An examination of sleep quality in middle school students in the context of smartphone addiction and morningness-eveningness preference

Background and objectives Problematic use of smartphones among healthcare workers can affect their performance, patient care, safety, care outcomes, and patient satisfaction. The aim of this study was to determine the prevalence of problematic use of smartphones and the relationship between the problematic use of smartphones and sleep quality among healthcare workers in Qassim, Saudi Arabia. Methods We enrolled 393 healthcare workers conveniently selected online for this cross-sectional survey. We assessed the problematic use of smartphones using the short version of the Smartphone Addiction Scale. For sleep quality, we used the Pittsburgh Sleep Quality Index (PSQI). Linear regression was used to assess the association of problematic use of smartphones with sleep quality. IBM SPSS Statistics, version 23.0 (IBM Corp., Armonk, NY) was used for analyses. Results The prevalence of smartphone addiction (SMA) was 59.0%, and 30.5% were at high risk for addiction. The mean PSQI score was 11.56 ± 2.1 out of 21. It was found that female gender was associated with poor sleep quality (adjusted B = 0.45, p-value = 0.049). On the other hand, SMA was also significantly associated with poor sleep quality (adjusted B = 0.90, p-value = 0.016). Conclusion There is a high prevalence of problematic use of smartphones among healthcare workers, which is associated with poor sleep quality. Given the significant occurrence of problematic smartphone use among healthcare professionals and its detrimental effects on sleep quality, it is crucial for public health initiatives to devise and execute suitable preventive measures, such as smartphone use policies at work and education of workers.

Background: Smart phone addiction can disturb sleep quality in medical students by upsetting internal biological clock (SCN) and melatonin level which in turn adversely affects academic performance of medical students.
 Objectives: To correlate smart phone addiction (SPA), poor sleep quality and low academic score in medical students and its association with gender.
 Methodology: A Cross- sectional descriptive study was conducted on medical students of 4th and final year MBBS (who were mobile phone addict for more than one year). The percentage of last professional exam was taken as academic score. A proposed SPA diagnostic criterion was used to diagnose smart phone addict students. For SPA severity and sleep quality assessment Problematic Mobile Phone Use Questionnaire (PMPU-Q) and Pittsburgh Sleep Quality Index (PSQI) were used.
 Results: A total of 74 subjects having mean (SD) age of 22.24 (1.929) years presented with negative correlation between academic score and PSQI (p< 0.05) were included in the study. There was no significant association between academic score and smart phone addiction. The male students had worse score in dependency and dangerous use on PMUQ scale. The female students were worse in dangerous and problematic use of smart phone on PMUQ scale.
 Conclusion: The SP dependency and poor subjective sleep quality of male students were negatively associated with their academic score. The female students with low academic score were worse at PSQI score, their academic score was not significantly correlated with their smart phone addiction.
 Keywords: Smartphone addiction, Sleep Quality, Academic Score

Introduction: The widespread integration of smartphones into daily life has raised concerns about their potential impact on Sleep Quality (SQ). Excessive smartphone use, particularly before bedtime, may contribute to sleep disturbances through prolonged screen exposure, cognitive stimulation and night-time interruptions. Given the critical role of sleep in overall health and wellbeing, understanding the association between problematic smartphone use and sleep disturbances is essential. Aim: To examine the association between Problematic Mobile Phone Use (PMPU) and SQ among adults, highlighting its potential impact on sleep patterns and overall wellbeing. Materials and Methods: This cross-sectional study was conducted at Indira Medical College and Hospitals, Tiruvallur, Tamil Nadu, India from January to June 2024. A total of 92 adult participants were included in the study. Problematic Smartphone Use (PSU) was assessed using a modified PMPU Questionnaire, while SQ was evaluated using the Pittsburgh Sleep Quality Index (PSQI). Correlation analysis, multiple regression and logistic regression were performed to examine relationships and identify predictors of SQ. Results: A significant positive correlation was observed between PMPU scores and PSQI scores (r=0.61, p-value <0.001). Multiple regression analysis identified the PMPU score as the strongest independent predictor of poor SQ (β=0.512, p-value <0.001), even after controlling for age, gender and mobile phone use duration. The association was strongest among younger participants (18-25 years: r=0.68, p-value <0.001) and slightly more pronounced in females (r=0.65, p-value <0.001). Logistic regression confirmed that each 10-point increase in PMPU score raised the odds of poor SQ by 2.35 times (OR=2.35, 95% CI: 1.62-3.41, p-value <0.001). Conclusion: This study demonstrates a significant association between PMPU and decreased SQ. Beyond screen time and content, PMPU represents an important factor influencing sleep health.

