A Conversational Agent (PracticePal) to Support the Delivery of a Brief Behavioral Activation Treatment for Depression in Rural India: Development and Pilot-Testing Study
Notice bibliographique
Résumé
BACKGROUND: Brief psychosocial interventions, such as the Healthy Activity Program (HAP), which are based on behavioral activation and delivered by nonspecialist providers (NSPs), have emerged as cost-effective solutions for the treatment of depression. HAP treatment outcomes are improved by the engagement of patients in activation-focused homework assignments and their adherence to these assignments during therapy. Currently, patients are expected to complete these homework assignments using a paper workbook. OBJECTIVE: The aim of this study was to describe the user-centered development process of PracticePal, a chatbot designed to enhance patient engagement and homework adherence, and to evaluate its feasibility and acceptability as a therapy aid in India. METHODS: We used a user-centered approach to co-develop PracticePal, incorporating conversational flows and video scripts in Hindi. The chatbot was piloted with 30 participants having depression who were receiving the HAP from 15 nonspecialist counselors in primary care in rural Madhya Pradesh, India. The feasibility and acceptability of PracticePal were assessed through engagement data, in-depth interviews with a subset of 6 participants, and focus group discussions with 11 counselors. Treatment completion rates and changes in depressive symptoms were explored as secondary outcomes. RESULTS: Average patient engagement spanned 29 days (95% CI 24-34) during the 60-day treatment period. The engagement of patients with PracticePal increased as their treatment progressed, particularly after the third HAP session. Of the 30 patients, 20 (67%) accessed more than half of the multimedia content available on the chatbot. On average, there was a greater frequency of self-initiated engagement (1558 out of a total of 1835 times, 84.9%) across all sessions compared with reminder prompts (277 out of 1835 times, 15.1%). All 30 patients completed treatment and experienced a reduction in the mean Patient Health Questionnaire-9 score from 13 (95% CI 12.6-13.6; signifying moderate severity) to 4 (95% CI 2.9-4.7; signifying none/minimal severity). Patients found the chatbot's reminders for activities, mood tracking, and video messages helpful and observed that it could help others in their social network. NSPs also reported improved participation of patients in the homework tasks compared with the paper workbook. A few patients faced challenges with low internet bandwidth, and those with limited literacy suggested increasing the amount of video content for easier accessibility. CONCLUSIONS: The PracticePal chatbot is a feasible and acceptable therapy aid to complement a psychological treatment, with promising potential to enhance the effectiveness of NSP-delivered psychosocial interventions in low-resource settings. Future steps include conducting a fully powered randomized controlled trial to assess its effectiveness in improving mental health outcomes.
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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 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».