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

Predictors of the Intention to Stop Using Smart Devices at Bedtime Among University Students in Saudi Arabia: Cross-Sectional Survey

BACKGROUND

The widespread use of smart devices, particularly among university students, has raised concerns about their impact on sleep quality. Bedtime usage of smart devices is associated with sleep disruptions and poor sleep quality.

OBJECTIVE

This study aimed to explore the behavioral and perceptual factors influencing university students' intention to stop using smart devices at bedtime in Saudi Arabia.

METHODS

A cross-sectional survey was conducted in June 2024 and distributed via social media platforms to university students (aged ≥18 years). The questionnaire collected data on demographics, smart device usage habits, perceived negative effects on sleep, and physical sleep disturbances. The Pittsburgh Sleep Quality Index was used to assess sleep quality. Path analysis was performed to evaluate relationships between the outcome variables, intended to stop using smart device usage, and 3 latent variables: sleep quality smartphone usage, sleep quality perceived negative effects, and sleep quality during the past month. Model fit was assessed using chi-square, comparative fit index, and root mean square error of approximation.

RESULTS

Of the 774 participants, 90.43% (700/774) reported using their smart devices every night and 72.48% (561/774) believed bedtime device use negatively affected them the next morning. The most frequently reported next-morning symptoms were fatigue or drowsiness (480/774, 62.01%). Common purposes for bedtime device use were staying in touch with friends or family (432/774, 55.81%), entertainment (355/774, 45.86%), and filling up spare time (345/774, 44.57%). Overall, 58.26% (451/774) expressed an intention to stop bedtime device use within the next 3 months. Path analysis demonstrated that frequent nightly use (path coefficient=0.36) and after-lights-off usage (0.49) were positively associated with the intention to stop, whereas spending ≥3 hours on devices (-0.35) and engaging in multiple activities (-0.18) had negative associations. The strongest predictors of the intention to stop were perceived negative effects on next-morning well-being (0.71) and difficulty breathing comfortably during sleep (0.64). Model fit was excellent (comparative fit index=0.845 and root mean square error of approximation=0.039).

CONCLUSIONS

Perceived negative effects on sleep quality and physical sleep disturbances are strong predictors of the intention to stop using smart devices at bedtime among university students in Saudi Arabia. Interventions aimed at improving sleep hygiene should focus on raising awareness about the impact of smart device use on well-being and addressing behaviors such as late-night usage and heavy screen time. Public health strategies should target both psychological and physiological aspects of bedtime smart device use to improve sleep quality in this population.

Unveiling the grip of mobile phone addiction: an in-depth review

Mobile Phone Addiction represents an emergent addictive disorder that gravely jeopardizes the physical and mental health of adolescents worldwide, necessitating exhaustive research. Current reviews of MPA are in dire need of updates and enhancements. Therefore, this review aggregates the extant research spanning the past two decades on the prevalence, pathogenesis, comorbidities, assessment, and treatment of MPA, aiming to furnish a reference for future investigations into this condition.

The effects of mobile phone addiction on bedtime procrastination in university students: the masking effect of physical activity and anxiety

PURPOSE

Good sleep is one of the necessary conditions to ensure the normal performance of the physiological and psychological functions of college students. This study aimed to explore the relationship between mobile phone addiction and bedtime procrastination among Chinese college students and the mediating mechanisms of physical exercise and anxiety between the two, with a view to seek ways to prevent and intervene in college students' sleep procrastination and improve their sleep quality.

METHODS

Using SPSS 29.0 analysis with Bootstrap's method, 3,800 first-year students, sophomores, and juniors were given the Mobile Phone Addiction Tendency Scale, Bedtime Procrastination Scale, Physical Activity Scale, and Anxiety Scale. The results of the analyses included mediation tests and effect analyses of anxiety and physical activity.

RESULTS

The correlation analysis revealed significant positive correlations between mobile phone addiction and bedtime procrastination (r = 0.149, p < 0.01) as well as anxiety (r = 0.497, p < 0.01). Additionally, there was a significant negative correlation between mobile phone addiction and physical activity (r = -0.447, p < 0.01). Physical activity was also found to have significant negative correlations with anxiety (r = -0.506, p < 0.01) and bedtime procrastination (r = -0.424, p < 0.01). Furthermore, anxiety showed a significant positive correlation with bedtime procrastination (r = 0.334, p < 0.01). Physical activity and anxiety acted as substantial mediators between mobile phone addiction and nighttime procrastination. Both mediators had considerable masking effects, with the mediating effect amounting to 50.3% and 25.1%, respectively. Physical exercise and anxiety played a chain mediating role between mobile phone addiction and bedtime procrastination, and the masking effect was also significant, with a mediating effect size of 13.4%.

CONCLUSIONS

This study reveals the special characteristics of the influencing factors and pathways of bedtime procrastination in this group of college students, providing targeted evidence for the prevention and intervention of bedtime procrastination in college students. It also has an important reference value for the effects of exercise and comprehensive intervention to improve bedtime procrastination and enhance the quality of sleep in college students.

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

Objective

This 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.

Methods

From 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).

Results

The 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.

Conclusion

The 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.