Background A differentiated analysis of the structural relationships between social factors and health-related quality of life (HRQOL) in older German adults has not yet been conducted. In this analysis, we aimed to examine the relationships between sociodemographic, socioeconomic, psychosocial, and behavioural factors and both physical and mental HRQOL in older German adults. Methods A community-based postal survey was used to collect cross-sectional data from German adults aged 65 and older (n = 1687, 33% response proportion, 52% female). Physical and mental dimensions of HRQOL were assessed using Short Form 36, version 2. Multiple linear regression models were used to analyse the associations between social factors and both physical and mental HRQOL. Results Health locus of control, physical activity, and income were positively associated with both physical HRQOL (Adj. R2 = 0.34, p < 0.001) and mental HRQOL (Adj. R2 = 0.22, p < 0.001), whereas age was negatively associated with both. Alcohol use was positively associated with physical HRQOL, and social support was positively associated with mental HRQOL. Conclusions A differentiated understanding of the relationships between social factors and HRQOL assists in group-specific targeting of health interventions. Demand-oriented interventions should consider underlying social factors to reduce socially determined inequities in HRQOL among older German adults. Depending on the focus of the intervention, it may be helpful to take specific social conditions into account. The results may be transferable to municipalities in high-income European countries. Key messages
With this analysis, we aimed to examine the associations between social factors and dietary risk behavior in older adults. Data were collected through a full-population postal survey of German adults aged 65 years or older (n = 1687, 33% response proportion, 52% female, mean age = 76 years). Using principal component analysis (PCA), a data-driven Dietary Risk Behavior Index (DRB) was computed. Dietary risk behavior was defined as consumption frequencies of vegetables/fruit, whole grains, and dairy products below national dietary recommendations. By performing a multiple linear regression, we analyzed associations between sociodemographic, socioeconomic, psychosocial, and behavioral factors and dietary risk behavior. Physical activity, female gender, socioeconomic status, social support, and age (in the male sample) were negatively associated with dietary risk behavior. Alcohol consumption and smoking were positively associated with dietary risk behavior. A group-specific analysis revealed a higher goodness-of-fit for the low socioeconomic status group, older adults aged 65–79 years, and women. A comprehensive understanding of the relationships between social factors and dietary risk behavior in older adults assists the group-specific targeting of dietary-related interventions. Demand-oriented dietary interventions should account for underlying social conditions to reduce inequity in dietary risk behavior among older adults. The results of this work may be transferable to municipalities in high-income European countries.
Background Statistics show that the number of received psychosocial counselling sessions remains at a constantly high level or has even increased since the COVID-19 pandemic situation in 2020. The objective of this work is to identify factors associated with students’ mental health to improve prevention and promotion in mental health at universities. Methods The analyses were based on a cross-sectional data set collected by an online survey among 1,842 students from a German University of Applied Sciences in 2014. Descriptive statistics as well as nine different multiple linear regression models were calculated with IBM® SPSS® Statistics software. Mental health indicators used were mental health-related quality of life (mental HRQOL), depression, and anxiety, which were analysed in a gender-specific manner. Results The analyses showed that the mean of the mental HRQOL score of the SF-36 for the student sample (46.68) was lower than the values for German (48.76) or American (51.34) norm samples. A key finding was the differences in mental health indicators between male and female students. Women reported worse mental health status in comparison to men. Female gender (ß of -.09; p < 0.01), age (ß of -1.05; p < 0.01), underweight (ß of -.09; p < 0.05), smoking (ß of -.10; p < 0.05) and drug consumption (ß of -.15; p < 0.001) were negatively associated with mental health indicators. In our sample, a moderate consumption of alcohol within the female population (ß of .12; p < 0.01) and physical activity within the male sample (ß of .09; p < 0.05) were positively associated with mental health indicators. Conclusion The gender-specific differences of students’ mental health and its associations could be an important result for counselling services at universities to adjust methods according to gender. Contrary to the general societal perception, students have lower mental health than a norm sample even before the pandemic. Due to the additional mental stress caused by the pandemic, it can be assumed that mental health problems have increased even more. Universities should therefore pay more attention to the mental health of their students.
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