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Published on: May 10, 2022
The Challenges in Designing a Prevention Chatbot for Eating Disorders: Observational Study
William W Chan1,2, Ellen E Fitzsimmons-Craft3, Arielle C Smith3
1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, United States.
Rule-based chatbots show promise for scalable eating disorder (ED) prevention, but understanding user responses remains a challenge. Workarounds can improve user experience and reduce errors in these digital health tools.
Area of Science:
- Digital Health
- Mental Health Technology
- Computational Linguistics
Background:
- Chatbots offer scalable, accessible mental health prevention.
- Current chatbot development for eating disorder (ED) prevention is nascent.
- Focus on rule-based systems for targeted interventions.
Purpose of the Study:
- To address challenges in designing and refining a rule-based chatbot for ED prevention.
- To target adult women at risk for developing EDs.
- To present solutions for improving chatbot interaction.
Main Methods:
- Analysis of 52,129 user comments from 2409 participants over 6 months.
- Identification of inappropriate responses and technical glitches.
- Iterative refinement of chatbot responses by the research team.
Main Results:
- Chatbot limitations in understanding and responding to unanticipated user input were common.
- Workarounds were developed to mitigate these issues.
- User experience was negatively impacted by certain chatbot responses.
Conclusions:
- Rule-based chatbots can reach large populations cost-effectively.
- Understanding and responding to unexpected user input is a key limitation.
- Chatbots are most effective for information delivery and simple conversations; workarounds can enhance performance.
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