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Algorithm-Driven Fourth Generation Multi-Theory Model for Alcohol and Drug Education.

In this age of technological advancements, algorithms are gaining importance. An algorithm is a rubric or a set of directions to be implemented in solving complex problems typically utilizing a computer interface. According to Merriam Webster Dictionary, an algorithm is "a step-by-step procedure for solving a problem or accomplishing some end." In medicine, as a result of randomized controlled trials and health services research coupled with engineering innovations, algorithms are becoming popular (Chen-Ying, Wei-Chen, Po-Tsun, Ching-Heng, & Chi-Chun, 2017; Golden, 2017; Kirby et al., 2016). A somewhat stunted use of this approach is appearing in the field of education in the format of grading rubrics for evaluation of online programs (Vitale, 2010; Wagner, Suh & Cruz, 2011). In health behavior research, while the field has grown over the years, use of algorithms is currently nonexistent.

Health behavior research, as it relates to alcohol and drug education programs, evolved from the first-generation knowledge-based programs starting in the 1960's that primarily relied on knowledge, attitudes and practices (KAP) surveys. Such programs are still being used in many developing countries and by local public health departments in the United States (Sharma, 2017). The second-generation programs were skill-building programs such as the refusal-skills programs used in the US and European countries to combat drug problems among teens or the problem-solving skills programs used among school children to deal with stress-coping or improving academic performance or providing alternatives to risky behaviors and a myriad of other health and social issues (Sharma, Petosa, & Heaney, 1999). The third-generation alcohol and drug-education interventions relied on explicit behavioral theories and have been labeled by some as evidence-based programs. Tebb and colleagues (2016) provide a systematic review of the use of theory in computer-based interventions to reduce alcohol use among adolescents and young adults. Likewise, Melendez-Torres and colleagues (2016) have summarized interventions using theory to reduce drug problems among youth. The currently emerging fourth-generation programs are multi-theory programs that focus on crucial constructs from proven theoretical models, break down the behavior-change process into initiation and maintenance and are brief and precise. One such model is the multi-theory model (MTM) of health behavior change (Sharma, 2015) that has been applied to a variety of health behaviors in a variety of target populations which the readers can search for and read. In this paper, we describe an algorithm for its application in Figure 1.

This is the first attempt in health-behavior research to operationalize an algorithm utilizing a theoretical model. It starts with the first decision of whether to imitate the behavior change or to reinforce it. In alcohol and drug education this can pertain to termination of alcohol use, termination of drug use, reinforcement of protective skills among youth for prevention and so on. The second step is the loop of participatory dialogue that establishes the advantages of behavior change over any potential disadvantages and at the same time building behavioral confidence. Following these steps, changes in physical environment need to be fostered that support the behavior change. For sustaining the behavior change, once again the trinity loop of practice for change, emotional transformation and changes in social environment need to be cultivated. Details on these constructs of MTM can be assimilated through several publications on this topic. We hope this attempt to reify an algorithm which is in its nascent stage will spark more interest and debate among drug and alcohol educators which will lead to its adoption in actual interventions and that will build protective behaviors and terminate harmful ones around drugs and alcohol use.

Manoj Sharma, MBBS, Ph.D., MCHES[R]

Editor, Journal of Alcohol & Drug Education Professor, Behavioral & Environmental Health Jackson State University 350 W. Woodrow Wilson Drive Jackson, MS 39213

manoj.sharma@jsums.edit (E-mail)


Malvika Sharma, MD

Rardin Family Practice Wexner Medical Center The Ohio State University 2231 North High Street Columbus, OH 43201


Chen-Ying, H., Wei-Chen, C., Po-Tsun, L., Ching-Heng, L., & Chi-Chun, L. (2017). Comparing deep neural network and other machine-learning algorithms for stroke prediction in a large-scale population-based electronic medical claims database. Conference Proceedings of the Annual International Conference of IEEE Engineering in Medicine and Biology Society, 2017, 3110-3113. doi:10.1109/EMBC.2017.8037515.

Golden, J. A. (2017). Deep-learning algorithms for detection of lymph node metastases from breast cancer: Helping artificial intelligence be seen. JAMA, 318(22), 2184-2186. doi:10.1001/jama.2017.14580.

Kirby, J. C., Speltz, P., Rasmussen, L. V., Basford, M., Gottesman, O., Peissig, P. L. Denny, J. C. (2016). PheKB: A catalog and workflow for creating electronic phenotype algorithms for transportability. Journal of American Medical Informatics Association, 23(6), 1046-1052. doi:10.1093/jamia/ocv202.

Melendez-Torres, G. J., Dickson, K., Fletcher, A., Thomas, J., Hinds, K., Campbell, R. Bonell C. (2016). Positive youth development programmes to reduce substance use in young people: Systematic review. The International Journal of Drug Policy, 36, 95-103. doi:10.1016/j.drugpo.2016.01.007.

Sharma, M., Petosa, R., & Heaney, C. A. (1999). Evaluation of a brief intervention based on social cognitive theory to develop problem-solving skills among sixth-grade children. Health Education & Behavior. 26(4), 465-477.

Sharma, M. (2015). Multi-theory model (MTM) for health behavior change. WebmedCentral Behaviour, 6(9), WMC004982. Retrieved from

Sharma, M. (2016). A new theory for health behavior change: Implications for alcohol and drug education. [Editorial]. Journal of Alcohol and Drug Education, 60(1), 5-8.

Sharma, M. (2017). Trends and prospects in public health education: A commentary. [Editorial]. Social Behavior Research & Health, 7(2), 67-72. Retrieved from lang=en

Tebb, K. P., Erenrich, R. K., Jasik, C. B., Berna, M. S., Lester, J. C, & Ozer, E. M. (2016). Use of theory in computer-based interventions to reduce alcohol use among adolescents and young adults: A systematic review. BMC Public Health, 16, 517. doi:10.1186/sl2889-016-3183-x.

Vitale, A. T. (2010). Faculty development and mentorship using selected online asynchronous teaching strategies. Journal of Continuing Education in Nursing, 47(12), 549-556. doi:10.3928/00220124-20100802-02.

Wagner, M. L., Suh, D. C., & Cruz, S. (2011). Peer-and self-grading compared to faculty grading. American Journal of Pharmaceutical Education, 75(7), 130. doi:10.5688/ajpe757130.
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Title Annotation:EDITORIAL
Author:Sharma, Manoj; Sharma, Malvika
Publication:Journal of Alcohol & Drug Education
Date:Apr 1, 2019
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