Background COVID-19 has been reported to affect the sleep quality of Chinese residents; however, the epidemic’s effects on the sleep quality of college students during closed-loop management remain unclear, and a screening tool is lacking. Objective This study aimed to understand the sleep quality of college students in Fujian Province during the epidemic and determine sensitive variables, in order to develop an efficient prediction model for the early screening of sleep problems in college students. Methods From April 5 to 16, 2022, a cross-sectional internet-based survey was conducted. The Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and the sleep quality influencing factor questionnaire were used to understand the sleep quality of respondents in the previous month. A chi-square test and a multivariate unconditioned logistic regression analysis were performed, and influencing factors obtained were applied to develop prediction models. The data were divided into a training-testing set (n=14,451, 70%) and an independent validation set (n=6194, 30%) by stratified sampling. Four models using logistic regression, an artificial neural network, random forest, and naïve Bayes were developed and validated. Results In total, 20,645 subjects were included in this survey, with a mean global PSQI score of 6.02 (SD 3.112). The sleep disturbance rate was 28.9% (n=5972, defined as a global PSQI score >7 points). A total of 11 variables related to sleep quality were taken as parameters of the prediction models, including age, gender, residence, specialty, respiratory history, coffee consumption, stay up, long hours on the internet, sudden changes, fears of infection, and impatient closed-loop management. Among the generated models, the artificial neural network model proved to be the best, with an area under curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 0.713, 73.52%, 25.51%, 92.58%, 57.71%, and 75.79%, respectively. It is noteworthy that the logistic regression, random forest, and naive Bayes models achieved high specificities of 94.41%, 94.77%, and 86.40%, respectively. Conclusions The COVID-19 containment measures affected the sleep quality of college students on multiple levels, indicating that it is desiderate to provide targeted university management and social support. The artificial neural network model has presented excellent predictive efficiency and is favorable for implementing measures earlier in order to improve present conditions.

UNSTRUCTURED Background: The objective of this study was to address the prevalent issue of sleep disturbance among college students, which can lead to a range of mental and physical disorders. The identification of potential predictors and the development of an accurate prediction model are essential steps for the early detection of and appropriate intervention in sleep disturbances. However, previous studies have encountered notable limitations. Objective: This study aimed to provide a fresh perspective by developing and validating a model for the prediction of sleep quality among college students, which will improve the accuracy of predictions and facilitate timely interventions. Mehods: We analyzed data from 20,645 college students between 5 April and 16 April 2022 in Fujian Province, China.First, the Pittsburgh Sleep Quality Index (PSQI) scale, a self-designed general data questionnaire, and a sleep quality influencing factor questionnaire were conducted among the participants. Second, the collected data were used to select appropriate variables by comparing the outcomes of a multinomial logistic regression, LASSO regression, and Boruta feature selection. The data were then divided into a training–testing set (70%) and an independent validation set (30%) using stratified sampling. We developed and validated six machine learning techniques, which included an artificial neural network, a decision tree, a gradient-boosting tree, a k-nearest neighbor, a naïve Bayes, and a random forest. Finally, an online sleep evaluation website was established based on the best-fitting prediction model. Results: The mean global PSQI score was 6.02±3.112, and the sleep disturbance rate was 28.9% (defined as a global PSQI score of > 7 points). The LASSO regression model was preferred because it contained only the following eight predictors: age, specialty, respiratory history, coffee consumption, staying up late, long hours online, sudden changes, and impatient closed-loop management. Among the generated models, the artificial neural network (ANN) model was proven to have the best performance, with a cutoff, AUROC, accuracy, sensitivity, specificity, precision, F1-score, and KAPPA of 0.710, 0.713 (95%CI 0.696-0.730), 0.669 (95%CI 0.669-0.669), 0.682 (95%CI 0.699-0.665), 0.637 (95%CI 0.665-0.610), 0.822 (95%CI 0.837-0.807), 0.745 (95%CI 0.729-0.795), and 0.284 (95%CI 0.313-0.255), respectively. In addition, it had a Brier score of 0.182. The calibration curves showed good agreement between the predictions and the observations. A decision curve analysis demonstrated that the model could achieve a net benefit. A clinical impact curve confirmed the high clinical efficiency of the prediction model. Conclusions: The prediction model, which incorporated eight predictors, was built using a LASSO regression and an ANN to estimate the probability of sleep disturbance among college students. This model may be utilized as an intuitive and practical tool for sleep quality predictions to support better management and healthcare on college campuses.

