Association of Web-Based Physical Education With Mental Health of College Students in Wuhan During the COVID-19 Outbreak: Cross-Sectional Survey Study.

BACKGROUND: The COVID-19 outbreak has affected people's health worldwide. For college students, web-based physical education is a challenge, as these course are normally offered outdoors. OBJECTIVE: The aim of this study was to use data from a web-based survey to evaluate the relationship between the mental health status of college students and their sports-related lifestyles. Problems related to web-based physical education were also examined. METHODS: A web-based survey was conducted by snowball sampling from May 8 to 11, 2020. Demographic data, mental health status, and sports-related lifestyles of college students in Wuhan as well as issues related to web-based physical education were collected. Mental health status was assessed by the Depression, Anxiety, and Stress Scale (DASS-21). RESULTS: The study included 1607 respondents from 267 cities. The average scores of the DASS-21 subscales (2.46 for depression, 1.48 for anxiety, and 2.59 for stress) were significantly lower in our study than in a previous study (P<.05). Lower DASS-21 scores were significantly correlated with regular exercise, maintaining exercise habits during the outbreak of COVID-19, exercising more than 1 to 2 times a week, exercise duration >1 hour, and >2000 pedometer steps (all P<.05). None of the three forms of web-based physical education was preferred by more than 50% of respondents. Frequent technical problems were confronted by 1087/1607 students (67.6%). Shape-up exercises (846/1607, 52.6%), a designed combination of exercises (710/1607, 44.2%), and Chinese kung fu (559/1607, 34.8%) were suggested sports for web-based physical education. CONCLUSIONS: Mental status was significantly correlated with regular exercise and sufficient exercise duration. Professional physical guidance is needed for college students in selected sports. Exercises not meeting students' preferences, frequent technical problems, and the distant interaction involved in web-based physical education were the main problems that should be solved in future.

Frequency-Specific Changes of Resting Brain Activity in Parkinson's Disease: A Machine Learning Approach.

The application of resting state functional MRI (RS-fMRI) in Parkinson's disease (PD) was widely performed using standard statistical tests, however, the machine learning (ML) approach has not yet been investigated in PD using RS-fMRI. In current study, we utilized the mean regional amplitude values as the features in patients with PD (n = 72) and in healthy controls (HC, n = 89). The t-test and linear support vector machine were employed to select the features and make prediction, respectively. Three frequency bins (Slow-5: 0.0107-0.0286 Hz; Slow-4: 0.0286-0.0821 Hz; conventional: 0.01-0.08 Hz) were analyzed. Our results showed that the Slow-4 may provide important information than Slow-5 in PD, and it had almost identical classification performance compared with the Combined (Slow-5 and Slow-4) and conventional frequency bands. Similar with previous neuroimaging studies in PD, the discriminative regions were mainly included the disrupted motor system, aberrant visual cortex, dysfunction of paralimbic/limbic and basal ganglia networks. The lateral parietal lobe, such as right inferior parietal lobe (IPL) and supramarginal gyrus (SMG), was detected as the discriminative features exclusively in Slow-4. Our findings, at the first time, indicated that the ML approach is a promising choice for detecting abnormal regions in PD, and a multi-frequency scheme would provide us more specific information.