Predicting Content Preference: Applying Lessons Learned from the Commercial Web to Therapeutic Software


Objective: An automated web-based program was developed to deliver a form of motivational enhancement therapy to individuals with drinking problems. The objective of this study was to evaluate its ability to predict user preferences for specific psychoeducational modules based on responses to questionnaire items from the evaluation portion of the program.
Methods: Nine items from three standardized alcohol assessment questionnaires were used to predict viewing of one or more educational modules related to alcohol cessation. The instruments included the Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), the Alcohol Use Disorders Identification Test (AUDIT), and the Decisional Balance Questionnaire.
Results: Statistically significant associations were found between some of the item scores and module viewing. In general, subjects who viewed a specific educational module more strongly endorsed the putatively related questionnaire items.
Conclusion: Due to a lack of a traditional therapeutic alliance, it can be difficult to engage users in a therapy which is delivered by an automated program. Accurately predicting content preference in real time based on individual user characteristics is a promising strategy for increasing user commitment to the treatment.

Internet; Alcohol Abuse; Psychotherapy
Author biographies

Daniel Z. Lieberman

Author photo Daniel Z. Lieberman, M.D. is an associate professor of Psychiatry and Behavioral Sciences, director of the Clinical Psychiatric Research Center, and chairman of the Institutional Review Board at George Washington University in Washington, DC, USA. He has spoken on depression and substance abuse to audiences at the Department of Health and Human Services, the Department of Commerce, and the Office of Drug and Alcohol Policy. His primary research interest is developing automated interventions to increase access to evidence-based psychotherapies. In order to create a unified and coherent therapeutic experience for users of his research web sites, in addition to developing the clinical content, he also designs and programs the sites using HTML, CSS, JavaScript, and VB.NET.

Suena H. Massey

Author photo Suena H. Massey, M.D. is an assistant professor of Psychiatry and Behavioral Sciences, and the principal investigator of the Clinical Psychiatric Research Center at George Washington University in Washington, DC, USA. Dr. Massey obtained her bachelor's degree from Yale University and her medical degree at Cornell University Medical College, before completing psychiatric residency training at George Washington University. Her current clinical and research interests include mood and substance abuse disorders, particularly in regard to making it easier for primary care physicians to identify alcohol abuse at an early stage, and identifying factors associated with substance use cessation among pregnant women.

Vilmaris Quiñones Cardona

Author photo Vilmaris Quiñones Cardona, M.P.H. is a medical student at the University of Puerto Rico School of Medicine in San Juan, PR. She obtained a Bachelors of Science and a Masters of Public Health from The George Washington University (GWU) in Washington, DC. Her primary research interests include mood and substance abuse disorders, minority health, and improving healthcare access and quality among the underserved. Her research experiences include coordinating industry-sponsored mood disorder clinical trials at the Clinical Psychiatric Research Center at GWU and investigating innovating approaches for reducing health disparities at the National Institutes of Health, National Center on Minority Health and Health Disparities in Bethesda, MD.

Kenneth P. Williams

Author photo Kenneth P. Williams, M.D. is an assistant professor of research and the associate director of the Clinical Psychiatric Research Center in the Department of Psychiatry and Behavioral Sciences at The George Washington University Medical Center in Washington, D.C. Dr. Williams has been a sub-investigator on more than 55 clinical trials in which he served as the lead clinical interviewer and research diagnostician. His research interests include mood disorders, and understanding factors associated with low participation of minority groups in clinical research. After completing his medical degree at East Carolina University School of Medicine, he completed his psychiatric residency training at the George Washington University Medical Center, including a year as the Chief Resident of the outpatient division.

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. Institute of Electrical and Electronics Engineers, Inc.(IEEE) Transactions On Knowledge And Data Engineering, 17, 734-749.

Babor, T. F., de la Fuente, J. R., Saunders, J., & Grant, M. (1992). The alcohol use disorders identification test: Guidelines for use in primary health care. Geneva, Switzerland: World Health Organization.

Babor, T. F., & Grant, M. (1989). From clinical research to secondary prevention: International collaboration of the Alcohol Use Disorders Identification Test (AUDIT). Alcohol Health & Research World, 13, 371-374.

Beutler, L. E., Zetzer, H., & Yost, E. (1997). Tailoring interventions to clients: effects on engagement and retention. NIDA Research Monographs, 165, 85-109.

Carey, K. B., Maisto, S. A., Carey, M. P., & Purnine, D. M. (2001). Measuring readiness-to-change substance misuse among psychiatric outpatients: I. Reliability and validity of self-report measures. Journal of Studies on Alcohol, 62(1), 79-88.

Copeland, J., & Martin, G. (2004). Web-based interventions for substance use disorders: a qualitative review. Journal on Substance Abuse Treatment, 26(2), 109-116.

Cunningham, J. A., Sobell, L. C., Sobell, M. B., Agrawal, S., & Toneatto, T. (1993). Barriers to treatment: why alcohol and drug abusers delay or never seek treatment. Addictive Behaviors, 18, 347-353.

Gega, L., Norman, I. J., & Marks, I. M. (2007). Computer-aided vs. tutor-delivered teaching of exposure therapy for phobia/panic: randomized controlled trial with pre-registration nursing students. International Journal of Nursing Studies, 44, 397-405.

