Diet and Cardiovascular Risk in University Marching Band, Dance Team and Cheer Squad Members: a cross-sectional study
© Sharma et al; licensee BioMed Central Ltd. 2008
Received: 03 December 2007
Accepted: 18 April 2008
Published: 18 April 2008
Cardiovascular disease (CVD) is the leading cause of death in the United States. Diets high in fat, especially saturated fat, are often linked to obesity, hypertension and hypercholesterolemia, all risk factors for CVD. The purpose of this study was to determine the association between diet and CVD risk factors in members of a university marching band, dance team and cheer squad.
In 2004, 232 marching band, dance team and cheer squad members completed a self-administered survey evaluating dietary intake. Body mass index (BMI), waist-to-hip ratio (WHR), blood pressure, fasting serum glucose and cholesterol were measured. Unpaired t-test and Pearson's chi square test were used to determine baseline differences by gender. Multiple linear regression analysis was used to determine the cross-sectional association between dietary intake of various food groups such as grains, meats, fruits & vegetables, dairy, water, alcohol and risk factors for CVD namely BMI, WHR, blood glucose, total cholesterol, and blood pressure (BP).
45% of the participants were overweight; 30% of females and 4.3% of males had WHR ≥ 0.80 and 0.95 respectively. Almost 8% were hyperglycemic, 10% hypercholesterolemic, 15% had high systolic and 9% had high diastolic BP. Less than 50% consumed the recommended servings of grains, fruits and vegetables, dairy and water and 58% consumed alcohol. Higher grains intake was positively associated with higher BMI (Adjusted β = 1.97, p = 0.030, 95% CI: 0.19, 3.74) and; higher alcohol intake was also positively associated with higher BMI (Adjusted β = 0.15, p = 0.002, 95% CI: 0.06, 0.24).
These results warrant the evaluation of existing college-based health programs and development of new interventions to improve dietary habits and promote a healthy lifestyle in these athletes.
Dietary habits are typically developing in childhood and established by young adulthood . Development of healthy eating habits in young individuals could be vital for prevention of cardiovascular disease (CVD) since CVD is the leading cause of death in the United States . Risk factors for CVD include obesity, hypertension, hypercholesterolemia, insulin resistance, physical inactivity, and smoking  and recent data have shown a significant rise in the prevalence of obesity in children, adolescents and adults . Diets high in fat, especially saturated fat, are often linked to obesity, hypertension and hypercholesterolemia [1, 5].
Data suggest that caloric intake is increasing among all ethnicities, age and socioeconomic groups, and diets contain more energy-dense, nutrient-poor foods . Evidence also suggests that consumption of foods from fast-food restaurants, which typically serve foods high in total and saturated fat, has doubled during the past 30 years . Consumption of unhealthy foods may be replacing that of fruits and vegetables (FV), which are known to provide essential vitamins, minerals and other nutrients . Even though high levels of whole grain consumption have been linked to reduced rates of CVD and colon cancer, most adults do not consume enough of these foods . Alcohol represents another dietary component that is associated with CVD risk factors. While moderate consumption of alcohol is protective against CVD, high levels of consumption are correlated with obesity, CVD and all-cause mortality . Also, evidence regarding the readiness to change dietary habits among adults also indicates that most adults are either not aware or not prepared to change their current dietary habits thus adding a layer of difficulty in trying to promote healthful eating habits . Given the strong link between diet and disease , it is important to evaluate and address dietary behaviors and readiness for change at a young age for effective prevention of CVD and related co-morbidities.
University marching band, dance team and cheer squad members perform physical activity for prolonged periods of time. It has been estimated that these athletes exercise at an intensity of 4.5–6 metabolic equivalents (METs) . This MET category also includes activities such as tennis, recreational swimming, golfing, bicycling and lawn mowing. Even though these band members perform intense physical activity, they represent a largely unstudied population in terms of physical characteristics, fitness levels, dietary habits, prevalence of overweight and CVD risk factors. Their dietary habits and physical fitness could be of vital importance to their well-being and performance, especially in an environment of increased heat and humidity.
The primary purpose of this study was to assess prevalence of CVD risk factors, measure dietary intake, readiness to change dietary intake and finally, examine the relationship between dietary intake and risk factors for CVD among members of an urban university marching band, dance team and cheer squad. We hypothesized that dietary factors such as low intake of grains, FV, dairy and high intake of alcohol are significantly associated with the CVD risk factors such as body mass index (BMI), waist to hip ratio (WHR), blood pressure, blood glucose and blood cholesterol.
