The use of the Internet has a promoting effect on the physical health of the older people. However, previous studies are mostly focused on the perspective of the overall population, or limited to the direct effects, ignoring the exploration of the mechanism of action and the perspective of the older people. Based on the data of the China Family Panel Survey (CFPS) in 2014, 2016, 2018, and 2020, this study found that the use of the Internet has a significant effect on the physical health of the older people, especially among the population groups of females, rural residents, and those living in central and western regions of China. In addition, this study also found that the use of the Internet by the older people can increase their exercise frequency, thereby improving their physical health. Therefore, to promote active aging, this study proposes to further increase the popularity of the Internet among the older people, encourage the introduction of age-appropriate Internet systems and sports facilities, create an online fitness platform for the older people, and promote scientific fitness programs for them.

Purpose: The popularization of the internet has promoted the implementation of China's national fitness strategy and created conditions for Chinese residents to participate in sports. The internet is an essential medium for disseminating sports knowledge, and the use of the internet can change sport participation behaviors. Therefore, the internet can be used to popularize sports knowledge and promote the participation of all people in sports and thus improve the health of the entire population. This study attempts to empirically analyse how the use of the internet changes sport participation behavior.Method: Utilizing data from the 2017 China General Social Survey, a probit model, ivprobit model, and bias-corrected non-parametric percentile bootstrap test were used to analyse the impact of internet use on sport participation behavior.Results: The empirical results show that internet use significantly increased the probability of participation in sports by Chinese residents. Heterogeneity test results showed that internet use was more effective in promoting sport participation in middle-aged groups, groups of older persons, unmarried groups, and groups with a high school education or above. The mediating effect test results showed that internet use influenced residents' participation in sports by promoting social interaction, leisure and entertainment, and learning and recharging.Conclusions: The internet has changed participation in sports; specifically, the use of the internet promotes sport participation. Additionally, internet use has a more obvious impact on improving the sport participation behavior of middle-aged, older, unmarried, and middle- and higher-educated individuals. Internet social interaction, internet entertainment and internet learning are effective channels to encourage Chinese residents to participate in sports and improve their health.

Background: With the aging of the population, health issues among older people have drawn increasing societal attention. The rapid development of intelligent technology provides innovative solutions to addressing these challenges. This study aims to explore the impact of smart wearable devices on older people’s health outcomes while discussing the mechanism of physical exercise, to provide a new perspective and exercise strategy for maximizing the positive role of smart wearable devices in older adult health management.Methods: Utilizing data from the China Longitudinal Aging Social Survey (CLASS), this study analyzed 21,881 samples. Descriptive statistics was computed using SPSS, followed by bidirectional fixed effect model analysis and intermediate effect test via Stata.Results: Bidirectional fixed effect model showed that the use of smart wearable devices could significantly improve the physical health of the older people (β = 0.325, p < 0.001). The effect was more significant in those aged 70 years or older (β = 0.385, p < 0.001), those without chronic diseases (β = 0.590, p < 0.001), and those living with their children (β = 0.452, p < 0.001). The mediating mechanism test results reported that exercise frequency (β = 0.037, p < 0.05) and exercise duration (β = 0.063, p < 0.001) were two positive mediating pathways for smart wearable devices to affect the health of the older adults.Conclusion: This study reveals the positive impact of smart wearable devices on the physical health level of the older people. To maximize benefits, we propose a dual-path optimization strategy: (1) Develop age-appropriate functions and use methods to improve device accessibility among older adult people, and (2) Generate a personalized exercise prescription for the older people to promote regular physical activity and exercise habits to improve their health level.

Using data from China Family Panel Studies (CFPS) and based on the Probit and Tobit models, this study investigates the impact of air pollution on residents’ outdoor exercise behaviour from the microscopic level. Specifically, this study examined the effects of PM2.5 index changes on residents’ decision to participate in outdoor exercise and the duration of outdoor exercise participation. The empirical results show that the increase of PM2.5 index has a significant inhibitory effect on residents’ participation in outdoor exercise, and has passed the robustness test and endogeneity test. Further testing found that the inhibitory effect was significantly different between urban and rural areas, and in the central, north-eastern and western regions where economic development was relatively backward, the conclusion that air pollution inhibited residents’ outdoor exercise behaviour still holds true. However, the level of air pollution had no significant effect on the outdoor exercise behaviour of residents in the eastern region. So, while air pollution discourages residents from participating in outdoor exercise, the results are more applicable to less economically developed areas.

This study examined the impact of COVID-19 on 28 indicators of match technical performance (MTP) for football attacking players upon their return to play. Analyzing data from 100 players in the Big Five European football leagues, covering 1500 matches each before and after COVID-19 over 3 years (2020–2023), revealed significant differences in 76% of players’ MTP indicators. Notably, 14 indicators, particularly the five indicators linked to scoring, significantly decreased post-COVID-19. On average, players needed 3.09 matches to regain pre-infection MTP levels. The impact varied across player groups, with those in the elite group showing a milder effect; they required an average of 2.64 matches for recovery, compared to the control group’s 3.55 matches. We found that, with increasing age, the majority of players’ MTP indicators did not exhibit significant changes, both before and after they contracted COVID-19. In conclusion, the study highlighted the negative impact of COVID-19 on football attacking players’ MTP. Players in the elite group experienced fewer adverse effects than those in the control group. This insight assisted coaches and managers in evaluating the impact of COVID-19 and similar virus-induced illnesses on players’ MTP, enabling them to formulate training regimens for recovery and specific match tactics upon players' return to play.

Monitoring the mental workload of construction workers is effective in detecting risky subjects because cognitive overload may threaten their safety. This study aimed to measure workers’ mental workload caused by heat exposure using heart rate variability (HRV) and eye movement features. Inexperienced pipe workers (n = 30) were invited to perform an installation task in a normothermic (26 °C, 50% RH) and a hyperthermic (33 °C, 50% RH) condition. Their HRV and eye movement features were recorded as the inputs of training models classifying mental workload between the two thermal conditions, using supervised machine learning algorithms, including Support Vector Machines (SVM), KNearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF). The results show that applying eight HRV features through the KNN algorithm could obtain the highest classification accuracy of 90.00% (Recall = 0.933, Precision = 0.875, F1 = 0.903, AUC = 0.887). This study could provide a new perspective for monitoring the mental workload of construction workers, and it could also provide a feasible approach for the construction industry to monitor workers’ mental workload in hot conditions.