Consumption of Energy Drinks among Italian University students: a cross-sectional multicenter study
Abstract
The aim of the study was to evaluate the caffeinated Energy Drinks (EDs) consumption among a large sample of Italian undergraduates and its association with some of the major lifestyle risk factors. Students attending twelve public Italian universities were involved between October 2021 and May 2022. Information on socio-demographic characteristics, ED consumption, and on health-related behaviors of participants was collected by the use of a web-based questionnaire. A total of 2165 students participated in the study and 15.2% of them reported having used caffeinated EDs in the last six months, mainly once a month (41.5%). In comparison with non-users, ED users showed a higher proportion of males (p < 0.001) and a higher father’s educational level (p = 0.003), came mainly from Northern universities (p = 0.004) and life sciences degree courses (p < 0.001). Besides, ED users reported higher BMI values (p = 0.003), more particular dietary regimens (p < 0.001), higher levels of weekly moderate–vigorous physical activity (p < 0.001) and participation in sports (p < 0.001) and in team sports (p = 0.003), and higher proportion of smokers (p < 0.001) and alcohol drinkers (p = 0.005). ED use was negatively related with female gender (OR 0.546; 95% CI 0.374–0.798), the Mediterranean diet (OR 0.587; 95% CI 0.362–0.951) and coming from the center of Italy (OR 0.500; 95% CI 0.275–0.909) and positively associated with tobacco smoke (OR 1.712; 95% CI 1.176–2.492) and participation in a team…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
Read the paper
Related studies
- The Matrisome: In Silico Definition and In Vivo Characterization by Proteomics of Normal and Tumor Extracellular Matrices
- The Escherichia coli MG1655 in silico metabolic genotype: Its definition, characteristics, and capabilities
- In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling
- In silico prediction of splice-altering single nucleotide variants in the human genome
- In silico clinical trials: how computer simulation will transform the biomedical industry
- In silico optimization of a guava antimicrobial peptide enables combinatorial exploration for peptide design
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.