The RMICS has been used in dozens of investigations with a range of ages (primary adult married couples, but also preteen siblings, high school dating couples, and engaged couples), populations (e.g., general married population, marital clinics, cancer patients and their spouses, families at risk for adolescent drug abuse, Vietnam veterans), and research purposes (Heyman, 2004). RMICS has been used with a wide range of North American couples. Participants' ages have ranged from midteens to late 70s and their education has ranged from middle school to earned postgraduate degrees. Income and socioeconomic status have ranged from very low to very high. Race and ethnicity of participants have been largely White, although hundreds of interactions of racial and ethnic minorities have been successfully coded (Heyman, 2004).

[+]

Identified

As noted in a meta-analysis of 126 published empirical articles investigating relationship quality and physical health (Robles, Slatcher, Trombello, & McGinn, 2014), a sizable literature links hostile couple relationships to (a) problems with social-cognitive and affective processes, such as self-regulation of emotion and behavior; (b) physiological stress reactivity processes, especially in the neuroendocrine axes and in immune functioning; and (c) poor health outcomes. Men and women who displayed more hostility (as coded by RMICS) and who also had a mood disorder history have been shown to have significantly lower post-meal energy expenditure, higher post-meal insulin, and higher peak triglyceride responses (i.e. poorer metabolic responses to high-fat meals; Kiecolt-Glaser et al., 2015). In another study of healthy couples, individuals in a couple who demonstrated higher levels of hostile behaviors across two interactions had significantly poorer wound healing (Kiecolt-Glaser et al., 2005). Thus, hostile partner behaviors that are coded by RMICS might be a mechanism for change for health behaviors and outcomes.

[+] PMCID, PUBMED ID, or CITATION

Text Citation: Robles, T. F., Slatcher, R. B., Trombello, J. M., & McGinn, M. M. (2014). Marital quality and health: A meta-analytic review. Psychological Bulletin, 140, 140. doi:10.1037/a0031859

Text Citation: Kiecolt-Glaser, J. K., Jaremka, L., Andridge, R., Peng, J., Habash, D., Fagundes, C. P., ... & Belury, M. A. (2015). Marital discord, past depression, and metabolic responses to high-fat meals: Interpersonal pathways to obesity. Psychoneuroendocrinology, 52, 239-250.

Text Citation: Kiecolt-Glaser, J. K., Loving, T. J., Stowell, J. R., Malarkey, W. B., Lemeshow, S., Dickinson, S. L., & Glaser, R. (2005). Hostile marital interactions, proinflammatory cytokine production, and wound healing. Archives of general psychiatry, 62(12), 1377-1384.

Measured

RMICS codes discriminate between both men and women in distressed and non-distressed relationships (Heyman, 2004) as determined by the commonly used Dyadic Adjustment Scale, using the standard cutoff of 97 and below as the criterion for relationship distress (DAS; Spanier, 1976). In a study of couples entering a treatment program for husband-to-wife physical aggression, positive treatment response (i.e. lower level of aggression) was significantly predicted by low levels of husbands' reciprocity of wives' hostility. Communication variables as coded by RMICS (e.g., base rates of hostility; frequency of certain behavioral sequences) predicted dropout and treatment response over and above the effects of marital adjustment and husbands' psychological abuse (Heyman, Brown, Feldbau, & O'Leary, 1999).

[+] PMCID, PUBMED ID, or CITATION

Text Citation: Heyman, R. E. (2004). Rapid Marital Interaction Coding System. In P. K. Kerig & D. H. Baucom (Eds.) Couple observational coding systems (pp. 67-94). Mahwah, NJ: Lawrence Erlbaum Associates.

Text Citation: Heyman, R. E., Brown, P. D., Feldbau, S. R., & O’Leary, K. D. (1999). Couples’ communication variables as predictors of dropout and treatment response in wife abuse treatment programs. Behavior Therapy, 30, 165-190.

Text Citation: Spanier, G. B. (1976). Measuring dyadic adjustment: New scales for assessing the quality of marriage and similar dyads. Journal of Marriage and the Family, 15-28.

[+] Demographics

The RMICS has been used in dozens of investigations with a range of ages (primary adult married couples, but also preteen siblings, high school dating couples, and engaged couples), populations (e.g., general married population, marital clinics, cancer patients and their spouses, families at risk for adolescent drug abuse, Vietnam veterans), and research purposes (Heyman, 2004). RMICS has been used with a wide range of North American couples. Participants' ages have ranged from midteens to late 70s and their education has ranged from middle school to earned postgraduate degrees. Income and socioeconomic status have ranged from very low to very high. Race and ethnicity of participants have been largely White, although hundreds of interactions of racial and ethnic minorities have been successfully coded (Heyman, 2004).

Influenced

This measure has not been influenced yet.

Validated

This measure has not been validated yet.

Access Measure

SOBC Validation Process

The Science of Behavior Change (SOBC) program seeks to promote basic research on the initiation, personalization and maintenance of behavior change. By integrating work across disciplines, this effort will lead to an improved understanding of the underlying principles of behavior change. The SOBC program aims to implement a mechanisms-focused, experimental medicine approach to behavior change research and to develop the tools required to implement such an approach. The experimental medicine approach involves: identifying an intervention target, developing measures to permit verification of the target, engaging the target through experimentation or intervention, and testing the degree to which target engagement produces the desired behavior change.

Within the SOBC Measures Repository, researchers have access to measures of mechanistic targets that have been (or are in the processing of being) validated by SOBC Research Network Members and other experts in the field. The SOBC Validation Process includes three important stages of evaluation for each proposed measure: Identification, Measurement, and Influence.

The first stage of validation requires a measure to be Identified within the field; there must be theoretical support for the specific measure of the proposed mechanistic target or potential mechanism of behavior change. This evidence may include references for the proposed measure, or theoretical support for the construct that the proposed measure is intended to assess. The second stage of validation requires demonstration that the level and change in level of the chosen mechanistic target can be Measured with the proposed measure (assay). For example, if the proposed measure is a questionnaire, the score on the measure should indicate the activity of the target process, and it must have strong psychometric properties. The third stage of validation requires demonstration that the measure can be Influenced; there must be evidence that the measured target is malleable and responsive to manipulation. Evidence relating to each stage includes at least one peer-reviewed publication or original data presentation (if no peer-reviewed research is available to support the claim) and is evaluated by SOBC Research Network Members and experts in the field.

Once a measure has gone through these three stages, it will then either be Validated or Not validated according to SOBC Research Network standards. If a measure is Validated, then change in the measured target was reliably associated with Behavior Change. If a measure is Not validated, then change in the measured target was not reliably associated with Behavior Change. Why would we share measures that are not validated? The SOBC Research Network values open, rigorous, and transparent research. Our goal is to make meaningful progress and develop replicable and effective interventions in behavior change science. Therefore, the SOBC sees value in providing other researchers in the field with information regarding measures that work and measures that fall short for specific targets. Further, a measure that is not validated for one target in one population may be validated in another target or population.

Want to learn more? For any questions regarding the SOBC Validation Process or Measures Repository, please email info@scienceofbehaviorchange.org.

Identified

Has the mechanism been identified as a potential target for behavior change? This section summarizes theoretical support for the mechanism.

Measured

Have the psychometric properties of this measure been assessed? This section includes information such as content validity, internal consistency, and test-retest reliability.

Influenced

Has a study manipulation led to change in the mechanism? This section addresses evidence that this measure is modifiable by experimental manipulation or clinical intervention.

Not Validated

Has a change in this mechanism been associated with behavior change? This section addresses empirical evidence that causing change in the measure reliably produces subsequent behavior change.