This study examined baseline pre-season data collected in fall of 2004. This baseline analysis was part of the main study, the primary aim of which was to promote healthy eating habits and physical fitness for the prevention of obesity-related conditions and CVD among marching band, dance team and cheer squad members in an urban university (Bush et al., in preparation).
Members of a mid-sized university marching band located in the southwest part of the United States (N = 275) participated in the study. The study was approved for the use of human subjects by the university Committee for the Protection of Human Subjects. The study investigators attended practice sessions during pre-season to recruit volunteers for the study. They informed the band members of the purpose of the study and the study procedures. During this time the investigators asked all interested band members to provide their contact information and review the informed consent document. Incentives were offered to the participants in the form of free fitness testing and nutrition counseling as a part of the study. The response rate was 93%. Informed consent was obtained from all participants prior to start of the study. Band members were also informed that they could refuse to partake in any portion of the testing procedures without negative consequences. During the pre-season, which is a 2-week band camp, participants attended a testing session in the investigator's laboratory. Participants refrained from any regular exercise programs two days prior to the testing by not participating in band activities during that period. They completed a self-administered demographic, nutrition and medical history questionnaire. Since 232 participants completed the nutrition survey, the final sample for the present study utilized for the analysis was N = 232. Please note that detailed description of the study protocol is presented elsewhere . All data collection discussed below was performed on campus by the investigators and trained personnel.
Characteristics of the study participants (N = 232).
Total N = 232
Male N = 140
Female N = 92
Mean ± SD
Mean ± SD
Mean ± SD
19.3 ± 1.5
19.4 ± 1.5
19.1 ± 1.6
Body weight (lbs)
168.1 ± 46.8
179.9 ± 43.5
150.2 ± 46.2
Body height (cm)
170.9 ± 8.3
175.6 ± 6.1
163.9 ± 6.0
Body Mass Index
26.1 ± 6.6
26.6 ± 6.3
25.3 ± 6.9
Waist to Hip ratio
0.81 ± 0.07
0.83 ± 0.06
0.77 ± 0.07
Blood pressure, fasting blood glucose and total cholesterol
Prevalence of cardiovascular disease risk factors of study participants (N = 232).
Total N (%)
Male (N = 140) N (%)
Female (N = 92) N (%)
§Body Mass Index
- Normal (18.5 – 24.9 kg/m2)
- Overweight (25.0 – 29.9)
- Obese (>30.0)
- 0.80 – 0.94
Fasted Blood Glucose
- Normal (≤ 110 mg/dl)
- High (>110 mg/dl)
Fasted Blood Cholesterol
- Normal (≤ 200 mg/dl)
- High (>200 mg/dl)
Systolic Blood Pressure
- Normal (≤ 140 mmHg)
- High (>140 mmHg)
Diastolic Blood Pressure
- Normal (≤ 90 mmHg)
- High (>90 mmHg)
Participants (N = 232) completed a self-administered cross-sectional survey evaluating the number of servings of various foods eaten per day based on the recommendations of the 2000 USDA Food Guide Pyramid  as well as dietary stages of change . The survey consisted of questions regarding the participants' eating behavior and dietary Stages of Change (pre-contemplation, contemplation, preparation, action and maintenance) using the Transtheoretical Model (TTM)  for all the food groups mentioned above. This allowed for assessment of the participants' readiness to make dietary changes. The TTM has been validated in previous studies as an effective method of evaluating readiness to change dietary behaviors in adults [21–23]. Our survey instrument was developed from these studies which indicate that self-assessment of diet using standard staging questions can be accurate reflections of intake. Staging questions were designed to categorize participants regarding their readiness to change according to the TTM. Participants were staged according to responses to the following questions: 1) How many servings of __________ (e.g., food group variable) do you usually eat in a day? 2) Have you been eating this amount for more than 6 months? 3) In the future do you intend to increase this amount, decrease this amount, or stay the same? And 4) do you intent to make this change in the next 6 months?
The survey instrument also contained one question regarding their alcohol consumption: Do you drink alcohol? Subjects who answered yes on alcohol intake were asked about the number of servings of alcohol consumed per week.
