Asthma is prevalent in athletes and when untreated can impact both respiratory health and sports performance. Pharmacological inhaler therapy currently forms the mainstay of treatment; however, for elite athletes competing under the constraints of the World Anti-Doping Code (Code), a number of established therapies are prohibited both in and/or out of competition and/or have a maximum permitted dose. The recent release of medical information detailing inhaler therapy in high-profile athletes has brought the legitimacy and utilisation of asthma medication in this setting into sharp focus. This narrative review critically appraises recent changes to anti-doping policy and the Code in the context of asthma management, evaluates the impact of asthma medication use on sports performance and employs a theory of behaviour to examine perceived determinants and barriers to athletes adhering to the anti-doping rules of sport when applied to asthma.
Conclusions The experimental vapor‐liquid equilibrium data obtained with a mixture of methyl esters, and a mixture of a fatty acid and the corresponding methyl ester, were in agreement with calculated Raoult's law data at a pressure of 4.0±0.2 mm. Hg. The system lauric acid‐myristic acid was observed to be non‐ideal or not obeying Raoult's law at a pressure of 4.0±0.2 mm. Hg. An average rate of polymer formation of 0.23% per hour at 155–170° C. was determined for mixtures of lauric acid and myristic acid. The above work shows the feasibility of obtaining useful vapor‐liquid equilibrium data for fatty materials at low pressures.
Background and objectiveThe differential diagnosis for exercise-associated breathlessness is broad, however, when a young athletic individual presents with respiratory symptoms, they are most often prescribed inhaler therapy for presumed exercise-induced asthma (EIA). The purpose of this study was therefore to use a novel sound-based approach to assessment to evaluate the prevalence of exertional respiratory symptoms and characterise abnormal breathing sounds in a large cohort of recreationally active individuals.MethodsCross-sectional field-based evaluation of individuals completing Parkrun.Phase 1Prerace, clinical assessment and baseline spirometry were conducted. At peak exercise and immediately postrace, breathing was monitored continuously using a smartphone. Recordings were analysed retrospectively and coded for signs of the predominant respiratory noise.Phase 2A subpopulation that reported symptoms with at least one audible sign of respiratory dysfunction was randomly selected and invited to attend the laboratory on a separate occasion to undergo objective clinical workup to confirm or refute EIA.ResultsForty-eight participants (22.6%) had at least one audible sign of respiratory dysfunction; inspiratory stridor (9.9%), expiratory wheeze (3.3%), combined stridor+wheeze (3.3%), cough (6.1%). Over one-third of the cohort (38.2%) were classified as symptomatic. Ten individuals attended a follow-up appointment, however, only one had objective evidence of EIA.ConclusionsThe most common audible sign, detected in approximately 1 in 10 individuals, was inspiratory stridor, a characteristic feature of upper airway closure occurring during exercise. Further work is now required to further validate the precision and feasibility of this diagnostic approach in cohorts reporting exertional breathing difficulty.
Compact mass spectrometry (CMS) is a versatile and transportable analytical instrument that has the potential to be used in clinical settings to quickly and non-invasively detect a wide range of relevant conditions from breath samples. The purpose of this study is to optimise data preprocessing protocols by three proposed methods of breath sampling, using the CMS. It also lays out a general framework for which data processing methods can be evaluated. Methods This paper considers data from three previous studies, each using a different breath sampling method. These include a peppermint washout study using continuous breath sampling with a purified air source, an exercise study using continuous breath sampling with an ambient air source, and a single breath sampling study with an ambient air source. For each dataset, different breath selection (data preprocessing) methods were compared and benchmarked according to predictive performance on a validation set and quantitative reliability of m/z bin intensity measurements. Results For both continuous methods, the best breath selection method improved the predictive model compared to no preselection, as measured by the 95% CI range for Youden's index, from 0.68-0.86 to 0.86-0.97 for the exercise study and 0.69-0.82 to 1.00-1.00 for the peppermint study. The reliability of intensity measurements for both datasets (as measured by median relative standard deviation), was improved slightly by the best selection method compared to no preselection, from 18% to 14% for the exercise study and 7% to 5% for the peppermint study. For the single breath samples, all the models resulted in perfect prediction, with a 95% CI range for Youden's index of 1.00-1.00. The reliability of the proposed method was 38%. Conclusion The method of selecting exhaled breath from CMS data can affect the reliability of the measurement and the ability to distinguish between breath samples taken under different conditions. The application of appropriate data processing methods can improve the quality of the data and results obtained from CMS. The methods presented will enable untargeted analysis of breath VOCs using CMS to be performed.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.