Methodological approach to sleep state misperception in insomnia disorder: Comparison between multiple nights of actigraphy recordings and a single night of polysomnography recording
Bibliographic record
Abstract
STUDY OBJECTIVE: To provide a comprehensive assessment of sleep state misperception in insomnia disorder (INS) and good sleepers (GS) by comparing recordings performed for one night in-lab (PSG and night review) and during several nights at-home (actigraphy and sleep diaries). METHODS: Fifty-seven INS and 29 GS wore an actigraphy device and filled a sleep diary for two weeks at-home. They subsequently completed a PSG recording and filled a night review in-lab. Sleep perception index (subjective/objective × 100) of sleep onset latency (SOL), sleep duration (TST) and wake duration (TST) were computed and compared between methods and groups. RESULTS: GS displayed a tendency to overestimate TST and WASO but correctly perceived SOL. The degree of misperception was similar across methods within the GS group. In contrast, INS underestimated their TST and overestimated their SOL both in-lab and at-home, yet the severity of misperception of SOL was larger at-home than in-lab. Finally, INS overestimated WASO only in-lab while correctly perceiving it at-home. While only the degree of TST misperception was stable across methods in INS, misperception of SOL and WASO were dependent on the method used. CONCLUSIONS: We found that GS and INS exhibit opposite patterns and severity of sleep misperception. While the degree of misperception in GS was similar across methods, only sleep duration misperception was reliably detected by both in-lab and at-home methods in INS. Our results highlight that, when assessing sleep misperception in insomnia disorder, the environment and method of data collection should be carefully considered.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".