Moderators of digital cognitive-behavioral therapy for youth with sickle cell disease pain: secondary analysis of a randomized controlled trial
Bibliographic record
Abstract
ABSTRACT: Pain is the hallmark symptom of sickle cell disease (SCD). By adolescence, 20% of youth with SCD develop chronic SCD pain. Our randomized controlled trial found significant reductions in pain in youth receiving digital cognitive-behavioral therapy (CBT) vs education control. However, little is known about factors that moderate the effects of CBT in adolescents with SCD. This secondary data analysis aims to identify adolescent and family characteristics that moderate treatment effects on pain outcomes in 111 adolescents aged 12 to 18 with SCD (M = 14.9, SD = 1.9, girls = 59%) and their caregivers. Adolescents were randomly assigned to digital CBT (N = 57) or education control (N = 54). Digital CBT included separate content for parents/caregivers (ie, a website to learn problem-solving skills and behavioral and communication strategies) and youths (ie, a smartphone app and website to learn pain management skills). Outcomes were assessed at pretreatment, posttreatment (2 months), and follow-up (6 months). Potential moderators included pretreatment variables (ie, adolescent variables: age, executive functioning, anxiety, depression; parent variables: psychological distress, protective behaviors, family functioning). There was a significant overall effect modification on pain intensity outcomes from pretreatment parent psychological distress (P = 0.012), where CBT appeared more effective among those with elevated parental distress. Differential intervention effects were observed across multiple potential moderator groups, though most of these differences did not reach statistical significance. Our study underscores the importance of family factors in understanding the efficacy of digital CBT for adolescent SCD pain, pointing to the need for future research to optimize CBT through targeted family-focused strategies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".