Problematic technology use and sleep quality in young adulthood: novel insights from a nationally representative twin study

The association between cigarette smoking and sleep deprivation among adolescents in Gulf Cooperation Council countries: analysis of national surveys

OBJECTIVES

To examine the association between sleep deprivation and cigarette smoking among adolescents in the "Gulf Cooperation Council (GCC)" countries, accounting for relevant sociodemographic and behavioral factors.

METHODS

The present study was conducted between June and August 2024 using data from the most recent "Global School-based Student Health Survey (GSHS)" conducted in 5 GCC countries. The study included 21,105 adolescents aged 11-18 years. Multiple logistic regression models were used to examine the association in each country. A random-effects meta-analysis was conducted to synthesize results across countries.

RESULTS

Overall, 17.9% of adolescents reported sleep deprivation. The pooled adjusted odds ratio for the association between cigarette smoking and sleep deprivation was 1.75 (95% CI: 1.56-1.96), indicating that adolescents who smoked cigarettes had 75% higher odds of experiencing sleep deprivation compared to non-smokers. This association was consistent across all 5 GCC countries, with low heterogeneity (I² = 18.1%).

CONCLUSION

This study provides evidence of a significant positive association between sleep deprivation and cigarette smoking among adolescents in GCC countries. These findings emphasize the need for comprehensive public health interventions promoting both smoking prevention and sleep health among adolescents in the region.

Feature Contributions and Predictive Accuracy in Modeling Adolescent Daytime Sleepiness Using Machine Learning: The MeLiSA Study

Background: Excessive daytime sleepiness (EDS) among adolescents poses significant risks to academic performance, mental health, and overall well-being. This study examines the prevalence and risk factors of EDS in adolescents in Bangladesh and utilizes machine learning approaches to predict the risk of EDS. Methods: A cross-sectional study was conducted among 1496 adolescents using a structured questionnaire. Data were collected through a two-stage stratified cluster sampling method. Chi-square tests and logistic regression analyses were performed using SPSS. Machine learning models, including Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Gradient Boosting Machine (GBM), were employed to identify and predict EDS risk factors using Python and Google Colab. Results: The prevalence of EDS in the cohort was 11.6%. SHAP values from the CatBoost model identified self-rated health status, gender, and depression as the most significant predictors of EDS. Among the models, GBM achieved the highest accuracy (90.15%) and precision (88.81%), while CatBoost had comparable accuracy (89.48%) and the lowest log loss (0.25). ROC-AUC analysis showed that CatBoost and GBM performed robustly in distinguishing between EDS and non-EDS cases, with AUC scores of 0.86. Both models demonstrated the superior predictive performance for EDS compared to others. Conclusions: The study emphasizes the role of health and demographic factors in predicting EDS among adolescents in Bangladesh. Machine learning techniques offer valuable insights into the relative contribution of these factors, and can guide targeted interventions. Future research should include longitudinal and interventional studies in diverse settings to improve generalizability and develop effective strategies for managing EDS among adolescents.

Tracked and self-reported nighttime smartphone use, general health, and healthcare utilization: results from the SmartSleep Study

STUDY OBJECTIVES

Nighttime smartphone use is an increasing public health concern. We investigated whether nighttime smartphone use is associated with general health and primary healthcare utilization.

METHODS

Four thousand five hundred and twenty individuals (age 35.6 ± 9.7 years, 35% male) provided self-reported information on smartphone use frequency, symptoms of depression, and general health (one-item perceived health and cross-symptom composite score). A subset of the study sample (n = 3221) tracked their nighttime smartphone use. Primary healthcare utilization, i.e. the number of weeks in which at least one service from the patient's general practitioner (GP) was billed in 2020, was extracted from Danish population registries. Statistical analysis comprised logistic and multiple linear regression, controlling for sociodemographics.

RESULTS

Three hundred and nineteen individuals (7%) reported using their smartphone almost every night or more. More frequent self-reported nighttime smartphone use was associated with poor general health across all measures. Using the smartphone almost every night or more was associated with 2.8 [95% CI: 1.9, 4.1] fold higher odds of reporting poor health and with an average of 1.4 [95% CI: 0.7, 2.1] additional GP utilizations per year compared to no use. Associations were also found for the cross-symptom composite score across all symptoms. Further adjustment for symptoms of depression attenuated some associations. Smartphone use towards the end of the sleep period (sleep-offset use) was associated with poorer self-reported general health, but not with healthcare utilization.

CONCLUSIONS

Nighttime smartphone use frequency is associated with poor general health and healthcare utilization. Further studies should investigate the underlying causal structure and nighttime smartphone use as a transdiagnostic intervention target.

The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review

PURPOSE OF REVIEW

Social media use (SMU) and other internet-based technologies are ubiquitous in today's interconnected society, with young people being among the commonest users. Previous literature tends to support that SMU is associated with poor sleep and mental health issues in youth, despite some conflicting findings. In this scoping review, we summarized relevant studies published within the past 3 years, highlighted the impacts of SMU on sleep and mental health in youth, while also examined the possible underlying mechanisms involved. Future direction and intervention on rational use of SMU was discussed.

RECENT FINDINGS

Both cross-sectional and longitudinal cohort studies demonstrated the negative impacts of SMU on sleep and mental health, with preliminary evidence indicating potential benefits especially during the COVID period at which social restriction was common. However, the limited longitudinal research has hindered the establishment of directionality and causality in the association among SMU, sleep, and mental health. Recent studies have made advances with a more comprehensive understanding of the impact of SMU on sleep and mental health in youth, which is of public health importance and will contribute to improving sleep and mental health outcomes while promoting rational and beneficial SMU. Future research should include the implementation of cohort studies with representative samples to investigate the directionality and causality of the complex relationships among SMU, sleep, and mental health; the use of validated questionnaires and objective measurements; and the design of randomized controlled interventional trials to reduce overall and problematic SMU that will ultimately enhance sleep and mental health outcomes in youth.