Comparative Effectiveness of Daily Supportive Text Messages Versus Email Messages for Patients with Depression. Randomized Hybrid Type II Effectiveness-Implementation Trial
Bibliographic record
Abstract
Introduction: Background Major depressive disorder (MDD) is a global health problem accounting for about 40.5% of disability-adjusted life years caused by mental and substance use disorders. Barriers to accessing healthcare services have been reported, highlighting the need for innovative, accessible, and cost-effective psychological interventions. Several clinical trials have proven the effectiveness of supportive SMS text messaging in ameliorating depressive symptoms, however, this approach can only be accessible to individuals having cell phones. Objectives This paper aims to evaluate the effectiveness, feasibility, and user satisfaction of daily supportive email messaging as a non-inferior intervention compared to daily supportive text messaging as an add-on treatment for patients with depression. Methods This trial will be carried out using a hybrid type II implementation-effectiveness design. In addition to the usual care, patients with depression will be randomized to receive either supportive text messages or supportive email messages. The messages in both groups will have the same content and will be provided daily for 6 months. The implementation evaluation will be guided by the Consolidated Framework for Implementation Research and the RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework. Descriptive and inferential statistics will be employed in the analysis of the quantitative outcome measures, while thematic analysis will be used for Qualitative data. Results The results are expected to be available 18 months after the start of recruitment. The results will highlight the feasibility, acceptability, and effectiveness of using automated emails as a strategy for delivering supportive messages to patients with depression as non-inferior to text messaging. Conclusions The outcome of this trial will have a translational impact on routine patient care and access to mental health, as well as potentially support mental health policy decision-making for health care resource allocation. Disclosure of Interest None Declared
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".