Behavior Emotion Therapy System and You: Co-Design and Evaluation of a Mental Health Chatbot and Digital Human for Mild to Moderate Anxiety in Healthy Participants
Bibliographic record
Abstract
Background: Co-design is a collaborative approach involving end users, stakeholders, and designers in creating digital tools for health care. This study focuses on the co-design and evaluation of Behavior, Emotion, Therapy System, and You (BETSY), a mental health chatbot and digital human for mild to moderate anxiety. Objective: This study aims to develop and evaluate BETSY through a co-design process involving potential users. Methods: The study used a mixed-methods approach across 3 phases. Phase 1 involved recruiting 87 volunteer participants through social media for initial end-user requirements. Phase 2 focused on the design process based on end-user requirements using the expertise of 10 stakeholders in health care and health service user. Phase 3 evaluated the 2 prototyped interfaces (a text-only chatbot and a voice-activated digital human chatbot) with 45 healthy volunteers. Results: For phase 1, 61% (n=51) of participants had previous experience with chatbots and 86% expressed a willingness or potential willingness to use a chatbot for mental health support. Thematic analysis in phase 2 revealed key user preferences for a personalized, nonjudgmental chatbot with a clear identity and a focus on empathy. Privacy concerns and the need for clear interaction guidelines were highlighted. In phase 3, the users showed a strong preference for discussing anxiety related to work, relationships, and health. Both text-only and voice-activated digital human users considered BETSY valuable for managing mild to moderate anxiety and providing self-help exercises. Conclusions: The co-design process yielded valuable insights for developing BETSY. While users recognized its potential as an accessible first step before seeking professional help, they also identified areas for improvement, including more nuanced conversation capabilities and a broader range of exercises. BETSY's potential as a screening tool for health care was consistently acknowledged, suggesting its capability for alleviation of health care system burdens.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".