case study of American Indian adolescents in the Southwestern United States. Clinica Chimica Acta, 286, 81–95.
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Freedson, P. S., Cureton, K. J., & Heath, G. W. (2000). Status of field-based fitness testing in children and youth. American Journal of Preventive Medicine, 31(Suppl. 2), S77–S85. Institute of Medicine. (2012). Fitness measures and health outcomes in youth. Washington, DC: The National Academies Press. Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7, 40. Kriska, A. M., Saremi, A., Hanson, R. L., Bennett, P. H., Kobes, S., Williams, D. E., & Knowler, W. C. (2003). Physical activity, obesity, and the incidence of type 2 diabetes in a high-risk population. American Journal of Epidemiology, 158, 669–675. Meredith, M. D., & Welk, G. J. (Eds.). (2010). Fitnessgram and Activitygram: Test administration manual (updated 4th ed.). Champaign, IL: Human Kinetics. Morrow, J. R., Martin, S. B., & Jackson, A. W. (2010). Reliability and validity of the FITNESSGRAM: Quality of teacher-collected healthrelated fitness surveillance data. Research Quarterly for Exercise and Sport, 81(Suppl. 3), S24–S30. Ogden, C. L., Kuczmarski, R. J., Flegal, K. M., Mei, Z., Guo, S., Wei, R., . . . Johnson, C. L. (2002). Centers for Disease Control and Prevention 2000 growth charts for the United States: Improvements to the 1977 National Center for Health Statistics version. Pediatrics, 109, 45–60. Salbe, A. D., Weyer, C., Harper, I., Lindsay, R. S., Ravussin, E., & Tatranni, P. A. (2002). Assessing risk factors for obesity between childhood and adolescence: II. Energy metabolism and physical activity. Pediatrics, 110, 299–306. Styne, D. M. (2010). Childhood obesity in American Indians. Journal of Public Health Management and Practice, 16, 381 –387. Welk, G. J., Meredith, M. D., Ihmels, M., & Seeger, C. (2010). Distribution of health related physical fitness in Texas youth: A demographic and geographic analysis. Research Quarterly for Exercise and Sport, 81 (Suppl. 3), S6 –S15. Zephier, E., Himes, J. H., & Story, M. (1999). Prevalence of overweight and obesity in American Indian school children and adolescents in the Aberdeen area: A population study. International Journal of Obesity, 23, 28–32. Zephier, E., Himes, J. H., Story, M., & Zhou, X. (2006). Increasing prevalence of overweight and obesity in Northern Plains American Indian children. Archives of Pediatrics and Adolescent Medicine, 160, 34–39.
Research Quarterly for Exercise and Sport Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/urqe20
Health-Related Fitness of American Indian Youth a
b
c
Timothy A. Brusseau , Tom Finkelstein , Pamela H. Kulinna & Connie Pangrazi a
University of Utah
b
University of Arizona
c
c
Arizona State University Published online: 20 May 2014.
To cite this article: Timothy A. Brusseau, Tom Finkelstein, Pamela H. Kulinna & Connie Pangrazi (2014) Health-Related Fitness of American Indian Youth, Research Quarterly for Exercise and Sport, 85:2, 257-261, DOI: 10.1080/02701367.2014.893050 To link to this article: http://dx.doi.org/10.1080/02701367.2014.893050
PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions
Research Quarterly for Exercise and Sport, 85, 257–261, 2014 Copyright q SHAPE America ISSN 0270-1367 print/ISSN 2168-3824 online DOI: 10.1080/02701367.2014.893050
Health-Related Fitness of American Indian Youth Timothy A. Brusseau University of Utah
Tom Finkelstein Downloaded by [University of West Florida] at 13:03 06 October 2014
University of Arizona
Pamela H. Kulinna and Connie Pangrazi Arizona State University
A physically fit lifestyle is important for American Indian (AI) youth who are at risk for hypokinetic diseases, particularly type 2 diabetes. Some evidence exists on the physical activity patterns of AI youth, but there is little information on their health-related fitness. Purpose: The purpose of this study was to describe the health-related fitness levels of youth living in an AI community. Method: Participants included youth from 5th to 9th grade (N ¼ 85) in a Southwestern U.S. AI community. Youth were of AI descent and were 12.36 ^ 1.68 years of age. Participants completed 5 parts of the FITNESSGRAMw fitness test during physical education. The tests included the Progressive Aerobic Cardiorespiratory Endurance Run fitness test (cardiovascular fitness), curl-up (muscular endurance), pushup (muscular strength), sit-and-reach (flexibility), and body mass index (estimated body composition). Results: Results were similar to other youth studies with some of the students reaching the healthy fitness zone for muscular strength (28%), body composition (30%), flexibility (60%), aerobic fitness (63%), and muscular endurance (74%). Conclusions: Findings highlight the capacity for improvement for students across all of the components of health-related fitness. Keywords: FITNESSGRAMw, fitness levels, Native American, school health
