ArticleCureus2025
Smartphone Addiction Among Medical Students and Its Implications on Sleep Quality and BMI: A Cross-Sectional Study.
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Background Smartphone addiction has emerged as a common behavioral problem among medical students, potentially affecting sleep quality and body mass index (BMI). This study aimed to assess the prevalence of smartphone addiction and its association with sleep quality and BMI among medical students. Methods This cross-sectional study was conducted among 391 medical students at the Government Medical College and Hospital, Thiruvallur, from March to June 2025. Data were collected using a structured questionnaire, including the Smartphone Addiction Scale-Short Version (SAS-SV) and the Pittsburgh Sleep Quality Index (PSQI). BMI was calculated using self-reported height and weight. Smartphone addiction was defined as SAS-SV score ≥ 31 for males and ≥ 33 for females; poor sleep quality was defined as PSQI > 5. Statistical analyses included chi-square tests, t-tests, analysis of variance (ANOVA), Pearson's correlation, and logistic regression. The significance threshold was set at p < 0.05. Results Of the 391 participants (mean age 20.4 ± 1.4 years; 171 males (43.7%), 220 females (56.3%), 187 (47.8%) classified as addicted students). Addiction was more frequent in males (108/171, 63.2%) than in females (79/220, 35.9%) (chi-square p < 0.001). Second-year students showed the highest addiction prevalence (59/97, 60.8%). The mean SAS-SV score was significantly higher in addicted compared to non-addicted students (34.32 ± 8.47 vs 28.04 ± 7.93, t-test p < 0.001). Poor sleep quality (PSQI > 5) was present in 332 (84.9%) students, more common among the addicted group (170/187, 90.9%) compared to the non-addicted group (163/204, 79.9%) (chi-square p = 0.002). The mean BMI was 23.60 ± 6.06 kg/m², with no significant difference between the addicted groups (t-test p = 0.299). Multivariate logistic regression showed that smartphone addiction independently predicted poor sleep quality (adjusted odds ratio (AOR) = 2.11, 95% CI: 1.12-3.97, p = 0.021), while gender and BMI were not significant predictors. Conclusion Smartphone addiction is highly prevalent in medical students and is independently linked with poor sleep quality, but not with BMI. Interventions targeting smartphone use and sleep hygiene are needed in this population.
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