Chronic skin disease and levels of physical activity in 17 777 Spanish adults: a cross‐sectional study
Bibliographic record
Abstract
BACKGROUND: To date there is limited literature on the prevalence of chronic skin conditions and its association with levels of physical activity (PA) in Spain. AIM: To determine the prevalence of chronic skin disease and to compare levels of PA between people with and without chronic skin disease in a large representative sample of Spanish adults aged 15-69 years. METHODS: Data from the Spanish National Health Survey 2017 were analysed. Chronic skin disease was assessed using a yes/no question. PA was measured using the short form of the International Physical Activity Questionnaire. Total PA metabolic equivalent of task min/week were calculated, and PA was included in the analyses as a continuous and a five-category variable. RESULTS: This cross-sectional study included 17 777 adult participants (52.0% women; mean ± SD age 45.8 ± 14.1 years), of whom 940 (5.3%) had chronic skin disease. After adjusting for several potential confounders, there was a negative association between chronic skin disease and PA (OR = 0.87, 95% CI 0.76-1.00, P = 0.05), which was significant for men (OR = 0.76, 95% CI 0.62-0.93, P = 0.01) but not for women (OR = 0.97, 95% CI 0.81-1.16, P = 0.72). CONCLUSIONS: In this large representative sample of Spanish adults, the prevalence of chronic skin disease was low. Levels of PA were lower in men with than in men without chronic skin conditions, but this association was not seen in women.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".