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.

Concerns have been raised regarding the extensive use of social media sites by young adults and adolescents and the effects this use may have on their mental health and general functioning. However, definitions of health are expansive and diverse. In the present article we assess 3 broad areas of mental and physical health: depressive symptoms, mindful attention, and physical symptoms. Additionally, the fear of missing out (FoMO), which relates to social media use both in its experience and origins, has received a great deal of popular attention recently with relatively less attention from researchers. In order to test the associations between social media use, FoMO, and a range of mental and physical health outcomes, an online study was conducted with 386 undergraduates from a large, ethnically diverse university. Results of this study demonstrated that FoMO was positively associated with time spent on social media. Furthermore, experiencing higher levels of FoMO was associated with more depressive symptoms, less mindful attention, and more physical symptoms. Moreover, time spent on social media was no longer related to depressive symptoms and mindful attention when FoMO was included in the model. Findings from this study suggest that FoMO may be a more revelatory measure than simple assessments of social media use, and is associated with negative health outcomes.

There are many nonpharmacologic interventions tested in randomized clinical trials that demonstrate significant benefits for people living with Alzheimer's disease (AD) and ADrelated dementia, their care partners, or professional care providers. Nevertheless, with few exceptions, proven interventions have not been translated for delivery in real-world settings, such as home care, primary care, hospitals, community-based services, adult day services, assisted living, nursing homes, or other healthcare systems (HCSs). Using embedded pragmatic clinical trial (ePCT) methods is one approach that can facilitate dissemination and implementation (D&I) of dementia care interventions. The science of D&I can inform the integration of evidence-based dementia care in HCSs by offering theoretical frameworks that capture field complexities and guiding evaluation of implementation processes. Also, D&I science can suggest evidence-based strategies for implementing dementia care in HCSs. Although D&I considerations can inform each stage of dementia care intervention development, it is particularly critical when designing ePCTs. This article examines fundamental considerations for implementing dementia-specific interventions in HCSs and how best to prepare for successful dissemination upstream in the context of ePCTs, thereby illustrating the critical role of the D&I Core of the National Institute on Aging Imbedded Pragmatic Alzheimer's Disease and AD-Related Dementias Clinical Trials Collaboratory. The scientific premise of the D&I Core is that having the "end" in mind, upfront in the design and testing of dementia care programs, can lead to decision-making that optimizes the ultimate goal of wide-scale D&I of evidencebased dementia care programs in HCSs.

The present research examined how actor and partner attachment insecurity relates to biases in perceptions of partners' core relationship-relevant constructs. Across three dyadic studies ( N = 333, N = 666), we examined attachment anxiety and avoidance as predictors of over- or underestimation of partners' relationship satisfaction, commitment, and responsiveness, using partners' own reports as the reference point for evaluating bias. Actors higher in avoidance and actors with partners higher in avoidance perceived their partners to be less satisfied and committed. In addition, actors higher in avoidance and actors higher in anxiety displayed a pessimistic bias, perceiving their partners to be less satisfied and committed than their partners reported being. Finally, actors with partners higher in avoidance displayed an optimistic bias, perceiving their partners to be more satisfied and committed than their partners reported being. Results underscore the importance of adopting a dyadic perspective on perceptual biases in romantic relationships.

This study investigated authenticity as a moderator of the association between loneliness and depressive symptoms, anxiety, physical symptoms, and alcohol-related problems. It was expected that loneliness and health outcomes would be negatively related and that relationship would be weaker among those higher in authenticity. Significant interactions emerged between authenticity and loneliness for each outcome such that authenticity mitigated the relationship between higher loneliness and negative health outcomes. Results suggest that authenticity may be an underutilized resource for lonely individuals and warrants future investigation. The potential implications are diverse and could be incorporated in college adjustment and health promotion programs.