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.

Background and objectives: Quality of life and sleep quality of college students were extensively studied. The present study evaluated sleep quality and quality of life of college students in Taiwan by using the Pittsburgh Sleep Quality Index (PSQI) and Short-Form Health Survey (SF-36), respectively. Materials and Methods: Data of 1756 college students aged 20–24 years were collected in this study. Association rule analysis was also used to provide a graphics-based visualization of the relationships between data, enabling the rapid identification of data correlations. Results: The results showed that the average physical component scale (PCS) and average mental component scale (MCS) scores were 52.9 and 44.1, respectively. Based on their body mass index (BMI), participants were divided into underweight, normal, overweight, and obese groups. The results of one-way analysis of variance showed that the p values for the PSQI, PCS, and MCS scores were 3.5 × 10−5, 1.7 × 10−5, and 0.671, respectively. The normal and overweight groups had the lowest PSQI scores. The PCS score of the obese group was lower than that of normal and overweight groups. The p values of the t-test result among PSQI, BMI, PCS, and MCS groups were 0.002, < 2 × 10−16, and < 2 × 10−16, respectively. The good sleep quality group had higher PCS and MCS scores. Conclusions: In this study, the results of association rule analysis indicated two distinct groups: Group 1, with the characteristics of good sleep quality as revealed by the high MCS and PCS scores, and Group 2, with the characteristics of poor sleep quality as revealed by low MCS and PCS scores and underweight BMI.