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Zhou Z, Wang D, Sun J, Zhu M, Teng L. A Machine Learning-Based Prediction Model for the Probability of Fall Risk Among Chinese Community-Dwelling Older Adults. Comput Inform Nurs 2024; 42:913-921. [PMID: 39356834 DOI: 10.1097/cin.0000000000001202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2024]
Abstract
Fall is a common adverse event among older adults. This study aimed to identify essential fall factors and develop a machine learning-based prediction model to predict the fall risk category among community-dwelling older adults, leading to earlier intervention and better outcomes. Three prediction models (logistic regression, random forest, and naive Bayes) were constructed and evaluated. A total of 459 people were involved, including 156 participants (34.0%) with high fall risk. Seven independent predictors (frail status, age, smoking, heart attack, cerebrovascular disease, arthritis, and osteoporosis) were selected to develop the models. Among the three machine learning models, the logistic regression model had the best model fit, with the highest area under the curve (0.856) and accuracy (0.797) and sensitivity (0.735) in the test set. The logistic regression model had excellent discrimination, calibration, and clinical decision-making ability, which could aid in accurately identifying the high-risk groups and taking early intervention with the model.
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Affiliation(s)
- Zhou Zhou
- Author Affiliations: Wuxi School of Medicine, Jiangnan University, Jiangsu (Mr Zhou; Mss Wang, Sun, and Zhu; and Dr Teng); Traditional Chinese Medicine Hospital of Qinghai Province, Xining, Qinghai (Ms Wang), China
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Zhang L, Zhao S, Yang Z, Zheng H, Lei M. An Artificial Intelligence Platform to Stratify the Risk of Experiencing Sleep Disturbance in University Students After Analyzing Psychological Health, Lifestyle, and Sports: A Multicenter Externally Validated Study. Psychol Res Behav Manag 2024; 17:1057-1071. [PMID: 38505352 PMCID: PMC10949300 DOI: 10.2147/prbm.s448698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Accepted: 02/10/2024] [Indexed: 03/21/2024] Open
Abstract
Background Sleep problems are prevalent among university students, yet there is a lack of effective models to assess the risk of sleep disturbance. Artificial intelligence (AI) provides an opportunity to develop a platform for evaluating the risk. This study aims to develop and validate an AI platform to stratify the risk of experiencing sleep disturbance for university students. Methods A total of 2243 university students were included, with 1882 students from five universities comprising the model derivation group and 361 students from two additional universities forming the external validation group. Six machine learning techniques, including extreme gradient boosting machine (eXGBM), decision tree (DT), k-nearest neighbor (KNN), random forest (RF), neural network (NN), and support vector machine (SVM), were employed to train models using the same set of features. The models' prediction performance was assessed based on discrimination and calibration, and feature importance was determined using Shapley Additive exPlanations (SHAP) analysis. Results The prevalence of sleep disturbance was 44.69% in the model derivation group and 49.58% in the external validation group. Among the developed models, eXGBM exhibited superior performance, surpassing other models in metrics such as area under the curve (0.779, 95% CI: 0.728-0.830), accuracy (0.710), precision (0.737), F1 score (0.692), Brier score (0.193), and log loss (0.569). Calibration and decision curve analyses demonstrated favorable calibration ability and clinical net benefits, respectively. SHAP analysis identified five key features: stress score, severity of depression, vegetable consumption, age, and sedentary time. The AI platform was made available online at https://sleepdisturbancestudents-xakgzwectsw85cagdgkax9.streamlit.app/, enabling users to calculate individualized risk of sleep disturbance. Conclusion Sleep disturbance is prevalent among university students. This study presents an AI model capable of identifying students at high risk for sleep disturbance. The AI platform offers a valuable resource to guide interventions and improve sleep outcomes for university students.
