{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T07:19:23Z","timestamp":1787987963725,"version":"build-2784847793"},"reference-count":32,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Eur J Dental Education"],"abstract":"ABSTRACT<\/jats:title>\n \n Objective<\/jats:title>\n 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\u2010risk smartphone use within this academically demanding population.<\/jats:p>\n <\/jats:sec>\n \n Materials and Methods<\/jats:title>\n \n A cross\u2010sectional survey was conducted during the 2024\u20132025 academic year at Faculty of Dentistry, Atat\u00fcrk University including 485 students from all academic years (mean age\u2009=\u200922.31\u2009\u00b1\u20091.77; 63.1% female). Participants were classified into low\u2010 and high\u2010risk groups based on gender\u2010specific Smartphone Addiction Scale\u2013Short Version (\n SAS<\/jats:styled-content>\n \u2010\n SV<\/jats:styled-content>\n ) cut\u2010offs (\u2265\u200931 for males; \u2265\u200933 for females). Sleep quality was assessed using the Pittsburgh Sleep Quality Index (\n PSQI<\/jats:styled-content>\n ). For the separate analysis of sleep disturbance, the raw sum of\n PSQI<\/jats:styled-content>\n items 5b\u20135j was used, with a theoretical range of 0\u201327, rather than the conventional 0\u20133\n PSQI<\/jats:styled-content>\n sleep disturbance component score. Psychosocial status was evaluated using the Depression Anxiety Stress Scales\u201021 (\n DASS<\/jats:styled-content>\n \u201021). Group comparisons were performed using appropriate parametric or non\u2010parametric tests, and predictors of high\u2010risk smartphone use were identified through binary logistic regression analysis.\n <\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Overall, 54.5% of participants were classified as high\u2010risk users (mean\n SAS<\/jats:styled-content>\n \u2010\n SV<\/jats:styled-content>\n \u2009=\u200933.26\u2009\u00b1\u200910.57). Demographic variables such as age, sex and class year were not associated with risk classification (all\n p<\/jats:italic>\n \u2009>\u20090.90). Sleep onset latency was significantly shorter in the high\u2010risk group (19.90\u2009\u00b1\u200917.53\u2009min) compared to the low\u2010risk group (24.50\u2009\u00b1\u200918.36\u2009min;\n p<\/jats:italic>\n \u2009=\u20090.005), while total sleep duration showed no difference (\n p<\/jats:italic>\n \u2009=\u20090.531). Among\n PSQI<\/jats:styled-content>\n subcomponents, only sleep disturbance was significantly associated with high\u2010risk smartphone use (\n OR<\/jats:styled-content>\n \u2009=\u20091.05, 95%\n CI<\/jats:styled-content>\n : 1.01\u20131.09,\n p<\/jats:italic>\n \u2009=\u20090.029). 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\u2010risk users, whereas sleep medication use did not differ between groups. In the psychosocial model, stress scores predicted high\u2010risk smartphone use (\n OR<\/jats:styled-content>\n \u2009=\u20091.09, 95%\n CI<\/jats:styled-content>\n : 1.02\u20131.17,\n p<\/jats:italic>\n \u2009=\u20090.015), whereas anxiety and depression scores were non\u2010significant (\n p<\/jats:italic>\n \u2009\u2265\u20090.62).\n <\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n \n While sleep disturbance and stress predicted high\u2010risk PSU in domain\u2010specific models, sleep disturbance lost significance in the fully adjusted combined model, where daytime dysfunction emerged as the primary sleep\u2010related predictor and stress remained only marginally significant (\n p<\/jats:italic>\n \u2009=\u20090.049). These findings should therefore be interpreted with caution.\n <\/jats:p>\n <\/jats:sec>","DOI":"10.1111\/eje.70293","type":"journal-article","created":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T06:25:21Z","timestamp":1787984721000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Are Smartphones Associated With Stress and Poor Sleep Quality? 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