Are Smartphones Associated With Stress and Poor Sleep Quality? The Perspective of Dental Students.

OBJECTIVE

This study aimed to examine the association between problematic smartphone use, sleep quality and psychosocial factors among dental students, and to identify key behavioural and psychological predictors of high-risk smartphone use within this academically demanding population.

MATERIALS AND

METHODS

A cross-sectional survey was conducted during the 2024-2025 academic year at Faculty of Dentistry, Atatürk University including 485 students from all academic years (mean age = 22.31 ± 1.77; 63.1% female). Participants were classified into low- and high-risk groups based on gender-specific Smartphone Addiction Scale-Short Version (SAS-SV) cut-offs (≥ 31 for males; ≥ 33 for females). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). For the separate analysis of sleep disturbance, the raw sum of PSQI items 5b-5j was used, with a theoretical range of 0-27, rather than the conventional 0-3 PSQI sleep disturbance component score. Psychosocial status was evaluated using the Depression Anxiety Stress Scales-21 (DASS-21). Group comparisons were performed using appropriate parametric or non-parametric tests, and predictors of high-risk smartphone use were identified through binary logistic regression analysis.

RESULTS

Overall, 54.5% of participants were classified as high-risk users (mean SAS-SV = 33.26 ± 10.57). Demographic variables such as age, sex and class year were not associated with risk classification (all p > 0.90). Sleep onset latency was significantly shorter in the high-risk group (19.90 ± 17.53 min) compared to the low-risk group (24.50 ± 18.36 min; p = 0.005), while total sleep duration showed no difference (p = 0.531). Among PSQI subcomponents, only sleep disturbance was significantly associated with high-risk smartphone use (OR = 1.05, 95% CI: 1.01-1.09, p = 0.029). Categorical analyses further showed that poor subjective sleep quality, sleep disturbance, daytime dysfunction and elevated stress, anxiety and depression were significantly more frequent among high-risk users, whereas sleep medication use did not differ between groups. In the psychosocial model, stress scores predicted high-risk smartphone use (OR = 1.09, 95% CI: 1.02-1.17, p = 0.015), whereas anxiety and depression scores were non-significant (p ≥ 0.62).

CONCLUSION

While sleep disturbance and stress predicted high-risk PSU in domain-specific models, sleep disturbance lost significance in the fully adjusted combined model, where daytime dysfunction emerged as the primary sleep-related predictor and stress remained only marginally significant (p = 0.049). These findings should therefore be interpreted with caution.