This narrative review of the literature assessed whether regular physical exercise and sleep patterns, fasting and autophagy, altogether can be an adequate strategy for achieving healthy longevity and well-being within different stage of life. There are a large number of studies dealing with well-being and healthy longevity; however, few of them have given us a specific formula for how to live long and healthy. Despite all the advances that have been made to create adequate physical exercise programs, sleep patterns or nutritional protocols, the relation between different types of fasting, nutritional supplementation as well as regular physical exercise and sleep patterns have not yet been satisfactorily resolved to cause the best effects of autophagy and, therefore, well-being and healthy longevity. In this way, future studies should clarify more efficiently the relationship between these variables to understand the association between regular physical exercise, sleep patterns, fasting and autophagy for healthy longevity and well-being.

The development of a high-quality sports industry is crucial to China’s economic growth. This research quantitatively analyzed factors influencing the development of the sports industry for the period 2010–2019. The study selected variables pertaining to the gross national income per capita (X1), household final consumption expenditure per capita (X2), sports population (X3), number of fitness venues and facilities (X4), number of sporting events (X5), and number of sports-related business registrations (X6) and analyzed their relationship with the value added to the sports industry. By developing a ridge regression model, it can be determined that correlations (Pearson’s r) between six factors and the value added to the sports industry were all greater than 0.90, and that each factor had a positive impact on the industry (p < 0.05). After standardizing the ridge regression model with the z-score method, it was determined that the degree of influence of the six factors varied: X2 (βridge = 0.156), X3 (βridge = 0.153) and X5 (βridge = 0.153), X1 (βridge = 0.151), X4 (βridge = 0.136), and X6 (βridge = 0.121). The ridge regression model can give a reference model for predicting and optimizing the sustainable development of the sports industry in China.