Efficacious Web-Based Psychotherapy to Address Depression and Anxiety Among Patients Receiving Oncological and Palliative Care: an Open-Label Randomised Controlled Trial
Bibliographic record
Abstract
Introduction Oncological and palliative care patients face unique stressors which increase their risk of developing depression and anxiety. Cognitive behavioural therapy (CBT) and mindfulness has established success in improving this population’s mental health. Traditional face-to-face psychotherapy is costly, has long wait lists, often lacks accessibility, and has strict scheduling, each of which can make attending psychotherapy physically, mentally, and financially out of reach for oncological and palliative patients. Web-based CBT (e-CBT) is a promising alternative that has shown efficacy in this and other patient populations. Objectives To quantify the efficacy of online CBT and mindfulness therapy in oncological and palliative patients experiencing depression and anxiety symptoms. Methods Participants with depression or anxiety related to their diagnosis were recruited from care settings in Kingston, Ontario, and randomly assigned to 8 weekly e-CBT/mindfulness modules (N= 25) or treatment as usual (TAU; N=24). Modules consisted of CBT concepts, problem-solving, mindfulness, homework, and personalised feedback from their therapist through a secure platform (Online Psychotherapy Tool- OPTT) Participants completed PHQ-9 and GAD-7 in weeks 1, 4, and 8. (NCT04664270: REB# 6031471). Results Significant decreases in PHQ-9 and GAD-7 scores within individuals support the hypothesis of efficacy. At this time, 10 e-CBT/mindfulness and 12 TAU have completed the study. Decreases in PHQ-9 and GAD-7 scores within e-CBT group support the hypothesis of efficacy. Specifically, PHQ-9 scores decreased over the 3 repeated measures (ANOVA, 2 groups, 3 repeated measures and the decrease in GAD-7 scores was similarly large) Conclusions As hypothesized, the results suggest that e-CBT/mindfulness therapy is an affordable, accessible, and efficacious mental health treatment for this population. The virtual, asynchronous delivery format is particularly appropriate given the unique barriers. Disclosure of Interest N. Alavi Shareolder of: OPTT inc, Grant / Research support from: department psychiatry Queen’s University, M. Omrani Shareolder of: OPTT inc, A. Shirazi: None Declared, G. Layzell: None Declared, J. Eadie: None Declared, J. Jagayat: None Declared, C. Stephenson: None Declared, D. Kain: None Declared, C. Soares: None Declared, M. Yang: 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".