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Abstract Although stress and adversity are largely universal experiences, people exposed to greater hardship are at increased risk for negative health consequences. Recent studies identify accelerated biological aging as a mechanism that could explain how trauma and adversity gives rise to poor health, and advances in this area of study coincide with technological innovations in the measurement of biological aging, particularly epigenetic profiles consistent with accelerated aging derived from DNA methylation. In this review, we provide an overview of the current literature examining how adversity might accelerate biological aging, with a specific focus on social and health behaviors. The most extensive evidence in this area suggests that health-compromising behaviors, particularly smoking, may partially explain the association between adversity and accelerated aging. Although there is relatively less published support for the role of social behaviors, emerging evidence points to the importance of social connection as a mechanism for future study. Our review highlights the need to determine the extent to which the associations from adversity to accelerated aging are consistent with causal processes. As we consider these questions, the review emphasizes methodological approaches from the causal inference literature that can help deepen our understanding of how stress and trauma might result in poor health. The use of these methodologies will help provide evidence as to which behavioral interventions might slow aging and improve health, particularly among populations that more often experience adversity and trauma.

Abstract Given its centrality in scholarly and popular discourse, morality should be expected to figure prominently in everyday talk. We test this expectation by examining the frequency of moral content in three contexts, using three methods: (a) Participants’ subjective frequency estimates (N = 581); (b) Human content analysis of unobtrusively recorded in-person interactions (N = 542 participants; n = 50,961 observations); and (c) Computational content analysis of Facebook posts (N = 3822 participants; n = 111,886 observations). In their self-reports, participants estimated that 21.5% of their interactions touched on morality (Study 1), but objectively, only 4.7% of recorded conversational samples (Study 2) and 2.2% of Facebook posts (Study 3) contained moral content. Collectively, these findings suggest that morality may be far less prominent in everyday life than scholarly and popular discourse, and laypeople, presume.

Abstract Over the recent years, machine learning techniques have been employed to produce state-of-the-art results in several audio related tasks. The success of these approaches has been largely due to access to large amounts of open-source datasets and enhancement of computational resources. However, a shortcoming of these methods is that they often fail to generalize well to tasks from real life scenarios, due to domain mismatch. One such task is foreground speech detection from wearable audio devices. Several interfering factors such as dynamically varying environmental conditions, including background speakers, TV, or radio audio, render foreground speech detection to be a challenging task. Moreover, obtaining precise moment-to-moment annotations of audio streams for analysis and model training is also time-consuming and costly. In this work, we use multiple instance learning (MIL) to facilitate development of such models using annotations available at a lower time-resolution (coarsely labeled). We show how MIL can be applied to localize foreground speech in coarsely labeled audio and show both bag-level and instance-level results. We also study different pooling methods and how they can be adapted to densely distributed events as observed in our application. Finally, we show improvements using speech activity detection embeddings as features for foreground detection.

Martial dissolution is associated with risk for poor mental health outcomes, but less is known about the variables and processes that may explain this risk. In a sample of recently-separated adults ('N' = 138), this study examined the association of psychological overinvolvement—assessed using a composite of self-reported rumination, language use, and judge-rated recounting and reconstruing—with daily affect and psychological distress. We included objective measures of sleep, behavioral displays of distress, and social engagement as potential mediators of these associations. Consistent with the preregistered hypotheses, greater psychological overinvolvement predicted higher levels of psychological distress, lower happiness, and greater sadness five months later. Psychological overinvolvement also predicted change in sadness, but not happiness or psychological distress, over five months. Contrary to our predictions, none of the candidate mediators explained these associations. Exploratory analyses suggested that the self-reported rumination component of the psychological overinvolvement composite largely accounted for the association between psychological overinvolvement and the three outcomes. People’s tendency to become overinvolved in their psychological experience after divorce predicts increased risk for distress in the months following marital separation.

Background: Medical residency can be a time of increased psychological stress and sleep disturbance. We examine the prospective associations between self-reported sleep quality and resident wellness across a single training year. Methods: Sixty-nine (N=69) resident physicians completed the Brief Resident Wellness Profile (M=17.66, standard deviation [SD]=3.45, range: 0–17) and the Pittsburgh Sleep Quality Index (M=6.22, SD=2.86, range: 12–25) at multiple occasions in a single training year. We examined the 1-month lagged effect of sleep disturbances on residents’ self-reported wellness. Results: Accounting for residents’ overall level of sleep disturbance across the entire study period, both the concurrent (within-person) within-occasion effect of sleep disturbance (B=−0.20, standard error [SE]=0.06, p=0.003, 95% confidence interval [CI]: −0.33, −0.07) and the lagged within-person effect of resident sleep disturbance (B=−0.15, SE=0.07, p=0.037, 95% CI: −0.29, −0.009) were significant predictors of decreased resident wellness. Increases in sleep disturbances are a leading indicator of resident wellness, predicting decreased well-being 1 month later. Conclusions: Sleep quality exerts a significant effect on self-reported resident wellness. Periodic evaluation of sleep quality may alert program leadership and the residents themselves to impending decreases in psychological well-being.

Objective: Low levels of perceived social integration, or loneliness, are associated with increased risk for a range of poor health outcomes. Sleep disturbance plays a central role in the evolutionary theory of loneliness, which provides a mechanistic account of how low levels of social integration may negatively impact health. No studies, however, have examined whether the association between social integration and sleep disturbance is consistent with a causal effect after accounting for genes that are common to both variables. Method: Using twin data ('N' = 905 twin pairs) from the nationally-representative Midlife in the United States (MIDUS) survey, I evaluated a series of bivariate twin models exploring whether the phenotypic association between low social integration and sleep disturbance can be explained by shared genetics. In addition, the current study specified a series of quantitative models for studying gene x environment (G X E) interactions to determine whether the genetic and environmental influences on sleep disturbance differ as a function of social integration. Results: The phenotypic association between social integration and sleep disturbance was fully accounted for by genes that are common between the two variables, suggesting that within-twin pair differences in social integration do not exert a causal influence on sleep disturbance. Social integration, however, moderated the non-shared environmental influence on sleep disturbances, with the greatest environmental influences observed at the lowest levels of social integration. Conclusions: The results of this study suggest that an essential feature of the evolutionary model of loneliness may need refinement or elaboration. The moderation findings are discussed in terms of the fit with a stress-buffering model of social support in which environmental influences on sleep disturbance are strongest when social resources are low.