BACKGROUND Existing research has demonstrated that depression is positively related to smartphone addiction, but the role of sleep has not been discussed thoroughly, especially among engineering undergraduates affected by the coronavirus disease 2019 pandemic. AIM To evaluate sleep as a mediator of the association between smartphone addiction and depression among engineering undergraduates. METHODS Using a multistage stratified random sampling method, a cross-sectional survey was conducted among 692 engineering undergraduates from a top engineering university in China, and data were collected by self-reported electronic questionnaires. The data included demographic characteristics, such as age, gender, the Smartphone Addiction Scale-Short Version (SAS-SV), the 9-item Patient Health Questionnaire, and the Pittsburgh Sleep Quality Index. Pearson correlation and multiple linear regression analyses were used to examine the association between smartphone addiction and depression, while structural equation models were established to evaluate the possible mediating role of sleep. RESULTS Based on the cutoffs of the SAS-SV, the rate of smartphone addiction was 63.58 percent, with 56.21 percent for women and 65.68 percent for men, among 692 engineering students. The prevalence of depression among students was 14.16 percent, with 17.65 percent for women, and 13.18 percent for men. Smartphone addiction was positively correlated with depression, and sleep played a significant mediating effect between the two, accounting for 42.22 percent of the total effect. In addition, sleep latency, sleep disturbances, and daytime dysfunction significantly mediated the relationship between depression and smartphone addiction. The mediating effect of sleep latency was 0.014 [ P < 0.01; 95% confidence interval (CI): 0.006-0.027], the mediating effect of sleep disturbances was 0.022 ( P < 0.01; 95%CI: 0.011-0.040), and the mediating effect of daytime dysfunction was 0.040 ( P < 0.01; 95%CI: 0.024-0.059). The influence of sleep latency, sleep disturbances, and daytime dysfunction accounted for 18.42%, 28.95%, and 52.63% of the total mediating effect, respectively. CONCLUSION The results of the study suggest that reducing excessive smartphone use and improving sleep quality can help alleviate depression.

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The aim of this study was to observe the changes in glucose metabolism after antipsychotic (APS) therapy, to note the influencing factors, as well as to discuss the relationship between the occurrence of glucose metabolism disorders of APS origin and abnormal glycosylated hemoglobin (HbA1c) levels. One hundred and fifty-two patients with schizophrenia, whose fasting plasma glucose (FPG) and 2-h plasma glucose (2hPG) in the oral glucose tolerance test (2HPG) were normal, were grouped according to the HbA1c levels, one normal and the other abnormal, and were randomly enrolled into risperidone, clozapine and chlorpromazine treatment for six weeks. The FPG and 2hPG were measured at the baseline and at the end of the study. In the group with abnormal HbA1c and clozapine therapy, 2HPG was higher after the study [(9.5 ± 1.8) mmol/L] than that before the study [(7.2 ± 1.4) mmol/L] and the difference was statistically significant (P < 0.01). FPG had no statistically significant difference before and after the study in any group (P > 0.05). HbA1c levels and drugs contributing to 2HPG at the end of study had statistical cross-action (P < 0.01). In the abnormal HbA1c group, 2HPG after the study was higher in the clozapine treatment group [(9.5 ± 1.8) mmol/L] than in the risperidone treatment group [(7.4 ± 1.7) mmol/L] and the chlorpromazine treatment group [(7.3 ± 1.6) mmol/L]. The differences were statistically significant (P < 0.01). In the normal HbA1c group there was no statistically significant difference before and after the study in any group (P > 0.05). 2HPG before [(7.1 ± 1.6) mmol/L] and after the study [(8.1 ± 1.9) mmol/L] was higher in the abnormal HbA1c group than in the normal HbA1c group [(6.2 ± 1.4) mmol/L vs (6.5 ± 1.4) mmol/L] with the difference being statistically significant (P < 0.01 vs P < 0.001). As compared with normal HbA1c group, the relative risk (RR) of glucose metabolism disease occurrence was 4.7 in the abnormal HbA1c group with the difference being statistically significant (P < 0.001). Patients with abnormal HbA1c are more likely to have a higher risk of having glucose metabolism disorders after APS treatment.