Social Jetlag and Other Aspects of Sleep Are Linked to Non-Suicidal Self-Injury Among College Students

Prevalence, correlates, and mental health outcomes of social jetlag in Chinese school-age adolescents: A large-scale population-based study

BACKGROUND

This cross-sectional study aimed to examine the prevalence and correlates of social jetlag (SJL) in Chinese adolescents, as well as to test the relationships between SJL and mental health problems.

METHODS

A total of 106979 students (Mage = 13.0 ± 1.8 years; Nmale = 58296 [54.5 %]) from Shenzhen, China completed an online survey from May 24th to June 5th, 2022. Information on sociodemographics, lifestyles, sleep characteristics, anxiety symptoms, and depressive symptoms was collected by a self-administered questionnaire. Multivariate and binary logistic regression were adopted for data analysis.

RESULTS

17.8 % of participants experienced SJL ≥ 2 h. To adjust the accumulated sleep debt, sleep-corrected SJL (SJLsc) was calculated and 8.3 % of individuals self-reported SJLsc ≥ 2 h. Both SJL and SJLsc show an increasing trend with age. Risk factors of SJL included females, poor parental marital status, being overweight, physically inactive, smoking, drinking, and having a late chronotype. Moreover, males, having siblings, boarding at school, short sleep duration, experiencing insomnia, and frequent nightmares were significantly associated with an increased risk of SJLsc. After adjusting for all covariates, adolescents with SJLsc ≥ 2 h were more likely to have anxiety symptoms (OR: 1.35, 95 % CI: 1.24-1.48) and depressive symptoms (OR: 1.35, 95 % CI: 1.25-1.46) than those with SJLsc < 1 h.

CONCLUSIONS

SJL is common among Chinese school-age adolescents. This study is valuable for the development of prevention and intervention strategies for SJL in adolescents at the population level. Additionally, the strong links between SJLsc and emotional problems underscore the critical significance of addressing SJL as a key aspect of adolescent well-being.

Associations between multiple sleep dimensions and suicide and non-suicidal self-injury: a cross-sectional study of 3828 Chinese young people

PURPOSE

Suicide and non-suicidal self-injury (NSSI) are preventable concerns in young people. Suicidal ideation (SI), suicidal plans (SP) and suicidal attempt (SA) are closely related to death. Sleep problems are known risk factors for suicide and NSSI. This study aimed to explore the relationship between sleep, suicidality and NSSI.

METHODS

Participants were 3,828 middle school and college students aged 11-23 years from urban and rural areas of Henan Province. Sleep, suicidal phenomena and NSSI were assessed by applying self-reported questionnaires. Chi-squared tests were utilized to demonstrate the demographic data and sleep variables. The correlation between sleep, suicidality and NSSI were explored by using binary logistic regression, while adjusting socio-demographic characteristics with multivariate models.

RESULTS

Sleep variables except mid-sleep time were related to suicidal phenomena (P < 0.05). Greater social jet lag (SJL) [≥ 2 h (h)] was associated with increased risk of SI [Odds ratios (OR) = 1.72, 95% confidence intervals (CI):1.40-2.11], SP (OR = 2.10, 95%CI:1.59-2.79) and SA (OR = 1.50, 95%CI:1.00-2.26). Non-only child participants with SJL (≥ 2 h) had significantly increased odds of SI (OR = 1.75, 95%CI: 1.41-2.18) and SP (OR = 2.25, 95%CI: 1.66-3.05). Eveningness chronotype had the strongest correlation with SI (OR = 3.87, 95%CI:2.78-5.38), SP (OR = 4.72, 95%CI:2.97-7.50), SA (OR = 6.69, 95%CI:3.08-14.52) and NSSI (OR = 1.39, 95%CI:1.02-1.90).

CONCLUSION

Overlong or short sleep duration, SJL, eveningness chronotype and other sleep abnormalities (e.g., daytime dysfunction, low sleep efficiency) were associated with a higher prevalence of SI, SP and SA. Additionally, eveningness was significantly correlated with NSSI among young people. These findings suggested the importance of assessing and intervening in sleep habits to prevent suicide and NSSI in young people.

Major Depressive Disorder Prediction Based on Sleep-Wake Disorders Symptoms in US Adolescents: A Machine Learning Approach from National Sleep Research Resource

Background

There is substantial evidence from previous studies that abnormalities in sleep parameters associated with depression are demonstrated in almost all stages of sleep architecture. Patients with symptoms of sleep-wake disorders have a much higher risk of developing major depressive disorders (MDD) compared to those without.

Objective

The aim of the present study is to establish and compare the performance of different machine learning models based on sleep-wake disorder symptoms data and to select the optimal model to interpret the importance of sleep-wake disorder symptoms to predict MDD occurrence in adolescents.

Methods

We derived data for this work from 2020 to 2021 Assessing Nocturnal Sleep/Wake Effects on Risk of Suicide Phase I Study from National Sleep Research Resource. Using demographic and sleep-wake disorder symptoms data as predictors and the occurrence of MDD measured base on the center for epidemiologic studies depression scale as an outcome, the following six machine learning predictive models were developed: eXtreme Gradient Boosting model (XGBoost), Light Gradient Boosting mode, AdaBoost, Gaussian Naïve Bayes, Complement Naïve Bayes, and multilayer perceptron. The models' performance was assessed using the AUC and other metrics, and the final model's predictor importance ranking was explained.

Results

XGBoost is the optimal predictive model in comprehensive performance with the AUC of 0.804 in the test set. All sleep-wake disorder symptoms were significantly positively correlated with the occurrence of adolescent MDD. The insomnia severity was the most important predictor compared with the other predictors in this study.

Conclusion

This machine learning predictive model based on sleep-wake disorder symptoms can help to raise the awareness of risk of symptoms between sleep-wake disorders and MDD in adolescents and improve primary care and prevention.

Sleep continuity, timing, quality, and disorder are associated with suicidal ideation and suicide attempts among college students

Objective: To evaluate sleep continuity, timing, quality, and disorder in relation to suicidal ideation and attempts among college students. Participants: Eight hundred eighty-five undergraduates aged 18-25 in the southwestern United States. Methods: Participants completed questionnaires on sleep, suicide risk, mental health, and substance use. Differences in sleep variables were compared by lifetime and recent suicidal ideation and suicide attempts using covariate-adjusted and stepwise regression models. Results: A total of 363 (41.0%) individuals reported lifetime suicidal ideation, of whom 172 (47.4%) reported suicidal ideation in the last 3 months and 97 (26.7%) had attempted suicide in their lifetime. Sleep disturbances were prevalent among those with lifetime suicidal ideation or a lifetime suicide attempt. Insomnia was identified as the best predictor of recent suicidal ideation, but this relationship did not survive adjustment for covariates. Conclusions: Sleep continuity, quality, and sleep disorders are broadly associated with suicidal thoughts and behaviors among college students.