An Investigation of Further Strategies to Optimize Early Treatment Gains in Brief Therapies for Insomnia
Bibliographic record
Abstract
Objectives Identifying those who are most (and least) likely to benefit from a stepped-care approach to cognitive behavioral therapy for insomnia (CBT-I) increases access to insomnia therapies while minimizing resource consumption. The present study investigates non-targeted factors in a single-session of CBT-I that may act as barriers to early response and remission.Methods Participants (N = 303) received four sessions of CBT-I and completed measures of subjective insomnia severity, fatigue, sleep-related beliefs, treatment expectations, and sleep diaries. Subjective insomnia severity and sleep diaries were completed between each treatment session. Early response was defined as a 50% reduction in Insomnia Severity Index (ISI) scores and early remission was defined by < 10 on the ISI after the first session.Results A single-session of CBT-I significantly reduced subjective insomnia severity scores and diary total wake time. Logistic regression models indicated that lower baseline fatigue was associated with increased odds of early remission (B = −.05, p = .02), and lower subjective insomnia severity (B = −.13, p = .049). Only fatigue was a significant predictor of early treatment response (B = −.06, p = .003)Conclusions Fatigue appeared to be an important construct that dictates early changes in perceived insomnia severity. Beliefs about the relationship between sleep and daytime performance may hinder perceived improvements in insomnia symptoms. Incorporating fatigue management strategies and psychoeducation about the relationship between sleep and fatigue may target non-early responders. Future research would benefit from further profiling potential early insomnia responders/remitters.
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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 | 0.003 | 0.010 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".