Applying the quarter‐hour rule: can people with insomnia accurately estimate 15‐min periods during the sleep‐onset phase?
Bibliographic record
Abstract
The 'quarter-hour rule' (QHR) instructs the person with insomnia to get out of bed after 15 min of wakefulness and return to bed only when sleep feels imminent. Recent research has identified that sleep can be significantly improved using this simple intervention (Malaffo and Espie, Sleep, 27(s), 2004, 280; Sleep, 29 (s), 2006, 257), but successful implementation depends on estimating time without clock monitoring, and the insomnia literature indicates poor time perception is a maintaining factor in primary insomnia (Harvey, Behav. Res. Ther., 40, 2002, 869). This study expands upon previous research with the aim of identifying whether people with insomnia can accurately perceive a 15-min interval during the sleep-onset period, and therefore successfully implements the QHR. A mixed models anova design was applied with between-participants factor of group (insomnia versus good sleepers) and within-participants factor of context (night versus day). Results indicated no differences between groups and contexts on time estimation tasks. This was despite an increase in arousal in the night context for both groups, and tentative support for the impact of arousal in inducing underestimations of time. These results provide promising support for the successful application of the QHR in people with insomnia. The results are discussed in terms of whether the design employed successfully accessed the processes that are involved in distorting time perception in insomnia. Suggestions for future research are provided and limitations of the current study discussed.
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 | 0.005 | 0.023 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".