Incidence and Risk Factors of Insomnia in a Population-Based Sample
Bibliographic record
Abstract
INTRODUCTION: Despite the high prevalence of insomnia, there is little information about its incidence and risk factors. This study estimated the incidence of insomnia and examined potential risk factors in a cohort of good sleepers followed over a one-year period. METHODS: Participants were 464 good sleepers who completed 3 postal evaluations over a one-year period (i.e., baseline, 6 months, and 12 months). Questionnaires assessed sleep, psychological and personality variables, stressful life events and coping skills, and health-related quality of life. Participants were categorized into 3 subgroups: (a) good sleepers (i.e., participants who remained good sleepers at the 3 assessments), (b) insomnia symptoms incident cases (i.e., developed insomnia symptoms either at 6- or 12-month follow-up), and (c) insomnia syndrome incident cases (i.e., developed an insomnia syndrome either at 6- or 12- month follow-up). RESULTS: One-year incidence rates were 30.7% for insomnia symptoms and 7.4% for insomnia syndrome. These rates decreased to 28.8% and 3.9% for those without prior lifetime episode of insomnia. Compared to good sleepers and insomnia symptoms incident cases, insomnia syndrome incident cases presented a premorbid psychological vulnerability to insomnia, characterized by higher depressive and anxiety symptoms, lower extraversion, higher arousability, and poorer self-rated mental health at baseline. They also presented a higher level of bodily pain and a poorer general health. Five variables were associated with a new onset of an insomnia syndrome: previous episode of insomnia, positive family history of insomnia, higher arousability predisposition, poorer self-rated general health, and higher bodily pain. CONCLUSION: The one-year insomnia incidence rate was very high and several psychological and health factors were associated with new onset insomnia. Improved knowledge about the nature of these predisposing factors would be helpful to guide the development of effective public health prevention and intervention programs to promote better sleep quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".