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BACKGROUND: Increasingly, researchers attend to both positive and negative aspects of mental health. Such distinctions call for clarification of whether psychological well-being and ill-being comprise opposite ends of a bipolar continuum, or are best construed as separate, independent dimensions of mental health. Biology can help resolve this query--bipolarity predicts 'mirrored' biological correlates (i.e. well-being and ill-being correlate similarly with biomarkers, but show opposite directional signs), whereas independence predicts 'distinct' biological correlates (i.e. well-being and ill-being have different biological signatures). METHODS: Multiple aspects of psychological well-being (eudaimonic, hedonic) and ill-being (depression, anxiety, anger) were assessed in a sample of aging women (n = 135, mean age = 74) on whom diverse neuroendocrine (salivary cortisol, epinephrine, norepinephrine, DHEA-S) and cardiovascular factors (weight, waist-hip ratio, systolic and diastolic blood pressure, HDL cholesterol, total/HDL cholesterol, glycosylated hemoglobin) were also measured. RESULTS: Measures of psychological well-being and ill-being were significantly linked with numerous biomarkers, with some associations being more strongly evident for respondents aged 75+. Outcomes for seven biomarkers supported the distinct hypothesis, while findings for only two biomarkers supported the mirrored hypothesis. CONCLUSION: This research adds to the growing literature on how psychological well-being and mental maladjustment are instantiated in biology. Population-based inquiries and challenge studies constitute important future directions.
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This study examined the interplay of social engagement, sleep quality, and plasma levels of interleukin-6 (IL-6) in a sample of aging women (n = 74, aged 61-90, M age = 73.4). Social engagement was assessed by questionnaire, sleep was assessed by using the NightCap in-home sleep monitoring system and the Pittsburgh Sleep Quality Index, and blood samples were obtained for analysis of plasma levels of IL-6. Regarding subjective assessment, poorer sleep (higher scores on the Pittsburgh Sleep Quality Index) was associated with lower positive social relations scores. Multivariate regression analyses showed that lower levels of plasma IL-6 were predicted by greater sleep efficiency (P < 0.001), measured objectively and by more positive social relations (P < 0.05). A significant interaction showed that women with the highest IL-6 levels were those with both poor sleep efficiency and poor social relations (P < 0.05). However, those with low sleep efficiency but compensating good relationships as well as women with poor relationships but compensating high sleep efficiency had IL-6 levels comparable to those with the protective influences of both good social ties and good sleep.
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OBJECTIVE: To test the hypothesis that socioeconomic status (SES) would be associated with sleep quality measured objectively, even after controlling for related covariates (health status, psychosocial characteristics). Epidemiological studies linking SES and sleep quality have traditionally relied on self-reported assessments of sleep. METHODS: Ninety-four women, 61 to 90 years of age, participated in this study. SES was determined by pretax household income and years of education. Objective and subjective assessments of sleep quality were obtained using the NightCap sleep system and the Pittsburgh Sleep Quality Index (PSQI), respectively. Health status was determined by subjective health ratings and objective measures of recent and chronic illnesses. Depressive symptoms and neuroticism were quantified using the Center for Epidemiological Studies Depression Scale and the Neuroticism subscale of the NEO Personality Inventory, respectively. RESULTS: Household income significantly predicted sleep latency and sleep efficiency even after adjusting for demographic factors, health status, and psychosocial characteristics. Income also predicted PSQI scores, although this association was significantly attenuated by inclusion of neuroticism in multivariate analyses. Education predicted both sleep latency and sleep efficiency, but the latter association was partially reduced after health status and psychosocial measures were included in analyses. Education predicted PSQI sleep efficiency component scores, but not global scores. CONCLUSIONS: These results suggest that SES is robustly linked to both subjective and objective sleep quality, and that health status and psychosocial characteristics partially explain these associations.
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