UNSTRUCTURED Background: Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors for obstructive sleep apnea (OSA), and a variety of methods have been applied to predict the occurrence of OSA. Objective: This study aimed to develop and evaluate OSA prediction models using simple and accessible parameters, combined with multiple machine learning algorithms, and integrate them into a cloud-based mobile sleep medicine management platform for clinical use. Methods: The study data were obtained from the clinical data of 610 patients who underwent polysomnography (PSG) at the Sleep Medicine Center of the Second Affiliated Hospital of Fujian Medical University between January 2021 and December 2022. The participants were randomly divided into a training–test group (80%) and an independent validation group (20%). The logistic regression, artificial neural network, naïve Bayes, support vector machine, random forest, and decision tree algorithms were used with age, gender, BMI, and mean heart rate during sleep as predictors to build a risk prediction model for moderate-to-severe OSA. To evaluate the performance of the models, we calculated the area under the receiver operating curve (AUROC), accuracy, recall, specificity, precision, and F1-score for the independent validation set. In addition, the calibration curve, decision curve, and clinical impact curve were generated to determine clinical usefulness. Results: Age, gender, BMI, and mean heart rate during sleep were significantly associated with OSA. The ANN model had the best efficacy compared with the other prediction algorithms. The AUROC, accuracy, recall, specificity, precision, F1-score, and Brier score were 0.804, 0.699, 0.865, 0.615, 0.532, 0.659, and 0.165, respectively, for the ANN model. The AUROCs for the LR, NB, SVM, RF, and DT models were 0.802, 0.797, 0.792, 0.784, and 0.704, respectively. Conclusions: The six models based on four simple and easily accessible parameters effectively predicted moderate-to-severe OSA in patients with PSG screening, with the ANN model having the best performance. These models can provide a reliable tool for early OSA diagnosis, and their integration into a cloud-based mobile sleep medicine management platform could improve clinical decision making.

UNSTRUCTURED Background: The prevalence of respiratory diseases has increased significantly, resulting in a rise in the use of high-flow nasal cannula (HFNC) therapy. However, certain limitations still exist in the clinical practice of HFNC therapy, such as prolonged equipment training and limited healthcare resources, which can lead to unforeseen emergencies. Fortunately, with the advent of Internet of Things (IoT) technology, great potential has emerged for developing novel solutions for medical equipment management to overcome these challenges. By integrating technology, information, and services, the IoT-based HFNC equipment and remote management platform can provide real-time patient monitoring, timely adjustments, and early warnings for respiratory failure, leading to improved clinical outcomes and economic benefits. Therefore, our study explored the use of these innovative technologies in enhancing clinical effectiveness and resource use. Methods: In this study, we developed a remote management platform for respiratory equipment using the latest Internet of Things (IoT) and big data analysis technologies. Data on patients treated with high-flow nasal cannulas (HFNCs) were collected from 12 medical institutions in Fujian Province from December 2020 to December 2022. Patients were randomly allocated to either the ordinary HFNC group or the intelligent HFNC group. Basic patient information, medical history, laboratory indicators, total hospitalization costs and duration, comfort level, dryness score, and cough disorder scores were all recorded. The two groups were compared using the t tests and chi-square tests, and a P value of less than .05 was considered statistically significant. Advanced statistical methods were employed to ensure that the data were accurately analyzed and that the results were valid and reliable. Overall, we applied the latest scientific approaches and technologies to ensure the highest-quality data and analysis. Results: A total of 619 patients were enrolled in this study, with no statistically significant differences between the ordinary HFNC group and the intelligent HFNC group in terms of general information. However, the use of intelligent HFNCs was associated with a significantly reduced hospitalization cost and duration compared to the ordinary HFNC group. Furthermore, patients using intelligent HFNCs reported consistently lower levels of dryness and rated higher in terms of comfort compared to those using ordinary HFNCs. There were no statistically significant differences in blood oxygen level, complications, clinical outcome, impaired coughing, dyspnea, or assisted respiratory muscle mobilization between the two groups. Overall, our findings suggested that the use of intelligent HFNCs may be a promising and effective solution for the management of respiratory diseases, with potential benefits for both patients and healthcare systems. Conclusions: We successfully developed a remote management platform for respiratory equipment using Internet of Things (IoT) technology and big data analysis, with the intelligent high-flow nasal cannula (HFNC) as the core. Our findings indicated that patients who received treatment with the intelligent HFNC experienced improved comfort, shorter hospital stays, and reduced hospitalization costs compared to those who received traditional HFNC treatment. This platform not only provided precise oxygen therapy to patients with respiratory failure, but also supported physicians in analyzing conditions, enacting parameter settings, and issuing early sickness warnings. Consequently, the integration of IoT technology with intelligent HFNCs holds great potential in terms of cost and resource management and may present advantages compared to traditional HFNC methods.