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Ethnic and socioeconomic comparisons of fitness, activity levels, and barriers to exercise in high school females.

Cardiovascular disease, cancer, and diabetes account for almost 70% of mortality in the United States, (1) with nonwhite minorities at the greatest risk for morbidity and mortality related to disease. African Americans have higher rates of heart disease, stroke, obesity, and diabetes than their white counterparts. (1) The Hispanic population, the largest minority group in the United States, (2) has a higher incidence of diabetes and is 1.5 times more likely to be obese than non-Hispanic whites. (3) These diseases are directly linked to physical inactivity, low levels of physical fitness, obesity, and increased blood lipids. (4,5) All of these risk factors begin early in life and if continued into adulthood, can have a significant impact on health status as well as productivity and achievement later in life. (6)

Regular physical activity, as well as physical fitness during youth, has been shown to attenuate the health risks associated with adult obesity and decrease morbidity and mortality (7) as well as improve the risk factors for cardiovascular disease later in life; (5,8) yet less than one third of adult African American and Hispanic women report getting regular physical exercise. (9) In addition, members of lower socioeconomic groups are less likely to be physically active and thus more likely to be at risk for the diseases associated with physical inactivity. (10)

Previous research has demonstrated that perceived barriers to a particular behavior correlate negatively with health behaviors. (11) In a study of 603 students in grades 6 through 8, Ferguson et al (12) reported that perceived benefits, perceived athletic ability, and self-esteem were positively correlated with intent to exercise and current exercise behavior. In addition, students who had positive attitudes toward physical activity were more likely to exercise.

Physical activity begins to decline in the high school years. This decline is especially apparent in the female population. (13) While 20 minutes of vigorous activity 3 times per week is the minimum recommended for health-related fitness, (14) only 59% of high school females meet that standard compared with 73% of their male counterparts. (13) Contributing to the decline in physical activity and the increase in obesity is the fact that less than one half of all high school students attend physical education classes daily. (15) In Michigan, where the research reported in the current article was conducted, the picture is especially bleak. While 48% of males report attending physical education classes daily, only 30% of females report attending physical education classes on a daily basis. (15)

The purpose of this study was to determine if high school females differed in individual measures of health-related physical fitness, barriers to exercise, or activity level based on ethnicity or socioeconomic status (SES). A second purpose was to determine the amount of variance the variables barriers, mile run, and activity level contributed to the risk factor "percent fat" in African American, white, and Hispanic high school females.

METHODS

Design

This study was a cross-sectional study of female high school students who completed survey instruments and fitness testing during a regularly scheduled physical education class.

Sample

Explanations of the study and invitations to participate were sent to the 10 school districts within a 30-mile radius of a large Midwestern city. Seven districts agreed to participate, and 7 high schools were randomly selected from those districts for data collection. Parents were informed of the study by first-class mail and asked to call the primary investigator (PI) if they did not approve of their daughter's participation. Assent was assumed if no call was received or if the parent called, and after discussing the research with the PI, verbally agreed to allow their daughter to participate. Female students in physical education classes were invited to participate in the study and signed assent forms attesting to their consent. The original sample consisted of 1628 students. The final sample consisted of 1314 students (81%) who completed all aspects of data. Students were not retained for reasons including, but not limited to, incomplete survey instruments (7%), incomplete fitness testing (7%), and inability to match fitness tests to the survey instrument (5%). The study was approved by the Human Investigation Committee at a large Midwestern university.

Measures

Survey Instrument. The survey instrument was developed in several phases. In phase 1, a comprehensive review of the literature was conducted and survey questions used in previous studies examining similar concepts were noted. (16-18) In phase 2, an elicitation questionnaire that examined student beliefs about exercise was given to 30 Hispanic, 30 African American, and 40 white students in 3 classes. All 100 students returned usable questionnaires. In an effort to include differences based on ethnicity, the questionnaires were tallied separately, and a response was considered salient if it was selected by 25% of an ethnic group. Salient responses were combined with questions used in previous research to form the basis for the current questionnaire.

In phase 3, a panel of 3 experts reviewed the instrument for clarity of wording and instructions. Based on their recommendations, changes were made, and the instrument was then pilot tested on a group of 50 students, representing 3 different ethnicities, in a high school physical education class. Based on the recommendations of those students, final minor changes were made.

