Constructing a Decision Model for Health Club Members to Purchase Coaching Programs during the COVID-19 Epidemic

The recent COVID-19 epidemic has affected the global sports industry to a certain extent, and health clubs are no exception. To avoid unsustainable operations, health clubs need to restructure their programs to suit members' needs. Therefore, this study constructs a two-stage framework model to evaluate health club members' purchase of coaching programs. The first stage is to construct a hierarchy of evaluation, using the modified Delphi method, to select suitable criteria and extended sub-criteria, and add and delete them through expert discussion. In the second stage, we use the pairwise comparison matrix to calculate the weight of each criterion and sub-criterion to influence each other. Next, we evaluate and compare physical, online and offline, and live-stream coaching programs, by using network hierarchy analysis to identify the best class purchase plan during the epidemic and provide relevant suggestions. The results of the study found that during the epidemic, the primary sales were for weight training among physical programs (0.314), and activity classes among online and offline programs (0.633) as well as live-stream coaching programs (0.280). These findings have implications for health clubs in deciding which mode they need to adopt for sustainable operations.

The impact of online teaching in behavior intention for college students in Taiwan.

This paper aims to explore the change of learning mode of college students from physical courses to online courses due to the impact of the COVID-19 pandemic. The questionnaire survey method is used to conduct research on the behavior intentions of college students in online teaching under the pandemic. A total of 600 questionnaires are distributed, and 530 questionnaires are collected, for a recovery rate of 88%. A total of 493 supported questionnaires are received, for an effective recovery rate of 93%. Descriptive statistics of data analysis are used to analyze the distribution of background variables of college students, and a structural equation model is used to analyze and verify the impact of external variables (trust, convenience, perceived critical mass) on the technology acceptance model (perceived usefulness, perceived ease of use, attitude, and behavior intention). The results found no significant impact of the trust of college students in online teaching on the perceived usefulness, and significant impact of trust on the perceived ease of use. There is significant impact of convenience on perceived usefulness, and no significant impact of convenience on perceived ease of use. There is no significant impact of perceived critical mass on perceived usefulness, and significant impact of perceived critical mass on perceived ease of use. There is significant impact of perceived ease of use on perceived usefulness, and significant impact of perceived usefulness on attitude. There is significant impact of perceived ease of use on attitude, and significant impact of attitude on behavior intention. Based on the research results, practical suggestions and research suggestions are proposed in this research, which can be used as a reference for college students to use online courses for learning.

Dynamic changes of pulmonary diffusion capacity in survivors of non-critical COVID-19 during the first six months.

BACKGROUND: The dynamic trends of pulmonary function in coronavirus disease 2019 (COVID-19) survivors since discharge have been rarely described. We aimed to describe the changes of lung function and identify risk factors for impaired diffusion capacity. METHODS: Non-critical COVID-19 patients admitted to the Guangzhou Eighth People's Hospital, China, were enrolled from March to June 2020. Subjects were prospectively followed up with pulmonary function tests at discharge, three and six months after discharge. FINDINGS: Eighty-six patients completed diffusion capacity tests at three timepoints. The mean diffusion capacity for carbon monoxide (DLCO)% pred was 79.8% at discharge and significantly improved to 84.9% at Month-3. The transfer coefficient of the lung for carbon monoxide (KCO)% pred significantly increased from 91.7% at discharge to 95.7% at Month-3. Both of them showed no further improvement at Month-6. The change rates of DLCO% pred and KCO% pred were significantly higher in 0-3 months than in 3-6 months. The alveolar ventilation (VA) improved continuously during the follow-ups. At Month-6, impaired DLCO% pred was associated with being female (OR 5.2 [1.7-15.8]; p = 0.004) and peak total lesion score (TLS) of chest CT > 8.5 (OR 6.6 [1.7-26.5]; p = 0.007). DLCO% pred and KCO% pred were worse in females at discharge. And in patients with impaired diffusion capacity, females' DLCO% pred recovered slower than males. INTERPRETATION: The first three months is the critical recovery period for diffusion capacity. The impaired diffusion capacity was more severe and recovered slower in females than in males. Early pulmonary rehabilitation and individualized interventions for recovery are worthy of further investigations.

Constructing tongue coating recognition model using deep transfer learning to assist syndrome diagnosis and its potential in noninvasive ethnopharmacological evaluation.

ETHNOPHARMACOLOGICAL RELEVANCE: Tongue coating has been used as an effective signature of health in traditional Chinese medicine (TCM). The level of greasy coating closely relates to the strength of dampness or pathogenic qi in TCM theory. Previous empirical studies and our systematic review have shown the relation between greasy coating and various diseases, including gastroenteropathy, coronary heart disease, and coronavirus disease 2019 (COVID-19). However, the objective and intelligent greasy coating and related diseases recognition methods are still lacking. The construction of the artificial intelligent tongue recognition models may provide important syndrome diagnosis and efficacy evaluation methods, and contribute to the understanding of ethnopharmacological mechanisms based on TCM theory. AIM OF THE STUDY: The present study aimed to develop an artificial intelligent model for greasy tongue coating recognition and explore its application in COVID-19. MATERIALS AND METHODS: Herein, we developed greasy tongue coating recognition networks (GreasyCoatNet) using convolutional neural network technique and a relatively large (N = 1486) set of tongue images from standard devices. Tests were performed using both cross-validation procedures and a new dataset (N = 50) captured by common cameras. Besides, the accuracy and time efficiency comparisons between the GreasyCoatNet and doctors were also conducted. Finally, the model was transferred to recognize the greasy coating level of COVID-19. RESULTS: The overall accuracy in 3-level greasy coating classification with cross-validation was 88.8% and accuracy on new dataset was 82.0%, indicating that GreasyCoatNet can obtain robust greasy coating estimates from diverse datasets. In addition, we conducted user study to confirm that our GreasyCoatNet outperforms TCM practitioners, yet only consuming roughly 1% of doctors' examination time. Critically, we demonstrated that GreasyCoatNet, along with transfer learning, can construct more proper classifier of COVID-19, compared to directly training classifier on patient versus control datasets. We, therefore, derived a disease-specific deep learning network by finetuning the generic GreasyCoatNet. CONCLUSIONS: Our framework may provide an important research paradigm for differentiating tongue characteristics, diagnosing TCM syndrome, tracking disease progression, and evaluating intervention efficacy, exhibiting its unique potential in clinical applications.