Pilot testing and focus groups affirmed understanding of question terminology in the survey and did not report any problems with the determination of what their perception of a serving was for the various food groups. Data from surveys were double entered and cross checked with error rates of <1%.
Unpaired t-test and Pearson's chi square test were used to determine baseline differences in participant characteristics by gender and ethnicity. Data were also analyzed to determine if the participants who did not complete the nutrition survey were different in their CVD risk factor characteristics than those who did. Multiple linear regression analysis was used to determine the association between dietary intake of various food groups such as grains, meats, fruits & vegetables, dairy, water and alcohol (independent variables) and risk factors for CVD namely BMI, blood glucose, blood total cholesterol, and blood pressure (dependent variables). All CVD risk factors were used as continuous variables in the analysis. Bivariate and multivariate analysis was performed with each CVD risk factor as the outcome variable and each food group as the predictor variable. Covariates adjusted for in the multivariate analysis included age, gender, BMI and ethnicity. Results presented are those from the regression models that were statistically significant.
Staging for grains, FV, meats, dairy and water was determined using a staging algorithm. While the nutrition survey provided information on all five Dietary Stages of Change categories, due to small cell sizes it was necessary to collapse the stages of change from five stages to two stages. These were named the 'cognitive stage' (pre-contemplation, contemplation and preparation) since these participants have not made any overt behavior changes and; the 'behavior stage' (action and maintenance) since these participants have either made overt behavior changes for less than or more than 6 months and the behavior change is observable. Significance level for all analysis was set at p < 0.05.
Physical characteristics and indicators of cardiovascular risk
Table 1 shows the baseline physical characteristics of the study participants. The participants (mean ± SD) were 19.3 ± 1.5 yrs and had a BMI of 26.1 ± 6.6. Sixty-five percent and 35% of the study population were White and non-White, respectively. Table 1 shows significant gender differences for body weight, body height and WHR. Overall, males had a higher WHR compared to females.
Table 2 shows the indicators of CVD risk for the study participants, subdivided into the categories of risk threshold for BMI, WHR, blood glucose, blood total cholesterol, and systolic and diastolic BP. The values are presented as the number of participants expressing the risk factor value. Overall, 45.0% of the participants were overweight or obese, 7.8% of had high blood glucose, 10.3% had high blood total cholesterol, 14.7% had high systolic blood pressure and 8.6% had high diastolic blood pressure. Results showed that 30.0% of the females had a WHR ≥ 0.80 and 4.3% of the males had WHR ≥ 0.95. It is important to note here that when data using the larger sample (N = 275 participants) was examined, results showed a significantly higher prevalence of elevated blood glucose (13.6%) and elevated blood cholesterol (22.6%) as compared to the 232 participants used in the present study. The prevalence of overweight and obesity, WHR and elevated blood pressure were not significantly different between the two samples.
Dietary habits of study participants: Number of servings of foods consumed per day (N = 232).
Frequency N (%)
Males (N = 140) N (%)
Females (N = 92) N (%)
0 to 5 servings/day
6 to 11 servings/day
0 to 2 servings/day
3 to 4 servings/day
§Fruits and Vegetables
1 to 4 servings/day
5 to 9 servings/day
> 9 servings/day
1 to 2 servings/day
3 to 4 servings/day
0 to 7 glasses/day
≠Alcohol (N = 157)
1–2 servings per week
3–4 servings per week
>= 5 servings per week
Dietary stages of change
Dietary Stages of Change of various food groups for study participants.
Grains (N = 191)
Meats (N = 219)
Fruits and Vegetables (N = 217)
Dairy (N = 218)
Water (N = 225)
Association of diet and indicators of CVD risk
Multiple linear regression analysis was performed to determine if the dietary intake of the various food groups such as grains, FV, meats, dairy as well as alcohol and water intake were significant predictors of the CVD risk factors namely BMI, systolic blood pressure, diastolic blood pressure, blood cholesterol and blood glucose. Multivariate analysis after adjusting for confounders such as age, gender and ethnicity showed that higher grains intake was positively associated with higher BMI (Adjusted β = 1.97, p = 0.030, 95% CI: 0.19, 3.74) and higher alcohol intake was also positively associated with higher BMI (Adjusted β = 0.15, p = 0.002, 95% CI: 0.06, 0.24). Other associations of interest between the food groups and CVD risk factors were not statistically significant.
