Relationship between socioeconomic status and quality of life in older adults: a path analysis

2014 ◽  
Vol 24 (7) ◽  
pp. 1697-1705 ◽  
Author(s):  
A. Bielderman ◽  
M. H. G. de Greef ◽  
W. P. Krijnen ◽  
C. P. van der Schans
Keyword(s):  
Quality Of Life ◽  
Older Adults ◽  
Socioeconomic Status ◽  
Path Analysis

scholarly journals Disparities in Mental Health Quality of Life Between Hispanic and Non-Hispanic White LGB Midlife and Older Adults and the Influence of Lifetime Discrimination, Social Connectedness, Socioeconomic Status, and Perceived Stress

2016 ◽  
Vol 39 (9) ◽  
pp. 991-1012 ◽  
Author(s):  
Hyun-Jun Kim ◽  
Karen I. Fredriksen-Goldsen
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Older Adults ◽  
Socioeconomic Status ◽  
Perceived Stress ◽  
Social Connectedness ◽  
Health Quality ◽  
Health Quality Of Life ◽  
Hispanic White

We assessed factors contributing to ethnic and racial disparities in mental health quality of life (MHQOL) among lesbian, gay, and bisexual (LGB) midlife and older adults. We utilized cross-sectional survey data from a sample of non-Hispanic White and Hispanic LGB adults aged 50 and older. Structural equation modeling was used to test the indirect effect of ethnicity/race on MHQOL via explanatory factors including social connectedness, lifetime discrimination, socioeconomic status (SES), and perceived stress. Hispanics reported significantly lower levels of MHQOL, compared to non-Hispanic Whites. In the final model, the association between ethnicity/race and MHQOL was explained by higher levels of perceived stress related to lower SES, higher frequency of lifetime discrimination, and lack of social connectedness among Hispanic LGB adults. This study suggests that perceived stress related to social disadvantage and marginalization plays an important role in MHQOL disparities among Hispanic LGB midlife and older adults.

scholarly journals The Effect of Social Networks and Internet Emotional Relationships on Mental Health and Quality of Life in Students: a Path Analysis Model

2021 ◽  
Author(s):  
Fatemeh Aliverdi ◽  
Zahra Mehdizadeh Tourzani ◽  
Leili Salehi ◽  
Mostafa Qorbani ◽  
Zohreh Mahmoodi
Keyword(s):  
Quality Of Life ◽  
Mental Health ◽  
Social Networks ◽  
Socioeconomic Status ◽  
Path Analysis ◽  
Causal Relationship ◽  
Negative Relationship ◽  
Direct Path ◽  
Emotional Relationships

Abstract Background: Social networks and relationships create a sense of belonging and social identity and therefore have a major effect on mental health and quality of life, especially in young people. The present study was conducted to determine the effect of social networks and Internet emotional relationships on mental health and quality of life in students. Materials and Methods: The present descriptive analytical study was conducted in 2021 on 350 students at Alborz University of Medical Sciences selected by convenience sampling. Data were collected using five questionnaires: Socioeconomic Status, Social Networks, Internet Emotional Relationships Mental Health, Quality of Life and a checklist of demographic details. Data were analyzed in SPSS-25, PLS-3, and Lisrel-8.8.Results: According to the path analysis results, mental health had the most significant positive causal relationship with Internet emotional relationships in the direct path (B=0.22) and the most negative relationship with socioeconomic status (B=-0.09). Mental health was assessed using DASS-21, in which higher scores mean higher mental disorder. Quality of life had the highest negative causal relationship with DASS-21 score in the direct path (B=-0.26) and the highest positive relationship with socioeconomic status in the indirect path (B=0.023). The mean duration of using social networks (B=-0.067) and Internet emotional relationships (B=-0.089) had the highest negative relationship with quality of life.Conclusion: The use of the internet and virtual networks, Internet emotional relationships and unfavorable socioeconomic status were associated with mental disorders and reduced quality of life in the students. Since students are the future of any country, it is necessary for policymakers to further address this group and their concerns.

scholarly journals Spirituality and quality of life in older adults: a path analysis model