Sexual Function and Problematic Use of Smartphones and Social Networking Sites

Introduction Sleep disorders constitute a significant public health concern recognized by the World Health Organization (WHO) in the ICD (International Classification of Diseases), with notable implications for young populations. Research demonstrates that disrupted sleep patterns significantly impair mental recovery processes and emotional stability [1]. Poor sleep quality contributes to mental health deterioration through disruption of emotional regulation and neurobiological mechanisms. Inadequate sleep compromises hypothalamic-pituitary-adrenal axis function, increasing cortisol production and stress perception, potentially leading to depressive symptoms [2]. Young adults represent the population stratum with the highest smartphone and electronic device usage rates, sometimes developing behavioural dependencies. Studies show that light exposure to these devices before falling asleep significantly disrupts sleep quality [3]. Moreover, excessive smartphone use is associated with reduced cognitive performance, negatively affecting work efficiency and academic achievement [4]. This study investigates the interactions between sleep disorders, mental health, electronic device usage, and academic performance among university students. We specifically examine how sleep quality and quantity influence students' psychological functioning, with particular attention to psychological distress. Methods The Pittsburgh Sleep Quality Index (PSQI) [5], the Kessler Psychological Distress Scale (K10) [6] and the Smartphone Application-Based Addiction Scale (SABAS) [7] were used to assess sleep quality, mental distress and problematic smartphone use, respectively. Descriptive statistics were expressed as Mean (SD), for continuous variables, and as count/percentages for categorical variables. “Good sleepers” and “Poor sleepers” were compared using Chi-square test or Fisher's exact test for categorical variables, and Student's t-test or the Wilcoxon-Mann-Whitney test for continuous variables, with significance at P < 0.05. Logistic regression identified independent predictors of poor sleep quality (PSQI > 5). Variables with significant univariate association (p < 0.05) were included in the multivariate model, with results expressed as odds ratios (OR) with 95% confidence intervals (95% CI). Results This cross-sectional study involved 208 students from the University of Palermo, with 58.7% (n=122) enrolled in medical degree programs. The average age of the sample was 22±1.99 years, and 71.6% were female. The analysis revealed that 61.54% (n=128) of students had inadequate sleep quality. Univariate analysis showed that their exam completion rate (80.1%) was lower than that one reported for good sleepers (83.5%) (p < 0.05). On average, daily smartphone use was higher among poor sleepers (6.46±3.03 vs 5.57±2.22 hour/day, p < 0.05), and a significant association was found between poor sleep quality and the risk of problematic smartphone use (OR=2.83, 95%CI [ 1.27-7.00], p < 0.05). Furthermore, results from K10 revealed that reporting severe psychological distress was significantly associated to poor sleep quality (OR=13.25, 95%CI= [5.34-37.28], p < 0.001). The multivariate analysis confirmed that higher daily smartphone usage, measured in hours, is associated with poor sleep quality (AdjOR=1.21; 95% CI [1.02-1.45]) and, notably, subjects with high probability of severe psychological distress have significantly higher likelihood of being classified as poor sleepers (AdjOR = 9.59, 95% CI = [3.57-28.82]). Discussion Our analysis revealed a strong association between psychological distress (K10 scale) and poor sleep quality among university students. Students experiencing significant psychological distress showed markedly higher likelihood of being poor sleepers, confirming bidirectional relationships between mental health and sleep, as documented in previous research. Daily smartphone uses also emerged as a significant predictor of poor sleep quality, aligning with literature on electronic devices' detrimental effects on sleep hygiene. Smartphone light emissions, particularly blue light, suppress melatonin production and disrupt circadian rhythms [8]. These findings emphasize the importance of addressing sleep health within university mental health and academic support initiatives. The strong psychological distress-sleep quality association suggests interventions targeting either aspect may benefit the other. Universities should consider implementing screening programs to identify students at risk of sleep disorders, especially those reporting psychological distress symptoms. Additionally, digital hygiene education should be incorporated into student wellness programs to mitigate electronic devices' negative impact on sleep. Conclusions The study highlights the link between psychological distress, smartphone use, and sleep quality in university students. The strong connection between mental health struggles and sleep issues underscores the need to integrate sleep health into mental health services. Universities should promote well-being and responsible technology use to enhance academic performance and overall student health.

