Association of depression with sexual and daily activities: a community study of octogenarian men

Decoding emotional resilience in aging: unveiling the interplay between daily functioning and emotional health

Background

EPs pose significant challenges to individual health and quality of life, attracting attention in public health as a risk factor for diminished quality of life and healthy life expectancy in middle-aged and older adult populations. Therefore, in the context of global aging, meticulous exploration of the factors behind emotional issues becomes paramount. Whether ADL can serve as a potential marker for EPs remains unclear. This study aims to provide new evidence for ADL as an early predictor of EPs through statistical analysis and validation using machine learning algorithms.

Methods

Data from the 2018 China Health and Retirement Longitudinal Study (CHARLS) national baseline survey, comprising 9,766 samples aged 45 and above, were utilized. ADL was assessed using the BI, while the presence of EPs was evaluated based on the record of "Diagnosed with Emotional Problems by a Doctor" in CHARLS data. Statistical analyses including independent samples t-test, chi-square test, Pearson correlation analysis, and multiple linear regression were conducted using SPSS 25.0. Machine learning algorithms, including Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR), were implemented using Python 3.10.2.

Results

Population demographic analysis revealed a significantly lower average BI score of 65.044 in the "Diagnosed with Emotional Problems by a Doctor" group compared to 85.128 in the "Not diagnosed with Emotional Problems by a Doctor" group. Pearson correlation analysis indicated a significant negative correlation between ADL and EPs (r = -0.165, p < 0.001). Iterative analysis using stratified multiple linear regression across three different models demonstrated the persistent statistical significance of the negative correlation between ADL and EPs (B = -0.002, β = -0.186, t = -16.476, 95% CI = -0.002, -0.001, p = 0.000), confirming its stability. Machine learning algorithms validated our findings from statistical analysis, confirming the predictive accuracy of ADL for EPs. The area under the curve (AUC) for the three models were SVM-AUC = 0.700, DT-AUC = 0.742, and LR-AUC = 0.711. In experiments using other covariates and other covariates + BI, the overall prediction level of machine learning algorithms improved after adding BI, emphasizing the positive effect of ADL on EPs prediction.

Conclusion

This study, employing various statistical methods, identified a negative correlation between ADL and EPs, with machine learning algorithms confirming this finding. Impaired ADL increases susceptibility to EPs.

Subthreshold depression - concept, operationalisation and epidemiological data. A scoping review

Purpose: In diagnostic systems (e.g., DSM-5, ICD-10), depression is defined categorically. However, the concept of subthreshold depression (SD) has gained increasing interest in recent years. The purpose of the present paper was to review, based on a scoping review, the relevant papers in this field published between October 2011 and September 2020.Materials and methods: Of the 1,160 papers identified, 64 records could be included in further analysis. The scoping review was conducted using both electronic and manual methods.Results: The main result of the analysis is that the operationalisation criteria used are highly heterogeneous, which also leads to very heterogenous epidemiological data.Conclusions: Clear conclusions are not possible scrutinising the reported results. Most definitions seem to be arbitrary, with considerable overlap (e.g., between SD and minor depression). The review also revealed that the impact of SD on quality of life and related parameters appear to be in the range of the respective impact of major depression (MD) and therapeutic approaches might be helpful for SD and also for the prevention of conversion from SD to MD. Keeping the presented difficulties in mind, a proposal for the definition of SD is made in the present paper in order to facilitate the discussion leading to more homogeneous criteria.

Are andropause symptoms related to depression?

BACKGROUND

Andropause is a middle-age condition in which men experience changes in their physical, spiritual and emotional health. The association between andropause and psychological symptoms such as depression are not very clear yet.

AIMS

The objective of this study was therefore to determine the association between the 'Aging Males Symptoms Scale' (AMS) and depression.

METHODS

A cross sectional study was conducted among 521 old men. To collect data, the AMS and the Patient Health Questionnaires 2 and 9 were used to screen depression, in addition to questions on background and fertility. Multiple linear regression analysis was used to assess the association between andropause symptoms and depression.

RESULTS

Based on our results and the AMS score, 51.5% of the study population had clinical symptoms of androgen disorder, 3.7% of which had severe symptoms. There was a strong correlation between the AMS score and depression. Depression, diabetes, cigarette smoking and spousal age retained their significant associations even after entering the relevant demographic, anthropometric, smoking and disease variables in the multivariable model. As a positive predictive factor, depression had the strongest association with AMS.

CONCLUSIONS

Based on our results, there is a direct association between andropause symptoms and depression, where the increasing AMS score corresponds with the severity of depression.

DISCUSSION

Our results show the need of screening for depression when evaluating andropause symptoms.