The final instrument consisted of 15 questions. Seven questions assessed perceived barriers to exercise, 5 assessed activity level, and 3 were demographic questions (age, gender, and race/ethnicity). The reading level, according to the SMOG formula, (19) was grade 7, month 3. For most items, students were asked to select from a Likert-type series of potential responses. All items were coded from 1 to 5, with 5 indicating either the highest activity level or the highest barrier. When necessary, items were reverse scored for analysis. Barrier questions can be found in Table 4. The activity-level questions began with the statement: "Physical activity means doing exercises like walking fast, jogging, running, bike riding, skating, dancing, swimming, or any other activity that makes you breathe faster and your heart beat faster." Sample questions include: "This week I was physically active 3-5 times." "I am more physically active than others my age and sex." "I am more physically active than my parents."

Psychometric Properties of the Instrument. Factor analysis was run and the individual items loaded on the intended subscale verifying their validity. The subscale "barriers" was totaled giving a composite score for that factor and tested for internal reliability using Cronbach's alpha. The alpha score for barriers (.92) was found to be reliable. The subscale "activity level" was totaled giving a composite score for that factor and tested for internal reliability using Cronbach's alpha. The alpha score for activity level (.87) was also found to be reliable.

In order to examine test-retest reliability, the test was administered to 35 female students in a physical education class of mixed ethnicities, on 2 occasions, 2 weeks apart. The interclass correlation coefficient was .82 for barriers and .89 for activity level.

Ethnicity and SES. Students self-selected ethnicity from the following choices: African American, non-Hispanic white, Hispanic, mixed race (identify the races), and other. For the determination of SES, secretarial staff at the school were given the codes matched with student names. They returned the codes only to the researchers indicating whether the students were eligible for free school lunches (low SES = 46%) or ineligible for free school lunches (moderate or high socioeconomic level = 54%).

Physical Fitness Testing. Health-related components of physical fitness were tested using the FITNESSGRAM. (20) The test was conducted by trained administrators. Students were familiarized with the procedure for each test prior to actually recording data. Aerobic capacity and body composition were tested, and body weight and height were recorded. The FITNESSGRAM manual gives directions for all tests, and the program comes with software that computes body mass index (BMI) from height and weight and percent fat from skinfold thickness at 2 sites. (20) For more complete instructions readers are referred to the user manual. (20) The following were tested.

Aerobic capacity was tested with the 1-mile run. Preparation for the test included instruction about pacing and practice pacing. Students were instructed to complete the distance as quickly as possible. Walking was permissible. Times were recorded in minutes and seconds.

Body composition was assessed using the triceps and calf skinfold measurements. A vertical measurement was taken on the triceps of the right arm halfway between the elbow and the acromium process of the scapula. The calf skinfold was measured on the inside of the right leg at the level of maximum calf girth. To decrease the chance of error, skinfold measurements were taken by professional exercise physiologists with a minimum of 7 years experience and greater than 1500 skinfold tests previously measured. Each measurement was taken 3 times. When the difference between 2 measurements was less than 1 mm, the score was recorded.

Height and weight were both measured on the 402KL Physician Balance Beam Scale, (Health O Meter, Sunbeam, Inc., San Francisco, CA). Subjects were instructed to stand on the scale, with their back to the measuring rod, place their hands on their hips, and take a deep breath. The measuring rod was lowered to their head, and they were asked to step off. Height was read and recorded to the nearest quarter inch. The scale was balanced to 0, and subjects were asked to step up on it and stand quietly while weight was measured to the nearest quarter pound. This process was repeated twice to ensure accurate measurements.

Data Analysis

To determine the main effects of SES, race/ethnicity and the possible interaction between the two, a 2 (high vs low SES) by 3 (Hispanic, African American, white) multivariate analysis of variance was run on the dependent variables, BMI, percent fat, mile run, activity level, and barriers. Due to the interaction between ethnicity and SES, separate logistic regression analyses were conducted for each dependent variable with race/ethnicity and SES entered as covariates. Since race/ethnicity proved to be an independent predictor in all but one instance, separate univariate analysis of variances were conducted on the dependent variables by ethnicity. When significance was found based on race/ethnicity, a Tukey post hoc was performed. Independent samples t tests were run to determine dependent variables that differed based on SES. Due to the number of dependent variables, a Bonferroni correction factor was applied and the significance level was set at .01. Finally, hierarchical regression analyses were conducted on each race/ethnicity to determine the amount of variance that the variables mile run, barriers, and activity level contributed to obesity as measured by percent fat.