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Affiliation(s)
- Lirong Zhang
- Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, 361024, People’s Republic of China
| | - Shaocong Zhao
- Department of Physical Education, Xiamen University of Technology, Xiamen, Fujian, 361024, People’s Republic of China
| | - Zhongbing Yang
- School of Physical Education, Guizhou Normal University, Guizhou, 550025, People’s Republic of China
| | - Hua Zheng
- College of Physical Education and Health Sciences, Chongqing Normal University, Chongqing, 401331, People’s Republic of China
| | - Mingxing Lei
- National Clinical Research Center for Orthopedics, Sports Medicine & Rehabilitation, Beijing, 100039, People’s Republic of China
- Department of Orthopedic Surgery, Chinese PLA General Hospital, Beijing, 100039, People’s Republic of China
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Hu B, Wu Q, Wang Y, Zhou H, Yin D. Factors associated with sleep disorders among university students in Jiangsu Province: a cross-sectional study. Front Psychiatry 2024; 15:1288498. [PMID: 38463428 PMCID: PMC10920341 DOI: 10.3389/fpsyt.2024.1288498] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 02/09/2024] [Indexed: 03/12/2024] Open
Abstract
Objective This study aims to establish the precise prevalence of sleep disorders among university students in Jiangsu Province. Utilizing a representative sample of students, we measured their sleep quality based on the Pittsburgh Sleep Quality Index (PSQI). Our objective is to quantitatively assess the magnitude of sleep quality and identify key factors. By detailed analysis of these relationships, our study seeks to provide actionable insights for the development of targeted interventions to enhance sleep quality within this population. Methods From October to November 2022, we conducted a cross-sectional web-based survey in Jiangsu Province, China. Using convenient cluster sampling in each college, a total of 8457 participants were selected. The PSQI was applied to assess sleep quality among university students. Data collected included sociodemographic details, scores from the Mobile Phone Dependence Index (MPAI) and psychological resilience measured by the Connor-Davidson Resilience Scale (CD-RISC). Results The overall prevalence of poor sleep quality among the participants was 39.30%. Binary logistic regression analysis revealed that higher physical activity (OR = 0.921; 95% CI: 0.779-1.090), earlier roommate bedtimes (OR = 0.799; 95% CI: 0.718-0.888), quieter dormitories (OR = 0.732; 95% CI: 0.647-0.828) and higher psychological resilience (OR = 0.982; 95% CI, 0.979-0.984) were protective factors linked to lower risk of poor sleep quality. Conversely, being a female student (OR = 1.238; 95% CI: 1.109-1.382), being a senior (OR = 1.582; 95% CI: 1.344-1.863), single-child status (OR = 1.195; 95% CI: 1.077-1.326), regular smoking (OR = 1.833; 95% CI: 1.181-2.847), regular alcohol consumption (OR = 1.737; 95% CI: 1.065-2.833), high academic stress (OR = 1.326; 95% CI: 1.012-1.736), high employment stress (OR = 1.352; 95% CI: 1.156-1.582), dissatisfaction with dormitory hygiene (OR = 1.140; 95% CI: 1.028-1.265), poor self-rated physical health (OR = 1.969; 95% CI: 1.533-2.529), poor self-rated mental health (OR = 2.924; 95% CI: 2.309-3.702) and higher mobile phone dependency were risk factors associated with an increased likelihood of poor sleep quality. Conclusion The sleep quality among university students should attract immediate attention. The development of public services and mental health education initiatives is crucial in enhancing the sleep health of this population.
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Affiliation(s)
- Bin Hu
- *Correspondence: Bin Hu, ; Dehui Yin,
| | | | | | | | - Dehui Yin
- Key Laboratory of Human Genetics and Environmental Medicine, School of Public Health, Xuzhou Medical University, Xuzhou, China
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Chen Q, Chen Z, Zhu X, Zhuang J, Yao L, Zheng H, Li J, Xia T, Lin J, Huang J, Zeng Y, Fan C, Fan J, Song D, Zhang Y. Artificial neural network-based model for sleep quality prediction for frontline medical staff during major medical assistance. Digit Health 2024; 10:20552076241287363. [PMID: 39398893 PMCID: PMC11467980 DOI: 10.1177/20552076241287363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2024] Open
Abstract
Background: The sleep quality of medical staff was severely affected during COVID-19, but the factors influencing the sleep quality of frontline staff involved in medical assistance remained unclear, and screening tools for their sleep quality were lacking. Methods: From June 25 to July 14, 2022, we conducted an Internet-based cross-sectional survey. The Pittsburgh Sleep Quality Index (PSQI), a self-designed general information questionnaire, and a questionnaire regarding the factors influencing sleep quality were combined to understand the sleep quality of frontline medical staff in Fujian Province supporting Shanghai in the past month. A chi-square test was used to compare participant characteristics, and multivariate unconditional logistic regression analysis was used to determine the predictors of sleep quality. Stratified sampling was used to divide the data into a training test set (n = 1061, 80%) and an independent validation set (n = 265, 20%). Six models were developed and validated using logistic regression, artificial neural network, gradient augmented tree, random forest, naive Bayes, and model decision tree. Results: A total of 1326 frontline medical staff were included in this survey, with a mean PSQI score of 11.354 ± 4.051. The prevalence of poor sleep quality was 80.8% (n = 1072, PSQI >7). Six variables related to sleep quality were used as parameters in the prediction model, including type of work, professional job title, work shift, weight change, tea consumption during assistance, and basic diseases. The artificial neural network (ANN) model produced the best overall performance with area under the curve, accuracy, sensitivity, specificity, precision, F1 score, and kappa of 71.6%, 68.7%, 66.7%, 69.2%, 34.0%, 45.0%, and 26.2% respectively. Conclusions: In this study, the ANN model, which demonstrated excellent predictive efficiency, showed potential for application in monitoring the sleep quality of medical staff and provide some scientific guidance suggestions for early intervention.
