The Mediating Role of Depression in Association Between Total Sleep Time and Instrumental Activities of Daily Living in China

Interaction effect between sleep duration and dynapenic abdominal obesity for predicting functional disability: A longitudinal study

OBJECTIVE

To assess the interaction of dynapenic abdominal obesity (DAO) and sleep duration on the risk of functional disability among middle-aged and older Chinese individuals.

METHODS

Data were extracted from the China Health and Retirement Longitudinal Study conducted in 2011, 2013, 2015, 2018, and 2020. A total of 6,343 participants were enrolled in this retrospective cohort study. Sleep duration was obtained through face-to-face interviews. Dynapenia (D) and abdominal obesity (AO) were defined by handgrip strength and waist circumference, respectively. Functional disability was assessed according to activities of daily living scales. Cox proportional hazard models analyzed the interactions of DAO and sleep duration on functional disability.

RESULTS

Over a mean follow-up of 10 years, 3,879 (61.2%) participants reported functional disability. Individuals with short sleep duration and D/AO (appropriate but short: RR = 1.42, 95% CI = 1.10-1.82, too short sleep: RR = 1.54, 95% CI = 1.16-2.06), long sleep duration and D/AO (appropriate but long: RR = 1.61, 95% CI = 1.11-2.33; too long: RR = 1.63, 95% CI = 1.15-2.32), were more likely to develop functional disability than those with normal sleep duration and ND/NAO in the fully adjusted model. The multiplicative interaction between the short sleep group and D/NAO or ND/AO were both significant. Middle-aged individuals and females were more susceptible to the effects of short sleep and DAO, while elderly individuals and males were more susceptible to the effects of long sleep and DAO.

CONCLUSIONS

Short and long sleep durations combined with DAO increase the risk of functional disability. Managing waist circumference and improving grip strength in middle-aged and older adults with abnormal sleep durations may help prevent functional disability.

Interaction effects of sleep duration and activities of daily living on depressive symptoms among Chinese middle-aged and older adult individuals: evidence from the CHARLS

Objectives

Evidence on the combined effect of sleep duration and activities of daily living (ADL) on depressive symptoms is scarce. This study aimed to explore the interaction effects between sleep duration and ADL limitations on depressive symptoms among Chinese individuals aged ≥45 years.

Methods

Data were extracted from the China Health and Retirement Longitudinal Study (CHARLS) wave 2020. Sleep duration was self-reported. The Center for Epidemiological Studies Depression Scale and a 12-item scale were employed to estimate depressive symptoms and ADL limitations, respectively. Logistic regression analysis was conducted to examine the interaction effects between sleep duration and ADL limitations on depressive symptoms.

Results

Logistic regression found that short sleep (OR = 1.69, 95% CI: 1.57-1.83), long sleep (OR = 0.87, 95% CI: 0.79-0.95), and ADL limitations [basic activities of daily living (BADL), OR = 1.82, 95% CI: 1.66-2.01; instrumental activities of daily living (IADL), OR = 1.88, 95% CI: 1.71-2.07] were associated with depressive symptoms. Furthermore, synergistic interaction effects on the depressive symptoms risk were identified between short sleep and IADL limitations (RERI = 1.08, 95% CI: 0.57-1.59) or BADL limitations (RERI = 1.13, 95% CI: 0.60-1.65). Conversely, antagonistic interaction effects were observed between long sleep and IADL limitations (RERI = 0.88, 95% CI: 0.39-1.38) or BADL limitations (RERI = 0.76, 95% CI: 0.25-1.27) on depressive symptoms.

Conclusion

The study revealed significant interactions between sleep duration and ADL limitations on depressive symptoms, suggesting that enhancing ADL's function and ensuring adequate sleep duration could effectively prevent depressive symptoms.

