0324 Protective and Risk Factors for Insomnia Over 5 Years in a Population-Based Sample of Adults
Bibliographic record
Abstract
Abstract Introduction Insomnia is a major public health concern and one of the most prevalent health issues among adults. Identifying protective and risk factors for insomnia could help identify vulnerable populations and design public health interventions to prevent some adverse medical and mental health consequences. The study objective was to identify protective and risk factors for insomnia in a population-based sample of adults. Methods Data is from a large epidemiological cohort study on the natural course of insomnia conducted in Canada before the COVID-19 pandemic. Insomnia and potential protective and risk factors for insomnia were measured annually over 5 years with validated questionnaires (e.g., Insomnia Severity Index, Pittsburgh Sleep Quality Index, State-Trait Anxiety Inventory, Beck Depression Inventory, Perceived Stress Scale, Coping Inventory for Stressful Situations, Ford Insomnia Response to Stress Test, Arousal Predisposition Scale, Life Experiences Survey). Risk factors for insomnia were divided as predisposing or precipitating factors. Results From a cohort of 3,413 participants 1,709 adults were identified as good sleepers at baseline and were included in the analyses. A total of 202 people developed an insomnia syndrome during the 5-year follow-up. Using survival analysis for discrete events, the following variables were significant predisposing factors for insomnia: anxiety (HR=1.037; p=0.018), depression (HR=1.082; p< 0.001), perceived stress (HR=1.073; p< 0.001), number of negative life events (HR=1.478; p=0.02), perceived worse general health (HR=1.606; p=0.006), pain (HR=1.475; p=0.002), vulnerability to stress (HR=1.089; p< 0.001), and maladaptive emotional coping (HR=1.044; p< 0.001). The following variables were identified as precipitating factors for insomnia: loss of income (HR=1.346; p=0.03), increased anxiety (HR=1.070, p< 0.001), depression (HR=1.119; p< 0.001) and perceived stress (HR=1.083; p=0.001), decline in perceived general health (HR=2.061; p=0.006), and increased pain (HR=1.353; p=0.013). Physical activity was not a significant protective factor for insomnia (p=0.54). Conclusion These results provide new information that could be helpful to prevent the onset and chronicity of insomnia by targeting specific precipitating factors, and people with predisposing factors for insomnia. This is important as insomnia tends to be a persistent condition and is known to increase adverse medical and mental health outcomes. Support (if any) This study was funded by the Canadian Institutes of Health Research.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".