Overall a high percentage of participants reported eating fewer grains, FV and drinking fewer glasses of water than recommended. Additionally, a majority of the participants were not ready to produce a change in their eating behaviors. Our results also identified a positive association between grain consumption, alcohol intake and BMI.
Forty-five percent of the participants were overweight or obese. The high prevalence of overweight/obesity that was observed in our study concurs with other studies on college-age students which report that 20% to 40% of college students are overweight or obese [24, 25]. The 2006 National College Health Assessment (NCHA) survey on 23,863 students reported that 31% of the U.S college students were overweight or obese based on self-reported height and weight . This lower prevalence of overweight in the NCHA statistics as compared to our study could be related to the ethnic distribution since 77% of the NCHA population was Caucasian as compared to 65% in our study; and Caucasians have a lower prevalence of obesity as compared to Hispanics of African Americans . Secondly, our study measured height and weight on all the participants. Usually in self-report, as done in NCHA, height is over-reported and weight is under-reported resulting in a lower estimate of BMI . The high percentage of females with a WHR ≥ 0.80 indicates an increased risk for development of atherosclerotic plaque and CVD in these participants . This high prevalence of overweight in these athletes, especially the females is quite concerning since it puts this population of athletes at increased risk for CVD. Also, given the rigorous training that these athletes often have to do under extreme weather conditions, it is important that they are in peak physical shape for optimal performance. These results warrant the need for design and implementation of targeted obesity-prevention programs for females in this population.
When compared to the 2000 Dietary Guidelines for Americans, our data indicate that less than 20% of participants in this urban marching band university were consuming 5 or more servings of FV per day. Similar results were observed in the 2006 NCHA survey, which reported that only 6.2% of the students were usually consuming 5 or more servings of FV a day [29, 30]. Also, more than 70% of the participants were not consuming the recommended servings for grains. The newer 2005 Dietary Guidelines for Americans  recommend 2 cups (4 servings) of fruits and 2.5 cups (5 servings) of vegetables, 5.5 ounces (2 servings) for meats and, 6-ounce equivalents (6 servings) of grains (of which 3 ounces are whole grains) per day at the 2000 calorie level. When compared to these newer guidelines, majority of our study participants are not meeting the recommendations for FV and grains and, are consuming more than the recommended amount for meats. Unfortunately, the current survey did not capture the total caloric intake to determine the participants' intake of various foods relative to the total calories. Also no data was collected on whole grain intake which would have clinical implications since a diet high in low fat dairy, whole grains, and FV along with exercise is considered to be protective against several chronic diseases including obesity, CVD, and cancer [29, 30]. The positive association between grains intake and BMI seen in our study could be suggestive of a high intake of simple carbohydrates which have been associated with overweight and obesity . It would be worthy for future studies to collect data on whole grain consumption and examine their relationship with CVD risk factors in this population of student athletes.
The results from the Dietary Stages of Change assessment suggest that the majority of participants are in the cognitive stages for the dietary intake of grains and FV indicating that they are not making any overt changes towards changing their eating behavior for grains and FV. These data imply that health promotion programs aimed at moving these athletes from the cognitive stages to the behavior stages by increasing knowledge and providing necessary skills to improve dietary habits are critical in this population. These data provide critical information regarding the study population's readiness to change their dietary habits which clearly indicates that, at this time, it would be counterproductive to provide action-oriented material about healthy dietary change behaviors. In fact, the focus needs to be more on raising awareness of poor dietary habits, pros of eating more whole grains, FV to move the participants towards action. It is also important to note that the Stages of Change model has potential for public health interventions as well as individual nutrition interventions by health care practitioners towards improving the dietary habits of this population. Additionally, environmental factors, such as availability of FV and whole grain foods on-campus, need to be evaluated. Psychosocial factors such as knowledge, attitudes, beliefs, self-efficacy, outcome expectations and social support influencing diet and weight status in these athletes also need to be assessed.
