Is there an association between physical activity and lung function in lung-healthy German adults? – Results from the KORA FF4 study
Bibliographic record
Abstract
Background: Being active is associated with beneficial health effects for subjects with chronic lung diseases. The association of physical activity (PA) with lung function in lung-healthy populations has been rarely analysed. Therefore, our aim was to investigate the association of accelerometer-based PA with spirometric parameters, maximal inspiratory mouth pressure (PImax) and pulmonary gas exchange capacity related to alveolar volume (TLCO/VA) in lung-healthy adults from the KORA FF4 cohort. Methods: Data was available from 341 participants (45% males, mean age 57 years, 47% never-smokers) without chronic lung diseases and FEV1/FVC ≥0.7 who completed lung function testing and wore ActiGraph GT3X accelerometers on the hip for up to seven days. According to their mean minutes/day spent in moderate to vigorous PA (MVPA), subjects were classified into activity quartiles. Other analysed PA variables were the achievement of at least one 10-minute bout of MVPA, and meeting the WHO activity recommendation threshold of 150 minutes/week of MVPA spent in bouts of at least 10 minutes. Linear regression models adjusted for possible confounders were applied. Results: Associations of MVPA quartiles with FEV1, FVC and GLI z-scores of FEV1 and FVC were found. In regression analyses, FVC was 157 ml higher in subjects engaging on average > 48 minutes/day in MVPA (4th quartile) compared to those with < 20 minutes/day (1st quartile). FEV1 was 139 ml higher comparing these quartiles. No associations were found for TLCO/VA. Engaging in MVPA for at least one 10-minute bout length was associated with higher PImax. Achieving the WHO PA recommendations was not associated with any lung function parameter. Conclusion: Weak, but positive associations of PA with lung function suggest that engaging in PA might benefit lung function of adults without chronic lung diseases.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".