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Affiliation(s)
- Qingquan Chen
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- The School of Public Health, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Zeshun Chen
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Xi Zhu
- The School of Public Health, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jiajing Zhuang
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Ling Yao
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Huaxian Zheng
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jiaxin Li
- Anyang University, Anyang, Henan Province, China
| | - Tian Xia
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jiayi Lin
- The School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Jiewei Huang
- The Graduate School of Fujian Medical University, Fuzhou, Fujian Province, China
| | - Yifu Zeng
- Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, Guangdong Province, China
| | - Chunmei Fan
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Jimin Fan
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Duanhong Song
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
- The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Yixiang Zhang
- The Sleep Disorder Medicine Center of the Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
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Du M, Li M, Yu X, Wang S, Wang Y, Yan W, Liu Q, Liu M, Liu J. Development and validation of prediction models for poor sleep quality among older adults in the post-COVID-19 pandemic era. Ann Med 2023; 55:2285910. [PMID: 38010392 PMCID: PMC10836252 DOI: 10.1080/07853890.2023.2285910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/15/2023] [Indexed: 11/29/2023] Open
Abstract
BACKGROUND Corona Virus Disease 2019 (COVID-19) has a significant impact on sleep quality. However, the effects on sleep quality in the post-COVID-19 pandemic era remain unclear, and there is a lack of a screening tool for Chinese older adults. This study aimed to understand the prevalence of poor sleep quality and determine sensitive variables to develop an effective prediction model for screening sleep problems during infectious diseases outbreaks. MATERIALS AND METHODS The Peking University Health Cohort included 10,156 participants enrolled from April to May 2023. The Pittsburgh Sleep Quality Index (PSQI) scale was used to assess sleep quality. The data were randomly divided into a training-testing cohort (n = 7109, 70%) and an independent validation cohort (n = 3027, 30%). Five prediction models with 10-fold cross validation including the Least Absolute Shrinkage and Selection Operator (LASSO), Stochastic Volatility Model (SVM), Random Forest (RF), Artificial Neural Network (ANN), and XGBoost model based on the area under curve (AUC) were used to develop and validate predictors. RESULTS The prevalence of poor sleep quality (PSQI >7) was 30.69% (3117/10,156). Among the generated models, the LASSO model outperformed SVM (AUC 0.579), RF (AUC 0.626), ANN (AUC 0.615) and XGBoost (AUC 0.606), with an AUC of 0.7. Finally, a total of 12 variables related to sleep quality were used as parameters in the prediction models. These variables included age, gender, ethnicity, educational level, residence, marital status, history of chronic diseases, SARS-CoV-2 infection, COVID-19 vaccination, social support, depressive symptoms, and cognitive impairment among older adults during the post-COVID-19 pandemic. The nomogram illustrated that depressive symptoms contributed the most to the prediction of poor sleep quality, followed by age and residence. CONCLUSIONS This nomogram, based on twelve-variable, could potentially serve as a practical and reliable tool for early identification of poor sleep quality among older adults during the post-pandemic period.
