Effect of Warm-Up Exercise on Exercise-Induced Bronchoconstriction
Bibliographic record
Abstract
PURPOSE: Exercise-induced bronchoconstriction (EIB) occurs when vigorous exercise induces bronchoconstriction. Preexercise warm-up routines are frequently used to elicit a refractory period and thus reduce or prevent EIB. This study aimed to conduct a systematic review to evaluate the effectiveness of preexercise routines to attenuate EIB. METHODS: A comprehensive literature search was performed, with steps taken to avoid publication and selection bias. Preexercise warm-up routines were classified into four groups: interval high intensity, continuous low intensity, continuous high intensity, and variable intensity (i.e., a combination of low intensity up to very high intensity). The EIB response was measured by the percent fall in the forced expiratory volume in 1 s (FEV1) after exercise, and the mean differences (MDs) and 95% confidence intervals (CI) are reported. RESULTS: Seven randomized studies met the inclusion criteria. The pooled results showed that high intensity (MD = -10.6%, 95% CI = -14.7% to -6.5%) and variable intensity (MD = -10.9%, 95% CI = -14.37% to -7.5%) exercise warm-up attenuated the fall in FEV1. However, continuous low-intensity warm-up (MD = -12.6%, 95% CI = -26.7% to 1.5%) and continuous high-intensity warm-up (MD = -9.8%, 95% CI = -26.0% to 6.4%) failed to result in a statistically significant reduction in bronchoconstriction. CONCLUSIONS: The most consistent and effective attenuation of EIB was observed with high-intensity interval and variable intensity preexercise warm-ups. These findings indicate that an appropriate warm-up strategy that includes at least some high-intensity exercise may be a short-term nonpharmacological strategy to reducing EIB.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".