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<p>Depression, anxiety, and stress are significant global health burdens worsened by restricted access to care. Conversational agents (CAs), encompassing both AI(Artificial Intelligence)- and rule-based systems, provide scalable mental health interventions. This systematic review and meta-analysis analyzed the efficacy of CA interventions in 48 randomized controlled trials covering 28,071 participants. It demonstrated small-to-moderate but significant effects in minimizing symptoms of depression (SMD: −0.27), anxiety (SMD: −0.20), and stress (SMD: −0.26). A Robust Variance Estimation (RVE) approach was used to address within-study dependencies, with results consistent with the random-effects model. Subgroup analyses investigated sources of heterogeneity and indicated greater effects in clinical populations and shorter-duration interventions. Meta-regression identified no appreciable effect of outcome timing. Risk of bias was largely low, although some studies had issues due to missing data or selective reporting. Publication bias was negligible. These results provide evidence for the utilization of conversational agents as effective interventions for lessening psychological symptoms. Additional research is required to evaluate their long-term effectiveness and how best to integrate them into mental healthcare systems.</p>

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Effectiveness of AI and rule-based conversational agents for depression, anxiety and stress: A meta-analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月29日

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  • Saeed Mokhtari Masoumi Alamdarloo,
  • Ali Mirzakhani,
  • Mohamad Azhdarloo,
  • Elaheh Haghani-Samani,
  • Abouzar Nazari

摘要

Depression, anxiety, and stress are significant global health burdens worsened by restricted access to care. Conversational agents (CAs), encompassing both AI(Artificial Intelligence)- and rule-based systems, provide scalable mental health interventions. This systematic review and meta-analysis analyzed the efficacy of CA interventions in 48 randomized controlled trials covering 28,071 participants. It demonstrated small-to-moderate but significant effects in minimizing symptoms of depression (SMD: −0.27), anxiety (SMD: −0.20), and stress (SMD: −0.26). A Robust Variance Estimation (RVE) approach was used to address within-study dependencies, with results consistent with the random-effects model. Subgroup analyses investigated sources of heterogeneity and indicated greater effects in clinical populations and shorter-duration interventions. Meta-regression identified no appreciable effect of outcome timing. Risk of bias was largely low, although some studies had issues due to missing data or selective reporting. Publication bias was negligible. These results provide evidence for the utilization of conversational agents as effective interventions for lessening psychological symptoms. Additional research is required to evaluate their long-term effectiveness and how best to integrate them into mental healthcare systems.