Accepted for/Published in: JMIR Formative Research
Date Submitted: Nov 14, 2019
Date Accepted: Sep 29, 2020
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Understanding Artificial Intelligence Chatbot Usage for Depression
ABSTRACT
Background:
Chatbots could be a scalable solution, providing an interactive means of engaging users in behavioral health interventions driven by artificial intelligence. Although some chatbots have shown promising early efficacy results, there is limited information about how people utilize these chatbots. Understanding usage patterns of a chatbot for depression represents a crucial step towards improving chatbot design, as well as providing information about chatbots’ strengths and limitations.
Objective:
To understand how users engage and are redirected through a chatbot for depression (Tess) to provide design recommendations.
Methods:
Interactions from 354 users of the Tess depression modules were analyzed to understand chatbot usage across and within modules. Descriptive statistics were used to analyze participant flow through each depression module, including characters per message, completion rate, and time spent per module. Slide plots were also used to analyze the flow across and within modules.
Results:
Users sent a total of 6,220 messages with a total of 86,298 characters, and on average engaged with Tess depression modules for forty-six days. There was large heterogeneity in user engagement across different modules, which appeared to be impacted by the length, complexity, content, and style of questions within the modules, and routing between modules.
Conclusions:
Overall, participants did engage with Tess, however, there was a heterogeneous usage pattern due to varying module designs. Major implications for future chatbots design and evaluation are discussed.
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Copyright
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