Background: More activity participation is an important means of handling depression and promoting positive aging, but the impact of changes in activity participation on the developmental trajectory of depression has not been fully studied. Objective: The purpose of this study is to study the relationship between current activity participation and depression in middle-aged and older people (≥45 years old) and the relationship between activity participation and the developmental trajectory of depression in later life in China. Methods: This study used data from the China Health and Retirement Longitudinal Study (CHARLS) across 7 years and included a total of 4818 middle-aged and older people (≥45 years old). Controlling for relevant control variables, the latent growth curve model and the cross-lag model were used to assess the effect of changes in activity participation on the depression trajectory in later life and the main lag relationship between activity participation and depression. Activity participation as well as depression were measured using the self-reported activity and health status based on the CHARLS questionnaire. Results: Among the 4818 respondents, the mean values of physical activity participation, social leisure activity participation, and depression ranged from 76.98 (SD 15.16) to 83.95 (SD 5.72), from 7.43 (SD 8.67) to 9.25 (SD 10.16), and from 7.61 (SD 5.72) to 8.82 (SD 6.51), respectively. Our findings revealed that activity participation could be related to depression. Physical activity participation predicted initial depression (β=–0.631, P<.001) and its trajectory (β=0.461, P<.001). However, social leisure activity participation predicted initial depression (β=–0.223, P<.001) but did not predict its trajectory (β=0.067, P=.159). Finally, cross-lag regression analysis further demonstrated the predictive effect of activity participation on depression. Conclusions: This study demonstrates the prediction of activity participation for future depression in the Chinese middle-aged and older populations. The data showed that activity participation is significantly associated with changes in depression and future depression among middle-aged and older people in China. The Chinese government should encourage middle-aged and older people to participate in various activities, which can effectively prevent the aggravation of depression and also have a positive significance for positive aging.

Amid the ongoing digital transformation of higher education, increasing attention has been paid to the impact of digital technologies on teaching quality—particularly in physical education settings that require high levels of interaction and physical engagement. This study examined 1158 university students across China using a cross-sectional design, integrating interpretable machine learning models with structural equation modeling to systematically assess how the frequency of use of seven common digital technologies influences teaching quality in physical education classes. The study also investigated the mediating roles of perceived teacher support and academic self-efficacy. Nine machine learning models and logistic regression analyses were conducted to predict teaching quality, employing feature permutation importance and SHapley Additive exPlanations (SHAP) to evaluate the relative contribution of each digital tool. Results identified multimedia courseware, e-textbooks, and question banks as the most influential predictors of teaching quality. The SHAP analysis further revealed that management software, educational websites, and multimedia materials had significant positive effects and exhibited synergistic interactions in improving teaching outcomes. Structural equation modeling showed that digital technology use had a significant direct positive effect on teaching quality (B = 0.347, p < 0.001). Moreover, perceived teacher support and academic self-efficacy not only served as independent mediators (95% CI = [0.146, 0.226], p < 0.001; 95% CI = [0.024, 0.063], p < 0.001) but also functioned as a chain mediating effect (95% CI = [0.036, 0.083], p < 0.001). This study is the first to integrate interpretable machine learning with structural modeling to elucidate the mechanisms through which digital technologies influence teaching quality in university-level physical education. The findings underscore the critical mediating roles of teacher support and student self-efficacy, providing both theoretical contributions and practical implications for enhancing digital pedagogy in higher education.

Background: Cognitive function and health-related quality of life (HRQoL) are important issues in diabetes care. According to the China Association for Aging, it is estimated that by 2030, the number of elderly people with dementia in China will reach 22 million. The World Health Organization reports that by 2044, the number of people with diabetes in China is expected to reach 175 million. Methods: Cohort analyses were conducted based on 854 diabetic patients aged ≥45 years from the third (2015) and fourth (2018) survey of the China Health and Retirement Longitudinal Study (CHARLS). Correlation analysis, repeated-measures variance analysis, and cross-lagged panel models were used to measure the difference in digital usage behavior in the established relationship. Results: The results show that the cognitive function of middle-aged and older diabetic patients is positively correlated with HRQoL. HRQoL at T1 could significantly predict cognitive function at T2 (PCS: B = 0.12, p < 0.01; MCS: B = 0.14, p < 0.01). This relationship is more associated with individual performance than digital usage behavior. Conclusions: Unidirectional associations may exist between cognitive function and HRQoL among middle-aged and older Chinese diabetes patients. In the future, doctors and nurses can recognize the lowering of self-perceived HRQoL of middle-aged and older diabetic patients, and thus draw more attention to their cognitive function, in turn strengthening the evaluation, detection, and intervention of their cognitive function.