Previous studies have highlighted the health disparity of American Indian (AI) youth. For example, AI youth have the highest rates of obesity and type 2 diabetes of youth in the United States (e.g., Castor et al., 2006; FagotCampagna, Burrows, & Williamson, 1999) and are at great risk (when compared with other youth) for other hypokinetic diseases (Kriska et al., 2003). When matched to an age and sex reference population at 5 and 10 years of age, AI youth were heavier and had a higher percentage of body fat (Salbe et al., 2002). AI youth are also 3 times more likely to be obese than other U.S. youth (Zephier, Himes, & Story, 1999). Eliminating these health disparities should be a priority. Submitted August 13, 2012; accepted October 7, 2013. Correspondence should be addressed to Timothy A. Brusseau, Department of Exercise and Sport Science, Sport Pedagogy and Physical Activity Assessment Laboratory, University of Utah, 250 S 1850 E Rm. 205, Salt Lake City, UT 84124. E-mail: [email protected]
An essential step in understanding chronic disease risk factors (including obesity) is to better understand the healthrelated fitness (Cooper et al., 2010) of individuals. The Institute of Medicine (2012) has suggested that collecting fitness data in youth will advance understanding of how fitness among young people translates into better health. The benefits associated with “health-enhancing” levels of fitness are numerous and well documented (e.g., Janssen & LeBlanc, 2010) and are linked to better health in adulthood. These advantages, however, are overshadowed by poor fitness (Freedson, Cureton, & Heath, 2000) among U.S. youth. Very little attention has been focused on understanding the health-related fitness of AI individuals. Previous work has primarily examined only the body composition of AI youth. For example, in 2002 –2003, large percentages of overweight AI youth ages 5 to 17 years old were reported to be 48.1% and 46.3% for boys and girls, respectively, above
Downloaded by [University of West Florida] at 13:03 06 October 2014
258
T. A. BRUSSEAU ET AL.
the 85th percentile of body mass index (BMI). AI children were consistently found to be over-represented when compared with national averages with respect to youthrelated BMI above the 95th percentile (all races; Caballero et al., 2003). More recently, Brusseau, Kulinna, TudorLocke, and Ferry (2013) determined that 67% of Southwestern U.S. AI children were overweight and 50% were obese. The high percentage of overweight and obese children in AI communities may lead to many health concerns, including type 2 diabetes and cardiovascular disease. Though findings suggest that as many as two in three AI students may be overweight/obese (e.g., Brusseau et al., 2013), investigations have not provided evidence related to youth health-related fitness levels. The examined fitness patterns can be used to create more successful interventions in AI communities. The testing process also benefits the students by allowing goal setting and tracking of results. Thus, the purpose of this study was to examine fitness levels (i.e., cardiovascular fitness, muscular strength, muscular endurance, flexibility, body composition) of youth in an AI community. A secondary purpose was to explore sex and BMI differences across fitness categories.
Fitness Testing Instrument The FITNESSGRAMw (Meredith & Welk, 2010) physical fitness test battery was selected to assess students’ fitness levels. Tests were administered during physical education (PE) classes during 1 month in fall 2011. Participants completed five components of the Fitnessgram (Meredith & Welk, 2010) physical fitness test to measure five fitness components: (a) aerobic fitness (Progressive Aerobic Cardiorespiratory Endurance Run fitness test [PACER]), (b) muscular endurance (curl-up test), (c) muscular strength (pushup test), (d) flexibility (sit-and-reach test), and (e) estimated body composition (BMI). The individual results were then compared to criterion-referenced health standards established by the Cooper Institute Scientific Advisory Board, specific to age and sex. For each item, the Cooper Institute Scientific Advisory Board has defined a “healthy fitness zone” (HFZ) and a “needs improvement-high risk”/“low-risk” zone based on the minimum level of capacity related to each area of youth physical fitness needed for good health. The results from each component stand alone (e.g., a student may be in the HFZ for aerobic fitness but be in the needs improvement-high risk zone for flexibility). However, when the tests are taken together, the results are used to provide an overall assessment of a student’s physical fitness.
