Lifestyle Factors Associated With Frequent Recurrent Headaches in Children and Adolescents
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Lifestyle behaviors have been postulated to affect headache frequency in youth and are often the primary target of self-management recommendations. Our study aimed to assess the association between various lifestyle factors and frequent recurrent headaches in children and youth. METHODS: Children and adolescents aged 5-17 years were enrolled in a large cross-sectional Canadian population-based health survey, completed on January 31, 2019. Headache frequency was dichotomized into "approximately once/week or less" or ">once/week" (defined as frequent recurrent headaches). The association between frequent headaches and meal schedules, screen exposure, physical activity, chronotype, and frequent substance use/exposure (alcohol, cigarettes, electronic cigarettes, and cannabis) was assessed using both unadjusted logistic regression models and models adjusted for age/sex. Fully adjusted models examined the odds of frequent headaches according to all exposures. Survey design effects were accounted for using bootstrap replicate weighting. RESULTS: = 0.005). DISCUSSION: Several lifestyle behaviors were associated with frequent headaches in children and youth, such as meal irregularity, late chronotype, prolonged screen exposure, and frequent substance use/exposure, suggesting that these are potential modifiable risk factors to target in this population.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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 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".