Purpose The aim of this study is to measure the trajectory of healthy ageing among Chinese middle-aged and older population, and explore the disparity of the trajectory, as well as contributing factors, between urban and rural areas in China. Methods A total of 9402 respondents aged 45 years and older interviewed in four waves (2011, 2013, 2015 and 2018) were selected from the China Health and Retirement Longitudinal Study. Healthy ageing score was calculated through item response theory. A latent growth mixture model (LGMM) was applied to distinguish the trajectory of healthy aging. A multinomial logistics regression model (MLRM) was used to explore the relationship between urban-rural areas and healthy aging trajectories, and further to explore associated factors in rural and urban areas separately. Results The healthy ageing score was lower in rural areas than urban areas in each survey wave. Five classes (“continuing-low”, “continuing-middle”, “continuing-middle-to-high”, “significantly-declining”, “continuing-high”) were grouped through LGMM. The MLRM results showed that urban living was significantly associated with a higher likelihood of being healthy (for [continuing-low/continuing-high]: β = − 1.17, RRR = 0.31, P < 0.001, 95% CI = 0.18–0.53; and for [continuing-middle/continuing-high]: β = − 0.53, RRR = 0.59, P < 0.001, 95% CI = 0.49–0.71). Conclusion Healthy ageing is a prominent objective in the development of a country, and rural-urban disparities are an essential obstacle to overcome, with the rural population more likely to develop a low level of healthy ageing trajectory. Prevention and standardized management of chronic diseases should be enhanced, and social participation should be encouraged to promote healthy ageing. The policy inclination and resource investment should be enhanced to reduce disparity in healthy ageing between urban and rural areas in China.
Background The prevalence of depression symptoms among medical students is particularly high, and it has increased during the COVID-19 epidemic. Sleep quality and state-trait anxiety are risk factors for depression, but no study has yet investigated the mediating role of state-trait anxiety in the relationship between poor sleep quality and depression symptoms in medical students. This study aims to investigate the relationship among depression symptoms, sleep quality and state-trait anxiety in medical university students in Anhui Province. Methods This was a cross-sectional survey of 1227 students’ online questionnaires collected from four medical universities in Anhui Province using a convenience sampling method. We measured respondents’ sleep quality, state-trait anxiety, and depression symptoms using three scales: the Pittsburgh Sleep Quality Index (PSQI), the State-Trait Anxiety Inventory (STAI) and the Self-rating Depression Scale (SDS). We analysed the mediating role of STAI scores on the association between PSQI scores and SDS scores through the Sobel-Goodman Mediation Test while controlling for covariates. P < 0.05 was considered statistically significant. Results A total of 74.33% (912) and 41.40% (518) of the respondents reported suffering from poor sleep quality and depression symptoms. Sleep quality, state-trait anxiety, and depression symptoms were positively associated with each other (β = 0.381 ~ 0.775, P < 0.001). State-trait anxiety partially mediated the association between sleep quality and depression symptoms (Sobel test Z = 15.090, P < 0.001), and this mediating variable accounted for 83.79% of the association when adjusting for potential confounders. Subgroup analysis further revealed that STAI scores partially mediated the association between PSQI scores and SDS scores in females and rural students and fully mediated the association between PSQI scores and SDS scores in males and urban students. Conclusions This study found that sleep quality and state-trait anxiety have a significant predictive effect on depression symptoms. State-trait anxiety mediated the relationship between sleep quality and depression symptoms, with a more complex mechanism observed among rural and female medical students. Multiple pathways of intervention should be adopted, such as encouraging students to self-adjust, providing professional psychological intervention and timely monitoring, enriching extracurricular activities, and making changes in policies regarding long shifts and working hours.
BackgroundDigital health has become a heated topic today and smart homes have received much attention as an important area of digital health. However, most of the existing studies have focused on discussing the impact of smart homes on people or the attitudes of older people towards smart homes. Only few studies have focused on relationship between health-related risks and use of smart homes.AimsTo investigate the association between health-related risks and the use of smart homes, provide new recommendations to promote the implementation of digital health strategies and achieve health for all.MethodsWe used data from 11,031 participants aged 18 and above. The population was clustered based on five health-related risk factors: perceived social support, family health, health literacy, media use, and chronic diseases self-behavioral management. A total of 23 smart homes were categorized into three sub-categories: entertainment smart home, functional smart home, and health smart home. We analyzed demographic characteristics and utilization rate of smart home across different cluster.ResultsThe participants were clustered into three groups: low risk, meddle risk, and high risk. The utilization rate of smart home was the most popular in the low risk group (total smart home: 86.97%; entertainment smart home: 61.07%, functional smart home: 77.42%, and health smart home: 75.33%; p < 0.001). For entertainment smart home, smart TV had the highest utilization rate (low risk: 45.73%; middle risk: 43.52%, high risk: 33.38%, p<0.001). For functional smart home, smart washing machine (low risk: 37.66%, middle risk: 35.11%, high risk: 26.49%; p<0.001) and smart air conditioner (low risk: 35.95%, middle risk: 29.13%, high risk: 24.61%) were higher than other of this category. For health smart home, sports bracelet has the highest utilization rate (low risk: 37.29%, middle risk: 24.49%, high risk: 22.83%).ConclusionHealth-related risks are an important factor affecting the use of smart homes. Joint efforts of government and product manufacturers are needed to broaden the smart home market and promote the implementation of digital health strategies.
Background: To investigate the relationship between depression and sleep quality and state-trait anxiety among undergraduate students in medical colleges and universities in Anhui Province, to enhance the attention to the mental health of undergraduate students in medical colleges and universities, and to provide a basis for mental health intervention strategies for undergraduate students in medical colleges and universities. Methods: The Pittsburgh Sleep Quality Index, State-Trait Anxiety Inventory, and Depression Self-Rating Scale were used to investigate 1300 college students in four medical undergraduate colleges and universities in Anhui Province. Results: The Pittsburgh Sleep Quality Index score was (5.87+2.944), the State-Trait Anxiety Inventory score was (84.10+17.276), and the Depression Self-Rating Scale score was (49.04+10.845). Depression status and Pittsburgh Sleep Quality Index were positively correlated (r=0.381, p<0.001), and Pittsburgh Sleep Quality Index and State-Trait Anxiety were positively correlated (r=0.428, p<0.001). Conclusions: Taking four undergraduate medical colleges and universities in Anhui Province as an example, sleep quality of college students in medical colleges and universities was positively correlated with depressive state, sleep quality and state-trait anxiety were positively correlated, and sleep quality was influencing depressive state through state-trait anxiety.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.