Greater than fifty-seven percent of the participants indicated consuming 1 or more servings of alcohol per week with more than 10% of them drinking 5 or more servings of alcohol per week. These results concur with the NCHA statistics which reported that 70% of college students used alcohol in the last 30 days . While moderate consumption of alcohol is protective against CVD , high consumption has adverse effects on health . Alcohol is detrimental to athletic performance since it is a diuretic, gives problems with heat regulation, causes hypoglycemia, reduces reaction-times, endurance and increases risk for injury . Our study also found a positive association of alcohol intake and BMI. This concurs with the literature that shows frequent consumption of alcohol is associated with weight gain given its high caloric content . Also, given that the average age of the study participants was 19 years; our results suggest a high prevalence of underage drinking in this population. Unfortunately, midcourse reviews of the Healthy People 2010 and Healthy Campus 2010 have shown no change nationally in alcohol use and abuse [36, 37]. We strongly recommend using evidence-based programs that are intentionally and cautiously researched, planned, implemented and evaluated in college campuses, especially among athletes. Using health marketing, ecological/environmental, policy and social marketing approaches could potentially work to influence positive health behaviors of students. Additionally, existing alcohol and substance abuse awareness programs need to be evaluated.
Perhaps the most alarming finding of our study was the high percentage of participants with high WHR, elevated blood glucose, cholesterol and systolic and diastolic blood pressure. The major risk factors for CVD include levels of cholesterol (total cholesterol, low-density lipoprotein, and high density lipoprotein), BMI, blood pressure, cigarette smoking and diabetes [17, 38]. Studies have shown that total cholesterol have similar predictive values as LDL for CVD secondary to the atherogenic properties of cholesterol . The European SCORE project advocates the use of total cholesterol in isolation . However, recent literature has identified TC/HDL or LDL/HDL ratios having superior predictive values with respect to CVD . Thus, it is a limitation of the current study that LDL, HDL values were not determined and it would be worthy for future studies to examine these measures.
These results could indicate the presence of components of metabolic syndrome among participants which is considered to be a risk factor for CVD . Huang et al.  examined the prevalence of metabolic syndrome among 163 college students. They concluded that 27% of the population was overweight and 11.7% had high blood cholesterol . Our study did not assess the prevalence of metabolic syndrome in this population which would have provided a greater insight into the health status and CVD risk among this population of collegiate athletes.
Strengths and limitations
This study is among the first to evaluate the dietary habits and examine their relationship to CVD risk factors in marching band, dance team and cheer squad members, dance team and cheer squad. The large sample size lends sufficient power to the study. Currently there is little data available on the dietary habits as well as CVD risk factors in this population. While not confirmatory, these results are an appropriate starting point for further in-depth exploration into these relationships between diet and CVD risk factors in student athletes and also pave the way for developing future research and health promotion/health education programs targeting this population.
These strengths notwithstanding, our study has its limitations. We had a high response rate since the study was conducted on campus and the participants had incentives in the form of free nutrition and fitness counseling sessions conducted by exercise physiologists and licensed dietitians which can be very expensive if the participants were to obtain these on their own. This high response rate could have biased our results away from the null. Secondly, only about 84% (N = 232) of the total respondents completed the nutrition survey. Also, it appears that the prevalence of the CVD risk factors was higher in the larger sample (N = 275) as compared to those who did not complete the survey which could have biased our results towards the null. However, all attempts were made by study investigators to contact the participants who did not complete the nutrition survey.
Another limitation is that this study relied on self-reported measures of dietary intake which could bias the validity . However, these measures are commonly used in epidemiologic studies with reasonable accuracy . Also, no data was collected on intake of fast foods or fat intake, both of which have been shown in studies to be consumed at high levels among children, adolescents and college students . While the analysis was stratified on gender, it was not stratified by type of sport. This was primarily because majority of the sample were band members with not sufficient number of participants in the other sports to detect significant differences by sport. Of the 31 total cheerleaders and dance members (N = 14 cheerleaders and N = 17 dance members) who consented to participate in the study, only seven responded to the nutrition survey. Since cheerleading and dance are considered a 'weight-conscious' sport as compared to participating in the band, collapsing the three together could probably have biased the results towards the null. However, given the small sample of cheerleaders and dance members (N = 7) in the present study, this is highly unlikely. Finally, given the cross-sectional nature of our study, causality cannot be established and since this was secondary data analysis, the analysis is exploratory.
