Age, skin color, self-rated health, and depression associated with co-occurrence of obesogenic behaviors in university students
a cross-sectional study
Abstract
BACKGROUND: The university context plays an important role in the health-disease process since students are potentially vulnerable to obesogenic behaviors that can influence long-term health. OBJECTIVE: To estimate the prevalence of and factors associated with the co-occurrence of obesogenic behaviors among university students. DESIGN AND SETTING: This was a cross-sectional study at a Brazilian public university. METHODS: This study was conducted with all university students in the first and second semesters of 2019 at Universidade Federal de Ouro Preto, Minas Gerais, Brazil. Data were collected between April and September 2019, using a self-administered questionnaire. The outcome was the co-occurrence of obesogenic behaviors, measured as the sum of three risk behaviors: inadequate eating practices, leisure-time physical inactivity, and sedentary behavior. A Venn diagram was used to evaluate the simultaneous occurrence of risk behaviors. Pearson’s chi-square test and multivariate logistic regression were used for statistical analyses. RESULTS: A total of 351 students participated in the study. Inadequate eating practices constituted the most prevalent isolated risk behavior (80.6%), which was also the most prevalent when combined with sedentary behavior (23.6%). University students aged 20 years or younger, with non-white skin color, poor self-rated health, and symptoms of depression had increased chances of simultaneous occurrence of obesogenic behaviors. CONCLUSION: These findings highlight the importance of developing and implementing actions to reduce combined obesogenic behaviors in the university environment. Institutions should focus on creating an environment that promotes health-protective behaviors such as physical activity and healthy eating.
Downloads
References
Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6-10. PMID: 30253139; https://doi.org/10.1016/j.metabol.2018.09.005.
Maia EG, Mendes LL, Pimenta AM, Levy RB, Claro RM. Cluster of risk and protective factors for obesity among Brazilian adolescents. Int J Public Health. 2018;63(4):481-90. PMID: 29143071; https://doi.org/10.1007/s00038-017-1053-7.
Nimptsch K, Konigorski S, Pischon T. Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine. Metabolism. 2019;92:61-70. PMID: 30586573; https://doi.org/10.1016/j.metabol.2018.12.006.
Di Cesare M, Sorić M, Bovet P, et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med. 2019:17(1): 212. PMID: 31760948; https://doi.org/10.1186/s12916-019-1449-8.
Streb AR, Duca GFD, Silva RPD, Benedet J, Malta DC. Simultaneidade de comportamentos de risco para a obesidade em adultos das capitais do Brasil [Simultaneity of risk behaviors for obesity in adults in the capitals of Brazil]. Cien Saude Colet. 2020;25(8):2999-3007. PMID: 32785536; https://doi.org/10.1590/141381232020258.27752018.
Malta DC, Silva AG, Cardoso LSM, et al. Noncommunicable diseases in the Journal Ciência & Saúde Coletiva: a bibliometric study. Cien Saude Colet. 2020;25(12):4757-69. PMID: 33295499; https://doi.org/10.1590/1413812320202512.16882020.
Hardy LL, Grunseit A, Khambalia A, et al. Co-occurrence of obesogenic risk factors among adolescents. J Adolesc Health. 2012;51(3):265-71. PMID: 22921137; https://doi.org/10.1016/j.jadohealth.2011.12.017.
Uddin R, Lee EY, Khan SR, Tremblay MS, Khan A. Clustering of lifestyle risk factors for non-communicable diseases in 304,779 adolescents from 89 countries: A global perspective. Prev Med. 2020;131:105955. PMID: 31862205; https://doi.org/10.1016/j.ypmed.2019.105955.
da Silva Brito AL, Hardman CM, de Barros MV. Prevalência e fatores associados à simultaneidade de comportamentos de risco à Saúde em adolescente [Prevalence and factors associated with the co-occurrence of health risk behaviors in adolescents]. Rev Paul Pediatr. 2015;33(4):423-30. PMID: 26298656; https://doi.org/10.1016/j.rpped.2015.02.002.
Bista B, Dhungana RR, Chalise B, Pandey AR. Prevalence and determinants of non-communicable diseases risk factors among reproductive aged women of Nepal: Results from Nepal Demographic Health Survey 2016. PloS One. 2020;15(3):0218840. PMID: 32176883; https://doi.org/10.1371/journal.pone.0218840.
de Winter AF, Visser L, Verhulst FC, Vollebergh WAM, Reijneveld S. Longitudinal patterns and predictors of multiple health risk behaviors among adolescents: The TRAILS study. Prev Med. 2016;84:76-82. PMID: 26656404; https://doi.org/10.1016/j.ypmed.2015.11.028.
Meader N, King K, Moe-Byrne T, et al. A systematic review on the clustering and co-occurrence of multiple risk behaviours. BMC Public Health. 2016;16:657. PMID: 27473458; https://doi.org/10.1186/s12889-016-3373-6.