Gossop, M., Marsden, J., Stewart, D., & Rolfe, A. (1999). Treatment retention and 1 year outcomes for residential programmes in England. Drug and Alcohol Dependence, 57(2), 89-98.

Gotham, H. J. (2004). Diffusion of mental health and dsbstance abuse treatments: Development, dissemination, and implementation. Clinical Psychology: Science and Practice, 11(2), 160-176.

Greist, J. H., Marks, I. M., Baer, L., Kobak, K. A., Wenzel, K. W., Hirsch, M. J., et al. (2002). Behavior therapy for obsessive-compulsive disorder guided by a computer or by a clinician compared with relaxation as a control. Journal of Clinical Psychiatry, 63(2), 138-145.

Hanani, U., Shapira, B., & Shoval, P. (2001). Information filtering: Overview of issues, research and systems. User Modeling and User-Adapted Interaction, 11, 203-259.

Henry, W. P., Strupp, H. H., Butler, S. F., Schacht, T. E., & Binder, J. L. (1993). Effects of training in time-limited dynamic psychotherapy: changes in therapist behavior. Journal of Consulting and Clinical Psychology, 61, 434-440.

Herlocker, J. L., Konstan, J. A., Terveen, L. G., & Riedl, J. T. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5-53.

Karuga, G. G., Khraban, A. M., Nair, S. K., & Rice, D. O. (2001). AdPalette: an algorithm for customizing online advertisements on the fly. Decision Support Systems, 32(2), 85-106.

Kenwright, M., & Marks, I. M. (2004). Computer-aided self-help for phobia/panic via internet at home: a pilot study. British Journal of Psychiatry, 184, 448-449.

Lenert, L., Munoz, R. F., Perez, J. E., & Bansod, A. (2004). Automated e-mail messaging as a tool for improving quit rates in an internet smoking cessation intervention. Journal of the American Medical Informormatics Association, 11, 235-240.

Leonhardt, D. (2006, June 7). What netflix could teach Hollywood. The New York Times.

Li, Y., Lu, L., & Xuefeng, L. (2005). A hybrid collaborative filtering method for multiple-interests and multiple-content recommendation in e-commerce. Expert Systems with Applications, 28(1), 67-77.

Lieberman, D. Z. (2003a). An automated treatment for jet lag delivered through the internet. Psychiatric Services, 54, 394-396.

Lieberman, D. Z. (2003b). Determinants of satisfaction with an automated alcohol evaluation program. CyberPsychology and Behavior, 6, 677-682.

Lieberman, D. Z. (2005). Clinical characteristics of individuals using an online alcohol evaluation program. American Journal on Addictions, 14(2), 155-165.

Lieberman, D. Z., & Massey, S. H. (2008). Pathways to change: the effect of a Web application on treatment interest. American Journal on the Addictions, 17, 265-270.

Marks, I. M., Cavanagh, K., & Gega, L. (2007). Hands-on help: Computer-aided psychotherapy. East Sussex, UK: Psychology Press.

Miller, N. S., & Flaherty, J. A. (2000). Effectiveness of coerced addiction treatment (alternative consequences): a review of the clinical research. Journal of Substance Abuse Treatment, 18(1), 9-16.

Miller, W. R. (1992). Stages of change readiness and treatment eagerness scale (SOCRATES). Albuquerque, NM: Center on Alcoholism, Substance Abuse, and Addictions.

Miller, W. R., & Rolllnick, S. (1991). Motivational interviewing. New York: The Guilford Press.

Miller, W. R., Sovereign, R. G., & Krege, B. (1988). Motivational interviewing with problem drinkers: II. the drinker's check up as a preventive intervention. Behavioral Psychotherapy, 16, 251-268.

Mojtabai, R., Olfson, M., & Mechanic, D. (2002). Perceived need and help-seeking in adults with mood, anxiety, or substance use disorders. Archives of General Psychiatry, 59(1), 77-84.

Netflix Consumer Press Kit. (n.d.). Retrieved October 31, 2008, from

Netflix Investor Press Kit. (n.d.). Retrieved October 31, 2008, from

Parsons, J., Ralph, P., & Gallagher, K. (2004). Using viewing time to infer user preference in recommender systems. Paper presented at the AAAI Workshop in Semantic Web Personalization, San Jose, California.

Prochaska, J. O., & Velicer, W. F. (1997). The transtheoretical model of health behavior change. American Journal of Health Promotion, 12(1), 38-48.

Riemer, K., & Totz, C. (2003). The many faces of personalization - An integrative economic overview of mass customization and personalization. In M. M. Tseng & F. T. Piller (Eds.), The customer centric enterprise: advances in mass customization and personalization (pp. 35-70). Heidelberg, Germany: Springer.

Wright, J. H., & Beck, A. T. (1983). Cognitive therapy of depression: Theory and practice. Hospital & Community Psychiatry, 34, 1119-1127.

Wright, J. H., Wright, A. S., Albano, A. M., Basco, M. R., Goldsmith, L. J., Raffield, T., et al. (2005). Computer-assisted cognitive therapy for depression: maintaining efficacy while reducing therapist time. American Journal of Psychiatry, 162, 1158-1164.

Wright, J. H., Wright, A. S., Salmon, P., Beck, A. T., Kuykendall, J., Goldsmith, L. J., et al. (2002). Development and initial testing of a multimedia program for computer-assisted cognitive therapy. American Journal of Psychotherapy, 56(1), 76-86.





HTML views