In sum, our results suggest a trend toward significant health risks in this elite members of a marching band unless an intervention of diet and exercise are performed. A high percentage of members of this urban marching band have a poor dietary intake and demonstrate a relatively high prevalence of overweight, hyperglycemia, hypercholesterolemia, and hypertension that increase their risk for CVD as well as other lifestyle-related illnesses. These results are especially important since these athletes participate in rigorous activities often under extreme weather conditions throughout the season and need to be in sound health for optimal performance. The unhealthy eating behaviors and a relatively high prevalence of alcohol consumption identified in the current study imply the need to evaluate existing health promotion programs aimed at improving the lifestyle of college students on campus and for evidence-based programs to be implemented. Finally, ACHA tools such as Standards of Practice for Health Promotion in Higher Education  and Healthy Campus 2010  should be used to develop, test and implement on-campus health promotion programs geared towards improving the dietary habits and reducing the prevalence of CVD risk factors in this population.
Informed consent was obtained from all participants prior to the study and the study protocol was approved by the University of Houston, Committee for Protection of Human Subjects.
The authors of this manuscript would like to acknowledge the Moore's School of Music for funding the study. We would also like to extend our appreciation to the research assistants responsible for recruitment, data collection and entry.
- American Heart Association Nutrition Committee, Lichtenstein AH, Appel LJ, Brands M, Carnethon M, Daniels S, Franch HA, Franklin B, Kris-Etherton P, Harris WS, Howard B, Karanja N, Lefevre M, Rudel L, Sacks F, Van Horn L, Winston M, Wylie-Rosett J: Diet and lifestyle recommendations revision 2006: A scientific statement from the American heart association nutrition committee. Circulation. 2006, 114: 82-96. 10.1161/CIRCULATIONAHA.106.176158.View ArticleGoogle Scholar
- Centers for Disease Control and Prevention: Chronic disease overview. [http://www.cdc.gov/nccdphp/overview.htm#4]
- Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB: Prediction of coronary heart disease using risk factor categories. Circulation. 1998, 97: 1837-1847.View ArticlePubMedGoogle Scholar
- Ogden CL, Carroll MD, Curtin LR, McDowell MA, Tabak CJ, Flegal KM: Prevalence of overweight and obesity in the united states, 1999–2004. JAMA. 2006, 295: 1549-1555. 10.1001/jama.295.13.1549.View ArticlePubMedGoogle Scholar
- Elmer PJ, Obarzanek E, Vollmer WM, Simons-Morton D, Stevens VJ, Young DR, Lin PH, Champagne C, Harsha DW, Svetkey LP, Ard J, Brantley PJ, Proschan MA, Erlinger TP, Appel LJ, PREMIER Collaborative Research Group: Effects of comprehensive lifestyle modification on diet, weight, physical fitness, and blood pressure control: 18-month results of a randomized trial. Ann Intern Med. 2006, 144: 485-495.View ArticlePubMedGoogle Scholar
- Troiano RP, Briefel RR, Carroll MD, Bialostosky K: Energy and fat intakes of children and adolescents in the United States: Data from the national health and nutrition examination surveys. Am J Clin Nutr. 2000, 72: 1343S-1353S.PubMedGoogle Scholar
- French SA, Story M, Neumark-Sztainer D, Fulkerson JA, Hannan P: Fast food restaurant use among adolescents: Associations with nutrient intake, food choices and behavioral and psychosocial variables. Int J Obes Relat Metab Disord. 2001, 25: 1823-1833. 10.1038/sj.ijo.0801820.View ArticlePubMedGoogle Scholar
- Popkin BM, Duffey K, Gordon-Larsen P: Environmental influences on food choice, physical activity and energy balance. Physiol Behav. 2005, 86: 603-613. 10.1016/j.physbeh.2005.08.051.View ArticlePubMedGoogle Scholar
- Flight I, Clifton P: Cereal grains and legumes in the prevention of coronary heart disease and stroke: A review of the literature. Eur J Clin Nutr. 2006, 60: 1145-1159. 10.1038/sj.ejcn.1602435.View ArticlePubMedGoogle Scholar
- Baranowski T, Cullen KW, Baranowski J: Psychosocial correlates of dietary intake: Advancing dietary intervention. Annu Rev Nutr. 1999, 19: 17-40. 10.1146/annurev.nutr.19.1.17.View ArticlePubMedGoogle Scholar
- Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, Willett WC, Hu FB: Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr. 2001, 73: 61-67.PubMedGoogle Scholar
- Erdmann LD, Graham RE, Radlo SJ, Knepler PL: Adolescents' energy cost in marching band. Percept Mot Skills. 2003, 97: 639-646.View ArticlePubMedGoogle Scholar
- Bush: In preparation.
