Excessive Daytime Sleepiness Should Be Systematically Assessed in Individuals With Insomnia: A Population‐Based Study Employing a Virtual Agent‐Based Digital Tool
Bibliographic record
Abstract
Insomnia and excessive daytime sleepiness (EDS) often co-occur, despite involving distinct physiological mechanisms. The KANOPÉE application, a fully autonomous virtual agent that collects sleep-related data and delivers personalised behavioural recommendations over a 17-day period, offers a unique opportunity to better understand this unexpected phenotype. Our primary aim was to characterise these 'sleepy insomniacs', and our secondary aim was to evaluate their response to this digital sleep intervention. Among 21,590 participants, individuals with an Epworth Sleepiness Scale score ≥ 11 and an Insomnia Severity Index score ≥ 15 were classified as 'sleepy insomniacs'. Comorbidities (i.e., obstructive sleep apnea syndrome, restless legs syndrome, depression, and sleep medication use) were first described and then excluded for further analyses. At baseline, 4843 (47.9%) of the 10,114 participants with insomnia also reported EDS and were categorised as 'sleepy insomniacs'. Half of this subgroup reported at least one comorbidity, with depression being the most common. After excluding participants with comorbidities, 3239 individuals (44.3%) remained in the 'sleepy insomniacs' category. These individuals were more likely to experience middle or late insomnia symptoms compared to those with insomnia without EDS but responded similarly to the digital sleep intervention. In conclusion, EDS is highly prevalent among individuals with insomnia symptoms. While comorbidities, particularly depression, explained the co-occurrence in approximately half of the sample, a substantial proportion of participants without comorbidities also exhibited this unexpected phenotype. The association with specific insomnia subtypes highlights the need for further investigation. Notably, a 17-day digital sleep intervention proved effective in treating 'sleepy insomniacs'.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".