Examining the Effects of Cognitive Behavioral Therapy With a Virtual Agent on User Motivation and Improvement in Psychological Distress and Anxiety: Two-Session Experimental Study
Notice bibliographique
Résumé
BACKGROUND: Cognitive behavioral therapy (CBT) is a valuable treatment for mood disorders and anxiety. CBT methods, such as cognitive restructuring, are employed to change automatic negative thoughts to more realistic ones. OBJECTIVE: This study extends on previous research conducted by the authors, focused on the process of correcting automatic negative thoughts to realistic ones and reducing distress and anxiety via CBT with a virtual agent. It was aimed to investigate whether the previously applied virtual agent would achieve changes in automatic negative thoughts when modifications to the previous experimental paradigm are applied and when user motivation is taken into consideration. Furthermore, the potential effects of existing participant knowledge concerning CBT or automatic thoughts were explored. METHODS: A single-group, 2-session experiment was conducted using a within-group design. The study recruited 35 participants from May 15, 2023, to June 2, 2023, via Inter Group Corporation, with data collection following from June 5 to June 20, 2023, at Nara Institute of Science and Technology, Japan. There were 19 male and 16 female participants (age range: 18-50 years; mean 33.66, SD 10.77 years). Participants answered multiple questionnaires covering depressive symptomatology and other cognitive variables before and after a CBT session. CBT was carried out using a virtual agent, who participants conversed with using a CBT dialogue scenario on the topic of automatic negative thoughts. Session 2 of the experiment took place 1 week after session 1. Changes in distress and state anxiety were analyzed using a Wilcoxon signed-rank test and t-test for paired samples. The relationships of motivation with cognitive changes and distress or anxiety changes were investigated via correlation analysis. Multiple linear regression was used to analyze the potential predictive qualities of previous knowledge of CBT and automatic negative thoughts regarding outcome measures. RESULTS: Significant reductions in distress (all P<.001) and state anxiety (all P<.003) emerged throughout the first and second experimental sessions. The CBT intervention increased participants' recognition of their negative thinking and their intention to change it, namely their motivation to change it. However, no clear correlations of motivation with changes in distress or anxiety were found (all P>.04). Participants reported moderate subjective changes in their cognition, which were in part positively correlated with their motivation (all P<.007). Lastly, existing knowledge of CBT did not predict reductions in distress during the first session of the experiment (P=.02). CONCLUSIONS: CBT using a virtual agent and a CBT dialogue scenario was successful in reducing distress and anxiety when talking about automatic negative thoughts. The promotion of client motivation needs to be critically considered when designing interventions using CBT with a virtual agent, and further experimental investigations on the causal influences between motivation and outcome measures need to be conducted.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».