RESULTS

The final sample consisted of 1314 (367 African American, 306 Hispanic, and 641 white) females aged 16.2 [+ or -] 0.9 years (mean [+ or -] SD). Significant main effects were found for ethnicity, [F.sub.10,2610] = 4.5, p < .001; SES, [F.sub.5,1304] = 3.3, p < .001; and the ethnicity x SES interaction, [F.sub.10,2610] = 2.9, p < .001. Subsequent post hoc analyses revealed ethnic differences in percent fat, BMI, mile run, perceived barriers, and activity levels (Table 1). In general, white students scored lower than African Americans who in turn scored lower than Hispanic students. As expected, students who were identified as low SES scored lower than those identified as high SES (Table 2).

Hierarchical multiple regression analysis revealed that perceived barriers contributed a larger portion of the variance to percent fat in minority students than white students (Table 3). Perceived barrier questions and responses can be found in Table 4. Logistic regression analysis revealed that race/ethnicity remained a significant predictor of BMI, percent fat, 1-mile run, and perceived barriers even when controlling for SES (Table 5).

DISCUSSION

To our knowledge, this is the first study that has examined the fitness levels and barriers to exercise by race/ ethnicity and SES in high school females. The limited number of studies in existence has compared African Americans to whites, (17,21) or examined only 1-mile walk times and BMI. (22) Due to the fact that Hispanics now constitute the largest minority group in the country, (2) their inclusion in this study makes a significant contribution to the existing literature. The most important finding of this study is that fitness levels, including percent fat, BMI, and 1-mile run times differ among African American, Hispanic, and white high school females and between females from different socioeconomic groups. The results of this study demonstrate that this difference is already established by the age of 16 years, which is particularly disconcerting in light of the research that indicates physical inactivity and obesity are both independent risk factors for a variety of morbidities and mortality. (5) Additionally, the results of this study show that there are significant differences among ethnicities and socioeconomic groups in the perceived barriers to participating in regular exercise.

This study stands in contrast to a study by Desmond et al (17) examining the relationship between African American and white adolescents' fitness status. They measured fitness status by using a modified Harvard Step Test and found no difference in female fitness levels based on ethnicity. In Desmond et al's study, 51% of African American females were in "poor" shape as compared to 35% of white females. In the current study, 57% of African American females, 72% of Hispanic females, and 29% of white females had aerobic fitness levels that classified them as "poor." It is possible that unknown variables account for the differences between these 2 studies, but it is more likely that physical activity and fitness levels have declined in the past decade, and could reflect the decline in participation in physical education classes. (23)

More current studies report similar findings. From a study population of 1668 females, Felton et al (23) found that African American girls reported significantly lower levels of physical activity and significantly higher BMIs than their white counterparts and that the difference was attributed to race. Beets and Pitetti (22) found significant ethnic differences in the 1-mile run/walk with these differences increasing with age for females.

The present study looked at a population of females aged 16 years, but existing literature suggests that the findings are not dissimilar to studies conducted in adults. Masse and Anderson (24) found that ethnicity and income were associated with physical activity in 246 African American and Hispanic adult females, and Burton et al (10) found that subjects of both sexes were less likely to engage in physical activity if they were socioeconomically disadvantaged.

In order to address the disparity in fitness levels that may lead to a disparity in health as these subjects age, it is important to note how the individual variables accounted for the variance. In African Americans and Hispanics, perceived barriers to exercise accounted for a significant proportion of the variance. Both races/ethnicities responded more positively to the barrier statements: "I do not exercise because it makes me tired" (African Americans, 65%; Hispanics, 68%; whites, 31%); "I am not physically active because it makes me sweat too much" (African Americans, 71%; Hispanics, 52%; whites, 31%); and "I do not exercise because I do not feel safe in my neighborhood" (African Americans, 71%; Hispanics, 62%; whites, 9%). In contrast, white students perceived very few barriers to exercise. The dislike of sweating and perspiring that both African Americans and Hispanics reported in this study has been reported previously. (25) In fact, Taylor et al found that physical activity was perceived as a barrier to maintaining attractive appearance in African American and Hispanic middle school females. (26) In the present study, whites were more likely to believe that exercise helped them to maintain an attractive appearance.

