Adapting the Number of Questions Based on Detected Psychological Distress for Cognitive Behavioral Therapy With an Embodied Conversational Agent: Comparative Study
Bibliographic record
Abstract
BACKGROUND: The high prevalence of mental illness is a critical social problem. The limited availability of mental health services is a major factor that exacerbates this problem. One solution is to deliver cognitive behavioral therapy (CBT) using an embodied conversational agent (ECA). ECAs make it possible to provide health care without location or time constraints. One of the techniques used in CBT is Socratic questioning, which guides users to correct negative thoughts. The effectiveness of this approach depends on a therapist's skill to adapt to the user's mood or distress level. However, current ECAs do not possess this skill. Therefore, it is essential to implement this adaptation ability to the ECAs. OBJECTIVE: This study aims to develop and evaluate a method that automatically adapts the number of Socratic questions based on the level of detected psychological distress during a CBT session with an ECA. We hypothesize that this adaptive approach to selecting the number of questions will lower psychological distress, reduce negative emotional states, and produce more substantial cognitive changes compared with a random number of questions. METHODS: In this study, which envisions health care support in daily life, we recruited participants aged from 18 to 65 years for an experiment that involved 2 different conditions: an ECA that adapts a number of questions based on psychological distress detection or an ECA that only asked a random number of questions. The participants were assigned to 1 of the 2 conditions, experienced a single CBT session with an ECA, and completed questionnaires before and after the session. RESULTS: The participants completed the experiment. There were slight differences in sex, age, and preexperimental psychological distress levels between the 2 conditions. The adapted number of questions condition showed significantly lower psychological distress than the random number of questions condition after the session. We also found a significant difference in the cognitive change when the number of questions was adapted based on the detected distress level, compared with when the number of questions was fewer than what was appropriate for the level of distress detected. CONCLUSIONS: The results show that an ECA adapting the number of Socratic questions based on detected distress levels increases the effectiveness of CBT. Participants who received an adaptive number of questions experienced greater reductions in distress than those who received a random number of questions. In addition, the participants showed a greater amount of cognitive change when the number of questions matched the detected distress level. This suggests that adapting the question quantity based on distress level detection can improve the results of CBT delivered by an ECA. These results illustrate the advantages of ECAs, paving the way for mental health care that is more tailored and effective.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".