0082 The Structural Influence of Disordered Sleep on the Wake Intrusion Index
Bibliographic record
Abstract
Abstract Introduction Individually, insomnia and obstructive sleep apnea (OSA) are significant public health concerns. Insomnia affects nearly a third of the population, while OSA affects 9% to 38% of adults. Their co-occurrence (COMISA) results in severe health outcomes, including cardiometabolic and neurocognitive morbidity, impaired sleep quality, and reduced quality of life. Although both disorders disrupt sleep, it is difficult to disentangle the effect of each disorder on conventional physiological surrogates of sleep quality. This study attempts to quantify the contributions of OSA and insomnia to the Wake Intrusion Index (WII), a measure of subthreshold intrusions of wakefulness on sleep. We hypothesized that WII is influenced by both respiratory and non-respiratory arousal mechanisms. Methods We leveraged polysomnography data and self-reported insomnia symptoms from individuals in the Sleep Heart Health Study. WII was calculated based on the odds-ratio product, a measure of sleep depth. We used structural equation modeling to examine the shared and unique contributions of both respiratory indices such as the Apnea Hypopnea Index and the Oxygen Desaturation Index and frequency of different insomnia symptoms on the WII. Results We found that our data had a two-factor structure representing respiratory related arousals, containing all the respiratory variables, and non-respiratory related arousal which contained the questions related to insomnia. Both respiratory and non-respiratory latent variables influenced the WII, however the non-respiratory arousal (β=8.22) factor had a notably higher influence on the WII over the respiratory arousal factor (β=1.02). Conclusion Our study demonstrates that both respiratory and non-respiratory arousal mechanisms significantly influence the WII. These findings highlight the importance of addressing both components in treatment strategies to improve sleep quality and overall health outcomes in COMISA patients. Support (if any) The Sleep Heart Health Study (SHHS) was supported by National Heart, Lung, and Blood Institute cooperative agreements U01HL53916 (University of California, Davis), U01HL53931 (New York University), U01HL53934 (University of Minnesota), U01HL53937 and U01HL64360 (Johns Hopkins University), U01HL53938 (University of Arizona), U01HL53940 (University of Washington), U01HL53941 (Boston University), and U01HL63463 (Case Western Reserve University). The National Sleep Research Resource was supported by the National Heart, Lung, and Blood Institute (R24 HL114473, 75N92019R002).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".