Association of Sleep Quality and General, Mental, and Oral Health with Lifestyle Traits (Dietary Intake, Smoking Status) in Arthritis: A Cross-Sectional Study from the Canadian Community Health Survey (CCHS)
Bibliographic record
Abstract
Arthritis is associated with health challenges. Lifestyle traits are believed to influence arthritis development and progression; however, data to support personalized treatment regimens based on holistic lifestyle factors are missing. This study aims to provide a comprehensive list of associations between lifestyle traits and the health status of individuals with arthritis in the Canadian population, using binary logistic regression analysis on data from the Canadian Community Health Survey, which includes 104,359 respondents. Firstly, we explored the association between arthritis and various aspects of health status including self-reported lifestyle factors. Secondly, we examined the associations between self-reported dietary intake and smoking status with general, mental, and oral health, and sleep disturbance among individuals both with and without arthritis. Our analysis revealed that individuals with arthritis reported considerably poorer general, mental, and oral health, and poorer sleep quality compared to those without arthritis. Associations were also found between self-reported dietary intake and various measures of health status in individuals with arthritis. Smoking and exposure to passive smoking were associated not only with arthritis but also with compromised sleep quality and poorer general, mental, and oral health in people with and without arthritis. This study highlights the need for personalized and holistic approaches that may include a combination of dietary interventions, oral health improvements, sleep therapies, and smoking cessation for improved arthritis prevention and care.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".