Long-Term Maintenance of Therapeutic Gains Associated With Cognitive-Behavioral Therapy for Insomnia Delivered Alone or Combined With Zolpidem
Bibliographic record
Abstract
Study objectives: To document the long-term sleep outcomes at 12 and 24 months after patients with chronic insomnia were treated with cognitive-behavioral therapy (CBT), either singly or combined with zolpidem medication. Methods: Participants were 160 adults with chronic insomnia. They were first randomized for a six-week acute treatment phase involving CBT alone or CBT combined with nightly zolpidem, and randomized for a six-month extended treatment phase involving CBT, no additional treatment, CBT combined with zolpidem as needed, or CBT with zolpidem tapered. This paper reports results of the 12- and 24-month follow-ups on the main outcome measures derived from the Insomnia Severity Index and sleep diaries. Results: Clinical improvements achieved 6 months following the end of treatment were well-maintained in all four conditions, with insomnia remission rates ranging from 48% to 74% at the 12-month follow-up, and from 44% to 63% at the 24-month follow-up. Participants receiving CBT with zolpidem taper in the extended treatment phase had significantly better results than those receiving CBT with continued zolpidem as needed. The magnitude of improvements on sleep diary parameters was similar between conditions, with a slight advantage for the CBT with zolpidem taper condition. The addition of extended CBT did not alter the long-term outcome over improvements obtained during the initial 6-week CBT. Conclusions: The results suggest that CBT for insomnia, when delivered alone or in combination with medication, produce durable sleep improvements up to two years after completion of treatment. These long-term results indicate that even if a combined CBT plus medication approach provide an added benefit immediately after treatment, extending CBT while tapering medication produce better sustained improvements compared to continued use of medication as needed.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".