- Center for Disease Control and Prevention: Overweight and obesity: Defining overweight and obesity. [http://www.cdc.gov/nccdphp/dnpa/obesity]
- Rosendorff C, Black HR, Cannon CP, Gersh BJ, Gore J, Izzo JL, Kaplan NM, O'Connor CM, O'Gara PT, Oparil S: Treatment of hypertension in the prevention and management of ischemic heart disease: A scientific statement from the American heart association council for high blood pressure research and the councils on clinical cardiology and epidemiology and prevention. Circulation. 2007, 115: 2761-2788. 10.1161/CIRCULATIONAHA.107.183885.View ArticlePubMedGoogle Scholar
- American Diabetes Association: Diagnosis and classification of diabetes mellitus. Diabetes Care. 2007, 30 (Suppl 1): S42-7. 10.2337/dc07-S042.View ArticleGoogle Scholar
- Grundy SM, Cleeman JI, Merz CN, Brewer HB, Clark LT, Hunninghake DB, Pasternak RC, Smith SC, Stone NJ, National Heart, Lung, and Blood Institute, American College of Cardiology Foundation, American Heart Association: Implications of recent clinical trials for the national cholesterol education program adult treatment panel III guidelines. Circulation. 2004, 110: 227-239. 10.1161/01.CIR.0000133317.49796.0E.View ArticlePubMedGoogle Scholar
- Croft JB, Keenan NL, Sheridan DP, Wheeler FC, Speers MA: Waist-to-hip ratio in a biracial population: Measurement, implications, and cautions for using guidelines to define high risk for cardiovascular disease. J Am Diet Assoc. 1995, 95: 60-64. 10.1016/S0002-8223(95)00014-3.View ArticlePubMedGoogle Scholar
- United States Department of Health and Human Services, U.S. Department of Agriculture: Dietary Guidelines for Americans. 2005, Washington, DC: US Government Printing officeGoogle Scholar
- Prochaska JO, Velicer WF: The transtheoretical model of health behavior change. Am J Health Promot. 1997, 12: 38-48.View ArticlePubMedGoogle Scholar
- Greene GW, Rossi SR, Reed GR, Willey C, Prochaska JO: Stages of change for reducing dietary fat to 30% of energy or less. J Am Diet Assoc. 1994, 94: 1105-10. 10.1016/0002-8223(94)91127-4. quiz 1111-2.View ArticlePubMedGoogle Scholar
- Ni Mhurchu C, Margetts BM, Speller VM: Applying the stages-of-change model to dietary change. Nutr Rev. 1997, 55: 10-16.View ArticlePubMedGoogle Scholar
- Hargreaves MK, Schlundt DG, Buchowski MS, Hardy RE, Rossi SR, Rossi JS: Stages of change and the intake of dietary fat in African-American women: Improving stage assignment using the eating styles questionnaire. J Am Diet Assoc. 1999, 99: 1392-1399. 10.1016/S0002-8223(99)00338-7.View ArticlePubMedGoogle Scholar
- Huang TT, Harris KJ, Lee RE, Nazir N, Born W, Kaur H: Assessing overweight, obesity, diet, and physical activity in college students. J Am Coll Health. 2003, 52: 83-86.View ArticlePubMedGoogle Scholar
- American college health association – national college health assessment (ACHA-NCHA) web summary. 2007, [http://www.acha-ncha.org/data_highlights.html]
- Rush EC, Goedecke JH, Jennings C, Micklesfield L, Dugas L, Lambert EV, Plank LD: BMI, fat and muscle differences in urban women of five ethnicities from two countries. Int J Obes (Lond). 2007, 31: 1232-1239. 10.1038/sj.ijo.0803576.View ArticleGoogle Scholar
- Niedhammer I, Bugel I, Bonenfant S, Goldberg M, Leclerc A: Validity of self-reported weight and height in the french GAZEL cohort. Int J Obes Relat Metab Disord. 2000, 24: 1111-1118. 10.1038/sj.ijo.0801375.View ArticlePubMedGoogle Scholar
- Rimm EB, Stampfer MJ, Giovannucci E, Ascherio A, Spiegelman D, Colditz GA, Willett WC: Body size and fat distribution as predictors of coronary heart disease among middle-aged and older US men. Am J Epidemiol. 1995, 141: 1117-1127.PubMedGoogle Scholar
