Associations between dietary patterns,lifestyle factors,and body mass index among female nutrition students: A classification and regression tree analysis
Restricted accessResearch articleFirst published online 2025
Associations between dietary patterns,lifestyle factors,and body mass index among female nutrition students: A classification and regression tree analysis
Background: The university period is critical for establishing positive lifestyle habits that can reduce long-term risk for chronic diseases. Aim: To analyze the associations between dietary patterns, lifestyle factors, and body mass index (BMI) among female university students enrolled in the Nutrition and Dietetics program at the University of Antioquia. Methods: A cross-sectional analysis was conducted with 251 female students (22.0 ± 2.8 years) selected via proportion-based random sampling. Sociodemographic, anthropometric, lifestyle, and food frequency data were collected using standardized questionnaires administered by trained nutritionists. A Dietary Index (DI) was calculated from 35 food groups to assess diet quality. Kendall's Tau-C test was used to evaluate associations between specific food groups and BMI. Additionally, Classification and Regression Tree (CART) analysis was applied to identify the variables most strongly associated with BMI, modeled both as categorical and continuous outcomes. Results: Students with higher food spending (p = 0.013), a healthy DI (p = 0.015), and enrollment in the contributory health system (p = 0.027) were more likely to have a normal BMI. CART analysis revealed that better DI scores and reduced screen time were associated with lower BMI, with the DI emerging as the primary discriminating variable. Individual food group analysis showed significant associations between BMI and frequent consumption of high-sodium sauces (p = 0.005; r = 0.142), canned foods (p = 0.034; r = −0.111), and sugar-sweetened beverages (p = 0.003; r = −0.113), though these correlations were weak. Conclusion: University settings should prioritize interventions such as healthy food services, nutrition education workshops, and accessible recreational facilities to promote healthy lifestyles among students. These strategies may help reduce the risk of overweight and obesity and support long-term health.
AlhakbanyMAAlzamilHAAlabdullatifWA, et al. (2018) Lifestyle habits in relation to overweight and obesity among Saudi women attending health science colleges. Journal of Epidemiology and Global Health8(1–2): 13–19.
2.
AlmoraieNMAlothmaniNMAlomariWD, et al. (2025) Addressing nutritional issues and eating behaviours among university students: A narrative review. Nutrition Research Reviews38(1): 53–68. 10.1017/S0954422424000088.
3.
AlqassimiSElmakkiEAreeshiAS, et al. (2024) Overweight, obesity, and associated risk factors among students at the faculty of medicine, Jazan University. Medicina (Lithuania)60(6): 1–17.
4.
BedeFCumberSNNkfusaiCN, et al. (2020) Dietary habits and nutritional status of medical school students: The case of three state universities in Cameroon. Pan African Medical Journal35: 1–10.
5.
BenaichSMehdadSAndaloussiZ, et al. (2021) Weight status, dietary habits, physical activity, screen time and sleep duration among university students. Nutrition and Health27(1): 69–78.
6.
BernardoGLJomoriMMFernandesAC, et al. (2017) Nutrition and Culinary in the Kitchen Program: A randomized controlled intervention to promote cooking skills and healthy eating in university students – study protocol. Nutrition Journal16(1): 1143. 10.1186/s12937-017-0305-y.
7.
BousheyCArdJBazzanoL, et al. (2020) Dietary patterns and growth, size, body composition, and/or risk of overweight or obesity: A systematic review. USDA Nutrition Evidence Systematic Reviews: 1–79. https://doi.org/10.52570/NESR.DGAC2020.SR0101.
8.
BuendíaRZambranoMDíazÁ, et al. (2014) Puntos de corte de perímetro de cintura para el diagnóstico de obesidad abdominal en población colombiana usando bioimpedanciometría como estándar de referencia. Revista Colombiana de Cardiologia23(1): 19–25.
9.
CastroOBennieJVergeerI, et al. (2020) How sedentary are university students? A systematic review and meta-analysis. Prevention Science21(3): 332–343.
10.
DeliensTClarysPBourdeaudhuijID, et al. (2013) Weight, socio-demographics, and health behaviours. 1–9.
11.
Deossa-RestrepoGSegura-BujánMRestrepo-BetancurF (2021) Revista Habanera de Ciencias Médicas Dietetics university students from Mexico and Colombia. Revista Habanera De Ciencias Médicas20(4): 1–7.
12.
De SouzaRJMenteAMaroleanuA, et al. (2015) Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: Systematic review and meta-analysis of observational studies. BMJ (Online)351: 1–16.
13.
DetopoulouPDedesVSykaD, et al. (2023) Relation of minimally processed foods and ultra-processed foods with the Mediterranean diet score, time-related meal patterns and waist circumference: Results from a cross-sectional study in university students. International Journal of Environmental Research and Public Health20(4): 1–16.
14.
Díaz-TorrenteXQuintiliano-ScarpelliD (2020) Dietary patterns of breakfast consumption among Chilean University Students. Nutrients12(2): 552. 10.3390/nu12020552.
15.
DoggettAChaurasiaAChaputJP, et al. (2023) Using classification and regression trees to model missingness in youth BMI, height and body mass data. Health Promotion and Chronic Disease Prevention in Canada43(5): 231–242.
16.
EnglishLKArdJDBaileyRL, et al. (2021) Evaluation of dietary patterns and all-cause mortality A systematic review. JAMA Network Open4(8): e2122277.
17.
Foscarini-CraggsPLoweRLeeM (2021) Changes over time in the relationship between weight, body fat, motivation, impulsivity and eating behaviour. BMC Public Health21(1): 1–12.
18.
