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Effect of Smartphone Use on Sleep in Undergraduate Medical Students

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dc.contributor.author Goel, Ashish
dc.contributor.author Moinuddin, Arsalan
dc.contributor.author Tiwari, Rajesh
dc.contributor.author Sethi, Yashendra
dc.contributor.author Suhail, Mohammed K.
dc.contributor.author Mohan, Aditi
dc.contributor.author Kaka, Nirja
dc.contributor.author Sarthi, Parth
dc.contributor.author Dutt, Ravi
dc.contributor.author Ahmad, Sheikh F.
dc.contributor.author Attia, Sabry M.
dc.contributor.author Emran, Talha Bin
dc.contributor.author Chopra, Hitesh
dc.contributor.author Greig, Nigel H.
dc.date.accessioned 2024-05-23T06:08:12Z
dc.date.available 2024-05-23T06:08:12Z
dc.date.issued 2023-11-02
dc.identifier.issn 2227-9032
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/12450
dc.description.abstract Smartphone use, particularly at night, has been shown to provoke various circadian sleep–wake rhythm disorders such as insomnia and excessive daytime tiredness. This relationship has been mainly scrutinized among patient groups with higher rates of smartphone usage, particularly adolescents and children. However, it remains obscure how smartphone usage impacts sleep parameters in adults, especially undergraduate college students. This study sought to (1) investigate the association between smartphone use (actual screen time) and four sleep parameters: Pittsburgh sleep quality score (PSQI), self-reported screen time, bedtime, and rise time; (2) compare the seven PSQI components between good and poor sleep quality subjects. In total, 264 undergraduate medical students (aged 17 to 25 years) were recruited from the Government Doon Medical College, Dehradun, India. All participants completed a sleep questionnaire, which was electronically shared via a WhatsApp invitation link. Hierarchical and multinomial regression analyses were performed in relation to (1) and (2). The average PSQI score was 5.03 ± 0.86, with approximately one in two respondents (48.3%) having a poor sleep index. Smartphone use significantly predicted respondents’ PSQI score (β = 0.142, p = 0.040, R2 = 0.027), perceived screen time (β = 0.113, p = 0.043, R2 = 343), bedtime (β = 0.106, p = 0.042, R2 = 045), and rise time (β = 0.174, p = 0.015, R2 = 0.028). When comparing poor-quality sleep (PSQI ≥ 5) to good-quality sleep (PSQI < 5), with good-quality sleep as the reference, except sleep efficiency and sleep medications (p > 0.05), five PSQI components declined significantly: subjective sleep quality (β = −0.096, p < 0.001); sleep latency (β = −0.034, p < 0.001); sleep duration (β = −0.038, p < 0.001); sleep disturbances (β = 1.234, p < 0.001); and sleep dysfunction (β = −0.077, p < 0.001). Consequently, public health policymakers should take this evidence into account when developing guidelines around smartphone use—i.e., the when, where, and how much smartphone use—to promote improved sleep behaviour and reduce the rate of sleep–wake rhythm disorders. en_US
dc.language.iso en_US en_US
dc.publisher MDPI Publications en_US
dc.subject Public health en_US
dc.subject Smartphones en_US
dc.subject Mobile phones en_US
dc.subject Sleep en_US
dc.title Effect of Smartphone Use on Sleep in Undergraduate Medical Students en_US
dc.title.alternative A Cross-Sectional Study en_US
dc.type Article en_US


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