Advanced Analytic Methods Could Provide Insight Into Mechanisms of Behavior Change

Numerous evidence-based behavioral interventions for alcohol use disorder (AUD) are available, including cognitive behavioral therapy, couples therapy, 12-step facilitation, and motivational interviewing. A current focus of research on behavioral interventions for AUD is identifying the processes through which evidence-based interventions work. Known as mechanisms of behavior change (MOBCs), such as increased readiness to change, increased social support for abstinence, and reduced craving, these processes partly explain why some treatments for AUD help people reduce or stop their alcohol use. A more comprehensive understanding of MOBCs, particularly of when they exert their effects, could inform clinicians’ efforts in evaluating patient progress and making treatment decisions. In addition, identifying how specific events, actions, and processes contribute to MOBCs could provide clues into how these mechanisms develop, which would assist clinicians in targeting treatments more effectively.

Research on MOBCs has traditionally relied on mediation analysis frameworks, which are methods that help researchers assess the pathways that link a specific factor, like an MOBC, to an outcome, such as abstinence. To help move this area of research forward, Kevin Hallgren, Ph.D., of the University of Washington, and colleagues undertook an analysis of some statistical approaches beyond mediation analysis frameworks that may enhance understanding of how MOBCs work.

Sophisticated analytic techniques that consider behavioral change processes that occur during AUD treatment could provide a broader picture of how MOBCs operate. For example, an approach known as growth-curve modeling can shed light on the timing of MOBCs, such as when alcohol craving changes after abstinence. Other advanced analytical approaches can provide much-needed information about the complex relationships between factors that impact treatment, such as how a clinician’s behavior during treatment sessions influences a patient’s voicing of reasons to change, and vice versa.

In their study, Dr. Hallgren and colleagues note that a greater diversity of analytic methods to study MOBCs will lead to a better understanding of how patients successfully change and can improve the translational value of MOBCs research.