- Havas S, Heimendinger J, Reynolds K, Baranowski T, Nicklas TA, Bishop D, Buller D, Sorensen G, Beresford SA, Cowan A: 5 a day for better health: A new research initiative. J Am Diet Assoc. 1994, 94: 32-36. 10.1016/0002-8223(94)92037-0.View ArticlePubMedGoogle Scholar
- Heimendinger J, Chapelsky D: The national 5 A day for better health program. Adv Exp Med Biol. 1996, 401: 199-206.View ArticlePubMedGoogle Scholar
- Ma Y, Olendzki B, Chiriboga D, Hebert JR, Li Y, Li W, Campbell M, Gendreau K, Ockene IS: Association between dietary carbohydrates and body weight. Am J Epidemiol. 2005, 161: 359-367. 10.1093/aje/kwi051.PubMed CentralView ArticlePubMedGoogle Scholar
- Jackson R, Scragg R, Beaglehole R: Alcohol consumption and risk of coronary heart disease. BMJ. 1991, 303: 211-216.PubMed CentralView ArticlePubMedGoogle Scholar
- Dufour MC: Risks and benefits of alcohol use over the life span. Alcohol Health Res World. 1996, 20: 145-151.Google Scholar
- Nelson TF, Wechsler H: Alcohol and college athletes. Med Sci Sports Exerc. 2001, 33: 43-47.View ArticlePubMedGoogle Scholar
- Sung KC, Kim SH, Reaven GM: Relationship among alcohol, body weight, and cardiovascular risk factors in 27,030 korean men. Diabetes Care. 2007, 30: 2690-2694. 10.2337/dc07-0315.View ArticlePubMedGoogle Scholar
- Midcourse review healthy people 2010. [http://www.healthypeople.gov/data/midcourse/pdf/fa07.pdf]
- American College Health Association (ACHA): Concept for a healthy campus 2010 supplement. [http://www.csupomona.edu/~jvgrizzell/hc2010/hc2010_mission_retention_objs.pdf]
- Ridker PM, Rifai N, Cook NR, Bradwin G, Buring J: Non-HDL Cholesterol, Apolipoproteins A-1 and B100, standard lipid measures, lipid ratios, and CRP as risk factors for cardiovascular disease in women. JAMA. 2005, 294: 326-333. 10.1001/jama.294.3.326.View ArticlePubMedGoogle Scholar
- Natarajan S, Glick H, Criqui M, Horoqitz D, Lipsitz SR, Kinosian B: Cholesterol measures to identify and treat individuals at risk for coronary heart disease. Am J Prev Med. 2003, 25 (1): 50-57. 10.1016/S0749-3797(03)00092-8.View ArticlePubMedGoogle Scholar
- Conroy RM, Pyorala K, Fitzgerald AP: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project. Eur Heart J. 2003, 24: 987-1003. 10.1016/S0195-668X(03)00114-3.View ArticlePubMedGoogle Scholar
- Huang TT, Kempf AM, Strother ML, Li C, Lee RE, Harris KJ, Kaur H: Overweight and components of the metabolic syndrome in college students. Diabetes Care. 2004, 27: 3000-3001. 10.2337/diacare.27.12.3000.View ArticlePubMedGoogle Scholar
- Natarajan L, Flatt SW, Sun X, Gamst AC, Major JM, Rock CL, Al-Delaimy W, Thomson CA, Newman VA, Pierce JP, Women's Healthy Eating and Living Study Group: Validity and systematic error in measuring carotenoid consumption with dietary self-report instruments. Am J Epidemiol. 2006, 163: 770-778. 10.1093/aje/kwj082.View ArticlePubMedGoogle Scholar
- Trabulsi J, Schoeller DA: Evaluation of dietary assessment instruments against doubly labeled water, a biomarker of habitual energy intake. Am J Physiol Endocrinol Metab. 2001, 281: E891-9.PubMedGoogle Scholar
- Racette SB, Deusinger SS, Strube MJ, Highstein GR, Deusinger RH: Weight changes, exercise, and dietary patterns during freshman and sophomore years of college. J Am Coll Health. 2005, 53: 245-251. 10.3200/JACH.53.6.245-251.View ArticlePubMedGoogle Scholar
- American College Health Association (ACHA) Guidelines: Standards for practice for health promotion in higher education, Second Edition, 2004. [http://www.acha.org/about_acha/ctfs/com_hpshe.cfm]
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