Objective This study aims to explore the complex relationship between social engagement and depressive symptoms among older adults in China,focusing particularly on the moderating role of marital status.Methods This s...Objective This study aims to explore the complex relationship between social engagement and depressive symptoms among older adults in China,focusing particularly on the moderating role of marital status.Methods This study used data from the latest Chinese Longitudinal Healthy Longevity Survey(CLHLS).The analysis used the latent class analysis to delineate personality clusters and hierarchical linear regression,supplemented by the PROCESS macro,to investigate the effects of social engagement and marital status on depressive symptoms.Results The analysis encompassed 7,789 respondents(mean age:82.53[s=11.20]years),with 54%female.The personality analysis categorized participants into four clusters,with the majority(77.60%)classified as Confident Idealists,who exhibited the lowest levels of depressive symptoms.Hierarchical linear regression analysis yielded several significant findings:Higher levels of social engagement were significantly associated with fewer depressive symptoms(t=-7.932,P<0.001,B=-0.463).Marital status was a significant factor;married individuals reported fewer depressive symptoms compared to their unmarried counterparts(t=-6.368,P<0.001,B=-0.750).There was a significant moderating effect of marital status on the relationship between social engagement and depressive symptoms(t=-2.092,P=0.037,B=-0.217).Conclusion This study demonstrates that,among Chinese older adults,both social engagement and marital status significantly influence depressive symptoms.Higher social engagement,particularly in other activities like doing household chores,gardening,reading newspapers or books,and playing cards or Mahjong,is associated with fewer depressive symptoms,especially among married individuals.展开更多
The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been...The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been applied widely.However,such analysis processes are blackbox and the results lack explainability.Some approaches by constructing a domain model may tackle these issues.However,domain knowledge from an expert is required.In this study,we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge,in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data.An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness.Furthermore,the number of model parameters in our method was smaller than that in the baselines,which reduced model complexity.Moreover,the analysis process of the proposed approach was visible and explainable,which improved the interpretability of the analysis processes.展开更多
基金supported by National Natural Science Foundation of China[72174183].
文摘Objective This study aims to explore the complex relationship between social engagement and depressive symptoms among older adults in China,focusing particularly on the moderating role of marital status.Methods This study used data from the latest Chinese Longitudinal Healthy Longevity Survey(CLHLS).The analysis used the latent class analysis to delineate personality clusters and hierarchical linear regression,supplemented by the PROCESS macro,to investigate the effects of social engagement and marital status on depressive symptoms.Results The analysis encompassed 7,789 respondents(mean age:82.53[s=11.20]years),with 54%female.The personality analysis categorized participants into four clusters,with the majority(77.60%)classified as Confident Idealists,who exhibited the lowest levels of depressive symptoms.Hierarchical linear regression analysis yielded several significant findings:Higher levels of social engagement were significantly associated with fewer depressive symptoms(t=-7.932,P<0.001,B=-0.463).Marital status was a significant factor;married individuals reported fewer depressive symptoms compared to their unmarried counterparts(t=-6.368,P<0.001,B=-0.750).There was a significant moderating effect of marital status on the relationship between social engagement and depressive symptoms(t=-2.092,P=0.037,B=-0.217).Conclusion This study demonstrates that,among Chinese older adults,both social engagement and marital status significantly influence depressive symptoms.Higher social engagement,particularly in other activities like doing household chores,gardening,reading newspapers or books,and playing cards or Mahjong,is associated with fewer depressive symptoms,especially among married individuals.
基金supported in part by 2022–2024 Masaru Ibuka Foundation Research Project on Oriental Medicine,2020–2025 JSPS A3 Foresight Program(No.JPJSA3F20200001)2022–2024 Japan National Initiative Promotion for Digital Rural City,2022–2024 JST SPRING(No.JPMJSP2128)+1 种基金2023 and 2024 Waseda University Grants for Special Research Projects(Nos.2023C-216 and 2024C-223)2023–2024 Waseda University Advanced Research Center Project for Regional Cooperation Support,and 2023–2024 Waseda University Advanced Research Center for Human Sciences Project(No.BA080Z000300).
文摘The identification of latent risk factors that can induce to health risks or an abnormal status is an important task in healthcare data analyses.In recent years,health analyses based on neural network models have been applied widely.However,such analysis processes are blackbox and the results lack explainability.Some approaches by constructing a domain model may tackle these issues.However,domain knowledge from an expert is required.In this study,we propose an exploratory and interpretable approach to estimating latent health risk factors without relying on domain knowledge,in which feature selection and causal discovery are used to construct a domain model for uncovering complex relationships in health and medical data.An evaluation experiment conducted on two datasets by comparing the proposed approach with four baselines demonstrated that the proposed approach outperformed the baselines in terms of model fitness.Furthermore,the number of model parameters in our method was smaller than that in the baselines,which reduced model complexity.Moreover,the analysis process of the proposed approach was visible and explainable,which improved the interpretability of the analysis processes.
