Running on Fumes: An Analysis of Fine Particulate Matter's Impact on Finish Times in Nine Major US Marathons, 2003-2019

Overview

Journal Sports Med

Specialty Orthopedics

Date 2024 Dec 17

PMID 39690352

Authors

Elvira S Fleury

Gray S Bittker

Allan C Just

Joseph M Braun

Affiliations

Soon will be listed here.

Abstract

Background: Under controlled conditions and in some observational studies of runners, airborne fine particulate matter smaller than 2.5 microns in diameter (PM) is associated with exercise performance decrements.

Objective: To assess the association between event-day fine particulate matter air pollution (PM) and marathon finish times.

Methods: Using a spatiotemporal machine-learning model, we estimated event-day racecourse-averaged PM concentrations for nine major US marathons (2003-2019). We obtained 1,506,137 male and 1,058,674 female finish times from 140 event-years of public marathon data. We used linear and quantile mixed models to estimate the mean and percentile-specific year and heat index-adjusted effect of 1 µg/m higher event-day racecourse-averaged PM on marathon finish times in sex-stratified samples.

Results: Analyzing all finish times, 1 µg/m higher race-day PM was associated with 32-s slower average finish times among men (95% confidence limits (CL) 30, 33 s) and 25-s slower average finish times among women (95% CL 23, 27 s). Quantile-specific associations of event-day PM with finish times were larger for faster-than-median finishers. While PM was generally associated with slower finish times in single-event models, there was effect heterogeneity, and most 95% confidence intervals included the null.

Conclusion: Greater race-day PM was associated with slower average marathon finish times, with more pronounced effects in faster-than-median runners. While more research is needed to characterize effect heterogeneity across the performance spectrum, these findings show the impact of PM on marathon performance and the importance of considering data from multiple competitions when estimating PM effects from event-level data.

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