AI CBT chatbot access reduces anxiety and depression symptoms in Brazilian primary care patients
This quasi-experimental study used a fuzzy regression discontinuity design to estimate the causal effect of providing access to an AI-powered cognitive behavioral therapy chatbot (Saude Mental Digital) on anxiety and depressive symptoms. The study included 43,287 registered adult patients across 312 primary care units in Minas Gerais, Brazil. Patients with a composite vulnerability score above a threshold were eligible for chatbot access, while those below served as the comparator group. The primary outcome was the 12-week change in the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) composite score.
The analysis found a local average treatment effect (LATE) of -4.73 points on the PHQ-ADS (95% CI -6.91 to -2.55, p=0.001), indicating a reduction in symptoms among compliers—patients whose treatment status changed due to being near the eligibility threshold. Subgroup analyses showed larger effects in rural patients, those with less education, and female patients. The results were robust across alternative bandwidths, polynomial orders, and kernel specifications, with McCrary density tests showing no evidence of running variable manipulation (p=0.67).
Safety and tolerability data were not reported. The study's key limitation is that the estimated effect applies specifically to compliers near the eligibility threshold, not to the entire patient population. The authors note that incorporating patient perspectives on acceptability is critical for maximizing engagement and sustained therapeutic benefit.
For practice, these findings provide quasi-experimental evidence supporting the potential scalable deployment of AI-powered CBT tools within public primary care systems in low- and middle-income countries. However, clinicians should interpret the 4.73-point reduction as specific to patients whose treatment assignment was marginal at the eligibility cutoff, and recognize that patient engagement factors remain important for real-world implementation.