Complex assessment of relationship quality within dyads

Higher quality relationships have been linked to improved outcomes; however, the measurement of relationship quality often ignores its complexity and the possibility of co-occurring positivity and negativity across different contexts. The goal of this study is to test the added benefit of including multiple dimensions, contexts, and perspectives of relationship quality from both individuals in predicting marital functioning. The Social Relationships Index assessed positive and negative dimensions of relationship quality under neutral, positive, and support-seeking contexts for 183 heterosexual married couples. Models showed that the inclusion of multiple dimensions of relationship quality across all three contexts improved prediction of marital functioning for both women and men. The use of multidimensional multicontextual relationship quality assessments is highly recommended

Effects of couple interactions and relationship quality on plasma oxytocin and cardiovascular reactivity: Empirical findings and methodological considerations

Cardiovascular reactivity is a potential mechanism underlying associations of close relationship quality with cardiovascular disease. Two models describe oxytocin as another mechanism. The “calm and connect” model posits an association between positive relationship experiences and oxytocin levels and responses, whereas the “tend and befriend” model emphasizes the effects of negative relationship experiences in evoking oxytocin release. In this study of 180 younger couples, relationship quality had a small, marginally significant inverse association with plasma oxytocin levels, and neither positive nor negative couple interactions evoked change in plasma oxytocin. Negative couple interactions evoked significant cardiovascular reactivity, especially among women. Hence, in the largest study of these issues to date, there was little support for key tenets of the “calm and connect” model, and only very modest support for the ”tend and befriend” model. However, findings were consistent with the view that CVR contributes to the effects of relationship difficulties on health

Viewing Loved Faces Inhibits Defense Reactions: A Health-Promotion Mechanism?

We have known for decades that social support is associated with positive health outcomes. And yet, the neurophysiological mechanisms underlying this association remain poorly understood. The link between social support and positive health outcomes is likely to depend on the neurophysiological regulatory mechanisms underlying reward and defensive reactions. The present study examines the hypothesis that emotional social support (love) provides safety cues that activate the appetitive reward system and simultaneously inhibit defense reactions. Using the startle probe paradigm, 54 undergraduate students (24 men) viewed black and white photographs of loved (romantic partner, father, mother, and best friend), neutral (unknown), and unpleasant (mutilated) faces. Eye–blink startle, zygomatic major activity, heart rate, and skin conductance responses to the faces, together with subjective ratings of valence, arousal, and dominance, were obtained. Viewing loved faces induced a marked inhibition of the eye-blink startle response accompanied by a pattern of zygomatic, heart rate, skin conductance, and subjective changes indicative of an intense positive emotional response. Effects were similar for men and women, but the startle inhibition and the zygomatic response were larger in female participants. A comparison between the faces of the romantic partner and the parent who shares the partner’s gender further suggests that this effect is not attributable to familiarity or arousal. We conclude that this inhibitory capacity may contribute to the health benefits associated with social support.This research was funded by grant P07-SEJ-02964 from Junta de Andalucía (Spain)

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner's ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person's own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Machine learning uncovers the most robust self-report predictors of relationship quality across 43 longitudinal couples studies

Given the powerful implications of relationship quality for health and well-being, a central mission of relationship science is explaining why some romantic relationships thrive more than others. This large-scale project used machine learning (i.e., Random Forests) to 1) quantify the extent to which relationship quality is predictable and 2) identify which constructs reliably predict relationship quality. Across 43 dyadic longitudinal datasets from 29 laboratories, the top relationship-specific predictors of relationship quality were perceived-partner commitment, appreciation, sexual satisfaction, perceived-partner satisfaction, and conflict. The top individual-difference predictors were life satisfaction, negative affect, depression, attachment avoidance, and attachment anxiety. Overall, relationship-specific variables predicted up to 45% of variance at baseline, and up to 18% of variance at the end of each study. Individual differences also performed well (21% and 12%, respectively). Actor-reported variables (i.e., own relationship-specific and individual-difference variables) predicted two to four times more variance than partner-reported variables (i.e., the partner’s ratings on those variables). Importantly, individual differences and partner reports had no predictive effects beyond actor-reported relationship-specific variables alone. These findings imply that the sum of all individual differences and partner experiences exert their influence on relationship quality via a person’s own relationship-specific experiences, and effects due to moderation by individual differences and moderation by partner-reports may be quite small. Finally, relationship-quality change (i.e., increases or decreases in relationship quality over the course of a study) was largely unpredictable from any combination of self-report variables. This collective effort should guide future models of relationships

Profiling the mental health of diabetic patients: a cross-sectional survey of Zimbabwean patients

Objective The burden of diabetes mellitus has exponentially increased in low resource settings. Patients with diabetes are more likely to exhibit poor mental health which negatively affects treatment outcomes. However, patients with high levels of social support (SS) are likely to report optimal mental health. We sought to determine how SS affects the report of psychiatric morbidity and health-related quality of life (HRQoL) in 108 diabetic patients in Harare, Zimbabwe. Results The average age of participants was 54.1 (SD 18.6) years. Most of the participants were; females (69.4%), married (51.9%), and were of low level of income (43.5%). 37.1% of the participants exhibited signs of psychiatric morbidity [mean Shona Symptoms Questionnaire score—6.7 (SD 3.2)]. Further, patients also reported lower HRQoL [mean EQ-5D-VAS score—64.1 (SD 15.3)] and high levels of SS [mean Multidimensional Scale of Perceived Social Support score—43.7 (SD 11.5)]. Patients who received greater amount of SS had optimal mental health. Being female, unmarried, lower education attainment, having more comorbid conditions, being diagnosed with type 2 diabetes and having been diagnosed of diabetes for a longer duration were associated with poorer mental health. It is important to develop context-specific interventions to improve diabetic patients’ mental health