Objective It has been proved that physical activity and sedentary behavior are closely related to health status.However,individual physical activity levels and sedentary time are not constant over the course of life and often change with age.The aim of this study was to identify the physical activity(PA)and sedentary time(ST)trajectory and explore the association with all-cause mortality in Chinese adults.Methods The data from the China Health and Nutrition Survey from 2004 to 2015 were used to identify the trajectory patterns of physical activity and sedentary time over the course of life by using the deleted normal model in the multivariate baseline trajectory model,and the association between the grouping of physical activity and sedentary time trajectory and the risk of all-cause mortality was evaluated by the multivariate COX proportional hazard regression model.Results A total of 10576 participants with two or more rounds of complete follow-up data were included in the analysis.Four physical activity and sedentary time trajectories were identified.34.9％showed that PA level decreased and ST maintained a moderate level.The rest were low PA low ST(13.5％),high PA high ST(32.8％)and high PA high ST(13.8％).People with high ST in medium PA(HR=0.54,95％CI:0.37～0.78,P=0.001)and people with high ST in high PA(HR=0.27,95％CI:0.11～0.65,P=0.002)the risk of death was significantly lower than those with low PA and low ST,and there was no significant difference in the risk of death in the ST group with reduced PA(HR=0.82,95％CI:0.59～1.16,P=0.257).Conclusion This study identified four PA and ST tracks in Chinese adults,and emphasized the importance of long-term maintenance of high PA levels in reducing mortality risk.

Objective It has been proved that physical activity and sedentary behavior are closely related to health status.However,individual physical activity levels and sedentary time are not constant over the course of life and often change with age.The aim of this study was to identify the physical activity(PA)and sedentary time(ST)trajectory and explore the association with all-cause mortality in Chinese adults.Methods The data from the China Health and Nutrition Survey from 2004 to 2015 were used to identify the trajectory patterns of physical activity and sedentary time over the course of life by using the deleted normal model in the multivariate baseline trajectory model,and the association between the grouping of physical activity and sedentary time trajectory and the risk of all-cause mortality was evaluated by the multivariate COX proportional hazard regression model.Results A total of 10576 participants with two or more rounds of complete follow-up data were included in the analysis.Four physical activity and sedentary time trajectories were identified.34.9％showed that PA level decreased and ST maintained a moderate level.The rest were low PA low ST(13.5％),high PA high ST(32.8％)and high PA high ST(13.8％).People with high ST in medium PA(HR=0.54,95％CI:0.37～0.78,P=0.001)and people with high ST in high PA(HR=0.27,95％CI:0.11～0.65,P=0.002)the risk of death was significantly lower than those with low PA and low ST,and there was no significant difference in the risk of death in the ST group with reduced PA(HR=0.82,95％CI:0.59～1.16,P=0.257).Conclusion This study identified four PA and ST tracks in Chinese adults,and emphasized the importance of long-term maintenance of high PA levels in reducing mortality risk.

Cohort study is one of the important research methods in analytical epidemiology because of its clear time sequence relationship, which is better than other observational studies in demonstrating causal association. However, screening diagnosis or other methods are often used to exclude the individuals with outcome events during the enrollment process of the subjects in cohort studies. The accuracy of screening diagnosis and the effectiveness of exclusion will affect the accuracy of the baseline status assessment of the subjects included in the study, which may lead to the causal time sequence reversal of exposure-outcome in the estimation of causal effect. Landmark analysis can be used to control reverse causality by excluding subjects with potentially unknown expose-outcome timing. In this paper, we describe the basic principles and analytical steps of landmark analysis, and use data from the Chinese Longitudinal Healthy Longevity Survey to explore the relationship between physical activity and frailty, and introduce the specific application of landmark analysis for the purpose of facilitating its application and inferring causal effects more accurately in cohort studies.

With completing a baseline survey of a large natural population cohort, conducting regular follow-up has become a key factor in further improving the quality of cohort construction and ensuring its sustainable development. Typical cohort follow-up methods include repeat surveys, routine monitoring, and community-oriented surveillance. However, in practical applications, there are often issues such as high costs, difficulty, and high error rates. Telephone follow-up is an important supplementary method to the methods mentioned above, as it has the characteristics of low cost, fast response, and high quality. However, the with difficult organization, quality control is challenging, response rates are low, and management levels vary widely, which limits its widespread use in large-scale population cohort studies. Given the above problems, this study draws on customer relationship management based on the actual needs of the China Northwest Cohort follow-up. It relies on the REDCap electronic data collection platform to build a telephone follow-up management and quality control system. Targeted solutions are provided for key issues in telephone follow-up implementation, including organizational structure, project management, data collection, and process quality control, to improve the quality control level of telephone follow-up comprehensively and thereby enhance the quality and efficiency of follow-up. We hope to provide standardized follow-up programs and efficient quality control tools for newly established and existing cohort studies.