How a Fully Automated eHealth Program Simulates Three Therapeutic Processes: A Case Study

BACKGROUND: eHealth programs may be better understood by breaking down the components of one particular program and discussing its potential for interactivity and tailoring in regard to concepts from face-to-face counseling. In the search for the efficacious elements within eHealth programs, it is important to understand how a program using lapse management may simultaneously support working alliance, internalization of motivation, and behavior maintenance. These processes have been applied to fully automated eHealth programs individually. However, given their significance in face-to-face counseling, it may be important to simulate the processes simultaneously in interactive, tailored programs. OBJECTIVE: We propose a theoretical model for how fully automated behavior change eHealth programs may be more effective by simulating a therapist’s support of a working alliance, internalization of motivation, and managing lapses. METHODS: We show how the model is derived from theory and its application to Endre, a fully automated smoking cessation program that engages the user in several “counseling sessions” about quitting. A descriptive case study based on tools from the intervention mapping protocol shows how each therapeutic process is simulated. RESULTS: The program supports the user’s working alliance through alliance factors, the nonembodied relational agent Endre and computerized motivational interviewing. Computerized motivational interviewing also supports internalized motivation to quit, whereas a lapse management component responds to lapses. The description operationalizes working alliance, internalization of motivation, and managing lapses, in terms of eHealth support of smoking cessation. CONCLUSIONS: A program may simulate working alliance, internalization of motivation, and lapse management through interactivity and individual tailoring, potentially making fully automated eHealth behavior change programs more effective.