Designing Chatbots to Treat Depression in Youth: Qualitative Study
Abstract BackgroundDepression is a severe and prevalent mental disorder among youth that requires professional care; however, various barriers hinder access to effective treatments. Chatbots, one of the latest innovations in the research on digital mental health interventions,...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-06-01
|
| Series: | JMIR Human Factors |
| Online Access: | https://humanfactors.jmir.org/2025/1/e66632 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Abstract
BackgroundDepression is a severe and prevalent mental disorder among youth that requires professional care; however, various barriers hinder access to effective treatments. Chatbots, one of the latest innovations in the research on digital mental health interventions, have shown potential in addressing these barriers. However, most studies on how to design chatbots to treat depression have focused on adult populations or prevention in the general population.
ObjectiveThis study aimed to investigate the problems faced by youth with depression and their adaptive coping strategies, as well as attitudes, expectations, and design preferences for chatbots designed to treat depression.
MethodsWe conducted a qualitative study, consisting of a semistructured interview and a concurrent think-aloud session, in which participants interacted with a chatbot prototype with 14 youth with a current or remitted depressive episode.
ResultsThe participants reported a wide range of problems beyond core depressive symptoms, such as interpersonal challenges, concerns about school and the future, and problems with human therapists. Adaptive coping strategies varied, with most seeking social support or engaging in pleasant activities. Attitudes toward chatbots for depression treatment were predominantly positive, with participants expressing less anxiety about using a chatbot than about seeing a human therapist. Participants showed diverse and partially contradictory design preferences, which included diverse dialogue topics, such as discussing daily life, acute problems, and therapeutic exercises, as well as various preferences for personality, language use, and personalization of the chatbot.
ConclusionsOur study provides a comprehensive foundation for designing chatbots that meet the unique needs and design preferences of youth with depression. These findings can inform the design of engaging and effective chatbots tailored to this vulnerable population. |
|---|---|
| ISSN: | 2292-9495 |