Qualitative examination of satisfaction with three Expert System interventions to reduce cancer

Jennifer M Doucet, University of Rhode Island

Abstract

Expert Systems are computerized population programs that can provide tailored interventions for behavior changes. These systems have been used in various population samples throughout the United States, however, no one has qualitatively examined the experiences of participants. In this research, participants had three cancer risks (i.e. poor diet, sedentary lifestyle and smoking) and were provided interventions in one of three types of Expert Systems (i.e. Telecommunications, Modular or Integrated). The experiences and satisfaction of 56 participants across the United States using these Expert Systems were examined, with special attention given to demographic differences. Qualitative methodologies were employed to design and administer structured telephone interviews. Data were transcribed and analyzed using the qualitative management program, NVivo 7 and complimentary quantitative data were analyzed using SPSS. Eight themes were drawn from the data representing participants' experiences including: Reasons to Participate, Expectations, Likes, Style, Reaction to Feedback, Trust, Satisfaction and Suggestions. While participant data revealed pros and cons of participating in each Expert System, the Integrated group displayed greater levels of behavior change and higher rates of satisfaction. This information not only provides evidence of the positive experiences of participants in the Integrated Expert System, but helpful suggestions in making the other Systems more appealing to future participants. It is hoped these data and interpretations will be valued and utilized for improving Expert systems for behavior change in the future. ^

Subject Area

Psychology, Behavioral|Health Sciences, Public Health|Artificial Intelligence|Health Sciences, Oncology

Recommended Citation

Jennifer M Doucet, "Qualitative examination of satisfaction with three Expert System interventions to reduce cancer" (2009). Dissertations and Master's Theses (Campus Access). Paper AAI3401135.
http://digitalcommons.uri.edu/dissertations/AAI3401135

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