Prevalence and correlates of sleep disturbance among adolescents in the eastern seaboard of China

BACKGROUND

Sleep disturbances are serious public health issues that warrant increased attention, especially in adolescents. The aim of this study was to investigate the prevalence and factors associated with sleep disorders among urban adolescents in China.

METHODS

This study utilized an online survey to assess the demographic characteristics and mental health status of secondary school students in Lianyungang City. The Patient Health Questionnaire-9 (PHQ-9) was used to evaluate sleep disturbances in adolescents. The seven-item Generalized Anxiety Disorder (GAD-7) assessed anxiety symptoms, and the Perceived Social Support Scale (PSSS) was used to measure perceived social support.

RESULTS

Among 3443 adolescents, the prevalence of sleep disorders were 10.8%, with significantly higher proportions of sleep disorders (13.7% VS 8.3%, P < 0.001) among female adolescents when compared to males. Binary regression analysis revealed that anxiety symptoms (OR = 1.305, 95% CI: 1.269-1.342, P < 0.001) was risk factor for sleep disturbances, and significant other support (OR = 0.944, 95% CI: 0.896-0.994, P = 0.028) and good annual household income (OR = 0.616, 95% CI: 0.394-0.963, P = 0.034) were protective factors. Furthermore, multinomial logistic regression analysis showed that age, sex, and anxiety symptoms were associated with an elevated risk of experiencing more frequent sleep disturbances (all P < 0.05).

CONCLUSIONS

We have found that 10.8% of adolescents experience sleep disorders, and it is evident that various factors can influence healthy sleeping. These results underscore the significance of addressing these factors to enhance sleep health among this population.

A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging

AIM

Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined.

METHODS

Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms.

RESULTS

The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS.

CONCLUSIONS

The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.