0508 Exploring Dimensions of Daily Dysfunction in Insomnia and the Predictive Role of Physiological Sleep Features
Bibliographic record
Abstract
Abstract Introduction Insomnia significantly impairs daily functioning, but the underlying dimensional structure of daily dysfunction and its physiological predictors remain underexplored. This study aims to better characterize the daily dysfunction in individuals with and without insomnia and examines the physiological predictors of this dysfunction. Methods Data from the Sleep Apnea Global Interdisciplinary Consortium (SAGIC) were analyzed, including 1058 participants (50% female; age 45.3±14.7 years) without obstructive sleep apnea (OSA) or shift work. Insomnia was defined by self-reported difficulty falling or staying asleep ≥3 nights/week for >3 months (n=387, 57% female; age 49.0±14.5 years). The dimensionality of self-reported daily dysfunction items (e.g., bother, affecting work, affecting social life, affecting sex life, affecting others, irritability, trouble concentrating, fatigue, and sleepy) was assessed using exploratory factor analysis (EFA). The role of EEG metrics (macro and micro-architecture and Odds-Ratio-Product [ORP]) in determining daytime dysfunction was examined using linear regression analyses to evaluate the predictive value of sleep metrics, group differences, and interaction effects. Results EFA identified a single-factor structure, explaining 60.4% of the variability in measures of daily dysfunction (KMO = 0.93; > 0.80, Bartlett’s test: χ²(36) = 2452.844, p < 0.001). The single factor represents overall daily dysfunction, with higher values indicating greater severity. All variables load in the same direction, meaning that as values increase, dysfunction worsens. Linear regression analyses revealed that longer sleep onset latency (SOL) and higher ORP-9, reflecting fragmented and unstable sleep, significantly predicted greater daily dysfunction in individuals with insomnia. In the ORP model, the higher ORP-9 × group (insomnia yes/no) interaction predicted worse daily dysfunction, while higher ORP wake was unexpectedly associated with better functioning. This suggests that preserved arousal stability may buffer some negative effects of insomnia. The interaction examines whether the effect of ORP-9 on daytime dysfunction differs between the insomnia groups. The linear regression models accounted for 19–23% of the variance in daily dysfunction. Conclusion These findings highlight the role of fragmented sleep and altered arousal stability in daily dysfunction among individuals with insomnia, emphasizing the value of combined sleep metrics in understanding sleep-related functional impairments. Support (if any)
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".