IntroductionDespite the many benefits of smartphones, researchers have raised concerns over problematic smartphone use (PSU) and its negative effects on physical and psychological well-being. Studies examining PSU and its impact among adults remain limited. Hence, we aim to examine the prevalence of PSU among adults in Singapore, and explore its associations with smartphone activities, sleep quality, and psychological well-being, as well as age and gender-related differences in these associations.MethodsA household survey (n = 1200) was conducted among multi-ethnic Singapore adults aged 21 to 60. The survey employed a proportionate stratified random sampling approach. The Smartphone Addiction Scale–Short Version was used to determine risk of PSU. Adjusted multivariable logistic regressions, age-stratified (21–30, and above 30) analyses and sensitivity analyses were performed.ResultsThe survey response rate was 45.7%. PSU prevalence rate was estimated to be 34.0%. Adults at risk were younger (OR = 3.72, p < 0.001), had poor sleep quality (OR = 2.94), reported depressive (OR = 2.84, p = 0.001) or anxiety symptoms (OR = 2.44, p < 0.001), tend to use smartphones for social media (OR = 2.81, p = 0.002) or entertainment (OR = 2.72, p < 0.001). Protective factors include higher levels of social support (OR = 0.76, p = 0.007), using smartphones for calling family (OR = 0.39, p = 0.003) and friends (OR = 0.53, p = 0.030), and spending four hours or less of smartphone usage duration (OR = 0.40, p < 0.001). Sensitivity analyses confirmed these findings. Associations between PSU and poor sleep quality (OR = 3.72, p < 0.001), depressive (OR = 3.83, p < 0.001), and anxiety symptoms (OR = 2.59, p = 0.004) and social media usage (OR = 3.46, p < 0.001) were more pronounced in adults over 30. PSU was more prevalent among females in those aged 21–30 (OR = 2.60, p = 0.022). Social support appears to be a protective factor for adults over 30 (OR = 0.64, p < 0.001) but was not observed in those aged 21–30. Among males, younger age (21–30 years), poor sleep quality, depressive symptoms, and anxiety symptoms, and using social media and entertainment apps were significantly associated with PSU. Females showed similar associations. Social support appears to be a protective factor for females (OR = 0.70, p = 0.018), but this association was not observed for males. Shorter smartphone usage times were inversely associated with PSU in both genders.ConclusionA substantial proportion of adults exhibited PSU. Findings highlight the differential associations between PSU and psychological well-being, social support, interactions with technology, and sleep quality. These associations are influenced by age which has implications for preventive efforts.

Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination

Background: Sleep problems and eating disorders (EDs) are both serious public health concerns often seen in young adults. Yet, the underlying mechanisms for such associations are largely unknown. This study aims to examine potential serial multiple mediation effects of problematic smartphone use (PSU) and psychological distress (i.e., depressive and anxiety symptoms) in the relationship between sleep quality and disordered eating behaviors/attitudes (DEBs).Methods: A total of 4,325 students from two Tibet universities in China (2,657 females and 1,668 males) completed an online survey that included the following measurements: Eating Attitude Test-26 for disordered eating behaviors/attitudes, the Chinese Version of Pittsburgh Sleep Quality Index (CPSQI), Smartphone Addiction Scale—Short Version (SAS-SV) for problematic smartphone use, Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7) for psychological distress.Results: While the direct path linking sleep quality and DEBs was not found to be significant (Standardized β = 0.006, 95% CI = −0.0667~0.0970), both PSU (Standardized β = 0.016, 95% CI = 0.0256~0.0591) and anxiety symptoms (Standardized β = 0.014, 95% CI = 0.0203~0.0526) may mediate a link between sleep quality and DEBs; serial multiple mediation analysis revealed that a serial indirect pathway of “sleep quality -> PSU -> anxiety symptoms -> DEBs” existed(Standardized β = 0.001, 95% CI = 0.0002~0.0012). Similarly, while the direct path linking sleep quality and DEBs was not found to be significant (Standardized β = 0.006, 95% CI = −0.0667~0.0970), both PSU (Standardized β = 0.020, 95% CI = 0.0337~0.0692) and depressive symptoms (Standardized β = 0.015, 95% CI = 0.0139~0.0652) may mediate a link between sleep quality and DEBs; serial multiple mediation analysis revealed that a serial indirect pathway of “sleep quality -> PSU -> depressive symptoms -> DEBs” existed (Standardized β = 0.001, 95% CI = 0.0006~0.0038).Conclusions: Psychological and behavioral factors may comprehensively work together, leading to flow-on effects from sleep problems to disordered eating behaviors among university students. Appropriate interventions that target problematic smartphone use could thus potentially reduce anxiety and depression levels, which in turn will provide a buffer against the negative impact of poor sleep quality on eating disorder symptoms.

BackgroundSmartphones used unconsciously and in an uncontrolled manner make young people experience sleep problems. This study aimed to investigate the effects of university students’ smartphone addiction and nomophobia levels on sleep quality and excessive daytime sleepiness.MethodsThis study, which had a cross-sectional design, was conducted with 390 people who were first-year and senior students at Inonu University between November and December 2019. The Pittsburgh Sleep Quality Index, Epworth Sleepiness Scale, Nomophobia Scale, and Smartphone Addiction Scale were used in the present study used. For statistical analysis, the chi-squared test, the Student’s t-test, one-way ANOVA, Spearman’s rank correlation coefficient and binomial logistic regression analysis were used. The research has ethics committee approval.The error level was chosen as p = 0.05.ResultsThe smartphone use time of the students was finded to be 5.4±2.6 years, daily online time was 4.3±2.6 hours, and daily sleep time was 7.4±1.5 hours. The students received 78.3±25.8 points from the Nomophobia Scale, 90.3±29.7 from the Smartphone Addiction Scale, 7.2±2.8 from the Pittsburgh Sleep Quality Index, and 5.9±4.1 points from the Epworth Sleepiness Scale. A total of 54.4% of students had moderate, and 22.8% had severe nomophobic symptoms; 83.6% of the group had poor sleep quality, and 14.6% had excessive daytime sleepiness. A positive, moderate and significant relation was detected between the mean Nomophobia score and the mean Smartphone Addiction Scale score. It was also determined that those with less than 30 minutes of smartphone use before sleeping had low nomophobia, smartphone addiction and daytime sleepiness scores, and better sleep quality.ConclusionsNomophobic symptoms and smartphone addiction were observed to be high in university students. Most students had poor sleep quality. The awareness of students on healthy sleep, and conscious and controlled smartphone use should be increased.Key messages• Smartphone addiction increases the level of nomophobia moderately in university students.• Those who use a smartphone less than 30 minutes before going to sleep have better sleep quality and lower daytime sleepiness.

BackgroundThis study established a network structure between problematic smartphone use (PSU) and poor sleep quality (PSQ) to explore their symptomatic relationship in adolescents with depression.MethodsThe data were obtained from the baseline data of the Chinese Adolescent Depression Cohort, which included depressed adolescents aged between 12 and 18 years. PSU and PSQ were assessed via the Mobile Phone Addiction Index (MPAI) and Pittsburgh Sleep Quality Index (PSQI). This study utilized network analysis to identify the core and bridge symptoms between PSU and PSQ.ResultsThe core symptom of the network was ‘Anxiety and craving’ in the PSU. The symptoms ‘Anxiety and craving’, ‘Daytime dysfunction’ and ‘Sleep disturbances’ could function as bridges between PSU and PSQ. The symptom ‘Anxiety and craving’ played the most important role in the interaction between PSU and PSQ. It affects ‘Sleep disturbances’ that contribute to the harm of problematic smartphone use to sleep quality. The symptom ‘Daytime dysfunction’ of PSQ was the most severely affected by PSU.ConclusionsInterventions that target regulating negative emotion and reducing daytime tiredness would be more effective in managing problematic smartphone use and improving sleep quality.Clinical trial numberNot applicable.