Bidirectional association between ADL disability and depressive symptoms among older adults: longitudinal evidence from CHARLS

This study investigates the bidirectional relationship between Activities of Daily Living (ADL) disability and depressive symptoms (DS) among middle-aged and older adults in China, utilizing data from the China Health and Retirement Longitudinal Study (CHARLS) from 2015 to 2018. A total of 8994 participants were analyzed to assess the impact of ADL on the risk of depressive symptoms, while 9673 participants were included to examine the influence of depressive symptoms on the risk of ADL disability. Cox proportional hazards regression models were employed to evaluate these relationships, adjusting for sociodemographic, lifestyle, and health-related covariates. Results revealed that ADL disability significantly increased the risk of depressive symptoms (HR = 1.090, 95% CI 1.058-1.123), and depressive symptoms were associated with a higher risk of ADL disability (HR = 1.033, 95% CI 1.025-1.042). Subgroup analyses demonstrated that factors such as education level, social activity, and disability significantly modified the relationship between ADL and depressive symptoms, while age, marital status, and pain influenced the association between depressive symptoms and ADL. Sensitivity analyses confirmed the robustness of these findings. This study highlights the complex interplay between ADL and depressive symptoms, emphasizing the need for targeted interventions to mitigate the risk of depression and improve functional independence in aging populations.

The relationship between childhood hunger experiences and activities of daily living disability: a mediating role of depression

BACKGROUND

Hunger is an important public health issue. This study aims to explore the impact of childhood hunger experiences (CHEs) on activities of daily living (ADL) disability and to discover the mediating role of depression on this correlation.

METHODS

Data were derived from the China Health and Retirement Longitudinal Study 2011, 2013, 2015, 2018, and 2020. Depression was assessed using the Centre for Epidemiological Studies Depression Scale. ADL disability, basic ADL (BADL) disability and instrumental ADL (IADL) disability were assessed using the ADL scale. The relationships between CHEs, depression and ADL disability were estimated using probit methods, and the mediating effect of depression was estimated using the stepwise regression test and bootstrap methods.

RESULTS

A total of 9,905 sample were included in the study. The study found that CHEs increased ADL disability by approximately 9.1% (β = 0.091, 95% CI: 0.028, 0.154), and increased the probability of IADL disability by approximately 8.0% (β = 0.080, 95% CI: 0.016, 0.144). CHEs increased ADL disability by approximately 29.8% (β = 0.298, 95% CI: 0.202, 0.393) in samples under 60 years of age. CHEs indirectly increased ADL (β = 0.062, 95% CI: 0.000, 0.124) by increasing the probability of depression.

CONCLUSION

CHEs predicted ADL disability, and depression mediated this correlation. There is a need to focus on the impact of CHEs on mental and physical health. In particular, focusing on mental health is necessary to prevent impairment of physical functioning.

The mediating effect of life satisfaction between daily living abilities and depressive symptoms in the Chinese older people: evidence from CHARLS 2020

Background

Depressive symptoms represent a significant public health challenge, impacting the mental well-being of older adults. Despite this, the understanding of how activities of daily living (ADL) abilities correlate with life satisfaction and depressive symptoms among older Chinese adults remains limited.

Aims

This study aims to investigate the relationship between ADL and depressive symptoms in older people Chinese individuals, with a specific focus on examining the mediating role of life satisfaction within this context.

Methods

The study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2020. A cohort of 8,343 individuals aged 60 years and above was included. The analysis was conducted using STATA 17.0 and SPSS 26.0, employing descriptive statistics, chi-square tests, Pearson correlations, and mediation analysis using the percentile Bootstrap method with 5,000 resamples to explore the interrelations among ADL, life satisfaction, and depressive symptoms.

Results

ADL is positively correlated with life satisfaction (r = 0.129, p < 0.01) and negatively correlated with depressive symptoms (r = -0.313, p < 0.01). Additionally, life satisfaction and depressive symptoms are negatively correlated with each other (r = -0.360, p < 0.01). In the model of the mediating effect, ADL directly, significantly and negatively predicts depressive symptoms in the Chinese older people (β = -0.193, t = -17.827, p < 0.001). After incorporating life satisfaction into the regression equation, the direct predictive effect of ADL on depressive symptoms remains significant (β = -0.177, t = -17.099, p < 0.001); furthermore, ADL has a significant positive predictive effect on life satisfaction (β = 0.007, t = 4.959, p < 0.001) and life satisfaction significantly negatively predicts depressive symptoms (β = -2.235, t = -27.799, p < 0.001). Furthermore, the direct effect of ADL on depressive symptoms (-0.177) and its mediating effect (-0.016) account for 91.71% and 8.29% of the total effect (-0.193), respectively.

