The association between parameters of physical activity and olfactory function—the importance of frequency and moderate intensity
Bibliographic record
Abstract
Introduction Overall physical activity (PA) has been linked to decreased olfactory dysfunction and could improve olfactory function. Additionally, decreased olfactory function can contribute to reduced overall PA levels, given its association with neurodegenerative disorders. This study aims to examine the relationship between specific PA parameters (duration, frequency, intensity) and olfactory function in adults. Methods A total of 3,527 participants from the National Health and Nutrition Examination Survey (NHANES) 2013–2014 underwent assessments for weekly PA duration, frequency, and intensity, alongside a smell test (including odors such as chocolate, strawberry, grape, onion, smoke, natural gas, leather, and soap). Correlation analyses and binary logistic regressions using SPSS were used to evaluate associations. Results The total smell score exhibited small yet significant positive correlations with the duration, frequency, and volume of moderate PA (correlation coefficients ranging between 0.05 and 0.08; all p ≤ 0.05) and frequency of vigorous PA (correlation coefficient of 0.05; p < 0.05). For moderate PA, the duration, frequency, and volume were significantly and positively associated with the ability to correctly detect the smell of grapes while the frequency was significantly and positively associated with the ability to identify smoke and leather odors (odds ratios ranging from 1.01 to 1.07; p < 0.05). For vigorous PA, the frequency of PA was positively associated with the detection of grape smell (odds ratio of 1.05; p < 0.05). Conclusion Some parameters of an active lifestyle are associated with improved odds of accurately identifying odors by up to 7.4%. Moderate PA duration, frequency, and volume were linked to better olfactory scores, while high-intensity PA had limited associations.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".