A Conversational Agent (PracticePal) to Support the Delivery of a Brief Behavioral Activation Treatment for Depression in Rural India: Development and Pilot-Testing Study
Bibliographic record
Abstract
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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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".