A cognitive–behavioral digital health intervention for sickle cell disease pain in adolescents: a randomized, controlled, multicenter trial
Bibliographic record
Abstract
ABSTRACT: Severe acute and chronic pain are the most common complications of sickle cell disease (SCD). Pain results in disability, psychosocial distress, repeated clinic visits/hospitalizations, and significant healthcare costs. Psychosocial pain interventions that teach cognitive and behavioral strategies for managing pain have been effective in other adolescent populations when delivered in person or through digital technologies. Our aim was to conduct a multisite, randomized, controlled trial to improve pain and coping in youth aged 12 to 18 years with SCD using a digital cognitive-behavioral therapy program (iCanCope with Sickle Cell Disease; iCC-SCD) vs Education control. We enrolled 137 participants (ages 12-18 years, 59% female) and analyzed 111 adolescents (107 caregivers), 54 randomized to Education control and 57 randomized to iCC-SCD. Ninety-two percent of youth completed posttreatment assessments and 88% completed 6-month follow-up. There was a significant effect of treatment group (iCC-SCD vs Education) on reduction in average pain intensity from baseline to 6-month follow-up (b = -1.32, P = 0.009, 95% CI [-2.29, -0.34], d = 0.50), and for the number of days with pain, adolescents in the iCC-SCD group demonstrated fewer pain days compared with the Education group at 6-month follow-up (incident rate ratio = 0.63, P = 0.006, 95% CI [0.30, 0.95], d = 0.53). Treatment effects were also found for coping attempts, momentary mood, and fatigue. Several secondary outcomes did not change with intervention, including anxiety, depression, pain interference, and global impression of change. Future studies are needed to identify effective implementation strategies to bring evidence-based cognitive-behavioral therapy for sickle cell pain to SCD clinics and communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".