ABSTRACT
Given current pressures to increase the public health contributions of behavioral interventions, intervention scientists may wish to consider moving beyond the classical treatment package approach that focuses primarily on achieving statistical significance. They may wish also to focus on goals directly related to optimizing public health impact. The Multiphase Optimization Strategy (MOST) is an innovative methodological framework that draws on engineering principles to achieve more potent behavioral interventions. MOST is increasingly being adopted by intervention scientists seeking a systematic framework to engineer an optimized intervention. As with any innovation, there are challenges that arise with early adoption. This article describes the solutions to several critical questions that we addressed during the first-ever iterative application of MOST. Specifically, we describe how we have applied MOST to optimize an online program (myPlaybook) for the prevention of substance use among college student-athletes. Our application of MOST can serve as a blueprint for other intervention scientists who wish to design optimized behavioral interventions. We believe using MOST is feasible and has the potential to dramatically improve program effectiveness thereby advancing the public health impact of behavioral interventions.
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References
Collins LM, Murphy SA, Nair VN, Strecher VJ. A strategy for optimizing and evaluating behavioral interventions. Ann Behav Med. 2005; 30: 65-73.
Collins LM, Murphy SA, Strecher VJ. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007; 32(5 Suppl): S112-S118.
Collins LM, Baker TB, Mermelstein RJ, et al. The multiphase optimization strategy for engineering effective tobacco use interventions. Ann Beh Med. 2011; 41(2): 208-226.
Collins LM, Chakraborty B, Murphy SA, Strecher V. Comparison of a phased experimental approach and a single randomized clinical. Clin Trials. 2009; 6(1): 5-15.
Strecher VJ, McClure JB, Alexander GL, et al. Web-based smoking-cessation programs: results of a randomized trial. Am J Prev Med. 2008; 34(5): 373-381.
Caldwell LL, Smith EA, Collins LM, et al. Translational research in South Africa: evaluating implementation quality using a factorial design. Child Youth Care Forum. 2012; 41: 119-136.
McClure J, Derry H, Riggs K, et al. Questions about quitting (Q2): design and methods of a multiphase optimization strategy (MOST) randomized screening experiment for an online, motivational smoking cessation intervention. Contemp Clin Trials. 2012; 33(5): 1094-1102.
Doumas DM, Haustveit T, Coll KM. Reducing heavy drinking among first year intercollegiate athletes: a randomized controlled trial of web-based normative feedback. J Appl Sport Psych. 2010; 22(3): 247-261.
Turrisi R, Larimer M, Mallett K, et al. A randomized clinical trial evaluating a combined alcohol intervention for high-risk college students. J Stud Alcohol Drugs. 2009; 70(4): 555-567.
Yusko DA, Buckman JF, White HR, Pandina RJ. Alcohol, tobacco, illicit drugs, and performance enhancers: a comparison of use by college student athletes and nonathletes. J Am Coll Health. 2008; 57(3): 281-290.
Yusko DA, Buckman JF, White HR, Pandina RJ. Risk for excessive alcohol use and drinking-related problems in college student athletes. Addict Beh. 2008; 33(12): 1546-1556.
Hansen W, Dusenbury L, Bishop D, Derzon J. Substance abuse prevention program content: systematizing the classification of what programs target for change. Health Educ Res. 2007; 22(3): 351-360.
Berkowitz AD. An overview of the social norms approach. In: Lederman LC, Stewar LP, eds. Changing the culture of college drinking. Cresskill: Hampton; 2004: 193-214.
Perkins HW. The emergence and evolution of the social norms approach to substance abuse prevention. In: Perkins HW, ed. The social norms approach to preventing school and college age substance abuse: a handbook for educators, counselors, and clinicians. San Francisco: Jossey-Bass; 2003: 3-18.
Becker MH, Radius SM, Rosenstock IM, Drachman RH, Schuberth KC, Teets KC. Compliance with a medical regimen for asthma: a test of the health belief model. Public Health Rep. 1978; 93(3): 268-277.
Rosenstock IM. Historical origins of the health belief model. Health Educ Behav. 1974; 2(4): 328-335.
Ajzen I, Fishbein M. Understanding attitudes and predicting social behavior, vol. 278. Englewood Cliffs, New Jersey: Prentice-Hall; 1980.
Fishbein M, Ajzen I. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading: Addison-Wesley; 1975.
Neighbors C, Lewis MA, Atkins DC, et al. Efficacy of web-based personalized normative feedback: a two-year randomized controlled trial. J Consult Clin Psych. 2010; 78(6): 898.
Collins LM, Dziak JJ, Li R. Design of experiments with multiple independent variables: a resource management perspective on complete and reduced factorial designs. Psych Methods. 2009; 14(3): 202-224.
Wu C-F, Hamada M. Experiments: planning, analysis, and parameter design optimization. New York: Wiley; 2000.
Chakraborty B, Collins LM, Strecher VJ, Murphy SA. Developing multicomponent interventions using fractional factorial designs. Stat Med. 2009; 28(21): 2687-2708.
Nair V, Strecher V, Fagerlin A, et al. Screening experiments and the use of fractional factorial designs in behavioral intervention research. Am J Pub Health. 2008; 98(8): 1354-1359.
Dziak JJ, Nahum-Shani I, Collins LM. Multilevel factorial experiments for developing behavioral interventions: power, sample size, and resource considerations. Psych Methods. 2012; 17(2): 153-175.
FactorialPowerPlan [computer program]. Version 1.0. University Park: The Methodology Center, The Pennsylvania State University; 2013.
Chiauzzi E, Green TC, Lord S, Thum C, Goldstein M. My student body: a high-risk drinking prevention web site for college students. J Am Coll Health. 2005; 53(6): 263-274.
Rogers EM. Diffusion of innovations. New York: Free Press; 2003.
Acknowledgments
This project was funded by the National Institute on Drug Abuse (NIDA): 3R44DA023735.
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Implications
Research: Researchers should move beyond the “treatment package” approach to intervention development and evaluation and explore ways to optimize behavioral interventions to maximize public health impact.
Practice: Intervention scientists should investigate iterative approaches to intervention development that incorporate systematic and resource efficient principles of product engineering.
Policy: Resources should be committed to support behavioral interventions that have been optimized to a specific and meaningful criterion.
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Wyrick, D.L., Rulison, K.L., Fearnow-Kenney, M. et al. Moving beyond the treatment package approach to developing behavioral interventions: addressing questions that arose during an application of the Multiphase Optimization Strategy (MOST). Behav. Med. Pract. Policy Res. 4, 252–259 (2014). https://doi.org/10.1007/s13142-013-0247-7
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DOI: https://doi.org/10.1007/s13142-013-0247-7