Sleep the night before and after a treatment session: A critical ingredient for treatment adherence?
Bibliographic record
Abstract
OBJECTIVE: Sleep prepares key neural structures for next-day learning, and sleep obtained after learning promotes subsequent memory consolidation supporting long-term retention. This study examined whether sleep the night before and after a therapy session predicts aspects of treatment adherence. METHOD: As part of a randomized clinical trial, 188 adults (62.7% female, mean age = 47.5, 80.5% Caucasian) with persistent insomnia received cognitive-behavioral therapy for insomnia. Patients completed a sleep diary before and after treatment sessions. Minutes spent awake during the night (total wake time; TWT) and total sleep time (TST) were used as measures of sleep disturbance. At each treatment session, therapists rated participant understanding of the session and homework compliance from the previous session. RESULTS: Compared to longer TWT, before session shorter TWT was associated with increased treatment understanding the next day. After session shorter TWT was also associated with increased understanding, but not homework compliance the subsequent session compared to participants with longer TWT. Similar results were obtained for TST. CONCLUSIONS: Improving sleep may benefit patient adherence to treatment. Sleep may influence processes related to initial learning and subsequent consolidation of treatment information. Future studies should examine whether improved sleep within other psychiatric disorders is also an ingredient to the successful outcome of psychosocial interventions. (PsycINFO Database Record
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".