Digital cognitive behavioral therapy vs education for pain in adults with sickle cell disease
Bibliographic record
Abstract
ABSTRACT: Despite the burden of chronic pain in sickle cell disease (SCD), nonpharmacological approaches remain limited. This multisite, randomized trial compared digital cognitive behavioral therapy (CBT) with a digital pain/SCD education program ("Education") for managing pain and related symptoms. Participants were recruited virtually from seven SCD centers and community organizations in the United States. Adults (aged ≥18 years) with SCD-related chronic pain and/or daily opioid use were assigned to receive either CBT or Education for 12 weeks. Both groups used an app with interactive chatbot lessons and received personalized health coach support. The primary outcome was the change in pain interference at six months, with secondary outcomes including pain intensity, depression, anxiety, quality of life, and self-efficacy. Of 453 screened participants, 359 (79%) were randomized to CBT (n = 181) or Education (n = 178); 92% were Black African American, and 66.3% were female. At six months, 250 participants (70%) completed follow-up assessments, with 16 (4%) withdrawals. Engagement with the chatbot varied, with 76% connecting and 48% completing at least one lesson, but 80% of participants completed at least one health coach session. Both groups showed significant within-group improvements in pain interference (CBT: -2.13; Education: -2.66), but no significant difference was observed between them (mean difference, 0.54; P = .57). There were no between-group differences in pain intensity, depression, anxiety, or quality of life. High engagement with health coaching and variable engagement with digital components may explain the similar outcomes between interventions in this diverse, hard-to-reach population.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".