Effect of AI-Based Natural Language Feedback on Engagement and Clinical Outcomes in Fully Self-Guided Internet-Based Cognitive Behavioral Therapy for Depression: 3-Arm Randomized Controlled Trial

Abstract

BACKGROUND: Depression remains a major global cause of disability; yet, access to optimal mental health services is limited. Self-guided internet-based cognitive behavioral therapy (iCBT) offers a scalable alternative but is generally less effective than guided programs, showing limited antidepressant effects and incomplete symptomatic and functional recovery. Adherence remains a major barrier. Recent advances in artificial intelligence (AI), particularly natural language processing, enable automated advisory and empathic feedback that may enhance engagement and therapeutic impact. Although previous trials have reported promising effects, most used heterogeneous control conditions, making it difficult to isolate the specific contribution of AI within fully self-guided interventions. OBJECTIVE: This randomized controlled trial evaluated whether natural language processing-based AI feedback integrated into a fully self-guided iCBT program improves clinical outcomes and engagement compared with an otherwise identical iCBT program without AI support. METHODS: We recruited 1187 adults aged 20-60 years online and randomly assigned them to AI-augmented iCBT (AI-iCBT; n=396), iCBT without AI (n=397), or a waitlist control (n=394). Both active groups received 6 weekly sessions combining video-based psychoeducation and cognitive restructuring exercises. The AI-iCBT program additionally provided automated empathic and advisory feedback. The primary outcome was depressive symptom severity (Patient Health Questionnaire-9 [PHQ-9]) at week 7 and month 3, analyzed using mixed-effects models for repeated measures under an intention-to-treat framework. Secondary outcomes included a dichotomous PHQ-9 score of ≥10, Quick Inventory of Depressive Symptomatology, Generalized Anxiety Disorder-7, Sheehan Disability Scale, and weekly participation rates. Exploratory analyses assessed the impact of AI functions on engagement and antidepressant effects in the efficacy analysis set (EAS). RESULTS: In intention-to-treat analyses, no significant between-group differences were observed in mean PHQ-9 scores at week 7 or month 3, whereas engagement analyses showed a significant group × week interaction, with AI-iCBT participants demonstrating consistently higher odds of weekly participation (odds ratio 1.23, 95% CI 1.09-1.39; P<.001). Exploratory analyses indicated that activation of the empathic feedback function strongly predicted adherence (odds ratio 9.99, 95% CI 5.80-17.21; P<.001), while advisory feedback was not significant. In EAS analyses, iCBT showed significant short-term improvement versus control at postintervention, whereas at follow-up, only AI-iCBT showed a significantly lower proportion of participants with a PHQ-9 score of ≥10 compared with control (difference -0.15, 95% CI -0.30 to -0.01; P=.046). No serious adverse events were reported. CONCLUSIONS: AI support significantly improved adherence to a fully self-administered program. In EAS analyses, AI-iCBT also showed a significantly lower proportion of participants with PHQ-9 score of ≥10 at follow-up compared with control. Empathic feedback emerged as a key mechanism for sustaining engagement, suggesting that AI communication may help maintain participation in scalable digital mental health interventions. Further research is required. TRIAL REGISTRATION: University Hospital Medical Information Network Clinical Trials Registry (UMIN-CTR) UMIN000019228; https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000022220.

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