In order to encourage physical activity in adolescents, the issue of barriers must be addressed. One method of addressing and overcoming barriers to exercise is school-based health and physical education programs. (27) Given the decline in physical activity that occurs during adolescence for females, physical education class is likely to be the only opportunity most females have to exercise. (28) The physical education curriculum is a key factor in determining the attitudes young people have toward fitness; (28) therefore, carefully designed programs can help to overcome the barriers adolescents face regarding exercise and provide them with the resources necessary to make exercise a part of their lifestyle, thus potentially impacting lifetime health and future mortality rates.

IMPLICATIONS

The key finding of this study is that individual measures of health-related fitness, including percent fat, BMI, and 1-mile run times differ among African American, Hispanic, and white high school females. Combined with other research indicating that physical fitness during youth is an indicator of disease risk later in life, there seems to be strong support for the assertion that steps need to be taken to eliminate the disparity in fitness levels based on ethnicity or SES. If this assertion holds true, educators can play an important role in enlightening their students regarding the benefits of physical activity and the importance of maintaining a healthy weight. They can also monitor the changes in these variables that occur with age. Since this research indicates that there are significant differences based on ethnicity and SES, and that these 2 variables are related, those educators working in areas that contain high populations of Hispanics and African Americans or high populations of students from low socioeconomic groups can make a significant contribution to the prevention of future morbidity and mortality in these populations. In addition, future research should attempt to discern which programs are successful based on ethnicity or SES and attempts should be made to implement these programs in the target population.

LIMITATIONS

Based on the fact that this was a selected sample representing a small population of adolescents, this research cannot be assumed to have external validity for all adolescents. Second, the survey data was self-reported and thus lends itself to the possibility that some respondents may have answered in a socially desirable manner. Also, the instrument itself, although tested for reliability, was monothematic and may have resulted in response set bias from some respondents. Additionally, ethnicity differences in the dependent barriers examined may be due to cultural biases that are currently unknown.

Socioeconomic status was assessed based on students' eligibility for the federally funded school lunch program. While this allowed us to make a crude distinction between students of low or not low SES, it did not allow us to distinguish between middle- and upper-class students.

In the current study, the 1-mile run test was used to estimate the aerobic capacity component of fitness. Because exact measurement of maximal oxygen consumption requires extensive time and laboratory equipment, field tests are often used instead as predictors of maximum oxygen consumption. When attempts are made to decrease limitations inherent in these tests, the 1-mile run has been shown to correlate well with maximum oxygen consumption. (29) Limitations specific to the 1-mile run test are subject motivation, the ability of the subject to pace themselves, and the ability of the subject to tolerate discomfort. To address these potential limitations, every effort was made to provide students with adequate practice in pacing. In addition, the test was conducted during ideal temperature and humidity conditions.

Height was measured to the nearest quarter inch. This is not a precise measure and increases the possibility of error in the BMI calculation. Finally, skinfold measures only give an estimate of body fat. This estimate can be off by as much as 12%. Every effort was made to reduce error (using trained exercise physiologists, repeating the measure 3 times, etc), but the possibility for error is inherent in the measure. Despite these limitations, this research does have a number of strengths. It examined data for females across race/ethnicities and socioeconomic groups, and the results demonstrate that there is an established difference in health-related fitness variables as well as perceived barriers to fitness by the age of 16 years. Future research needs to focus on ways to address and correct these differences and also to determine if these differences exist at the elementary school level.

References

(1.) Health, United States, 2003: Trends in the Health of Americans. Hyattsville, Md: Centers for Disease Control and Prevention; 2003.

(2.) Miller S. (2003). United States Department of State, Washington, DC. Available at: http://usinfo.state.gov/usa/diversity/a012303.htm. Accessed June 15, 2005.

