Association between socioeconomic status and longitudinal sleep quality patterns mediated by depressive symptoms

Gender-specific link between sleep quality and body composition components: a cross-sectional study on the elderly

Sleep duration has been associated with overweight/obesity. Since sleep quality and body composition alter during aging, we conducted this study to determine if sleep quality is linked to body composition components in elderly people. This is a cross-sectional study conducted on 305 Iranian community-dwelling elderly aged ≥ 65 years. Sleep quality and body composition components were evaluated using Pittsburgh sleep quality index and bioelectric impedance analysis, respectively. The association of sleep quality and body composition components was examined using linear regression analysis. The prevalence of poor sleep quality and overweight/obesity was 48.9% and 54.4% in men and 77.0% and 79.3% in women, respectively. Women had significantly higher scores in most PSQI items than men, indicating their worse sleep quality compared to men. Women also had significantly higher body mass index (BMI), body fat percentage, and visceral adipose tissue and lower skeletal muscle and fat-free mass percentages than men. In the adjusted regression model, men showed positive associations between the third tertile of poor sleep quality and BMI (B = 1.35; 95% CI 0.08-2.61) and waist circumference (B = 4.14; 95% CI 0.39-7.89), but they did not demonstrate an association between sleep quality and body composition components. In the adjusted regression model for women, there were positive associations for BMI (B = 1.21; 95% CI 0.34-2.07), waist circumference (B = 2.95; 95% CI 0.99-4.91), body fat percentage (B = 2.75; 95% CI 1.06-4.45), and visceral adipose tissue (B = 7.80; 95% CI 1.73-13.87); also there were negative associations for skeletal muscle (B =  - 1.40; 95% CI - 2.39 - - 0.41) and fat-free mass (B =  - 2.76; 95% CI - 4.46 - -1.07) percentages. Except for waist circumference, other variables differed between men and women (P < 0.001). Weight management, prevention of muscle wasting, and improvement of sleep quality should be considered in a consortium when designing healthcare strategies for the elderly.

Socioeconomic Background and Self-Reported Sleep Quality in Older Adults during the COVID-19 Pandemic: An Analysis of the English Longitudinal Study of Ageing (ELSA)

The COVID-19 pandemic negatively impacted sleep quality. However, research regarding older adults' sleep quality during the pandemic has been limited. This study examined the association between socioeconomic background (SEB) and older adults' sleep quality during the COVID-19 pandemic. Data on 7040 adults aged ≥50 were acquired from a COVID-19 sub-study of the English Longitudinal Study of Ageing (ELSA). SEB was operationalized using educational attainment, previous financial situation, and concern about the future financial situation. Sociodemographic, mental health, physical health, and health behavior variables were included as covariates. Chi-squared tests and binary logistic regression were used to examine associations between SEB and sleep quality. Lower educational attainment and greater financial hardship and concerns were associated with poor sleep quality. The relationship between educational attainment and sleep quality was explained by the financial variables, while the relationship between previous financial difficulties and sleep quality was explained by physical health and health behavior variables. Greater financial concerns about the future, poor mental health, and poor physical health were independent risk factors for poor sleep quality in older adults during the pandemic. Healthcare professionals and service providers should consider these issues when supporting older patients with sleep problems and in promoting health and wellness.

Health-related quality of life and its association with socioeconomic status and diet diversity in Chinese older adults

Objectives

The study aimed at examining the combined association of socioeconomic status (SES) and diet diversity (DD) with health-related quality of life (HRQoL) and exploring whether DD played a mediating role in the relationship between varied SES and HRQoL among Chinese older persons.

Method

A multi-stage random sampling method was conducted in Shanxi Province of China, with 3,250 older adults participating in this cross-sectional survey. SES was divided into groups by quartiles and DD by means, and these variable groups were combined in pairs to generate a total of eight combinations. The PROCESS macro developed by Hayes was employed for the simple mediation analysis.

Results

Compared with the reference group (those with both high SES and high DD), older adults who were classified to have lower SES or DD had elevated odds of having worse HRQoL: low SES/ low DD (OR = 1.65, 95% CI 1.41-2.92); low SES/ high DD (OR = 1.45, 95% CI 1.17-1.80); middle low SES/ low DD (OR = 1.43, 95% CI 1.24-1.65); middle low SES/ high DD (OR = 1.23, 95% CI 1.03-1.47); upper high SES/ low DD (OR = 1.41, 95% CI 1.21-1.65); and high SES/ low DD (OR = 1.30, 95%CI 1.10-1.53). The mediation analysis revealed that DD mediated the relationship between SES and HRQoL (B=0.011, 95% CI 0.008-0.013), with its indirect effects accounting for 39.29% of the total effects.

Conclusions

These findings highlighted the role of DD as a mediator of the relationship between SES and HRQoL. As DD could be protective, modifiable, and easy for older adults to understand and implement, village clinics and community health stations should work collaboratively to design proper DD intervention measures for better HRQoL.

Introduction to Mediation Analysis and Examples of Its Application to Real-world Data

Traditional epidemiological assessments, which mainly focused on evaluating the statistical association between two major components-the exposure and outcome-have recently evolved to ascertain the in-between process, which can explain the underlying causal pathway. Mediation analysis has emerged as a compelling method to disentangle the complex nature of these pathways. The statistical method of mediation analysis has evolved from simple regression analysis to causal mediation analysis, and each amendment refined the underlying mathematical theory and required assumptions. This short guide will introduce the basic statistical framework and assumptions of both traditional and modern mediation analyses, providing examples conducted with real-world data.