Conclusion

ADL is inversely associated with the risk of depressive symptoms among older people Chinese individuals, with life satisfaction serving as a significant mediator in this relationship. Interventions aimed at improving life satisfaction in older people individuals with ADL impairments may effectively reduce or prevent the onset of depressive symptoms.

Abnormal sleep duration is associated with sarcopenia in older Chinese people: A large retrospective cross-sectional study

Aim

Abnormalities in sleep patterns are a common health problem for the older adults. The relationship between sarcopenia and sleep duration in older people is controversial. This research is to examine the association between sleep duration and sarcopenia.

Methods

We drew 21,095 adults from the China Health and Retirement Longitudinal Survey (CHARLS). Not only we explore the relationship between sleep duration and sarcopenia, but also compare sleep duration to three sarcopenia subcomponents. Moreover, the sensitivity analysis was conducted by the gender and residence area to ascertain the discrepancy, separately. Finally, using restricted cubic spline to find the non-linear association between them.

Results

Among 7,342 community older adults engaged by CHARLS in 2015, the incidence of possible sarcopenia and sarcopenia was 23.14 and 11.30%, separately. Sleep duration (≤6 h) [OR(95%CI) = 1.30(1.03-1.65), p < 0.05] and (≥8 h) [OR(95%CI) = 1.33(1.05-1.69), p < 0.05] were significantly linked with possible sarcopenia, while long sleep duration (≥8 h) [OR(95%CI) = 1.41(1.01-2.02), p < 0.05] was correlated strongly with sarcopenia. A non-linear relationship (U-shaped) between sarcopenia risk and sleep duration was found (p for non-linear = 0.009).

Conclusions

Our findings highlight the importance of sleep duration in the onset of sarcopenia and might assist older persons to maintain good sleeping habits.

Disability risk prediction model based on machine learning among Chinese healthy older adults: results from the China Health and Retirement Longitudinal Study

Background

Predicting disability risk in healthy older adults in China is essential for timely preventive interventions, improving their quality of life, and providing scientific evidence for disability prevention. Therefore, developing a machine learning model capable of evaluating disability risk based on longitudinal research data is crucial.

Methods

We conducted a prospective cohort study of 2,175 older adults enrolled in the China Health and Retirement Longitudinal Study (CHARLS) between 2015 and 2018 to develop and validate this prediction model. Several machine learning algorithms (logistic regression, k-nearest neighbors, naive Bayes, multilayer perceptron, random forest, and XGBoost) were used to assess the 3-year risk of developing disability. The optimal cutoff points and adjustment parameters are explored in the training set, the prediction accuracy of the models is compared in the testing set, and the best-performing models are further interpreted.

Results

During a 3-year follow-up period, a total of 505 (23.22%) healthy older adult individuals developed disabilities. Among the 43 features examined, the LASSO regression identified 11 features as significant for model establishment. When comparing six different machine learning models on the testing set, the XGBoost model demonstrated the best performance across various evaluation metrics, including the highest area under the ROC curve (0.803), accuracy (0.757), sensitivity (0.790), and F1 score (0.789), while its specificity was 0.712. The decision curve analysis (DCA) indicated showed that XGBoost had the highest net benefit in most of the threshold ranges. Based on the importance of features determined by SHAP (model interpretation method), the top five important features were identified as right-hand grip strength, depressive symptoms, marital status, respiratory function, and age. Moreover, the SHAP summary plot was used to illustrate the positive or negative effects attributed to the features influenced by XGBoost. The SHAP dependence plot explained how individual features affected the output of the predictive model.

Conclusion

Machine learning-based prediction models can accurately evaluate the likelihood of disability in healthy older adults over a period of 3 years. A combination of XGBoost and SHAP can provide clear explanations for personalized risk prediction and offer a more intuitive understanding of the effect of key features in the model.