(3.) Bott J. Latinas: healthy inside and out. HAP Wise Women. 2001;1(2):3.

(4.) Trost SG, Pate RR, Ward DS. Determinants of physical activity in active and low-active, sixth grade African American youth. J Sch Health. 1999;69(1):29-34.

(5.) Richardson CR, Kriska AM, Lantz PM, Hayward RA. Physical activity and mortality across cardiovascular disease risk groups. Med Sci Sports Exerc. 2004;36(11): 1923-1929.

(6.) Fulton JE, Garg M, Galuska DA, Rattay KT, Caspersen CJ. Public health and clinical recommendations for physical activity and fitness. Sports Med. 2004;34(9):581-599.

(7.) Blair SN, Brodney S. Effects of physical inactivity and obesity on morbidity and mortality: current evidence and research issues. Med Sci Sports Exerc. 1999;31(11):s646-s662.

(8.) Twisk JWR, Kemper HCG, vanMechelen W. Prediction of cardiovascular disease risk factors later in life by physical activity and physical fitness in youth: general comments and conclusions. Int J Sports Med. 2002;23:S44-S49.

(9.) Center of Disease Control. State specific birth rates for teenagers-United States, 1990-1996. MMWR Morb Mortal Wkly Rep. 1997;46(36): 837-842.

(10.) Burton NW, Turrell G, Oldenburg B. Participation in recreational physical activity: why do socioeconomic groups differ? Health Educ Behav. 2003;30(2):225-244.

(11.) Grundy SM, Blackburn G, Higgins M, Lauer R, Petri MG, Ryan D. Physical activity in the prevention and treatment sof obesity and its comorbidities. Med Sci Sports Exerc. 1999;31(11):s502-s508.

(12.) Ferguson KJ, Yesalis CE, Pomrehn PR, Kirkpatrick MB. Attitudes, knowledge, and beliefs as predictors of exercise intent and behavior in schoolchildren. J Sch Health. 1989;59(3): 112-115.

(13.) Grunbaum JA, Kann L, Kinchen S, et al. Youth Risk Behavior Surveillance--United States, 2003. MMWR Morb Mortal Wkly Rep. 2003;53:s2-s11.

(14.) American College of Sports Medicine. Guidelines for Exercise Testing and Prescription. 5th ed. Baltimore, Md: Williams & Wilkins; 1995.

(15.) Grunbaum J, Kann L, Kinchen S, et al. Youth Risk Behaviour Surveillance--United States, 2003. MMWR Morb Mortal Wkly Rep. 2004; 53(33-2):2-100.

(16.) Johnson CC, Li D, Eppling J, et al. A transactional model of social support, self-efficacy, and physical activity for children in the child and adolescent trial for cardiovascular health. J Health Educ. 2000;31(1):2-9.

(17.) Desmond SM, Price JH, Lock RS, Smith D, Stewart, PW. Urban black and white adololescents' physical fitness status and perceptions of exercise. J Sch Health. 1990;60(5):220-226.

(18.) Cardinal BJ, Engels H-J, Zhu W. Application of the transtheoretical model of behavior change to preadolescents' physical activity and exercise behavior. Pediatr Exerc Sci. 1998; 10:69-80.

(19.) McLaughlin G. SMOG grading: a new readability formula. J Reading. 1969; 12(8):639-646.

(20.) The Cooper Institute for Aerobics. FITNESSGRAM: Test Administration Manual. 2nd ed. Champaign, Ill: Human Kinetics; 1994.

(21.) Folsom A, Sprafka JM, Luepker R, Jacobs DR. Beliefs among black and white adults about causes and prevention of cardiovascular disease: the Minnesota Heart Survey. Am J Prev Med. 1988;4:121-127.

(22.) Beets MW, Pitetti KH. One-mile run/walk and body mass index of an ethnically diverse sample of youth. Med Sci Sports Exerc. 2004;36(10): 1796-1803.

(23.) Felton GM, Dowda M, Ward DS, et al. Differences in physical activity between black and white girls living in rural and urban areas. J Sch Health. 2002;72(6):250-255.

(24.) Masse LC, Anderson CB. Ethnic differences among correlates of physical activity in women. Am J Health Promot. 2003; 17(6):357-360.

