In recent years, the awareness of sustainable tourism has risen around the world. Many tourism industries combine sports to attract more customers to facilitate the development of the economy and the promotion of local culture. However, it is an important task to establish a comprehensive tourism evaluation framework for sustainable sports tourism. This study proposes a Multi-Criteria Decision-Making (MCDM) model to discuss the above issues, using the Bayesian Best Worst Method (Bayesian BWM) to integrate multiple experts’ judgments to generate the group optimal criteria weights. Next, the modified Visekriterijumska Optimizacija i Kompromisno Resenje (VIKOR) technique is combined with the concept of aspiration level to determine the performance of sports attractions and their priority ranks. In addition, this study adds a perspective of institutional sustainability to emphasize the importance of government support and local marketing. The effectiveness and robustness of the proposed model is demonstrated through potential sports tourism attractions in Taiwan. A sensitivity analysis and models comparison were also performed in this study. The results show that the proposed model is feasible for practical applications and that it effectively provides some management implications to support decision-makers in formulating improvement strategies.

Problematic smartphone use (PSU) is an expanded public health heed that requires more study to clarify the influence elements of different populations. The aim of this study was to investigate the relationship between smartphone use, and sleep quality, self-perceived health, and exercise participation in university students. A total of 1,575 Taiwanese undergraduate students from 7 universities participated in the study. Three questionnaires were completed by the study individuals. The results show the overall PSU rate was 11.8%. Average smartphone users were more likely to feel in good health, better sleep quality and less unsatisfactory exercise participation than those who were problematic smartphone users. Multiple logistic regression analysis indicated that PSU, low weekly exercise frequency, and poor sleep quality were significant indicators of poor self-perceived health. We concluded that both low physical activity and PSU did have negative impacts on self-perceived health and sleep quality for undergraduate students.