General Background: Sleep quality is a fundamental physiological need and a critical determinant of cognitive functioning, emotional regulation, and academic performance among university students. Specific Background: Early adult students frequently encounter academic demands and intensive smartphone use, both of which are associated with disrupted sleep patterns and reduced restorative rest. Knowledge Gap: Previous studies have primarily examined either academic procrastination or smartphone addiction separately, leaving limited empirical evidence on their simultaneous relationship with sleep quality among early adult students in East Java. Aims: This study investigates the relationships between academic procrastination, smartphone addiction, and sleep quality among early adult university students in East Java. Results: Using a quantitative correlational design with 272 students and multiple linear regression analysis, findings indicate significant negative associations between academic procrastination and sleep quality (B = –0.468; p &lt; 0.05) and between smartphone addiction and sleep quality (B = –0.327; p &lt; 0.05). Simultaneously, both variables account for 51.2% of the variance in sleep quality (R² = 0.512; F = 141.224; p &lt; 0.05), demonstrating that higher levels of academic delay and excessive smartphone use correspond with lower sleep quality. Novelty: This study integrates academic procrastination and smartphone addiction within a single regression model to explain variations in student sleep quality. Implications: The findings provide empirical evidence for developing campus-based time management education, digital well-being initiatives, and sleep hygiene programs targeting early adult students. Highlights: Task postponement behavior shows a statistically significant inverse association with restorative rest patterns. Excessive mobile device dependency demonstrates a significant negative relationship with nightly recovery conditions. Combined behavioral factors explain more than half of the variance in student sleep conditions. Keywords:Academic Procrastination; Smartphone Addiction; Sleep Quality; Early Adult Students; Multiple Linear Regression

Background:This study investigated the relationships between smartphone use, bedtime procrastination, and adolescent sleep quality. Specifically, the study investigated whether bedtime procrastination mediates the relationship between problematic smartphone use (PSU) and sleep quality.Methods:A sample of 245 adolescents aged 13-18 participated in the study. The Smartphone Addiction Scale—Short Version, Bedtime Procrastination Scale, and Adolescent Sleep–Wake Scale were used to assess PSU signs, bedtime procrastination, and sleep quality, respectively. In addition, smartphone usage habits were assessed through a purpose-built questionnaire.Results:It was found that 46.9% of the adolescents had PSU signs, they used smartphones for an average of 2.8 hours per day, 90.6% used smartphones in bed, they used smartphones in bed for an average of 4.9 days per week, and the average duration of smartphone use in bed was 2.3 hours per day. Correlation analyses showed that poor sleep quality was positively associated with the duration of smartphone use in bed, number of smartphone activities, PSU signs, and bedtime procrastination. The relationship between PSU and sleep quality is mediated by bedtime procrastination, according to mediation analysis.Conclusion:This study demonstrated the negative effect of smartphone use on sleep quality in adolescents. The results also indicate that bedtime procrastination may play a mediating role between PSU and sleep quality. Thus, interventions such as sleep hygiene targeting bedtime procrastination may improve adolescents’ sleep quality with PSU signs.

Sleep disorders are a significant health issue that urgently needs to be addressed among undergraduate students, and one of the potential underlying problems could be problematic smartphone use (PSU). This study aimed to clarify the relationship between PSU and poor sleep quality by investigating the independent and serial mediating roles of anxiety and depressive symptoms in a population of university students in Tibet, China. A total of 2993 Tibetan college students completed three waves of data surveys, with all participants completing questionnaires on PSU, anxiety, depressive symptoms, and sleep quality (Time 1 (T1) -Time 3 (T3)). Bootstrapped mediation analysis was used to explore the mediating role of anxiety and depressive symptoms in the longitudinal relationship between PSU and sleep quality. Both direct and indirect effects of PSU on poor sleep quality were found. PSU (T1) can had not only a direct negative influence on poor sleep quality (T3) among young adults (direct effect = 0.021, 95% CI = 0.010-0.033) but also an indirect negative impact via three pathways: the independent mediating effect of anxiety symptoms (T2) (indirect effect 1 = 0.003, 95% CI = 0.001-0.006), the independent mediating effect of depressive symptoms (T2) (indirect effect 2 = 0.004, 95% CI = 0.002-0.006), and the serial mediating effects of anxiety (T2) and depressive symptoms (T2) (indirect effect 3 = 0.008, 95% CI=0.005-0.011). These findings highlight the role of anxiety and depression symptoms as joint mediating factors in the relationship between PSU and sleep disturbances. Interventions focused on improving sleep that incorporate behavioural measures could benefit from treatment approaches targeting mental disorders.