(25.) Taylor WC, Yancey A, Leslie J, et al. Physical activity among African American and Latino middle school girls: consistent beliefs, expectations, and experiences across two sites. Women Health. 1999;30(2): 67-82.

(26.) Perry CL, Kelder SH. Models for effective prevention. J Adolesc Health. 1992;13:355-363.

(27.) Daley AJ, Buchanan J. Aerobics dance and physical self-perceptions in female adolescents: some implications for physical education. Res Q Exerc Sport. 1999;70(2):196-200.

(28.) Coakley J, White A. Making decisions: gender and sport participation among British adolescents. Sociol Sport J. 1992;9:20-35.

(29.) McArdle WD, Katch FJ, Katch VL. Exercise Physiology. 2nd ed. Philadelphia, Pa: Lea Febiger; 1986.

Mariane M. Fahlman, PhD, Associate Professor, (m.fahlman@wayne. edu), Wayne State University, Matthaei 262, Detroit, MI 48202; Heather L. Hall, PhD, Professor, (heatherh@elmhurst.edu), Elmhurst College, 190 Prospect Ave, Elmhurst, IL 60126-3096; and Robyn Lock, PhD, Associate Professor, (rlock@sfsu.edu), San Francisco State University, Gym 125, San Francisco, CA 94132.
Table 1
Dependent Variables Where There Were Significant Differences
Based on Ethnicity (Mean [+ or -] SEM) *

                                       African American
                                          (n = 367)

BMI (kg/[m.sup.2])                 25.4 [+ or -] 2.2 (1, 3)
Body fat (%)                       31.2 [+ or -] 5.2 (1, 3)
One-mile run (minutes)             12.5 [+ or -] 2.3 (1, 3)
Activity level (1 = strongly        2.2 [+ or -] 2.2 (1)
  disagree, 5 = strongly agree)
Barriers (1 = strongly disagree,    3.1 [+ or -] 0.6 (1)
  5 = strongly agree)

                                           Hispanic
                                          (n = 306)

BMI (kg/[m.sup.2])                 27.8 [+ or -] 3.0 (1, 2)
Body fat (%)                       35.1 [+ or -] 7.2 (1, 2)
One-mile run (minutes)             14.8 [+ or -] 4.9 (1, 2)
Activity level (1 = strongly        2.4 [+ or -] 1.8 (2)
  disagree, 5 = strongly agree)
Barriers (1 = strongly disagree,    3.5 [+ or -] 0.5 (2)
  5 = strongly agree)

                                            White
                                          (n = 641)

BMI (kg/[m.sup.2])                 22.5 [+ or -] 1.2 (2, 3)
Body fat (%)                       24.3 [+ or -] 2.2 (2, 3)
One-mile run (minutes)             10.5 [+ or -] 2.8 (2, 3)
Activity level (1 = strongly        3.7 [+ or -] 1.8 (1, 2)
  disagree, 5 = strongly agree)
Barriers (1 = strongly disagree,    1.4 [+ or -] 0.5 (1, 2)
  5 = strongly agree)

                                    F       p

BMI (kg/[m.sup.2])                 80.3   <.001
Body fat (%)                       85.2   <.001
One-mile run (minutes)             76.9   <.001
Activity level (1 = strongly       79.8   <.001
  disagree, 5 = strongly agree)
Barriers (1 = strongly disagree,   82.5   <.001
  5 = strongly agree)

* Like numbers (1, 2, 3) indicate significant difference between
ethnicities. (p = .000) High levels of activity and barriers are
represented by higher numbers. BMI, body mass index.

