Margaret L. Kern

Do Conscientious Individuals Live Longer? A Quantitative Review

OBJECTIVE Following up on growing evidence that higher levels of conscientiousness are associated with greater health protection, the authors conducted a meta-analysis of the association between conscientiousness-related traits and longevity. DESIGN Using a random-effects analysis model, the authors statistically combined 20 independent samples. In addition, the authors used fixed-effects analyses to examine specific facets of conscientiousness and study characteristics as potential moderators of this relationship. MAIN OUTCOME MEASURES Effect sizes were computed for each individual sample as the correlation coefficient r, based on the relationship between conscientiousness and mortality risk (all-cause mortality risk, longevity, or length of survival). RESULTS Higher levels of conscientiousness were significantly and positively related to longevity (r = .11, 95% confidence interval = .05-.17). Associations were strongest for the achievement (persistent, industrious) and order (organized, disciplined) facets of conscientiousness. CONCLUSION Results strongly support the importance of conscientiousness-related traits to health across the life span. Future research and interventions should consider how individual differences in conscientiousness may cause and be shaped by health-relevant biopsychosocial events across many years.

Health benefits: Meta-analytically determining the impact of well-being on objective health outcomes

Abstract This research synthesis integrates findings from 150 experimental, ambulatory and longitudinal studies that tested the impact of well-being on objective health outcomes. Results demonstrated that well-being positively impacts health outcomes (r=0.14). Well-being was found to be positively related to short-term health outcomes (r=0.15), long-term health outcomes (r=0.11), and disease or symptom control (r=0.13). Results from the experimental studies demonstrated that inductions of well-being lead to healthy functioning, and inductions of ill-being lead to compromised health at similar magnitudes. Thus, the effect of subjective well-being on health is not solely due to ill-being having a detrimental impact on health, but also to well-being having a salutary impact on health. Additionally, the impact of well-being on improving health was stronger for immune system response and pain tolerance, whereas well-being was not significantly related to increases in cardiovascular and physiological reactivity. These findings point to potential biological pathways, such that well-being can directly bolster immune functioning and buffer the impact of stress.

Psychological Language on Twitter Predicts County-Level Heart Disease Mortality:

Hostility and chronic stress are known risk factors for heart disease, but they are costly to assess on a large scale. We used language expressed on Twitter to characterize community-level psychological correlates of age-adjusted mortality from atherosclerotic heart disease (AHD). Language patterns reflecting negative social relationships, disengagement, and negative emotions—especially anger—emerged as risk factors; positive emotions and psychological engagement emerged as protective factors. Most correlations remained significant after controlling for income and education. A cross-sectional regression model based only on Twitter language predicted AHD mortality significantly better than did a model that combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity. Capturing community psychological characteristics through social media is feasible, and these characteristics are strong markers of cardiovascular mortality at the community level.

Automatic personality assessment through social media language.

Language use is a psychologically rich, stable individual difference with well-established correlations to personality. We describe a method for assessing personality using an open-vocabulary analysis of language from social media. We compiled the written language from 66,732 Facebook users and their questionnaire-based self-reported Big Five personality traits, and then we built a predictive model of personality based on their language. We used this model to predict the 5 personality factors in a separate sample of 4,824 Facebook users, examining (a) convergence with self-reports of personality at the domain- and facet-level; (b) discriminant validity between predictions of distinct traits; (c) agreement with informant reports of personality; (d) patterns of correlations with external criteria (e.g., number of friends, political attitudes, impulsiveness); and (e) test-retest reliability over 6-month intervals. Results indicated that language-based assessments can constitute valid personality measures: they agreed with self-reports and informant reports of personality, added incremental validity over informant reports, adequately discriminated between traits, exhibited patterns of correlations with external criteria similar to those found with self-reported personality, and were stable over 6-month intervals. Analysis of predictive language can provide rich portraits of the mental life associated with traits. This approach can complement and extend traditional methods, providing researchers with an additional measure that can quickly and cheaply assess large groups of participants with minimal burden.