Introduction Singapore, an urbanized multi-ethnic city-state in Southeast Asia with a very high smartphone penetrance, ranks among the most sleep deprived countries globally. This study examines the prevalence of poor sleep quality in Singapore and its association with smartphone use. Methods A cross-sectional household survey in eastern Singapore was conducted from October to December 2022. Subjects were included if they were residents aged 21 to 60 and own a smartphone. A simple random sampling design and a proportionate stratified approach by dwelling type was used to select 1200 households for the survey. Key variable data collected included sleep quality as measured using the Pittsburgh Sleep Quality Index (PSQI), problematic smartphone use (PSU) using the Smartphone Addiction Scale – Short Version, and smartphone usage patterns. Other variables included psychological symptoms of depression, anxiety, and stress using validated scales. Social support, sociodemographic, and lifestyle data were also recorded. Significant factors from bivariate analyses were included in adjusted multivariable logistic regression models. Results 400 participants (200 male, 200 female) were surveyed. Prevalence of poor sleep quality was 21%. PSU was associated with poor sleep quality (aOR 2.73, 95% CI 1.61 – 4.64, p&amp;lt; 0.001). Daily use of a smartphone in bed before sleep was associated with poorer sleep quality (aOR 2.70, 95% CI: 1.15 – 6.34, p=0.023), compared to no smartphone use. After lights out, even occasional smartphone use was associated with increased likelihood of having poor sleep quality (aOR 3.02, 95% CI: 1.31 – 6.95, p=0.010). Spending &amp;gt;4 hours compared to &amp;lt; 2h per day on the smartphone was also linked with poorer sleep quality (aOR 2.50, 95% CI: 1.15–5.43, p=0.020). Conversely, making smartphone calls to family (aOR 0.49, 95% CI: 0.25 – 0.96, p=0.040) or friends (aOR 0.39, 95% CI: 0.21 – 0.74, p=0.004) was negatively associated with poor sleep quality. Conclusion About a fifth of adults in Singapore suffer from poor sleep quality and it is significantly associated with smartphone usage patterns. It remains plausible that smartphone-mediated social connections could contribute to improved sleep quality; further research is required to explore this. Support (if any) This research was supported by CGH’s Joint Research and Innovation Grant (RIG202110-003PR).

The excessive use of smartphones can lead to significant physical, psychological, social, and academic problems and may create the conditions for the development of pervasive emotional and behavioral disorders. Over the past two decades, substantial scientific evidence has accumulated regarding the factors associated with smartphone addiction, as well as the consequences of this dysfunctional behavioral pattern. The correlational study presented here aimed to determine the predictive role of depression, anxiety, and psychological stress in explaining smartphone addiction among preadolescents and adolescents. Additionally, the study sought to identify the contribution of smartphone addiction to the occurrence of sleep disturbances. A standardized protocol (k = 71 items) was administered to 356 students in grades 5-8 to operationalize the main variables in regression models, with smartphone addiction and sleep quality as the criterion variables. Gender and participants’ place of residence had statistically significant effects on sleep quality but not on smartphone addiction. Depression, anxiety, and psychological stress demonstrated moderate positive correlations with smartphone addiction. Smartphone addiction was positively associated with lower sleep quality. Regression models revealed significant predictive contributions from depression, anxiety, and psychological stress, which together explained 20.7% to 23.6% of the variance in students’ smartphone addiction score. Conversely, smartphone addiction accounted for 20.2% of the variance in students’ sleep quality. The study’s findings are interpreted in relation to mechanisms that may explain the links between the factors contributing to smartphone addiction and the consequences of this problematic behavior.