Table 2
Dependent Variables Where There Were Significant Differences Based
on Socioeconomic Status (Mean [+ or -] SEM) *

                                  Low SES (n = 604)

BMI (kg/[m.sup.2])                36.5 [+ or -] 3.1
Body fat (%)                      34.6 [+ or -] 2.6
One-mile run (minutes)            10.2 [+ or -] 1.4
Activity level (1 = strongly       3.5 [+ or -] 2.1
  disagree, 5 = strongly agree)
Barriers (1 = strongly             3.6 [+ or -] 0.8
  disagree, 5 = strongly agree)

                                  High SES (n = 710)    t      p

BMI (kg/[m.sup.2])                20.2 [+ or -] 1.7    4.5   <.001
Body fat (%)                      21.7 [+ or -] 0.6    4.2   <.001
One-mile run (minutes)            14.2 [+ or -] 2.4    2.5   <.001
Activity level (1 = strongly       2.1 [+ or -] 1.2    2.3   <.001
  disagree, 5 = strongly agree)
Barriers (1 = strongly             1.4 [+ or -] 0.7    2.6   <.001
  disagree, 5 = strongly agree)

* All variables were significantly different based on SES. High levels
of activity and barriers are represented by higher numbers. BMI, body
mass index; SES, socioeconomic status.

Table 3
Hierarchical Regression Results for Dependent Variable Percent Fat *

                                      [R.sup.2]     F
                    r     [R.sup.2]    Change     Change     p

African American
  Model 1          .709     .503        .503       41.1     .001
  Model 2          .745     .555        .052        5.8    <.001
  Model 3          .765     .585        .030        3.4    <.001
Hispanic
  Model 1          .375     .141        .141       49.7    <.001
  Model 2          .487     .237        .097        3.8    <.001
  Model 3          .541     .293        .056        2.9    <.001
White
  Model 1          .059     .003        .003        .22     .136
  Model 2          .733     .537        .533       43.5    <.001
  Model 3          .843     .710        .173        8.6    <.001

* Model 1 predictors (barriers); Model 2 predictors (barriers, mile
run); Model 3 predictors (barriers, mile run, activity).

Table 4
Responses to Barrier Items Based on Ethnicity *

                                          African American

                                     Agree   Neutral   Disagree
                                      (%)      (%)       (%)

I am not physically active because    71        4         25
  it makes me sweat too much
I do not exercise because it makes    65        2         33
  me tired
I am not physically active because    71        3         26
  I do not feel safe in my
  neighborhood
I am not physically active because    43        1         56
  I don't feel like it
I am not physically active because    65        0         35
  exercise is uncomfortable
I am not physically active because    79        7         14
  exercise makes me look
  unattractive
I am not physically active because    50        0         50
  I don't want to get big muscles

                                              Hispanic

                                     Agree   Neutral   Disagree
                                      (%)      (%)       (%)

I am not physically active because    52        3         45
  it makes me sweat too much
I do not exercise because it makes    68        6         26
  me tired
I am not physically active because    62        2         36
  I do not feel safe in my
  neighborhood
I am not physically active because    80        5         15
  I don't feel like it
I am not physically active because    71        2         27
  exercise is uncomfortable
I am not physically active because    75        3         22
  exercise makes me look
  unattractive
I am not physically active because    45        2         53
  I don't want to get big muscles

                                               White

                                     Agree   Neutral   Disagree
                                      (%)      (%)       (%)

I am not physically active because    31       11         58
  it makes me sweat too much
I do not exercise because it makes    31        4         65
  me tired
I am not physically active because     9        0         65
  I do not feel safe in my
  neighborhood
I am not physically active because    28        3         69
  I don't feel like it
I am not physically active because    30        5         65
  exercise is uncomfortable
I am not physically active because    14        1         85
  exercise makes me look
  unattractive
I am not physically active because     7        4         89
  I don't want to get big muscles

Student responses strongly disagree and agree are represented as
"agree," and strongly disagree and disagree are  represented as
"disagree."

Table 5
Logistic Regression Results by Dependent Variable *

Dependent
Variable         Covariate     B      SE      p

BMI              Ethnicity   -.365   .155    .001
                 SES          .474   .249    .058
Percent fat      Ethnicity   -.333   .142   <.001
                 SES          .424   .251    .049
One-mile run     Ethnicity    .231   .106   <.001
                 SES          .356   .218    .038
Activity level   Ethnicity    .216   .250    .001
                 SES          .252   .213    .002
Barriers         Ethnicity    .237   .118    .001
                 SES          .399   .206    .042

BMI, body mass index; SES, socioeconomic status.
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Author:Fahlman, Mariane M.; Hall, Heather L.; Lock, Robyn
Publication:Journal of School Health
Geographic Code:1USA
Date:Jan 1, 2006
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