METHOD Data Collection Participants The participating school district enrolled approximately 360 students in Grades K –12, and schools were governed by the Bureau of Indian Affairs. The community is composed of families from several AI tribes. The school district serves a geographically dispersed community that is considered rural. All students shared the same school campus with an elementary building and a junior/senior high school building. There was no gymnasium available for elementary-aged (Grades 5 to 6) children. The older students (Grades 7 – 9) had updated teaching spaces including an aerobics room and weight room in addition to a gymnasium. The district suffered from high absenteeism rates (e.g., 14.5% per day for seventh grade), which influenced the number of student participants in this project. Approvals were gained prior to the beginning of the study from: (a) the university institutional review board, (b) the Educational Division of Tribal Council, and (c) the principal/administrators at the schools. Parental consent and student assent were also collected. All students in the fifth through ninth grades were invited to participate (n ¼ 150; schools had one class per grade level). Of the approximately 150 total students in Grades 5 through 9, 57% (or 85 students) had parental consent and student assent and completed all tests. Therefore, participants were 85 students in fifth through ninth grade (52% female, Mage ¼ 12.36 ^ 1.68 years old).
PE teachers were trained to use the Fitnessgram (Meredith & Welk, 2010) assessments during routine professional development sessions as part of a comprehensive healthy and active school initiative. Training sessions have been found to increase the reliability and validity of Fitnessgram testing (i.e., Morrow, Martin, & Jackson, 2010). The Fitnessgram training was performed by a university faculty member who held a Physical Best Health-Fitness Instructor certification (includes training in Fitnessgram protocol). All four PE teachers were given the needed Fitnessgram equipment and administration manuals. Prior to data collection, teachers had students practice the fitness tests at stations and practice the PACER test as a group two separate times so they were comfortable with the protocol and procedures for completing the fitness tests. Fitness testing was completed during four classes during a 2-week period. For elementary (Grades 5– 6) students, most fitness tests were administered on an outdoor playing field. Body composition assessments were conducted in a hallway away from other students. For secondary (Grades 7 – 9) students, testing was administered in the school gymnasium with body composition measured in the hallway just outside of the gymnasium. During the testing weeks, researchers were present to oversee the protocol and data-recording procedures to make sure the Fitnessgram administration protocol (Meredith &
Note. HFZ ¼ healthy fitness zone; aerobic fitness ¼ Progressive Aerobic Cardiorespiratory Endurance Run fitness test laps; body composition ¼ body mass index; muscular endurance ¼ curl-ups; muscular strength ¼ pushups; flexibility ¼ sit-and-reach; left ¼ left leg; right ¼ right leg. Overall, 7% of youth were in the HFZ for all five tests and 6% failed to be in the HFZ for any of the tests.
68 28 72 27 48 31.82 5.62 27.46 14.39 Right 9.30 SD.91 35.02 24.05 30.43 13.09 Left 9.36 SD 2.66 58 31 75 29 71 18.06 5.48 25.21 5.00 Right 9.43 SD 2.40 22.68 24.65 24.90 5.80 Left 9.37 SD 2.16 63 30 74 28 60 30.70 24.32 28.58 10.05 Left 9.41 SD 2.42 Aerobic Fitness Body Composition Muscular Endurance Muscular Strength Flexibility
27.83 5.50 26.31 11.78 Right 9.42 SD 2.69
% Met HFZ SD M
SD
% Met HFZ
M
Boys n ¼ 41
% Met HFZ SD
Descriptive results showed that a majority of participants were in the HFZ for aerobic fitness (63%), muscular endurance (74%), and flexibility (60%). Cumulatively, 30% of youth achieved the HFZ for estimated body composition. Twenty-eight percent of individuals met the HFZ for muscular strength. Table 1 provides the means, standard deviations, and percentage meeting the HFZ for the five Fitnessgram components. Independent t tests indicated that boys and girls scored statistically similarly for aerobic fitness, t(70) ¼ 1.87, p ¼ .07, r 2 ¼ .04; muscular endurance, t(67) ¼ 0.85, p ¼ .40, r 2 ¼ .01; flexibility, t(67) ¼ 2 0.12, p ¼ .91, r 2 , .01; and body composition, t(45) ¼ 2 0.37, p ¼ .71, r 2 , .01. Boys scored significantly, t(58) ¼ 2.43, p , .05, r 2 ¼ .09, higher for muscular strength. ANOVA results indicated that findings were similar for each Fitnessgram component for healthy-weight, overweight, and obese students: aerobic fitness, F(2, 36) ¼ 1.28, p ¼ .29, h2 ¼ .07, muscular endurance, F(2, 36) ¼ 0.50, p ¼ .61, h2 ¼ .03, muscular strength, F(2, 29) ¼ 0.32, p ¼ .73,
M
RESULTS
Fitness Components
Descriptive statistics were calculated (including means, standard deviations, and frequencies) for each fitness component. For BMI, students were classified as healthy weight, overweight, or obese (Ogden et al., 2002). The percentage of students who met the HFZ requirement for each component of physical fitness was also calculated. Independent t tests and analyses of variance (ANOVAs) were used to examine test differences by sex and weight status.
