Cognitive behavioral digital therapeutic effects on distress and quality of life in patients with cancer: National randomized controlled trial.
Bibliographic record
Abstract
OBJECTIVE: Cancer-specific psychological interventions like cognitive behavioral stress management (CBSM) demonstrate distress (e.g., anxiety/depression) and quality of life (QoL) benefits. Digital formats can expand access. METHOD: Patients (80.6% female; 76.5% White; 25-80 years) with Stage I-III cancer and elevated anxiety within 6 months of treatment (surgery/chemotherapy/radiation/immunotherapy) receipt were randomized 1:1 to a 10-module CBSM or health education control digital app and completed questionnaires at Weeks 0, 4, 8, 12. Primary outcomes of greater group-level anxiety (PROMIS-A) and depression symptom (PROMIS-D) reductions for CBSM were met and published; this secondary report evaluates individual-level response results for these outcomes and outcomes beyond anxiety and depression. Chi-square tests compared responder proportions using PROMIS-A/PROMIS-D symptom categories and two levels (≥5/≥7.5) of T-score point reductions. Changes across conditions over time for stress (Perceived Stress Scale), cancer-specific distress (Impact of Event Scale-Intrusions), and QoL (Functional Assessment of Cancer Therapy-General) were analyzed using repeated measures linear mixed-effects modeling (N = 449). Patient Global Impression of Change-Well-being was also examined. RESULTS: At Week 12, a greater proportion of CBSM (vs. control) participants reported normal-to-mild (vs. moderate-to-severe) PROMIS-A and PROMIS-D, and a greater proportion of CBSM participants at Week 8 or 12 had a ≥7.5 T-score reduction in PROMIS-A and a ≥5 T-score reduction in PROMIS-D (ps < .05). CBSM participants (vs. control) showed significantly greater reductions in Perceived Stress Scale and Impact of Event Scale-Intrusions and increases in Patient Global Impression of Change-Well-being and Functional Assessment of Cancer Therapy emotional and physical well-being (ps < .05), but not functional or social/family well-being. CONCLUSION: Digitized CBSM benefits distress and QoL. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".