Activities of daily living and non-exercise physical activity in older adults: findings from the Chinese Longitudinal Healthy Longevity Survey.

OBJECTIVES: Studies have shown that good cognitive function can moderate the relationship between non-exercise physical activity (NEPA) and activities of daily living (ADLs) disability to some extent, and this study mainly explores the relationship between ADL and NEPA and cognitive function in Chinese older adults. SETTING AND PARTICIPANTS: Data came from a nationally representative sample of 2471 Chinese old adults (aged 65+) from the 2011, 2014 and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey. PRIMARY AND SECONDARY OUTCOME MEASURES: A cross-lagged panel model combined with mediation analysis was used to determine the relationship between ADL and NEPA and the mediating effect of cognitive function on the ascertained ADL-NEPA relationship. RESULTS: The more frequently people over the age of 65 in China participate in NEPA, the lower the risk of ADL disability. Cognitive function partially mediated this expected relationship, accounting for 9.09% of the total NEPA effect on ADL. CONCLUSION: Participating in more NEPA could reduce the risk of ADL disability, and participating in NEPA may reduce the risk of ADL disability through cognitive function to some extent.

Development of rapid and effective risk prediction models for stroke in the Chinese population: a cross-sectional study.

OBJECTIVES: The purpose of this study was to use easily obtained and directly observable clinical features to establish predictive models to identify patients at increased risk of stroke. SETTING AND PARTICIPANTS: A total of 46 240 valid records were obtained from 8 research centres and 14 communities in Jiangxi province, China, between February and September 2018. PRIMARY AND SECONDARY OUTCOME MEASURES: The area under the receiver operating characteristic curve (AUC), sensitivity, specificity and accuracy were calculated to test the performance of the five models (logistic regression (LR), random forest (RF), decision tree (DT), extreme gradient boosting (XGBoost) and gradient boosting DT). The calibration curve was used to show calibration performance. RESULTS: The results indicated that XGBoost (AUC: 0.924, accuracy: 0.873, sensitivity: 0.776, specificity: 0.916) and RF (AUC: 0.924, accuracy: 0.872, sensitivity: 0.778, specificity: 0.913) demonstrated excellent performance in predicting stroke. Physical inactivity, hypertension, meat-based diet and high salt intake were important prediction features of stroke. CONCLUSION: The five machine learning models all had good predictive and discriminatory performance for stroke. The performance of RF and XGBoost was slightly better than that of LR, which was easier to interpret and less prone to overfitting. This work provides a rapid and accurate tool for stroke risk assessment, which can help to improve the efficiency of stroke screening medical services and the management of high-risk groups.