Feasibility, efficacy, and perceptions of an online writing intervention in patients with depressive disorders: A randomized, multi-methods pilot study
Bibliographic record
Abstract
Barriers to accessing treatment for Depressive Disorders highlight a need for scalable interventions. Expressive writing (EW), which involves daily writing about a personal issue, has been shown to alleviate depressive symptoms in various samples. Its simple format makes it amenable to online administration. We conducted a multi-methods study to evaluate the feasibility, efficacy, and perceived utility of online EW in patients with Depressive Disorders. We recruited 53 patients diagnosed with a Depressive Disorder from a psychiatry hospital. Half were randomized to complete four 20-minute sessions of EW online and the other half were randomized as controls. All participants completed measures before and after the intervention, and at a one-month follow up. Our primary outcome for feasibility was the number of participants completing at least three sessions of EW, and for efficacy, the primary outcome was depressive symptom severity. Secondary outcomes were anxiety symptom severity, functional impairment, and perceived personal problem complexity. Mixed effect models assessed the impact of condition (EW or control), time, and their interaction on each outcome. Qualitative interviews with 11 participants assigned to EW probed into their experiences, which were subject to reflexive thematic analysis. Most EW participants (73%) completed at least three EW sessions, but only 23% wrote for 20 minutes. There was no evidence that condition interacted with time to impact depressive symptoms (F = 0.10, p = .904), anxiety symptoms (F = 0.17, p = .847), functional impairment (F = 1.03, p = .363), or perceived problem complexity (F = 0.94, p = .394). In qualitative interviews, some participants reported EW allowed them to offload or process negative emotions and thoughts, whereas others reported it to be unhelpful. EW was feasible to administer online to patients with Depressive Disorders, with some reported benefits for emotional and cognitive processing. However, EW was not associated with improvements in symptoms and may not be an effective online intervention for Depressive Disorders in its current state. Providing more guidance, with some clinical oversight or support, may be a promising approach to modifying the intervention in future work. Trial registration: ClinicalTrials.gov NCT06699719.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".