Champion KE, Mather M, Spring B, et al. Clustering of Multiple Risk Behaviors Among a Sample of 18-Year-Old Australians and Associations With Mental Health Outcomes: A Latent Class Analysis. Front Public Health. 2018;6:135. PMID: 29868543; https://doi.org/10.3389/fpubh.2018.00135.
Laska MN, Pasch KE, Lust K, Story M, Ehlinger ED. Latent class analysis of lifestyle characteristics and health risk behaviors among college youth. Prev Sci. 2009;10(4):376-86. PMID: 19499339; https://doi.org/10.1007/s11121-0090140-2.
Olatona FA, Onabanjo OO, Ugbaja RN, Nnoaham KE, Adelekan DA. Dietary habits and metabolic risk factors for non-communicable diseases in a university undergraduate population. J Health Popul Nutr. 2018;37(1):21. PMID: 30115131; https://doi.org/10.1186/s41043-018-0152-2.
Busse H, Buck C, Stock C, et al. Engagement in Health Risk Behaviours before and during the COVID-19 Pandemic in German University Students: Results of a Cross-Sectional Study. Int J Environ Res Public Health. 2021;18(4):1410. PMID: 33546344; https://doi.org/10.3390/ijerph18041410.
World Health Organization. Global status repor t on noncommunicable diseases 2010. Geneva: World Health Organization; 2011. Available from: https://apps.who.int/iris/handle/10665/44579. Accessed in 2022 (May 16).
Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Guia alimentar para a população brasileira/Ministério da Saúde, Secretaria de Atenção à Saúde, Departamento de Atenção Básica. - 2.ed. 1. reimpr. - Brasília: Ministério da Saúde, 2014. Available from: https://bvsms.saude.gov.br/bvs/publicacoes/guia_alimentar_populacao_brasileira_2ed.pdf. Accessed in 2022 (May 04).
Gabe KT, Jaime PC. Development and testing of a scale to evaluate diet according to the recommendations of the Dietary Guidelines for the Brazilian Population. Public Health Nutr. 2019;22(5):785-96. PMID: 30744711; https://doi.org/10.1017/S1368980018004123.
Brasil. Ministério da Saúde. Secretaria de Atenção Primária à Saúde. Departamento de Atenção Básica. Teste “Como está a sua alimentação?”. Brasília: Ministério da Saúde; 2018. Available from: http://189.28.128.100/dab/docs/portaldab/publicacoes/guiadebolso_folder.pdf. Accessed in 2022 (May 04).
Gabe KT, Jaime PC. Dietary practices in relation to the Dietary guidelines for the Brazilian population: associated factors among Brazilian adults, 2018. Epidemiol Serv Saude. 2020;29(1):e2019045. PMID: 32215534; https://doi.org/10.5123/S1679-49742020000100019.
BRASIL. Ministério da Saúde. Secretaria de Vigilância em Saúde. Departamento de Análise em Saúde e Vigilância de Doenças Não Transmissíveis. Vigitel Brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2019 [recurso eletônico]/Ministério da Saúde, Secretaria de Vigilância em Saúde, Departamento de Análise em Saúde e Vigilância de Doenças não Transmissíveis. Brasília: Ministério da Saúde; 2020. Available from:https://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2019_vigilancia_fatores_risco.pdf. Accessed in 2022 (Mar 10).
Silva RMA, Andrade ACS, Caiaffa WT, Medeiros DSD, Bezerra VM. National Adolescent School-based Health Survey - PeNSE 2015: Sedentary behavior and its correlates. PLoS One. 2020;15(1):e0228373. PMID: 31999792; https://doi.org/10.1371/journal.pone.0228373.
World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. (WHO Technical Report Series n.894). Geneva, Switzerland: World Health Organization, 2000. Available from: https://apps.who.int/iris/handle/10665/42330. Accessed in 2022 (May 04).
de Onis M, Onyango AW, Borghi E, et al. Development of a WHO growth reference for school-aged children and adolescents. Bull World Health Organ. 2007;85(9):660-7. PMID: 18026621; https://doi.org/10.2471/blt.07.043497.
Barbosa BCRB, Guimarães NS, Paula W, Meireles AL. Práticas alimentares de estudantes universitários da área da saúde, de acordo com as recomendações do guia alimentar para a população brasileira. Demetra. 2020;15:e45855. https://doi.org/10.12957/demetra.2020.45855.
Vignola RC, Tucci AM. Adaptation and validation of the depression, anxiety and stress scale (DASS) to Brazilian Portuguese. J Affect Disord. 2014;155:104-9. PMID: 24238871; https://doi.org/10.1016/j. jad.2013.10.031.
Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics. 2015;16(1):169. PMID: 25994840; https://doi.org/10.1186/s12859-015-0611-3.
Hulsen T, de Vlieg J, Alkema W. BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams. BMC Genomics. 2008;9:488. PMID: 18925949; https://doi.org/10.1186/1471-2164-9-488.
