A meta-analysis on the impact of resistance training on phase angle in middle-aged and older individuals.

PURPOSE: To determine the impact of resistance training (RT) on phase angle (PhA) in middle-aged and older individuals via meta-analysis, explore effects in subgroups, and identify optimal RT protocol. MATERIALS AND METHODS: We searched five databases using predefined criteria, assessed literature quality per Cochrane 5.1 Handbook, and used Revman 5.3 for effect size aggregation, bias assessment, sensitivity analysis, and subgroup analysis. RESULTS: RT improved PhA in middle-aged and older individuals (d = 0.34, 95 % CI: 0.27-0.40, P < 0.05). Effective subgroups included Suspension (d = 0.62, 95 % CI: 0.33-0.90, P < 0.05), free-weights and machine (d = 0.36, 95 % CI: 0.28-0.45, P < 0.05), equipment training (d = 0.24, 95 % CI: 0.13-0.36, P < 0.05), and moderate-intensity RT (d = 0.34, 95 % CI: 0.27-0.42, P < 0.05). RT was conducted 2-3 times/week (d = 0.20, 95 % CI: 0.01-0.38, P < 0.05) or (d = 0.38, 95 % CI: 0.30-0.47, P < 0.05). PhA improved after 8 weeks (d = 0.37, 95 % CI: 0.23-0.51, P < 0.05), 12 weeks (d = 0.35, 95 % CI: 0.26-0.44, P < 0.05), and ≥ 24 weeks (d = 0.26, 95 % CI: 0.11-0.41, P < 0.05) of RT in aged and older individuals. Low- and high-intensity RT, elastic band training, and weekly exercises did not significantly improve PhA. CONCLUSIONS: RT enhances PhA in middle-aged and older adults. For optimal results, we recommend 2-3 weekly sessions of free weights and machine training lasting at least 8 weeks.

Demands for Community Services and Associated Factors among Residents in Smart Communities: A Case Study of Xuzhou City.

Smart community enables a sustainable and livable community future, in which residents' demands play an important role in its success. Though great efforts have been made to encourage residents' participation in the implementation of smart communities, inefficient service supply still exists. Thus, this study aimed to classify residents' demands for community services in smart communities and to explore relevant influencing factors based on the developed conceptual framework. Data from 221 respondents in Xuzhou city of China were analyzed by using binary logistic regression. The results indicated that more than 70% of respondents had demands for all community services in smart communities. Moreover, the demands were influenced by distinct factors, including sociodemographic characteristics, living characteristics, economic characteristics, and individual attitude characteristics. The types of community services in smart communities are clarified and fresh insights are provided into associated factors related to residents' demands for these services in this study, through which enhanced provision of community services and effective implementation of smart communities can be achieved.