Digital technology has become increasingly prevalent in higher education classrooms. However, the impact of different types and use frequencies of digital technology on college students’ classroom engagement can vary substantially. This study aims to develop an interpretable machine learning model to predict student classroom engagement based on various digital technologies and construct a structural equation model (SEM) to further investigate the underlying mechanisms involving perceived usefulness (PU), perceived ease of use (PEU), and academic self-efficacy (ASE). Nine machine learning algorithms were employed to develop interpretable predictive models, rank the importance of digital technology tools, and identify the optimal predictive model for student engagement. A total of 1158 eligible Chinese university students participated in this study. The results indicated that subject-specific software, management software, websites, and mobile devices were identified as key factors influencing student engagement. Interaction effect analyses revealed significant synergistic effects between management software and subject-specific software, identifying them as primary determinants of student engagement. SEM results demonstrated that digital technology usage frequency indirectly influenced student engagement through PU, PEU, and ASE, with both PU and ASE as well as PEU and ASE playing chain-mediated roles. The findings underscore the importance of integrating digital tools strategically in PE classrooms to enhance engagement. These insights offer practical implications for higher education institutions and policymakers.

Background: The sudden and unpredictable changes caused by the COVID-19 pandemic are a serious threat to the occupational stress and mental health of referees worldwide, which has not attracted widespread attention. The mental health of football referees has a certain influence on their job satisfaction or the accuracy of judgments. Methods: This study constructed a moderated mediation model to explore the buffer factors between occupational stress and mental health in Chinese soccer referees in the early stage of reopening soccer matches during the COVID-19 pandemic outbreak. Data from 317 Chinese soccer referees (aged 19–45) were collected through an online questionnaire in September–October 2022. Occupational stress, mental health, job burnout and perceived social support were measured, and moderated mediation model was analyzed. Results: The results of this study showed that occupational stress was negatively correlated with mental health through the mediating effect of job burnout and the moderated effect of perceived social support after controlling for demographic variables. Specifically, the association between occupational stress and mental health was weaker when perceived social support was higher and stronger. Conclusions: The results demonstrate that job burnout and perceived social support played important roles in buffering the negative effects of occupational stress on the mental health of Chinese soccer referees in the early stage of reopening soccer matches during the COVID-19 pandemic outbreak. The findings provide implications for mental health interventions in soccer referees during the public health crises.

Psychological issues have a complex and multidimensional impact on a college athlete’s training and performance. As the reserve talent of competitive sports, it is very important to investigate the psychological health of athletes. This study aims to explore the association between generalized anxiety disorder (GAD), post-traumatic stress disorder (PTSD), basic psychological needs (BPN), and mental health (MH) among Chinese college athletes. Chinese college athletes who were willing to participate in the study, had participated in national competitions, and had a Chinese athlete rating certificate were included in this study. Participants completed the Kessler Psychological Distress Scale (K10), the Generalized Anxiety Disorder Scale (GAD-7), the Impact of Event Scale-Revised (IES-R), and the Basic Needs Satisfaction in General Scale (BNSG-S). The study involved 665 college athletes (415 males and 250 females), with an average age of 20.43 years (SD = 1.68). We performed descriptive statistics, correlation analyses, and moderated chain mediation analyses using SPSS 22.0 and Hayes’ PROCESS macro. The results of the final model showed that basic psychological needs were positively correlated with mental health (r = 0.443, p < 0.001), while PTSD (r = −0.346, p < 0.001) and generalized anxiety disorder (r = −0.527, p < 0.001) were negatively correlated with mental health among college athletes. There were significant indirect impacts. According to the bootstrapping results, basic psychological needs and generalized anxiety disorder played a mediating role in 22.54% and 50.29% of the total effects of PTSD on mental health, respectively. Meanwhile, the chain mediating effect of basic psychological needs and generalized anxiety disorder (7.23%) was also significant. The study’s findings advance our understanding of the connections between PTSD and mental health and highlight the significant roles played by basic psychological needs and generalized anxiety disorder in that link among Chinese college athletes.