Optimizing Behavioral and Biobehavioral Interventions

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Behavioral and biobehavioral interventions appear throughout society. They are important in many areas of public health, such as substance misuse, HIV/AIDS, Hepatitis C, smoking cessation, cancer treatment, weight management, treatment of depression and other mental health problems, and prevention of child maltreatment. They are also important in enhancement of educational achievement and promotion of human well-being.

Among the challenges faced by scientists is how to use interventions to achieve the greatest societal benefits.  Societal benefit is a function of not only the effectiveness of an intervention, but also its reach. Thus to achieve the greatest societal benefits, it is necessary that interventions be not only effective, but also readily implementable, in other words, scalable. If an intervention is highly effective but too costly or complicated to be implemented widely, it can offer only limited societal benefits. By contrast, scalable interventions, that is, interventions that can be implemented widely and with fidelity without exceeding available resources, have the potential to reach many participants and thereby offer substantial societal benefits. Here the term resources is broadly defined to include, for example, the amount a payer (e.g. insurance company or school district) is willing to pay to implement the intervention; the amount of staff or classroom time that can be spared; and the amount of time participants are willing to devote to completing the intervention.

Scalability cannot be achieved without acknowledging two inescapable realities: (1) Where there are constraints on the resources available for implementation, in general intervention effectiveness will be somewhat less than what it would have been if there were no constraints; and (2) there are almost always constraints on implementation resources.  Intervention optimization is about maximizing effectiveness while working within the constraints, with the ultimate objective of offering the highest level of societal benefits.





The Multiphase Optimization Strategy (MOST)

The multiphase optimization strategy (MOST) provides a framework for engineering efficacious and effective behavioral interventions. Conceptually rooted in engineering, MOST emphasizes efficiency and careful management of resources to move intervention science forward systematically. MOST can be used to guide the evaluation of research evidence, develop optimized interventions, and enhance Type I and Type II translation of research.

Factorial Experiments: Why and How They Work

​Factorial and fractional factorial designs are frequently used in conducting optimization trials, and other optimization trial designs such as the sequential multiple-assignment randomized trial (SMART) and the micro-randomized trial (MRT) are close relatives of the factorial design.  In the pages below, we briefly explain the logic of the factorial design.  More information can be found in the articles listed in the citations.


Collins, L. M. (2018). Optimization of behavioral, biobehavioral, and biomedical interventions: the multiphase optimization strategy (MOST). Springer.

Collins, L. M., Dziak, J. J., & Li, R. (2009). Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psychological methods14(3), 202.

Collins, L. M., & Kugler, K. C. (2018). Optimization of multicomponent behavioral, biobehavioral, and biomedical interventions: Advanced topics. Springer.

Dziak, J. J., Nahum-Shani, I., & Collins, L. M. (2012). Multilevel factorial experiments for developing behavioral interventions: Power, sample size, and resource considerations. Psychological methods17(2), 153.

Nahum-Shani, I., Dziak, J. J., & Collins, L. M. (2018). Multilevel factorial designs with experiment-induced clustering. Psychological methods23(3), 458.


Last updated: May 6, 2020