Discussion
nonverbal behaviors, Anxiety, anxiety, communication and sensory modalities, depression
DISORDER, SECONDARY
This secondary analysis study explored the associations of objective and subjective user interaction measures as well as treatment response on Lumen, a novel voice-based coach, in a sample of racially and ethnically diverse adults with mild to moderate depression or anxiety or both. The number of conversational breakdowns during each session was relatively low on average, decreasing with sessions, and was not correlated with participant perceptions of workload, user experience, treatment alliance, or their depression or anxiety symptoms. The participants were consistently favorable in their evaluations of the workload and treatment alliance and were neutral to favorable regarding their user experience across the 8 PST sessions with Lumen. User experience was moderately to strongly correlated with treatment alliance across sessions. Both depression and anxiety symptoms improved, with participants on average achieving 3.4 (38%) and 3.2 (31%) reductions in their PHQ-9 and GAD-7 scores, respectively, by the end of the intervention. The treatment alliance predicted the symptom improvements in participants, with a higher mean treatment alliance associated with greater reductions in both depression and anxiety symptoms over the course of the intervention. Moreover, participants, in sessions where they reported higher treatment alliance (relative to their own mean), showed greater reductions in depression, but not anxiety symptoms.Conversational breakdowns are inevitable and even expected when interacting with current consumer-based voice assistant platforms. Lumen, which was developed on Amazon’s Alexa platform, faced similar, known challenges associated with the platform including those with natural language understanding, tone, and accent, leading to conversational breakdowns [Treatment alliance appeared to be the primary user interaction measure that correlated with both depression and anxiety symptoms. The strength of the correlations between treatment alliance and outcomes reported in this study is similar to, or even higher than, that for face-to-face (Even though several communication and sensory modalities (eg, nonverbal behaviors) cannot be used in digital therapies that are limited to voice-based interactions, treatment alliance is an important driver for treatment response for this new delivery modality [Treatment alliance could be assessed regularly (eg, at the end of each session) in digital interventions as it can provide real-time insight into treatment outcomes. Any issues associated with treatment alliance should be addressed immediately to help prevent intervention withdrawal and unsatisfactory treatment progress. For example, if specific items related to treatment goal setting showed room for improvement, the voice-based coach might be refined to confirm the accuracy of goals set by the participants and ask participants to rate the importance of achieving goals and confidence in achieving them; if not important or confident, the voice-based coach may ask participants to take additional steps to reflect and refine their goals and to make and execute realistic action plans. This can help the voice-based coach and participants reach a mutual agreement. However, more research is needed to explore how to capture treatment alliance features within a digital environment. In this study, we used the WAI-Tech survey, which kept the same subscales (task, bond, and goal) as the original WAI, but was adapted for digital interventions by rewording the items and omitting human elements [This study has several strengths. First, the sample is racially and ethnically diverse. Current mental health resources are often limited and underused, especially among these groups. This study provided promising results on the relationship between user interactions and treatment responses in this underserved, mostly minority sample, even though the small sample size precludes subgroup analysis. Second, to the best of our knowledge, Lumen is one of the first, voice-based coach applications for delivering behavioral therapy to treat mild to moderate depression and anxiety. Leveraging longitudinal data on intervention participants from the pilot RCT, this study assessed the repeated user interactions, both objectively and subjectively, with the digital platform through voice input whereby completing a structured 8-session PST program over 3 months. Third, findings from this longitudinal design supported the important role of treatment alliance in predicting treatment outcomes of this novel digital psychotherapy. These strengths address research gaps noted in the previous work [Several limitations are also worth noting. First, the study was based on a small sample of Lumen intervention participants (N=42) in an RCT, limiting the generalizability of the findings. Second, due to its exploratory nature, the study only investigated the relationship between overall scales of task-related workload, user experience, and treatment alliance. The differentiation in the subscales can be investigated in future research. For example, it is hypothesized that task and goal subscales of treatment alliance have higher relations with treatment outcomes than the bond subscale [In conclusion, conversational breakdowns were not associated with subjective user interaction measures or treatment responses of this voice-based PST coach in a sample of racially and ethnically diverse adults. Over the course of the intervention, participants exhibited a decreasing trend in conversational breakdowns and reported favorable user interactions. Higher individual mean treatment alliance predicted greater improvements in depression and anxiety, while higher session-based differences from individual mean predicted greater improvements in depression.Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health (award R61MH119237). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the paper; and decision to submit the paper for publication.Authors' Contributions: All authors contributed to the acquisition of data. NL, TK, CRR, JMS, and JM contributed to the drafting of the paper and conceptualization. LX contributed to the statistical analysis. All authors contributed to the critical revisions of the paper and data interpretation. JM and OAA contributed in obtaining funding.Conflicts of Interest: JM is a paid scientific consultant for Health Mentor, Inc (San Jose, California). OAA is the cofounder of KeyWise AI, serves on the advisory boards of Blueprint Health and Embodied Labs, and also serves as a consultant for Otsuka Pharmaceuticals. TK is a paid consultant for Pfizer, Inc, outside of this work. The other authors report no conflicts of interest.Correlations of user interaction with next session Patient Health Questionnaire-9 (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7) changes from session 1.