Randomized Trial of Self-Guided, Internet-Based, Cognitive Behavior Therapies for Migraine
Bibliographic record
Abstract
ABSTRACT: Background: Self-guided Internet-based cognitive behavior therapy (iCBT) for migraine interventions could improve access to care, but there is poor evidence of their efficacy. Methods: A three-arm randomized controlled trial compared: iCBT focused on psychoeducation, self-monitoring and skills training (SPHERE), iCBT focused on identifying and managing personal headache triggers (PRISM) and a waitlist control. The primary treatment outcome was a ≥ 50% reduction in monthly headache days at 4 months post-randomization. Results: 428 participants were randomized (mean age = 30.1). 240 participants (56.2%) provided outcome data at 4 months. Intention-to-treat (ITT) analysis with missing data imputed demonstrated that the proportion of responders with a ≥ 50% reduction was similar between combined iCBTs and waitlist (48.5/285, 17% vs. 16.6/143, 11.6%, p = 0.20), but analysis of completers showed both iCBT programs to be superior to the waitlist (24/108, 22.2% vs. 13/113, 11.5%, p = 0.047). ITT analysis with missing data imputed showed no difference between the two iCBTs (SPHERE: 24.8/143, 17.3% vs. PRISM: 23.7/142, 16.7%, p = 0.99). Uptake rates of the iCBTs were high (76.9% and 81.69% logged in at least once into SPHERE and PRISM, respectively), but adherence was low (out of those who logged in at least once, 19.01% [21/110] completed at least 50% modules in SPHERE and 7.76% [9/116] set a goal for trying out a given trigger-specific recommendation in PRISM). Acceptability ratings were intermediate. Conclusions: Self-guided iCBTs were not found to be superior in our primary ITT analysis. Low adherence could explain the lack of effects as completer analysis showed effects for both interventions. Enhancement of adherence should be a focus of future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".