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Affiliation(s)
- Min Du
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Manchang Li
- Anning First People’s Hospital, Kunming University of Science and Technology, Yunan, China
| | - Xuejun Yu
- Jinfang Community Health Center, Anning Medical Community, Yunan, China
| | - Shiping Wang
- Anning First People’s Hospital, Kunming University of Science and Technology, Yunan, China
| | - Yaping Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wenxin Yan
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Qiao Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Min Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute for Global Health and Development, Peking University, Beijing, China
| | - Jue Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
- Institute for Global Health and Development, Peking University, Beijing, China
- Ministry of Education, Key Laboratory of Epidemiology of Major Diseases (Peking University), Beijing, China
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA
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Ji W, Shi L, Lin X, Shen Z, Chen Q, Song D, Huang P, Zhao Z, Fan J, Hu Y, Xie M, Yang J, Chen X. The relationship between sleep quality and daytime dysfunction among college students in China during COVID-19: a cross-sectional study. Front Public Health 2023; 11:1253834. [PMID: 38026404 PMCID: PMC10667466 DOI: 10.3389/fpubh.2023.1253834] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 10/30/2023] [Indexed: 12/01/2023] Open
Abstract
Objective College Students' sleep quality and daytime dysfunction have become worse since the COVID-19 outbreak, the purpose of this study was to explore the relationship between sleep quality and daytime dysfunction among college students during the COVID-19 (Corona Virus Disease 2019) period. Methods This research adopts the form of cluster random sampling of online questionnaires. From April 5 to 16 in 2022, questionnaires are distributed to college students in various universities in Fujian Province, China and the general information questionnaire and PSQI scale are used for investigation. SPSS26.0 was used to conduct an independent sample t-test and variance analysis on the data, multi-factorial analysis was performed using logistic regression analysis. The main outcome variables are the score of subjective sleep quality and daytime dysfunction. Results During the COVID-19 period, the average PSQI score of the tested college students was 6.17 ± 3.263, and the sleep disorder rate was 29.6%, the daytime dysfunction rate was 85%. Being female, study liberal art/science/ engineering, irritable (due to limited outdoor), prolong electronic entertainment time were associated with low sleep quality (p < 0.001), and the occurrence of daytime dysfunction was higher than other groups (p < 0.001). Logistics regression analysis showed that sleep quality and daytime dysfunction were associated with gender, profession, irritable (due to limited outdoor), and prolonged electronic entertainment time (p < 0.001). Conclusion During the COVID-19 epidemic, the sleep quality of college students was affected, and different degrees of daytime dysfunction have appeared, both are in worse condition than before the COVID-19 outbreak. Sleep quality may was inversely associated with daytime dysfunction.
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Affiliation(s)
- Wei Ji
- The Second Clinical College of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Liyong Shi
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xinjun Lin
- The Second Clinical College of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Zhiyong Shen
- Department of Respiratory and Critical Care Medicine, Jinjiang City Hospital, Quanzhou, Fujian Province, China
| | - Qingquan Chen
- The School of Public Health, Fujian Medical University, Fuzhou, Fujian Province, China
| | - Duanhong Song
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Pengxiang Huang
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Zhihuang Zhao
- The Second Clinical College of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Jimin Fan
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Yiming Hu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mianmian Xie
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Jiaohong Yang
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
| | - Xiaoyang Chen
- Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian Province, China
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Jaisinghani D, Phutela N. Packets-to-Prediction: An Unobtrusive Mechanism for Identifying Coarse-Grained Sleep Patterns with WiFi MAC Layer Traffic. SENSORS (BASEL, SWITZERLAND) 2023; 23:6631. [PMID: 37514925 PMCID: PMC10383615 DOI: 10.3390/s23146631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 07/05/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
A good night's sleep is of the utmost importance for the seamless execution of our cognitive capabilities. Unfortunately, the research shows that one-third of the US adult population is severely sleep deprived. With college students as our focused group, we devised a contactless, unobtrusive mechanism to detect sleep patterns, which, contrary to existing sensor-based solutions, does not require the subject to put on any sensors on the body or buy expensive sleep sensing equipment. We named this mechanism Packets-to-Predictions(P2P) because we leverage the WiFi MAC layer traffic collected in the home and university environments to predict "sleep" and "awake" periods. We first manually established that extracting such patterns is feasible, and then, we trained various machine learning models to identify these patterns automatically. We trained six machine learning models-K nearest neighbors, logistic regression, random forest classifier, support vector classifier, gradient boosting classifier, and multilayer perceptron. K nearest neighbors gave the best performance with 87% train accuracy and 83% test accuracy.
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Affiliation(s)
- Dheryta Jaisinghani
- Department of Computer Science, College of Humanities, Arts, and Sciences, University of Northern Iowa, Cedar Falls, IA 50613, USA
| | - Nishtha Phutela
- Department of Computer Science and Engineering, BML Munjal University, Gurugram 122413, India
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