Prediction of sleep quality among university students after analyzing lifestyles, sports habits, and mental health - Zhang, Lirong; Zheng, Hua; Yi, Min; Zhang, Ying; Cai, Guoliang; Li, Changqing; Zhao, Liang (Frontiers in Psychiatry, 2022)
The study aimed to develop and validate a prediction model to evaluate the risk of poor sleep quality. We performed a cross-sectional study and enrolled 1928 college students from five universities between September and November 2021. The quality of sleep was evaluated using the Chinese version of the Pittsburgh sleep quality index (PSQI). Participants were divided into a training (n=1555) and a validation (n=373) group. The training group was used to establish the model and the validation group was used to validate predictive effectiveness of the model. Risk classification of all participants was performed based on the optimal threshold of the Model. Of all enrolled participants, 45.07% (869/1928) had poor sleep quality (PSQI score≧6 points). Multivariate analysis showed that older age, a higher grade, previous smoking, drinking, midday rest, chronic disease, anxiety, and stress were significantly associated with a higher rate of poor sleep quality, while preference to vegetable was significantly associated with better sleep quality, and all these variables were included to develop the prediction model. Area under the curve (AUC) was 0.765 (95% confident interval [CI]: 0.742-0.789) in the training group and 0.715 (95% CI: 0.664-0.766) in the validation group. Corresponding discrimination slopes were 0.207 and 0.167, respectively, and Brier scores were 0.195 and 0.221, respectively. Calibration curves showed favorable matched consistency between predicted and actual probability of poor sleep quality in both groups. Based on the optimal threshold, the actual probability of poor sleep quality was 29.03% (317/1092) in the low-risk group and 66.03% (552/836) in the high-risk group (P<0.001). A nomogram was presented to calculate the probability of poor sleep quality to promote the application of the model. The prediction model can be a helpful tool to stratify sleep quality specifically among university students. Some interventive measures or preventive strategies to quit smoking and drinking, eat more vegetables, avoid midday rest, treat chronic disease, and alleviate anxiety and stress may be considerably beneficial to improve sleep quality.