Back

Dietary practices and associated factors among Debre Berhan university students in Ethiopia

Urgie, B. M.; Tesfahun, E.; Melaku, E. E.; Mohammed, F. Z.

2025-07-25 nutrition
10.1101/2025.07.24.25332094 medRxiv
Show abstract

BackgroundThe transition of young people from high school to university is associated with increased autonomy, including dietary choices. However, data on this issue is scarce in the current study area. PurposeThis study aimed to identify dietary practices and associated factors among university students at Debre Berhan University, Ethiopia, 2023. MethodsAn institution-based cross-sectional study was carried out to select 771 students using a cluster sampling technique. Then, data were collected, loaded into Epi data version 3.1 and analyzed via SPSS version 20.0. Descriptive analyses as well as bi-variable and multivariable logistic regression analyses were performed to assess the strength of the associations between the variables. Adjusted odds ratios with 95% confidence intervals and p-values < 0.05 were used to assess the level of significance. ResultsIn this study, the prevalence of poor dietary practice was high at 77.3% at 95% CI (74.4-80.2). After adjusting the variables using logistic regression, variables such as using university meal service [AOR = 0.07, 95% CI (0.02, 0.22)], monthly pocket money < 500 birr [AOR = 0.29, 95% CI (0.13, 0.64), university cafe as a main source of food [AOR = 0.02, 95% CI (0.01, 0.05)], and 1-2 times eating of food out of student cafeteria [AOR = 0.20, 95% CI (0.06-0.68)] were associated with poor dietary practice. ConclusionPoor dietary practice among university students was high in this study. Therefore, the government and other responsible bodies should try to increase institutional-based nutritional education, create awareness, and improve campus meals containing different food groups.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.