Do Changes in the Weather Influence Joint Pain and Stiffness in Children Living with Juvenile Idiopathic Arthritis?
Bibliographic record
Abstract
Objectives Weather is a commonly reported factor related to flares of pain and stiffness in patients with juvenile idiopathic arthritis (JIA). However, limited empirical data exists on the influence of weather in childhood rheumatological conditions. In Canada, where climate variations are pronounced, understanding how weather influences JIA symptoms could help enhance disease management and improve patient well-being. This study aimed to investigate whether weather conditions, including humidity, temperature, precipitation, and wind speed, are associated with pain and stiffness in Canadian children with JIA. Methods In this cross-sectional study, data were extracted from the Canadian Alliance of Pediatric Rheumatology Investigators (CAPRI) Registry, comprising children recruited within 3 months of JIA diagnosis at 20 participating sites across Canada. Measures of joint pain (severity scale 1-10) and stiffness (rated as yes or no and quantified in minutes) during visits for 653 children were matched with the preceding 2 weeks’ weather data from the weather station nearest to the child’s home postal code from the Government of Canada website. We employed logistic regression to evaluate the relationship among stiffness, pain, and weather parameters (humidity, temperature, precipitation, windspeed) within 1, 7, and 14 days of symptom reporting. Multiple linear regression was used to evaluate pain severity, while controlling for age and sex. Results At participants’ baseline visit, higher average maximum humidity in the preceding week was associated with increased reported stiffness (OR=1.26, 95% CI 1.06-1.50, p=0.0076) and higher severity of joint pain (β=0.21, 95% CI 0.012-0.41, p=0.038). The association of humidity with stiffness also applied for the preceding day (OR=1.14, 95% CI 1.02-1.27, p=0.022) and 2 weeks (OR=1.24, 95% CI 1.02-1.50, p=0.031), but not for joint pain. Higher mean and maximum temperatures for the preceding day had a significant positive effect on joint pain severity (β=0.087, 95% CI 0.00-0.17; p=0.048 and β=0.086, 95% CI 0.013-0.16; p=0.021, respectively) holding all other variables constant. Total precipitation and windspeed in the preceding 1, 7, or 14 days were not associated with stiffness and joint pain. Conclusion We found significant associations between weather conditions, particularly humidity and temperature, and reported symptoms of joint pain and stiffness in children with JIA. Higher humidity was linked to increased stiffness and joint pain, while higher temperatures correlated with greater pain severity. Building on insights from this research can guide better management strategies for patients and their families, allowing them to anticipate and mitigate the impacts of adverse weather conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".