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AI CBT chatbot access reduces anxiety and depression symptoms in Brazilian primary care patients

AI CBT chatbot access reduces anxiety and depression symptoms in Brazilian primary care patients
Photo by Galina Nelyubova / Unsplash
Key Takeaway
Consider AI CBT chatbots as potential scalable tools in LMIC primary care, but recognize effects are estimated for threshold-compliant 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.

Study Details

Sample sizen = 43,287
EvidenceLevel 5
PublishedApr 2026
View Original Abstract ↓
Background: AI powered cognitive behavioral therapy CBT chatbots represent a scalable approach to addressing the global mental health treatment gap However causal evidence on their population level effectiveness in low and middle income countries LMICs remains limited and patient perspectives on acceptability and engagement are critical determinants of sustained use Brazils Estrategia de Saude da Familia ESF deployed an AI powered CBT chatbot Saude Mental Digital SMD to registered patients aged 18 and older at participating primary care units with eligibility determined by a composite vulnerability score exceeding a predetermined threshold Objective: To estimate the causal effect of AI powered CBT chatbot access on anxiety and depressive symptoms among primary care patients in Minas Gerais Brazil leveraging the eligibility score threshold as an exogenous source of variation Methods: We conducted a fuzzy regression discontinuity design fuzzy RDD study using linked administrative and clinical data from 312 ESF primary care units across Minas Gerais N 43287 patients January 2022 December 2024 The running variable was the composite vulnerability score with a threshold of 60 points determining chatbot eligibility The primary outcome was the 12 week change in the Patient Health Questionnaire Anxiety and Depression Scale PHQ ADS composite score Two stage least squares 2SLS estimation was used with local polynomial regression and triangular kernel weighting Bandwidth selection followed the Calonico Cattaneo Titiunik CCT optimal procedure Results: The fuzzy RDD estimated a local average treatment effect LATE of 473 points 95 CI 691 to 255 p 0001 on the PHQ ADS composite score at the eligibility threshold indicating clinically meaningful symptom reduction among compliers First stage estimates confirmed a strong 312 percentage point jump in chatbot uptake at the threshold F statistic 1274 Subgroup analyses revealed larger treatment effects among patients in rural municipalities 618 95 CI 902 to 334 those with lower educational attainment 582 95 CI 844 to 320 and women 537 95 CI 761 to 313 McCrary density tests confirmed no evidence of running variable manipulation p 067 Results were robust across alternative bandwidths polynomial orders and kernel specifications Conclusions: AI powered CBT chatbot access causally reduces anxiety and depressive symptoms among primary care patients near the eligibility threshold in Brazil with particularly pronounced benefits for rural less educated and female populations These findings provide quasi experimental evidence supporting the scalable deployment of AI powered CBT tools within public primary care systems in LMICs while underscoring the importance of incorporating patient perspectives on acceptability to maximize engagement and sustained therapeutic benefit
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