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Artificial Intelligence Chatbot for Depression: Descriptive Study of Usage.

Gilly Dosovitsky1, Blanca S Pineda1, Nicholas C Jacobson2

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Summary
This summary is machine-generated.

User engagement with the Tess chatbot for depression varied significantly across modules. Understanding these heterogeneous usage patterns is key to improving AI-driven behavioral health interventions.

Keywords:
artificial intelligencechatbotdepressionmobile healthtelehealth

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Area of Science:

  • Digital Health
  • Human-Computer Interaction
  • Mental Health Technology

Background:

  • Artificial intelligence (AI)-driven chatbots offer scalable solutions for behavioral health interventions.
  • While early efficacy of some chatbots is promising, user engagement patterns remain under-researched.
  • Understanding chatbot usage is vital for enhancing design and identifying limitations in mental health applications.

Purpose of the Study:

  • To investigate user engagement and navigation within the Tess chatbot for depression.
  • To derive design recommendations for AI-powered mental health chatbots based on usage patterns.

Main Methods:

  • Analysis of 354 users' interactions with Tess depression modules.
  • Descriptive statistics to assess participant flow, message characteristics, completion rates, and time spent.
  • Utilization of slide plots to visualize user pathways across and within modules.

Main Results:

  • Users sent 6220 messages totaling 86,298 characters, engaging with modules over an average of 46 days.
  • Significant heterogeneity observed in user engagement across modules.
  • Engagement variations were linked to module design factors like length, complexity, content, question style, and routing.

Conclusions:

  • Participants actively engaged with the Tess chatbot, but usage patterns were diverse.
  • Module design significantly influenced heterogeneous user engagement.
  • Findings provide critical implications for the future design and evaluation of mental health chatbots.