Design and Formative Evaluation of a Virtual Voice-Based Coach for Problem-solving Treatment: Observational Study

Thomas Kannampallil, Corina R. Ronneberg, Nancy E. Wittels, Vikas Kumar, Nan Lv, Joshua M. Smyth, Ben S. Gerber, Emily A. Kringle, Jillian A. Johnson, Philip Yu, Lesley E. Steinman, Olu A. Ajilore, Jun Ma

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Background: Artificial intelligence has provided new opportunities for human interactions with technology for the practice of medicine. Among the recent artificial intelligence innovations, personal voice assistants have been broadly adopted. This highlights their potential for health care-related applications such as behavioral counseling to promote healthy lifestyle habits and emotional well-being. However, the use of voice-based applications for behavioral therapy has not been previously evaluated. Objective: This study aimed to conduct a formative user evaluation of Lumen, a virtual voice-based coach developed as an Alexa skill that delivers evidence-based, problem-solving treatment for patients with mild to moderate depression and/or anxiety. Methods: A total of 26 participants completed 2 therapy sessions-an introductory (session 1) and a problem-solving (session 2)-with Lumen. Following each session with Lumen, participants completed user experience, task-related workload, and work alliance surveys. They also participated in semistructured interviews addressing the benefits, challenges and barriers to Lumen use, and design recommendations. We evaluated the differences in user experience, task load, and work alliance between sessions using 2-tailed paired t tests. Interview transcripts were coded using an inductive thematic analysis to characterize the participants' perspectives regarding Lumen use. Results: Participants found Lumen to provide high pragmatic usability and favorable user experience, with marginal task load during interactions for both Lumen sessions. However, participants experienced a higher temporal workload during the problem-solving session, suggesting a feeling of being rushed during their communicative interactions. On the basis of the qualitative analysis, the following themes were identified: Lumen's on-demand accessibility and the delivery of a complex problem-solving treatment task with a simplistic structure for achieving therapy goals; themes related to Lumen improvements included streamlining and improved personalization of conversations, slower pacing of conversations, and providing additional context during therapy sessions. Conclusions: On the basis of an in-depth formative evaluation, we found that Lumen supported the ability to conduct cognitively plausible interactions for the delivery of behavioral therapy. Several design suggestions identified from the study including reducing temporal and cognitive load during conversational interactions, developing more natural conversations, and expanding privacy and security features were incorporated in the revised version of Lumen. Although further research is needed, the promising findings from this study highlight the potential for using Lumen to deliver personalized and accessible mental health care, filling a gap in traditional mental health services.

Original languageEnglish
Article numbere38092
JournalJMIR Formative Research
Volume6
Issue number8
DOIs
StatePublished - Aug 1 2022

Keywords

  • artificial intelligence
  • behavioral therapy
  • mental health
  • problem-solving therapy
  • user evaluation
  • voice assistants

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