Sarah Colby1, Wenjun Zhou2, Morgan F Sowers3, Karla Shelnutt4, Melissa D Olfert5, Jesse Morrell6, Mallory Koenings7, Tandalayo Kidd8, Tanya M Horacek9, Geoffrey W Greene10, Onikia Brown11, Adrienne A White12, Sharon L Hoerr13, Carol Byrd-Bredbenner14, Kendra K Kattelmann15. 1. Associate Professor, University of Tennessee, Knoxville, Department of Nutrition, Knoxville, TN;, Email: scolby1@utk.edu. 2. Assistant Professor, University of Tennessee, Knoxville, Department of Business Analytics and Statistics, Knoxville, TN. 3. University of Tennessee, Knoxville, Department of Nutrition, Knoxville, TN. 4. Associate Professor, University of Florida, Department of Family, Youth, and Community Science, Gainesville, FL. 5. Associate Professor, West Virginia University, Department of Human Nutrition and Foods, Morgantown, WV. 6. Principal Lecturer, University of New Hampshire, Department of Molecular, Cellular, and Biomedical Sciences, Durham, NH. 7. Program Specialist, United States Department of Agriculture, National Institute of Food and Agriculture, Division of Nutrition, Institute of Food Safety and Nutrition, Washington, DC. 8. Associate Professor, Kansas State University, Department of Food, Nutrition, Dietetics and Health, Manhattan, KS. 9. Professor, Syracuse University, Department of Nutrition Science and Dietetics, Syracuse, NY. 10. Professor, University of Rhode Island, Department of Nutrition and Food Sciences, Kingston, RI. 11. Assistant Professor, Auburn University, Department of Nutrition, Dietetics, and Hospitality Management, Auburn, AL. 12. Professor, University of Maine, School of Food and Agriculture, Orono, ME. 13. Professor, Michigan State University, Department of Food Science and Human Nutrition, East Lansing, MI. 14. Professor, Rutgers University, Department of Nutritional Sciences, New Brunswick, NJ. 15. Distinquised Professor, South Dakota State University, Department of Health and Nutritional Sciences, Brookings, SD.
Abstract
OBJECTIVE: The study purpose was to identify clusters of weight-related behaviors by sex in a college student populations. METHODS: We conducted secondary data analysis from online surveys and physical assessments collected in Project Young Adults Eating and Active for Health (YEAH) with a convenience sample of students on 13 college campuses in the United States. We performed 2-step cluster analysis by sex to identify subgroups with homogeneous characteristics and behaviors. We used 8 derivation variables: healthy eating; eating restraints; external cues; stress; fruit/vegetable intake; calories from fat; calories from sugar-sweetened beverages; and physical activity. Contribution of derivation variables to clusters was analyzed with a MANOVA test. RESULTS: Data from 1594 students were included. Cluster analysis revealed 2-clusters labeled "Healthful Behavior" and "At-risk" for males and females with an additional "Laid Back" cluster for males. "At-risk" clusters had the highest BMI, waist circumference, elevated health risk, and stress and least healthy dietary intake and physical activity. The "Laid Back" cluster had normal weights and the lowest restrained eating, external cues sensitivity, and stress. CONCLUSION: Identified differences in characteristics and attitudes towards weight-related behaviors between males and females can be used to tailor weight management programs.
OBJECTIVE: The study purpose was to identify clusters of weight-related behaviors by sex in a college student populations. METHODS: We conducted secondary data analysis from online surveys and physical assessments collected in Project Young Adults Eating and Active for Health (YEAH) with a convenience sample of students on 13 college campuses in the United States. We performed 2-step cluster analysis by sex to identify subgroups with homogeneous characteristics and behaviors. We used 8 derivation variables: healthy eating; eating restraints; external cues; stress; fruit/vegetable intake; calories from fat; calories from sugar-sweetened beverages; and physical activity. Contribution of derivation variables to clusters was analyzed with a MANOVA test. RESULTS: Data from 1594 students were included. Cluster analysis revealed 2-clusters labeled "Healthful Behavior" and "At-risk" for males and females with an additional "Laid Back" cluster for males. "At-risk" clusters had the highest BMI, waist circumference, elevated health risk, and stress and least healthy dietary intake and physical activity. The "Laid Back" cluster had normal weights and the lowest restrained eating, external cues sensitivity, and stress. CONCLUSION: Identified differences in characteristics and attitudes towards weight-related behaviors between males and females can be used to tailor weight management programs.
Authors: Matthew J Fagan; Katie M Di Sebastiano; Wei Qian; Scott T Leatherdale; Guy Faulkner Journal: Int J Environ Res Public Health Date: 2021-04-07 Impact factor: 3.390
Authors: Andrea J Hanson; Kendra K Kattelmann; Lacey A McCormack; Wenjun Zhou; Onikia N Brown; Tanya M Horacek; Karla P Shelnutt; Tandalayo Kidd; Audrey Opoku-Acheampong; Lisa D Franzen-Castle; Melissa D Olfert; Sarah E Colby Journal: Int J Environ Res Public Health Date: 2019-07-11 Impact factor: 3.390