GómezGFisbergRNogueira PrevidelliÁ, et al. (2019) Diet quality and diet diversity in eight Latin American countries: Results from the Latin American study of nutrition and health (ELANS). Nutrients11(7): 1605.
19.
HastieTTibshianiRFriedmanJ (2009) Analysis on critical nodes in controlling complex networks using dominating sets. Springer: 649–654. 10.1109/SITIS.2013.106.
20.
KabirAMiahSIslamA (2018) Factors influencing eating behavior and dietary intake among resident students in a public university in Bangladesh: A qualitative study. PLoS ONE13(6): 1–17.
21.
KlusowskiJMTianPM (2024) Large scale prediction with decision trees. Journal of the American Statistical Association119(545): 525–537.
22.
LaneMMGamageEDuS, et al. (2024) Ultra-processed food exposure and adverse health outcomes: Umbrella review of epidemiological meta-analyses. BMJ384: e077310. 10.1136/bmj-2023-077310.
23.
LongWJ (1993) A comparison of logistic regression to decision-tree induction in a medical domain 1 Introduction 2 methodology. Health Care26: 74–97.
24.
MaYHeFJMacgregorGA (2015) High salt intake: Independent risk factor for obesity?Hypertension66(4): 843–849.
25.
McCartneyDDesbrowBKhalesiS, et al. (2021) Analysis of dietary intake, diet cost and food group expenditure from a 24-h food record collected in a sample of Australian university students. Nutrition and Dietetics78(2): 174–182.
26.
MemonARGuptaCCCrowtherME, et al. (2021) Sleep and physical activity in university students: A systematic review and meta-analysis. Sleep Medicine Reviews58: 101482.
27.
Meza-MirandaERParra-SotoSLDurán-AgüeroS, et al. (2024) Body weight in relation to hours of sleep, and physical activity in Latin American university students during the COVID-19 pandemic. Journal of American College Health72(6): 1753–1758.
28.
Monsalve ÁlvarezJMGonzález ZapataLI (2011) Diseño de un cuestionario de frecuencia para evaluar ingesta alimentaria en la universidad de Antioquia, Colombia. Nutricion Hospitalaria26(6): 1333–1344.
29.
Nacher JC and Akutsu T (2013) Analysis on critical nodes in controlling complex networks using dominating sets. In: 2013 International Conference on Signal-Image Technology & Internet-Based Systems, Kyoto, Japan, pp. 649–654. https://doi.org/10.1109/SITIS.2013.106.
30.
NewtonJDoorisMWillsJ (2016) Healthy universities: An example of a whole-system health-promoting setting. Global Health Promotion23(February): 57–65.
31.
Nuñez-LeyvaRELozano-LópezTECalizaya-MillaYE, et al. (2022) Excess weight and body fat percentage associated with waist circumference as a cardiometabolic risk factor in university students.Scientifica2022: 1–8.10.1155/2022/1310030.
32.
PhelpsNHSingletonRKZhouB, et al. (2024) Worldwide trends in underweight and obesity from 1990 to 2022: A pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults. The Lancet403(10431): 1027–1050.
33.
Pico FonsecaSQuiroz MoraCHernández CarrilloM, et al. (2021) Relationship between pattern of food consumption and body composition in university students: A cross-sectional study. Nutricion Hospitalaria38(1): 100–108.
34.
PlotnikoffRCCostiganSAWilliamsRL, et al. (2015) Effectiveness of interventions targeting physical activity, nutrition and healthy weight for university and college students: A systematic review and meta-analysis. International Journal of Behavioral Nutrition and Physical Activity12(1): 1–10.
35.
ReynoldsAMannJCummingsJ, et al. (2019) Carbohydrate quality and human health: A series of systematic reviews and meta-analyses. The Lancet393(10170): 434–445.
36.
SchwingshacklLSchwedhelmCHoffmannG, et al. (2017) Food groups and risk of all-cause mortality: A systematic review and meta-analysis of prospective studies. American Journal of Clinical Nutrition105(6): 1462–1473.
37.
Sinha RK (2024) Book review: Christoph Molnar. 2020. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. Metamorphosis 23(1), 92–93. https://doi.org/10.1177/09726225241252009.
38.
SprakeEFRussellJMCecilJE, et al. (2018) Dietary patterns of university students in the UK: A cross-sectional study. Nutrition Journal17(1): 1–17.
39.
TremblayAPérusseLBertrandC, et al. (2023) Effects of sodium intake and cardiorespiratory fitness on body composition and genetic susceptibility to obesity: Results from the Quebec family study. British Journal of Nutrition129(1): 77–86.
40.
TrujillanoJServiáLBadiaM, et al. (2025) Methodological review of classification trees for risk stratification: An application example in the obesity paradox. Nutrients17(11): 1–17.
41.
WellsJCSawayaALWibaekR, et al. (2020) The double burden of malnutrition: Aetiological pathways and consequences for health. The Lancet395(10217): 75–88.
42.
World Health Organization. (2000) Obesity: Preventing and Managing the Global Epidemic. In WHO Library Cataloguing-in-Publication Data. vol. 1Geneva, Switzerland: Report of a WHO consultation. World Health Organization, 1–268. 10.1007/BF00400469.
43.
YahiaNWangDRapleyM, et al. (2016) Assessment of weight status, dietary habits and beliefs, physical activity, and nutritional knowledge among university students. Perspectives in Public Health136(4): 231–244.
44.
ZhangXWangJLiJ, et al. (2018) A positive association between dietary sodium intake and obesity and central obesity: Results from the national health and nutrition examination survey 1999-2006. Nutrition Research55: 33–44.