Girls n ¼ 44
Data Analysis
Overall n ¼ 85
Welk, 2010) and procedures were followed systematically and accurately. The teams measured the fitness variables together to ensure accuracy (agreement of the two researchers for the values) in the measurements and recordings. If different scores were determined by the two teachers (there was , 1% disagreement between reviewers), a mean of the two scores was used. The Fitnessgram data were provided to the researchers for aggregation and reporting. Fitness testing data were used by teachers and students only to inform student goal setting and track progress toward goals for physical activity participation and improving fitness components. Researchers assessed body composition measured using BMI. It was estimated by measuring height without shoes to the nearest 0.5 cm using a portable stadiometer (Seca, Hanover, MD) and by measuring weight to the nearest 0.1 kg on a digital scale (Seca 882, Hanover, MD). BMI was calculated using kilograms per meter-squared.
TABLE 1 Mean, Standard Deviation, and Percent Meeting the Healthy Fitness Zone for Five FITNESSGRAMw Components by Sex Among 85 AI Youth
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h 2 ¼ .02, and flexibility, F(2, 34) ¼ 2.17, p ¼ .13, h2 ¼ .11.
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DISCUSSION Depending on the test, between 28% and 74% of participants were in the HFZ. A majority of youth in the current study performed in the HFZ for muscular endurance, aerobic fitness, and flexibility. However, a majority of AI youth failed to meet the HFZ for muscular strength and estimated body composition. Overall, 7% of the students were in the HFZ for all five components and 6% failed to reach the HFZ for any of the tests. In general, AI youth performed similarly to previous studies (e.g., Welk, Meredith, Ihmels, & Seeger, 2010) examining youth health-related fitness. The Texas schools data reported the percentage of youth reaching the HFZ for cardiovascular fitness at approximately 58% (Welk et al., 2010), similar to findings from the current study (i.e., 63%). These results are also similar to previous studies examining the physical activity patterns of AI youth (Brusseau, Kulinna, Kloeppel, & Ferry, 2012; Brusseau et al., 2013). It may be reasonable to conclude that AI youth are generally as active as and have similar cardiovascular fitness and similar levels of muscular endurance to individuals with other ethnic backgrounds in the United States (e.g., Welk et al., 2010). One area of fitness in which this sample of AI children excelled was for the component of flexibility. The current sample of students met the HFZ 60% of the time compared with the Texas sample, who were reportedly less flexible (48% met the HFZ; Welk et al., 2010). Flexibility has many benefits including increased range of motion, decreased risk for injury, better postural alignment, improved circulation, and prevention of low back pain or other spinal problems (Alter, 2004). The AI youth participating in this study were generally less fit than other samples when examining the fitness component of body composition. For example, BMI results indicated that 70% of the boys and girls were overweight or obese in the current sample while the reverse findings were reported by Welk and his colleagues (2010), with only 30% of the youth being categorized into the overweight or obese categories. Similarly, the current sample also performed poorly compared with other samples in the area of muscular strength. For example, only a small percentage (28%) of youth achieved the HFZ for the pushups. Again, for this second component of fitness, the results were reversed for the larger, more diverse Texas study participants (Welk et al., 2010), with about 70% of students reaching the HFZ for muscular strength. Our findings were consistent with other studies examining health-related fitness in youth with few sex
differences found (Welk et al., 2010). The present study also reinforces previous studies’ findings that have shown high percentages of overweight/obese AI youth, surpassing the national U.S. reference standards for any age (Brusseau et al., 2012, 2013; Caballero et al., 2003; Styne, 2010; Zephier, Himes, Story, & Zhou, 2006). In fact, Styne (2010) found that 40% to 50% of AI youth younger than 10 years old from many communities are classified as either overweight or obese, and Brusseau et al. (2013) also reported those numbers can reach as high as 70% in an AI community. Physical fitness helps to predict an individual’s future health status (Artero et al., 2012). Specifically, higher levels of aerobic fitness and muscular strength are associated with better health (Artero et al., 