Personality, Well-Being, and Health*

A lifespan perspective on personality and health uncovers new causal pathways and provides a deeper, more nuanced approach to interventions. It is unproven that happiness is a direct cause of good health or that negative emotion, worry, and depression are significant direct causes of disease. Instead, depression-related characteristics are likely often reflective of an already-deteriorating trajectory. It is also unproven that challenging work in a demanding environment usually brings long-term health risks; on the contrary, individual strivings for accomplishment and persistent dedication to ones career or community often are associated with sizeable health benefits. Overall, a substantial body of recent research reveals that conscientiousness plays a very significant role in health, with implications across the lifespan. Much more caution is warranted before policy makers offer narrow health recommendations based on short-term or correlational findings. Attention should be shifted to individual trajectories and pathways to health and well-being.

Stability of physical activity across the lifespan.

Physical activity is associated with various health-relevant psychosocial and physiological processes, but activity stability across extended time periods is inadequately understood. This lifespan longitudinal cohort study examined activity levels of 723 males and 554 females. Associations across time were computed and structural equation modeling compared a one factor model and a simplex model. Results showed activity levels are somewhat stable from childhood through middle and late adulthood. Further, a simplex model provided a better fit than a one factor model. Successful models and interventions to improve health will likely require a more nuanced, pattern-sensitive understanding of physical activity across time.

Developing Age and Gender Predictive Lexica over Social Media

Demographic lexica have potential for widespread use in social science, economic, and business applications. We derive predictive lexica (words and weights) for age and gender using regression and classification models from word usage in Facebook, blog, and Twitter data with associated demographic labels. The lexica, made publicly available,1 achieved state-of-the-art accuracy in language based age and gender prediction over Facebook and Twitter, and were evaluated for generalization across social media genres as well as in limited message situations.

A new life-span approach to conscientiousness and health: combining the pieces of the causal puzzle.

Conscientiousness has been shown to predict healthy behaviors, healthy social relationships, and physical health and longevity. The causal links, however, are complex and not well elaborated. Many extant studies have used comparable measures for conscientiousness, and a systematic endeavor to build cross-study analyses for conscientiousness and health now seems feasible. Of particular interest are efforts to construct new, more comprehensive causal models by linking findings and combining data from existing studies of different cohorts. Although methodological perils can threaten such integration, such efforts offer an early opportunity to enliven a life course perspective on conscientiousness, to see whether component facets of conscientiousness remain related to each other and to relevant mediators across broad spans of time, and to bolster the findings of the few long-term longitudinal studies of the dynamics of personality and health. A promising approach to testing new models involves pooling data from extant studies as an efficient and heuristic prelude to large-scale testing of interventions.

A multidimensional approach to measuring well-being in students: Application of the PERMA framework

Seligman recently introduced the PERMA model with five core elements of psychological well-being: positive emotions, engagement, relationships, meaning, and accomplishment. We empirically tested this multidimensional theory with 516 Australian male students (age 13–18). From an extensive well-being assessment, we selected a subset of items theoretically relevant to PERMA. Factor analyses recovered four of the five PERMA elements, and two ill-being factors (depression and anxiety). We then explored the nomological net surrounding each factor by examining cross-sectional associations with life satisfaction, hope, gratitude, school engagement, growth mindset, spirituality, physical vitality, physical activity, somatic symptoms, and stressful life events. Factors differentially related to these correlates, offering support for the multidimensional approach to measuring well-being. Directly assessing subjective well-being across multiple domains offers the potential for schools to more systematically understand and promote well-being.

Towards Assessing Changes in Degree of Depression through Facebook

Depression is typically diagnosed as being present or absent. However, depression severity is believed to be continuously distributed rather than dichotomous. Severity may vary for a given patient daily and seasonally as a function of many variables ranging from life events to environmental factors. Repeated population-scale assessment of depression through questionnaires is expensive. In this paper we use survey responses and status updates from 28,749 Facebook users to develop a regression model that predicts users’ degree of depression based on their Facebook status updates. Our user-level predictive accuracy is modest, significantly outperforming a baseline of average user sentiment. We use our model to estimate user changes in depression across seasons, and find, consistent with literature, users’ degree of depression most often increases from summer to winter. We then show the potential to study factors driving individuals’ level of depression by looking at its most highly correlated language features.