Are “night owls” or “morning larks” more likely to delay sleep due to problematic smartphone use? a cross-lagged study among undergraduates

Zhao, Chengjia; He, Jiankang; Xu, Huihui; Zhang, Jingjing; Zhang, Guohua; Yu, Guoliang

doi:10.1016/j.addbeh.2023.107906

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…Academic stress has also been identified as a significant factor associated with poor sleep quality among university students. Academic stress exacerbates sleep challenges, often causing students to sleep late (28). Taken together, entering university can be considered a challenging stage in the lives of adolescents, and high levels of perceived stress have been identified as a central predictor of poor sleep quality among university students (29).…”

Section: Introductionmentioning

confidence: 99%

Factors associated with sleep disorders among university students in Jiangsu Province: a cross-sectional study

Hu,

Wu,

Wang

et al. 2024

Front. Psychiatry

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ObjectiveThis study aims to establish the precise prevalence of sleep disorders among university students in Jiangsu Province. Utilizing a representative sample of students, we measured their sleep quality based on the Pittsburgh Sleep Quality Index (PSQI). Our objective is to quantitatively assess the magnitude of sleep quality and identify key factors. By detailed analysis of these relationships, our study seeks to provide actionable insights for the development of targeted interventions to enhance sleep quality within this population.MethodsFrom October to November 2022, we conducted a cross-sectional web-based survey in Jiangsu Province, China. Using convenient cluster sampling in each college, a total of 8457 participants were selected. The PSQI was applied to assess sleep quality among university students. Data collected included sociodemographic details, scores from the Mobile Phone Dependence Index (MPAI) and psychological resilience measured by the Connor-Davidson Resilience Scale (CD-RISC).ResultsThe overall prevalence of poor sleep quality among the participants was 39.30%. Binary logistic regression analysis revealed that higher physical activity (OR = 0.921; 95% CI: 0.779-1.090), earlier roommate bedtimes (OR = 0.799; 95% CI: 0.718-0.888), quieter dormitories (OR = 0.732; 95% CI: 0.647-0.828) and higher psychological resilience (OR = 0.982; 95% CI, 0.979-0.984) were protective factors linked to lower risk of poor sleep quality. Conversely, being a female student (OR = 1.238; 95% CI: 1.109-1.382), being a senior (OR = 1.582; 95% CI: 1.344-1.863), single-child status (OR = 1.195; 95% CI: 1.077-1.326), regular smoking (OR = 1.833; 95% CI: 1.181-2.847), regular alcohol consumption (OR = 1.737; 95% CI: 1.065-2.833), high academic stress (OR = 1.326; 95% CI: 1.012-1.736), high employment stress (OR = 1.352; 95% CI: 1.156-1.582), dissatisfaction with dormitory hygiene (OR = 1.140; 95% CI: 1.028-1.265), poor self-rated physical health (OR = 1.969; 95% CI: 1.533-2.529), poor self-rated mental health (OR = 2.924; 95% CI: 2.309-3.702) and higher mobile phone dependency were risk factors associated with an increased likelihood of poor sleep quality.ConclusionThe sleep quality among university students should attract immediate attention. The development of public services and mental health education initiatives is crucial in enhancing the sleep health of this population.

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Section: Introductionmentioning

confidence: 99%

Factors associated with sleep disorders among university students in Jiangsu Province: a cross-sectional study

Hu,

Wu,

Wang

et al. 2024

Front. Psychiatry

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“…On the other hand, su cient discourse and literature exist on procrastination, bedtime procrastination, academic procrastination, and related areas, as explained by Sharma and Kulshreshtha (2023).Recent studies suggest that several factors, including procrastination behavior, academic burnout, selfcontrol, fear of missing out, social anxiety, and self-esteem, have a direct impact on smartphone addiction (Liu, 2023). On the other hand, problematic smartphone use is linked to sleep procrastination (Zhao et al, 2024). A reciprocal relationship exists among college students between problematic smartphone use and later bedtime procrastination, each positively predicting the other (Chen et al, 2023).…”

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confidence: 99%

The Challenge of Procrastination: Gaining Insights from Overdue Books in University Libraries

Liu

2024

Preprint

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The late return of library books is a manifestation of procrastination, resulting in fines that impact the overall user experience at the library. Overdue or lost books can disrupt the library’s circulation services and affect the satisfaction of other patrons’ book needs. This study analyzes overdue readers at Nanjing Normal University Library from 2012 to 2023, examining factors such as the frequency of overdue incidents, borrowing habits, instances of book loss, duration of overdue periods, and the gender and background of the readers. The findings from path analysis suggest that the accumulation of fines is a deterrent for overdue behavior by influencing borrowing habits and serving as a warning against procrastination. Structural equation modeling analysis reveals a positive correlation between reader self-regulation and overdue attitudes, negatively impacting book circulation. Additionally, the study observes the application of the law of large numbers to the occurrence of book overdue, with a weak correlation between overdue incidents and instances of book loss.

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“…Based on findings from cross-lagged panel models (CLPM), Zhao et al (2024) concluded that problematic smartphone use (PSU) may lead to the occurrence of sleep delay and vice versa. However, it is well established that prospective effects in CLPM may be spurious due to correlations with residuals and regression to the mean (e.g.…”

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confidence: 99%

Inconclusive prospective effect of sleep delay on problematic smartphone use: A simulated reanalysis and comment on Zhao et al. (2024)

Sorjonen,

Melin

2024

Preprint

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Based on findings from analyses with cross-lagged panel models, Zhao et al. (2024) concluded that sleep delay may increase problematic smartphone use (PSU). Here, we simulated data to resemble the data used by Zhao et al. We used triangulation and fitted complementary models to the simulated data and found contradicting increasing and decreasing effects of initial sleep delay on subsequent change in PSU. These divergent findings suggest that it is premature to assume an increasing effect of sleep delay on PSU and the conclusion by Zhao et al. in this regard can be challenged. It is important for researchers to be aware that correlations, including adjusted cross-lagged effects, do not prove causality in order not to overinterpret findings, something that appears to have happened to Zhao et al. We recommend researchers to triangulate by fitting complementary models to their data in order to evaluate if observed effects may be due to true causal effect or if they appear to be spurious.

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Are “night owls” or “morning larks” more likely to delay sleep due to problematic smartphone use? a cross-lagged study among undergraduates

Cited by 10 publications

References 52 publications

Factors associated with sleep disorders among university students in Jiangsu Province: a cross-sectional study

Factors associated with sleep disorders among university students in Jiangsu Province: a cross-sectional study

The Challenge of Procrastination: Gaining Insights from Overdue Books in University Libraries

Inconclusive prospective effect of sleep delay on problematic smartphone use: A simulated reanalysis and comment on Zhao et al. (2024)

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