BACKGROUND
Generative artificial intelligence (Gen-AI) chatbots hold promise for building highly personalized, effective mental health treatments at scale, while also addressing user engagement and retention issues common among digital therapeutics.
METHODS
A RCT with (N=210) with clinically significant symptoms of major depressive disorder (MDD), generalized anxiety disorder (GAD), or at clinically high risk for feeding and eating disorders (CHR-FED). Randomly assigned to a 4-week Therabot intervention (N=106) or waitlist control (WLC; N=104).
RESULTS
Therabot users showed significantly greater reductions in symptoms of MDD (mean changes: −6.13 [standard deviation {SD}=6.12] vs. −2.63 [6.03] at 4 weeks; −7.93 [5.97] vs. −4.22 [5.94] at 8 weeks; d=0.845–0.903), GAD (mean changes: −2.32 [3.55] vs. −0.13 [4.00] at 4 weeks; −3.18 [3.59] vs. −1.11 [4.00] at 8 weeks; d=0.794–0.840), and CHR-FED (mean changes: −9.83 [14.37] vs. −1.66 [14.29] at 4 weeks; −10.23 [14.70] vs. −3.70 [14.65] at 8 weeks; d=0.627–0.819) relative to controls at postintervention and follow-up. Therabot was well utilized (average use >6 hours), and participants rated the therapeutic alliance as comparable to that of human therapists.
CONCLUSIONS
This is the first RCT demonstrating the effectiveness of a fully Gen-AI therapy chatbot for treating clinical-level mental health symptoms. The results were promising for MDD, GAD, and CHR-FED symptoms. Therabot was well utilized and received high user ratings. Fine-tuned Gen-AI chatbots offer a feasible approach to delivering personalized mental health interventions at scale, although further research with larger clinical samples is needed to confirm their effectiveness and generalizability.
0 Comments