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sara Lima ◽  
Lurdes Teixeira ◽  
Raquel Esteves ◽  
Fátima Ribeiro ◽  
Fernanda Pereira ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Older Adults ◽  
Path Analysis ◽  
Analysis Model ◽  
Path Analysis Model

scholarly journals Sleep Health, Depression, Pain and Quality of Life among Older Adults during the First Months of the Covid-19 Pandemic

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. 865-865
Author(s):  
Rebecca Lorenz ◽  
Devita Stallings ◽  
Janice Palmer ◽  
Helen Lach
Keyword(s):  
Quality Of Life ◽  
Older Adults ◽  
Socioeconomic Status ◽  
Social Engagement ◽  
Online Survey ◽  
Personal Characteristics ◽  
Poor Sleep ◽  
Physical And Mental Health ◽  
Sleep Health

Abstract To slow the spread of Covid-19, many states instituted restrictions on group size for religious services, exercise, and social engagement. We are beginning to understand the effect of these mandates on older adults. The purpose of this study was to examine the relationships between sleep health, depression, pain, and quality of life (QOL) among older adults during the initial months of the Covid-19 pandemic. Older adults completed an anonymous online survey to collect data including personal characteristics, behaviors, and health conditions during May-September 2020. Sleep Health was assessed with a survey of satisfaction, timing, efficiency, and duration of sleep along with daytime alertness. Pearson correlations were used to explore relationships between age, education, socioeconomic status, pain, depression, and QOL. Participants (N=509) were primarily female (n=392, 77%), white (n=466; 92%), college educated (n=471, 93%) and with a mean age of 75.6 years (SD=5.0; range 63-93 years). Mean Sleep Health score was 7.4 (SD=2.1; range 0-10). Higher (better) Sleep Health scores were associated with education (r=.15, p<01) and socioeconomic status (r=.17, p<.01) and lower scores with depression (r= -.35, p<.01), pain (r= -.23, p<.01), and QOL (r= -.26, p<.01). Poorer Sleep Health among older adults during the initial months of the pandemic were associated with depression, pain, and reduced QOL. Sleep, depression, and pain have reciprocal relationships that may have lasting consequences on physical and mental health among older adults. These findings suggest that poor sleep health should be identified and treated to improve QOL among older adults.

scholarly journals Cognitive function and its associations in older adults from Amazonas, Brazil

2018 ◽  
Vol 23 ◽  
pp. 1-8
Author(s):  
Maria Antonieta de Campos Tinôco ◽  
Élvio Rúbio Gouveia ◽  
Andreas Ihle ◽  
Matthias Kliegel ◽  
Jefferson Jurema ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Older Adults ◽  
Socioeconomic Status ◽  
Cognitive Function ◽  
Linear Regression Analysis ◽  
Depression Scale ◽  
Mini Nutritional Assessment ◽  
Old Adults ◽  
Age Related

The objectives of this study were: (1) to investigate the age-related differences in cognitive function (CF), nutritional status (MNA), physical activity (AF), quality of life (QoL), depression, social satisfaction (SS) and socioeconomic status (SES), and (2) to explore the relationships between CF and the previous variables. This cross sectional study included 268 men and 433 women (aged 71.4 ± 7.0 years). CF was determined with the Cognitive Telephone Screening Instrument (COGTEL) and the Mini-Mental State Examination (MMSE). Correlates were as follows: Mini Nutritional Assessment (MNA), PA (Baecke questionnaire modied for older adults), Quality of life (QoL SF- 12), Geriatrics Depression Scale (GDS), Satisfaction and Social Support Scale, and Socioeconomic status (SES). All instruments were applied in a face to face interview. An independent t-test identied signicantly higher scores in young-old adults (≤ 69 years) for CF (p < 0.001), PA (p = 0.046) and SES (p = 0.007), compared to old-old adults (≥ 70 years). e results of multiple linear regression analysis indicated that the most signicant CF correlates were SES (β = 0.45; p < 0.001), age (β = -0.12; p < 0.001), SS (β = 0.12; p = 0.001), GDS (β = -0.11; p = 0.003) and QoL (β = 0.08; p = 0.017). The overall regression model explained 36% of the total variance in the COGTEL. The oldest and the more depressed adults obtained lower scores for FC. The present study suggests that, between the correlates studied, SES was the strongest predictor in the explanation of CF in older adults.

Sign in / Sign up

Export Citation Format

Share Document