Multiuser Application for the Diagnosis and Treatment of Depression in Women’s Self-Help Groups: Pilot Randomized Controlled Trial
Bibliographic record
Abstract
Background: Depression in women results in elevated morbidity rates, functional impairment, diminished quality of life, and an increased risk of suicide. Numerous obstacles impede access to mental health treatment for women in India. Digital mental health solutions can bridge the treatment gap, but it is important to tailor these solutions to the context and to end-users. Objective: We conducted a pilot randomized controlled trial to test the feasibility, acceptability, and preliminary effectiveness of a mental health app deployed in community-based organizations in improving depression outcomes. Methods: The Multiuser Interactive Health Response Application (MITHRA) is a multiple-user mobile app used in community-based organizations for screening, tracking, and supporting stepped-care treatment for depression. MITHRA is based on the healthy activity program, a brief psychological intervention based on behavioral activation. It includes audio, video, and enhanced touchscreen capabilities to overcome the barrier of illiteracy and lack of access. It was developed in collaboration with a participatory design group consisting of primary and secondary end-users and is available on tablets installed in self-help groups (SHGs), which are community-based organizations in India. The SHGs were randomized to MITHRA (n=3) or enhanced usual care (EUC; n=3). During SHG meetings, women completed the Patient Health Questionnaire-9 (PHQ-9). Based on their PHQ-9 scores, they were assigned different modules. In the EUC SHGs, women viewed one module of education on symptoms of depression. Primary outcomes include feasibility and acceptability, and secondary outcomes include depressive symptoms and functioning. Repeated-measures ANOVA was performed to compare the change in the outcome scores over time between study groups. A P value of<.05 was considered statistically significant. Results: MITHRA was found to be feasible and acceptable. A total of 96% of intervention arm participants completed at least half of their assigned modules. Although not powered for effectiveness outcomes, in this trial, we found that the change at 6 months from baseline in depressive symptoms (PHQ-9) were significantly different between MITHRA and EUC (P=.037), with greater improvement in the intervention group. Similarly, significant improvement in the World Health Organization Disability Assessment Scale score was noted in the MITHRA group (P=.005). Conclusions: MITHRA is feasible and acceptable for use in women's SHGs. Larger studies should examine the effectiveness of this approach in identifying and treating depression.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| 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 | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".