The specific processes through which social support exerts its influence in daily life are not well understood. Its salutary effects as an environmental variable might be construed as effects of individual differences and related, contextualized personality processes. We investigated the unique effects of personality and social support on daily stress experiences across two 2-day periods (N=391). In line with our preregistered hypotheses, both personality and social support affected the probability of reporting a stressor in daily life and moderated within-person stress reactivity. No incremental effects of social support, yet unique effects of personality emerged when all variables were joined into one model. Our results demonstrate that while both constructs moderate the link between stressor exposure and perceived stress when tested in isolation, their conceptual overlap may produce effects in favor of personality.

Background Previous research from our group found that recent depressive symptoms were associated with 3-year change in carotid intima-media thickness (CA-IMT), a biomarker of cardiovascular disease risk, in an initially healthy sample of older adults. Trait measures of anxiety, anger, and hostility did not predict 3-year CA-IMT progression in that report. Purpose The current study sought to reexamine these associations at a 6-year follow-up point. Methods Two-hundred seventy-eight participants (151 males, mean age = 60.68 years) from the original sample completed an additional IMT reading 6 years following the initial baseline assessment. Results Though not significant at 3-years, trait-anger emerged as a predictor of IMT progression at the 6-year point. When examined in separate regression models, both depression and trait-anger (but not anxiety or hostility) predicted 6-year IMT change (b = .017, p = .002; b = .029, p = .01, respectively). When examined concurrently, both depression and anger were independently associated with 6-year IMT progression (b = .016, p = .010, b = .028, p = .022, respectively). Exploratory analyses suggest that the relative contributions of anger and depression may differ for males and females. Conclusions The use of sequential follow-ups is relatively unique in this literature, and our results suggest a need for further research on the timing and duration of psychosocial risk exposures in early stages of cardiovascular disease.

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Objective: Interpersonal and emotional functioning are closely linked, and reciprocally influence one another. Contemporary Integrative Interpersonal Theory (CIIT) offers a useful framework to study these patterns. Stress processes offer several candidate targets for empirical investigation with methods that allow for fine-grained analyses in the context of daily life. Method: Four samples of adults (Sample 1 N=145; Sample 2 N=160; Sample 3 N=297; Sample 4=89 dyads, 178 individuals) completed ecological momentary assessment protocols focused on a variety of interpersonal and emotional experiences (Sample 1 Observation N= 14,219; Sample 2 Observation N=8,137; Sample 3 Observation N=5,400; Sample 4 Observation N=5,537). Samples were enriched for aggressive and self-harming behavior (Sample 1), trait hostility (Sample 2), interpersonal problems (Sample 3), and personality disorder features (Sample 4). Results: Using multilevel dynamic structural equation modeling, we investigated how emotions and interpersonal functioning operate over brief timescales in daily life. We found evidence for a normative vicious socio-emotional cycle across all four samples, whereby negative emotions led to interpersonal conflict (i.e., perceptions of and enacting cold, antagonistic, or quarrelsome behavior) which in turn led to increased negative emotions. Although individuals differed in the strength of this process, it was unrelated to trait negative affectivity. Conclusions: Viewing these results through the lens of CIIT, we discuss multiple intervention points highlighted by these dynamic results whereby the vicious cycle can be changed.

BACKGROUND Physical and psychological symptoms are common during chemotherapy in cancer patients, and real-time monitoring of these symptoms can improve patient outcomes. Sensors embedded in mobile phones and wearable activity trackers could be potentially useful in monitoring symptoms passively, with minimal patient burden. OBJECTIVE The aim of this study was to explore whether passively sensed mobile phone and Fitbit data could be used to estimate daily symptom burden during chemotherapy. METHODS A total of 14 patients undergoing chemotherapy for gastrointestinal cancer participated in the 4-week study. Participants carried an Android phone and wore a Fitbit device for the duration of the study and also completed daily severity ratings of 12 common symptoms. Symptom severity ratings were summed to create a total symptom burden score for each day, and ratings were centered on individual patient means and categorized into low, average, and high symptom burden days. Day-level features were extracted from raw mobile phone sensor and Fitbit data and included features reflecting mobility and activity, sleep, phone usage (eg, duration of interaction with phone and apps), and communication (eg, number of incoming and outgoing calls and messages). We used a rotation random forests classifier with cross-validation and resampling with replacement to evaluate population and individual model performance and correlation-based feature subset selection to select nonredundant features with the best predictive ability. RESULTS Across 295 days of data with both symptom and sensor data, a number of mobile phone and Fitbit features were correlated with patient-reported symptom burden scores. We achieved an accuracy of 88.1% for our population model. The subset of features with the best accuracy included sedentary behavior as the most frequent activity, fewer minutes in light physical activity, less variable and average acceleration of the phone, and longer screen-on time and interactions with apps on the phone. Mobile phone features had better predictive ability than Fitbit features. Accuracy of individual models ranged from 78.1% to 100% (mean 88.4%), and subsets of relevant features varied across participants. CONCLUSIONS Passive sensor data, including mobile phone accelerometer and usage and Fitbit-assessed activity and sleep, were related to daily symptom burden during chemotherapy. These findings highlight opportunities for long-term monitoring of cancer patients during chemotherapy with minimal patient burden as well as real-time adaptive interventions aimed at early management of worsening or severe symptoms.