Couples-based health behavior change interventions: A relationship science perspective on the unique opportunities and challenges to improve dyadic health
Bibliographic record
Abstract
Epidemiological studies indicate that better marital quality is associated with less morbidity and premature mortality. A number of interpersonal processes related to marital quality are also associated with health-relevant surrogate biomarkers across different physiological systems. Despite these replicated correlational findings, few interventions have harnessed interpersonal processes as potential interventions to enhance health. Building on Dr. Janice Kiecolt-Glaser's model of relationships and health, we propose that couples-based health behavior change interventions may represent an effective way to decrease dysregulation across autonomic, endocrine and immune systems and, ultimately, improve dyadic health. Given that the cohabiting partner is an essential part of the social context in which the behavior change is being pursued, it is important to consider the relational issues triggered by dyadic interventions. Using a relationship science perspective, this article reviews the literature on couples' concordance in health behaviors and health outcomes, the potential pathways underlying this concordance, theories of the couple as a self-sustaining social system, dyadic adaptation of individual self-regulation strategies, effective and ineffective social support and social control in couple relationships, the integration of relationship-building and health behavior change strategies, and the consideration of key moderators related to the nature of the relationship and the context surrounding the relationship. These findings highlight the importance of adopting a relationship science perspective when designing and testing dyadic interventions to improve health outcomes. The data reviewed provide insights on how to optimize couples-based health behavior change interventions to reduce physiological dysregulation and improve dyadic health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".