Variations in Hypoxia Impairs Muscle Oxygenation and Performance during Simulated Team-Sport Running
Authors: François Billaut
Alice J Sweeting
Ramón F Rodriguez
William G Hopkins
Matthew C Varley
Robert J Aughey
Journal: Frontiers in physiology (2017)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: altitude; near-infrared spectroscopy; non-motorized treadmill; repeated sprints.

Abstract:  Purpose: To quantify the effect of acute hypoxia on muscle oxygenation and power during simulated team-sport running. Methods: Seven individuals performed repeated and single sprint efforts, embedded in a simulated team-sport running protocol, on a non-motorized treadmill in normoxia (sea-level), and acute normobaric hypoxia (simulated altitudes of 2,000 and 3,000 m). Mean and peak power was quantified during all sprints and repeated sprints. Mean total work, heart rate, blood oxygen saturation, and quadriceps muscle deoxyhaemoglobin concentration (assessed via near-infrared spectroscopy) were measured over the entire protocol. A linear mixed model was used to estimate performance and physiological effects across each half of the protocol. Changes were expressed in standardized units for assessment of magnitude. Uncertainty in the changes was expressed as a 90% confidence interval and interpreted via non-clinical magnitude-based inference. Results: Mean total work was reduced at 2,000 m (-10%, 90% confidence limits ±6%) and 3,000 m (-15%, ±5%) compared with sea-level. Mean heart rate was reduced at 3,000 m compared with 2,000 m (-3, ±3 min-1) and sea-level (-3, ±3 min-1). Blood oxygen saturation was lower at 2,000 m (-8, ±3%) and 3,000 m (-15, ±2%) compared with sea-level. Sprint mean power across the entire protocol was reduced at 3,000 m compared with 2,000 m (-12%, ±3%) and sea-level (-14%, ±4%). In the second half of the protocol, sprint mean power was reduced at 3,000 m compared to 2,000 m (-6%, ±4%). Sprint mean peak power across the entire protocol was lowered at 2,000 m (-10%, ±6%) and 3,000 m (-16%, ±6%) compared with sea-level. During repeated sprints, mean peak power was lower at 2,000 m (-8%, ±7%) and 3,000 m (-8%, ±7%) compared with sea-level. In the second half of the protocol, repeated sprint mean power was reduced at 3,000 m compared to 2,000 m (-7%, ±5%) and sea-level (-9%, ±5%). Quadriceps muscle deoxyhaemoglobin concentration was lowered at 3,000 m compared to 2,000 m (-10, ±12%) and sea-level (-11, ±12%). Conclusions: Simulated team-sport running is impaired at 3,000 m compared to 2,000 m and sea-level, likely due to a higher muscle deoxygenation. 

Retracted: No

PubPeer comments:
 In 2014, Alan Welch presented his work along with Emma Knight to the AIS where they looked into a method of statistics called Magnitude Based Inference (MBI). MBI has never been published in a statistical or mathematical journal but is heavily used by the sports science field. You can view the presentation here https: maths.anu.edu.au files AIS-slides.pdf . After presenting MBI was supposedly discontinued at the AIS after Alan and Emma showed the method to be fundamentally flawed. In 2018 a 538 article was written about MBI uptake within the sports science field. https: fivethirtyeight.com features how-shoddy-statistics-found-a-home-in-sports-research This article notes to further work was done by Kristin Sainani, showing that MBI has numerous issues. She gives reproducible mathematics to show this. https: www.ncbi.nlm.nih.gov pubmed 29683920 In 2019 there was an article written in The Age talking about how still MBI is being used. https: www.smh.com.au national cold-water-poured-on-scientific-studies-based-on-statistical-cult-20191108-p538t6.html This article was done post AIS banning the method and still MBI does not have a mathematical proof, uses no version control in its excel spreadsheets. , 
 Magnitude-based inference, or as it is now known, magnitude-based decisions (MBD), does not give  false results . It turns out to be equivalent to several hypothesis tests with well-defined and acceptable Type-2 error rates (failed discovery of substantial effects, 5% for the non-clinical version used in this study), and the Type-1 rates (false discovery of substantial effects) for the non-clinical version are only ever slightly more than 5% and usually a lot less. An article substantiating these assertions first-authored by a mathematics professor and intended for a statistical journal is in preparation. I am also publishing something at Sportscience shortly that will be more accessible to researchers without substantial training in statistics. Meantime, for some recent background, see my posting and other postings each side of it in the datamethods forum: https: discourse.datamethods.org t what-are-credible-priors-and-what-are-skeptical-priors 580 53 It is worth noting one of statistician Sander Greenland s postings to that conversation in the datamethods forum:  Earlier applications of MBI did not necessarily reach incorrect conclusions. Instead when a report that used MBI is seen as supplying data relevant to a topic, that report needs to be scrutinized to see if its conclusions hold up or fail in light of critical insights about what MBI was actually producing (as opposed to the original MBI descriptions). Note that this advice involves study-specific examinations, and thus is a far cry from simply discarding everything that was published using MBI. Being able to follow it in a particular case hinges on how well the report described its design, execution and data, and if the needed summary statistics were given or computable from the report; if not, the report authors would need to supply those items. You will find that we supply compatibility limits and identify smallest important effects for the main outcomes in all our papers using MBI MBD, so it is a simple matter for readers to evaluate for themselves whether precision of the estimates is adequate for a probabilistic conclusion, in the manner that epidemiologist Ken Rothman and psychologist Geoff Cumming evaluate outcomes. Rothman uses 90% confidence compatibility limits in his introductory text on epidemiology three times more frequently than 95% CIs. Cumming s default is 95%. MBI MBD uses 90% CIs for non-clinical effects, and 99% on the harm side and 50% on the benefit side for clinical effects, all for very good reasons. , 
 How long has MBI been out for, as yet there has not been any mathematical formulation in a peer reviewed statistics or mathematics journal. I believe Sander Greenlands comments mean well, however it doesn t represent what is underlying the spreadsheets which Kristin, Alan and Emma have all shown to be misleading at best. All critiques of MBI are done with reproducible simulations and show the underlying formula. No response has. ,