2012). The low muscular strength levels of the current sample, along with 37% of youth not meeting the HFZ for aerobic fitness and 70% failing to meet the HFZ for estimated body composition, highlight the public health concerns for this population. AI youth are already at increased risk for many hypokinetic diseases (Kriska et al., 2003), and the health-related fitness scores from the current study highlight the risks for premature mortality and chronic disease. Limitations There are a few limitations to note. First, the sample size was not representative of national AI youth fitness levels; the data were from one Southwestern U.S. AI community. Second, the small sample size limited additional statistical analyses. Lastly, the large standard deviations make comparisons with additional studies difficult and do not allow for meaningful between-group comparisons. Future Research The current sample included 70% of participants outside of the HFZ for BMI. Although a majority of youth were overweight or obese, many youth were still able to reach the HFZ for cardiovascular health. Fitness testing across other AI communities is needed to make findings more generalizable. Furthermore, only a few studies have been published exploring the health-related fitness of youth using the Fitnessgram. A nationally representative sample of youth fitness testing may be needed to make generalizations about fitness. Additional studies examining AI youth’s healthrelated fitness trends would also benefit from random selection of participants from multiple AI communities.
WHAT DOES THIS ARTICLE ADD? The current study examined fitness levels of youth in an AI community and thus provided baseline data of AI youth fitness levels for future investigation and for designing
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interventions. Results suggested a capacity for improvement for all of the components of health-related fitness. Seventy percent of youth failed to reach the HFZ for body composition and muscular strength, and these areas may need to be targeted. Due to the association between fitness and better health, improvements in health-related fitness are needed (Artero et al., 2012).
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Freedson, P. S., Cureton, K. J., & Heath, G. W. (2000). Status of field-based fitness testing in children and youth. American Journal of Preventive Medicine, 31(Suppl. 2), S77–S85. Institute of Medicine. (2012). Fitness measures and health outcomes in youth. Washington, DC: The National Academies Press. Janssen, I., & LeBlanc, A. G. (2010). Systematic review of the health benefits of physical activity and fitness in school-aged children and youth. International Journal of Behavioral Nutrition and Physical Activity, 7, 40. Kriska, A. M., Saremi, A., Hanson, R. L., Bennett, P. H., Kobes, S., Williams, D. E., & Knowler, W. C. (2003). Physical activity, obesity, and the incidence of type 2 diabetes in a high-risk population. American Journal of Epidemiology, 158, 669–675. Meredith, M. D., & Welk, G. J. (Eds.). (2010). Fitnessgram and Activitygram: Test administration manual (updated 4th ed.). Champaign, IL: Human Kinetics. Morrow, J. R., Martin, S. B., & Jackson, A. W. (2010). Reliability and validity of the FITNESSGRAM: Quality of teacher-collected healthrelated fitness surveillance data. Research Quarterly for Exercise and Sport, 81(Suppl. 3), S24–S30. Ogden, C. L., Kuczmarski, R. J., Flegal, K. M., Mei, Z., Guo, S., Wei, R., . . . Johnson, C. L. (2002). Centers for Disease Control and Prevention 2000 growth charts for the United States: Improvements to the 1977 National Center for Health Statistics version. Pediatrics, 109, 45–60. Salbe, A. D., Weyer, C., Harper, I., Lindsay, R. S., Ravussin, E., & Tatranni, P. A. (2002). Assessing risk factors for obesity between childhood and adolescence: II. Energy metabolism and physical activity. Pediatrics, 110, 299–306. Styne, D. M. (2010). Childhood obesity in American Indians. Journal of Public Health Management and Practice, 16, 381 –387. Welk, G. J., Meredith, M. D., Ihmels, M., & Seeger, C. (2010). Distribution of health related physical fitness in Texas youth: A demographic and geographic analysis. Research Quarterly for Exercise and Sport, 81 (Suppl. 3), S6 –S15. Zephier, E., Himes, J. H., & Story, M. (1999). Prevalence of overweight and obesity in American Indian school children and adolescents in the Aberdeen area: A population study. International Journal of Obesity, 23, 28–32. Zephier, E., Himes, J. H., Story, M., & Zhou, X. (2006). Increasing prevalence of overweight and obesity in Northern Plains American Indian children. Archives of Pediatrics and Adolescent Medicine, 160, 34–39.
