Background: How specific activities influence cognitive decline among different age groups, especially the late middle-aged and the early old, remains inadequately studied. Objective: To examine the association between specific activities with trajectories of cognitive functions in different age groups in China. Methods: A longitudinal cohort study was conducted based on data from the China Health and Retirement Longitudinal Study (CHARLS). Mixed effects growth models were applied to analyze the association between specific activities and cognitive functions. Results: Interacting with friends (infrequent: β= 0.13, confidence interval [CI] = 0.03 to 0.22; daily: β= 0.19, CI = 0.09 to 0.28), playing Mah-jong or other games (infrequent: β= 0.12, CI = 0.02 to 0.22; daily:β= 0.26, CI = 0.10 to 0.42), infrequent providing help to others (β= 0.24, CI = 0.11 to 0.37), and going to a sport (infrequent: β= 0.31, CI = 0.08 to 0.54); daily: β= 0.22, CI = 0.05 to 0.38) are significantly associated with participants’ memory. Infrequently playing Mah-jong or other games (β= 0.30, CI = 0.17 to 0.43) and daily sports (β= 0.24, CI = 0.03 to 0.45) are significantly associated with better mental status. Effect of each activity varies among population of different age, education level, gender, and residence. Conclusion: This study identifies four social activities that are associated with better cognitive functions, and provides a comprehensive, in-depth understanding on the specific protective effect of each activity among different subgroups.

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Based on the calculation formulas of confidence intervals for different medical indexes, in this paper, by using the backward-deduction method of mathematics and combining with the knowledge of mathematical statistics, a method of obtaining the accurate p value from the confidence interval is given.

Background: Emerging evidence indicates that leisure activities are associated with higher risk of cognitive impairment and dementia among the older adults, but how specific activities influence cognitive decline among different age groups, especially the late middle-aged and the early old, remains inadequately studied. This study aims to examine association between specific activities with trajectories of cognitive functions in different age groups in China. Methods: This longitudinal cohort study included 14,161 Chinese individuals aged 45 years or above from the China Health and Retirement Longitudinal Study (CHARLS). Data were collected bi-annually from 2011 to 2015. Cognitive function, including memory and mental status, was measured by Telephone Interview of Cognitive Status (TICS) battery. Mixed effects growth models were applied to analyse the association between specific activities and cognitive functions.Results: Four activities, respectively interacting with friends, playing Mah-jong or other card games, going to a sport and providing help to others, were found to be significantly associated with participants’ cognitive functioning. All four activities are associated with better memory. Infrequently playing Mah-jong or other card games and daily sports are significantly associated with better mental status. In addition, specific effect of each activity varies among population of different age, education level, gender and residence. Conclusions: This study identifies four social activities that are associated with better cognitive functions, and provides a comprehensive, in-depth understanding on the specific protective effect of each activity among different subgroups. These findings have practical implications for feasible and personalized cognitive health interventions.

Background: Predicting the probability of the reversion from mild cognitive impairment (MCI) to cognitively normal (CN) status can inform preventive treatments at individual, institutional and social level. This study aims to build a prediction model using a machine learning approach for reversion from MCI to CN status. Method:The study included 7,422 participants above 65 years old with MCI from Chinese Longitudinal Health Longevity Survey (Figure 1). LightGBM was used to build a prediction model of 154 variables, including individual and household socioeconomic status, dietary/lifestyle, cardiometabolic, psychological factors and history of diseases. SHAP values were used to interpret the impact of the top 40 variables that contributed to the LightGBM model. Multivariable Cox regression with elastic net penalty was also conducted for comparison. Model performance was assessed according to a range of learning metrics including area under the receiver operating characteristic curve (AUC).Results: 1,604 (21.6%) participants reversed from MCI to CN with a median follow-up of 2.8 years. The top 40 features were presented in the figure 2. The concordance index of the LightGBM model was 0.71, which is much higher than the traditional multivariable Cox regression with elastic net penalty (0.66). Conclusion:The machine learning approach could develop a more accurate early identification of recovery of MCI patients. The predicting can guide the recovery process of MCI patients.