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