Digital cognitive behavioral therapy vs. education intervention in population with sickle cell disease experiencing pain: A systematic review
Bibliographic record
Abstract
Abstract Introduction: Chronic pain in sickle cell disease (SCD) patients impairs daily functioning and quality of life, despite advances in pharmacological treatments. Digital cognitive behavioral therapy (dCBT) and educational interventions present promising nonpharmacologic strategies. This systematic review assesses the efficacy of these interventions, as well as their potential for digital delivery in SCD pain management. Methods: Following PRISMA guidelines, we screened randomized controlled trials (RCTs) from PubMed MEDLINE, CNKI, Scopus, Web of Science, Cochrane, EMBASE, and CINAHL (01/20/2025). Our PROSPERO-registered protocol (CRD42025650053) guided selection. Results: From 142 studies, 81 were screened, 13 underwent full-text review, and two RCTs (n=470) met eligibility criteria. Studies from the US and Canada assessed dCBT versus education for SCD pain over six months in adults and adolescents, primarily Black/African American females. Neither intervention significantly reduced post-treatment pain, anxiety, or depression. However, cognitive behavioral therapy (dCBT) demonstrated superior mood improvements at six months and greater reductions in fatigue. Conclusion: These findings highlight critical factors influencing intervention effectiveness, including sample size, age, engagement, and intervention type. Future research should optimize dCBT implementation and engagement strategies to enhance outcomes. Understanding these elements is essential for developing effective digital interventions for SCD chronic pain management. Keywords: Sickle Cell Disease, Chronic Pain, Digital Cognitive Behavioral Therapy, Educational Interventions, Pain Management, Systematic Review, Randomized Controlled Trials, Adults, Adolescents.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".