Socioeconomic status has been found to be a significant predictor of quality of life, with individuals of higher socioeconomic status reporting better quality of life. However, social capital may play a mediating role in this relationship. This study highlights the need for further research on the role of social capital in the relationship between socioeconomic status and quality of life, and the potential implications for policies aimed at reducing health and social inequalities. The study used a cross-sectional design with 1792 adults 18 and older from Wave 2 of the Study of Global AGEing and Adult Health. We employed a mediation analysis to investigate the relationship between socioeconomic status, social capital, and quality of life. The results showed that socioeconomic status was a strong predictor of social capital and quality of life. In addition to this, there was a positive correlation between social capital and quality of life. We found social capital to be a significant mechanism by which adults’ socioeconomic status influences their quality of life. It is crucial to invest in social infrastructure, encourage social cohesiveness, and decrease social inequities due to the significance of social capital in the connection between socioeconomic status and quality of life. To improve quality of life, policymakers and practitioners might concentrate on creating and fostering social networks and connections in communities, encouraging social capital among people, and ensuring fair access to resources and opportunities.

This study aims to examine the factors affecting caregivers’ acceptance of the use of wearable devices by patients with dementia by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) model with 2 additional constructs: resistance to change (RC) and technology anxiety (TA). A structured online questionnaire was developed and distributed to caregivers who have either dealt previously with or were currently taking care of patients with dementia in sub-Saharan Africa. The selection of countries for the study was purely based on the availability of caregivers of patients with dementia and their readiness to take part in the survey. Questionnaire data from 350 respondents were analyzed and the hypothesis tested using partial least squares based on structural equation modeling. The results of the extended model show that social influence, effort expectancy, facilitating conditions, and behavioral intention (BI) all had P < .05, thus were statistically significant in explaining the perceived BI and actual use of health-care wearable devices among patients with dementia as reported by caregivers. However, P value for RC and TA was greater than .05, hence were insignificant.

This study aims to evaluate the effect of social capital (SC), social support (SS), and social network formation (SNF) on the quality of life of American adults during COVID-19. Using a probability sample of American adults aged 49+, 2370 respondents were selected from the National Social Life Health and Aging Project (NSHAP) dataset for analysis using an integrated partial least squares based on structural equation modeling (PLS-SEM)-K-fold cross-validation approach. The analysis showed that social capital assessed using civic engagement, social cohesion, socioeconomic status (SES), social support, and social network formation were significantly and positively associated with American adults’ quality of life during the COVID-19 pandemic. Furthermore, the results showed that using the PLS-SEM and K-fold cross-validation approach produced a medium predictive power of the overall model, confirming the importance of SC, SS, and SNF in predicting quality of life-outcomes. These findings suggest that efforts to promote the well-being of American adults, especially older adults, during the pandemic should focus on strengthening social capital, social support and social network formation.

The increasing prevalence of dementia has necessitated the introduction and use of healthcare wearable devices to augment the healthcare services delivered to people with dementia (PWDs). Unfortunately, evidence on user feedback and the real impact of the actual use of these devices on the quality of life of PWDs has not been fully explored, especially in the context of developing countries. This study, therefore, seeks to evaluate the impact of actual use behavior (AUB) of healthcare wearable devices on the quality of life (QoL) of PWDs in Ghana by using constructs from the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model and the SF-36 quality of life instrument. Data were collected from people with dementia and, in some cases, caregivers (acting as proxies) in three administrative regions of Ghana using structured questionnaires. Of the 420 questionnaires distributed, 356 were returned, giving a response rate of (356/420, 84.8%). Three hundred and twenty (320) responses were used for further analysis, employing the Partial Least Squares based on Structural Equation Modeling. The results showed a significant positive relationship between AUB and QoL (p < 0.001). Other predictors such as behavioral intention, facilitating conditions, effort expectancy and social influence were also found to significantly impact the actual use behavior of healthcare wearable devices among PWDs. The study also revealed that 4% (14) of patients rated their reported health transition associated with the use of healthcare wearable devices as excellent, 42.5% (136) as very good, and 39.4% (126) as good, respectively at the time of the study compared to a year ago. This study provided empirical evidence of the positive impact of the actual use of healthcare wearable devices on the quality of life of PWDs and further highlighted the factors that drive actual use behavior among PWDs in Ghana. Based on the findings of this study, stakeholders need to create the necessary conditions that will facilitate the adoption and use of healthcare wearable devices, as this will help improve the quality of life, reduce the burden of the disease on caregivers, and enable independent living of PWDs.

Background: Though extensive studies have been conducted on assessing the predictors of cognitive functioning among older adults in small community-based samples, very few studies have focused on understanding the impact of socioeconomic status (SES), demographic characteristics and other risk factors such as lifestyle and chronic diseases on the cognitive functioning among adults of all ages in a nationally representative population-based sample across low- and middle-income countries. This study, therefore, seeks to evaluate the impact of SES, demographic characteristics and risk factors on the cognitive functioning of adults across all ages in five selected developing countries. Methods: Data from 12,430 observations obtained from the WHO Study on Global AGEing and Adult Health (SAGE) Wave 1; consisting of 2,486 observations each for China, Ghana, India, the Russian Federation, and South Africa, were used for the study. A meta-regression and a five-step hierarchical linear regression were used to analyze the data, with cognitive functioning as the dependent variable. Independent variables used in this study include SES; assessed by household income and education, demographic characteristics, other risk factors such as lifestyle, self-reported memory difficulty and chronic diseases. Results: This study found that SES and lifestyle significantly predicted cognitive functioning in all the five selected countries as obtained by the pooled results of the meta-regression analysis. The hierarchical linear regression results also revealed that demographic characteristics such as age, type of residency, and self-reported memory difficulty significantly impact cognitive functioning in China, Ghana, Russia, and South Africa. Conclusion: The findings in this study provide new insights for policymakers, caregivers, parents, and individuals, especially those in developing countries, to implement policies and actions targeted at improving SES and eliminating risk factors associated with cognitive decline, as these measures could help improve the cognitive functioning among their populations.