Cognitive Behavioral Therapy for Children With Headaches: Will an App Do the Trick?
Bibliographic record
Abstract
Participants were enrolled into a pilot randomized-controlled 4-week trial comparing the efficacy and feasibility of app-based cognitive behavioral therapy (CBT) to a stretching program. Headache-related disability and quality of life were assessed using the Pediatric Migraine Disability Scale (PedMIDAS), Kidscree27, and Pediatric Quality of Life Inventory. Multivariable regression analysis were performed to assess the group effects in the presence of adherence and other covariates. Twenty participants completed the study. Adherence was significantly higher in the stretching than in the CBT app group (100% vs 54%, P < .034). When controlling for adherence and baseline scores, the stretching group showed greater reduction in PedMIDAS score (average: 29.2, P < .05) as compared to the CBT app group. However, in terms of the Quality-of-Life Indicators, pre- and postintervention raw scores were not significantly different between groups ( P > .05). App-based CBT was not superior to a stretching program in reducing headache-related disability in a select population of pediatric headache patients. Future studies should assess if implementing features to the CBT app, like tailoring to pediatric age groups, would improve outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.006 | 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".