Supportive Text Messages for Patients with Depression – A Randomized Controlled Trial
Bibliographic record
Abstract
Background Depression is projected to be the primary cause of disability worldwide by 2030. Our aim was to test the feasibility of a supportive text messaging mobile health intervention in improving treatment outcomes in depressed patients. Methods We performed a single-rater-blinded randomised trial involving 73 patients with Major Depressive Disorder. Patients in the intervention group (n = 35) received twice-daily supportive text messages for 3 months while those in the control group (n = 38) received a single text message every fortnight thanking them for participating in the study. Results After adjusting for baseline BDI scores, a significant difference remained in the three month mean BDI scores between the intervention and control groups: (20.8 (SD = 11.7) vs. 24.9 (SD = 11.5), F (1, 60) = 4.83, P = 0.03, ηp2 = 0.07). The mean difference in the BDI scores change was significant with an effect size (Cohen's d) of 0.67. Furthermore, after adjusting for baseline scores, a significant difference remained in the three month mean self-rated VAS scores (EQ-5D-5L scale) between the intervention and control groups, 65.7 (SD = 15.3) vs. 57.4 (SD = 22.9), F (1, 60) = 4.16, P = 0.05, ηp2 = 0.065. The mean difference in change mean self-rated VAS scores was also statistically significant with an effect size (Cohen's d) of 0.51. Conclusions Our findings suggest that supportive text messages are a potentially useful psychological intervention for depression, especially in underserved populations. Further studies are needed to explore the implications of our findings in larger clinical samples.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".