Percorrer por autor "Chen, Cheng"
A mostrar 1 - 2 de 2
Resultados por página
Opções de ordenação
- Impact of physical exercise interventions on functional fitness in older adultsPublication . Chen, Cheng; Freire, Cristiana; Fu, Zhiyang; Teixeira, Inês; Adegas, Matilde; Gomes, Rafael; Rabaçal, Ricardo; Silva, Sara; Malheiro, Alexandra; Leite, Luciano Bernardes; Reis, António; Monteiro, António M.; Yang, Jing; Xiaolin, Yao; Forte, PedroAging is associated with a decline in functional fitness, compromising the autonomy of older adults. This systematic review evaluated recent randomized clinical trials (2015–2024) on the effects of physical exercise in individuals aged ≥65 years. Ninety-five studies were included, showing that multicomponent programs, resistance training, and supervised protocols improved muscular strength, mobility, balance, gait speed, and body composition. Interventions lasting more than 12 weeks with high adherence yielded more favorable outcomes. Effects on cognitive function were inconsistent. Structured exercises tailored to the capacities of older adults are effective strategies to promote functional independence and healthy aging, although study heterogeneity limits some conclusions.
- Why they do not move: An explainable machine learning analysis of physical activity barriers in obese adolescents and tool translationPublication . Chen, Cheng; Chen, Kai; Du, Wenqian; Li, Shengtao; Gou, Wenling; Yang, Jing; Forte, Pedro; Zhang, Xiaoran; Shangguan, Yuwen; Huang, Yongyu; Zhang, Hao; Zhang, Xiaofei; Lin, Zhiyi; Yao, Xiaolin; Li, HuanDespite the well-documented benefits of physical activity, insufficient activity remains highly prevalent among adolescents with obesity. This study is the first to apply interpretable machine learning methods to identify the barriers, facilitators, and U-shaped determinants of physical activity in this population. Methods: We analyzed data from 1,041 adolescents with obesity from the China Education Panel Survey. A range of personal, family, and school-level variables were incorporated to construct six machine learning models for predicting physical activity attainment. The Shapley Additive Explanations (SHAP) method, a game-theoretic approach for explainable artificial intelligence, was used to identify key predictive factors and quantify their relative contributions. Results: The Random Forest model demonstrated the best performance, achieving an accuracy of 85.30% and an AUC of 0.720 on the test set. SHAP analysis revealed several key factors associated with physical activity. Positive facilitators included parents with an education level beyond high school (≥ 3.27 for mothers, ≥ 3.51 for fathers), higher school rankings (≥ 3.77), adequate school sports facilities (≥ 1.44), and a personal interest in sports. Negative barriers included excessive screen time (≥ 5.51 h) and school location in central urban areas (≥ 4.23). Notably, U-shaped relationships were identified for academic workload, sleep problems, and self-perceived appearance. Specifically, moderate levels of these factors were associated with lower physical activity, whereas both low and high extremes promoted activity. Conclusion: This study demonstrates that physical activity among adolescents with obesity is shaped by a complex interplay of individual, family, and school-level factors, with parental education emerging as the strongest predictor. The identification of specific risk thresholds (e.g., screen time ≥ 5.51 h) and U-shaped relationships offers precise, actionable targets for intervention. These findings underscore the need for multi-level strategies: families should prioritize fostering cultural capital, schools should ensure facility accessibility beyond regular hours, and policymakers must address environmental constraints in urban settings. To facilitate the translation of these insights into practice, a web-based tool has been deployed to help physical education teachers identify at-risk students and design targeted interventions.