Hilger J, Loerbroks A, Diehl K. Eating behaviour of university students in Germany: Dietary intake, barriers to healthy eating and changes in eating behaviour since the time of matriculation. Appetite. 2017;109:100-7. PMID: 27864073; https://doi.org/10.1016/j. appet.2016.11.016.
Abdelhafez AI, Akhter F, Alsultan AA, Jalal SM, Ali A. Dietary Practices and Barriers to Adherence to Healthy Eating among King Faisal University Students. Int J Environ Res Public Health. 2020;17(23):8945. PMID: 33271893; https://doi.org/10.3390/ijerph17238945.
Soriano-Ayala E, Amutio A, Franco C, Mañas I. Promoting a healthy lifestyle through mindfulness in university students: a randomized controlled trial. Nutrients. 2020;12(8):2450. PMID: 32824061; https://doi.org/10.3390/nu12082450.
Souza RK, Backes V. Autopercepção do consumo alimentar e adesão aos Dez Passos para Alimentação Saudável entre universitários de Porto Alegre, Brasil [Self-perception of food consumption and observance of the Ten Steps to Healthy Eating among university students in Porto Alegre, Brazil]. Cienc Saude Colet. 2020;25(11):4463-72. PMID: 33175054; https://doi.org/10.1590/1413-812320202511.35582018.
Syed NK, Syed MH, Meraya AM, et al. The association of dietary behaviors and practices with overweight and obesity parameters among Saudi university students. PloS One. 2020;15(9):e0238458. PMID: 32911507; https://doi.org/10.1371/journal.pone.0238458.
Ferreira NL, Claro RM, Mingoti SA, Lopes ACS. Coexistence of risk behaviors for being overweight among Brazilian adolescents. Prev Med. 2017;100:135-42. PMID: 28412185; https://doi.org/10.1016/j.ypmed.2017.04.018.
Jezewska-Zychowicz M, Gębski J, Guzek D, et al. The associations between dietary patterns and sedentary behaviors in Polish adults (LifeStyle study). Nutrients. 2018;10(8):1004. PMID: 30071656; https://doi.org/10.3390/nu10081004.
Rocha BMC, Goldbaum M, César CLG, Stopa SR. Sedentary behavior in the city of São Paulo, Brazil: ISA-Capital 2015. Rev Bras Epidemiol. 2019;22:e190050. PMID: 31460665; https://doi.org/10.1590/1980-549720190050.
Cureau FV, Duarte PM, Teixeira FS. Simultaneidade de comportamentos de risco para doenças crônicas não transmissíveis em universitários de baixa renda de uma cidade do Sul do Brasil. Cad Saude Colet. 2019;27(3):316-24. https://doi.org/10.1590/1414-462X201900030178.
da Silva TPR, Matozinhos FP, Gratão LHA, et al. The coexistence of obesogenic behaviors among Brazilian adolescents and their associated factors. BMC Public Health. 2022;22(1):1290. PMID: 35788201; https://doi.org/10.1186/s12889-022-13708-6.
Fleary SA, Freund KM. Social Disparities in Obesogenic Behaviors in Adolescents. J Racial Ethn Health Disparities. 2018;5(1):24-33. PMID: 28130748. https://doi.org/10.1007/s40615-017-0339-z.
Barreto SM, Figueiredo RC. Chronic diseases, self-perceived health status and health risk behaviors: gender differences. Rev Saude Publica. 2009;43 Suppl 2:38-47. PMID: 19936497; https://doi.org/10.1590/S0034-89102009000900006.
Francisco PMSB, Assumpção D, Borim FSA, Senicato C, Malta DC. Prevalence and co-occurrence of modifiable risk factors in adults and older people. Rev Saude Publica. 2019;53:86. PMID: 31644769; https://doi.org/10.11606/s1518-8787.2019053001142.
Barros MB, Zanchetta LM, Moura EC, Malta DC. Self-rated health and associated factors, Brazil, 2006. Rev Saude Publica. 2009;43 Suppl 2:27-37. PMID: 19936496; https://doi.org/10.1590/S0034-89102009000900005.
Jao NC, Robinson LD, Kelly PJ, Ciecierski CC, Hitsman B. Unhealthy behavior clustering and mental health status in United States college students. J Am Coll Health. 2019;67(8):790-800. PMID: 30485154; https://doi.org/10.1080/07448481.2018.1515744.
Michels N. Poor Mental Health Is Related to Excess Weight via Lifestyle: A Cross-Sectional Gender- and Age-Dependent Mediation Analysis. Nutrients. 2021;13(2):406. PMID: 33525320; https://doi.org/10.3390/nu13020406.
Sousa TF, Loch MR, Lima AJO, Franco DC, Barbosa AR. Coocorrência de fatores de risco à saúde em universitários de uma instituição de ensino superior brasileira [Co-occurrence of risk factors to health among university students of a Brazilian tertiary education institution]. Cienc Saude Colet. 2021;26(2):729-38. PMID: 33605347; https://doi.org/10.1590/1413-81232021262.07062019.
