Normal Variability of Weekly Musculoskeletal Screening Scores and the Influence of Training Load across an Australian Football League Season
Authors: Andrew M Stewart
Alireza Esmaeili
William G Hopkins
George P Elias
Brendan H Lazarus
Amber E Rowell
Robert J Aughey
Journal: Frontiers in Physiology (2018)
Discipline: BIOLOGY & BIOCHEMISTRY
Keywords: athlete monitoring; calf; groin; hamstring; injury prevention

Abstract:  Aim: The sit and reach test (S&amp;R), dorsiflexion lunge test (DLT), and adductor squeeze test (AST) are commonly used in weekly musculoskeletal screening for athlete monitoring and injury prevention purposes. The aim of this study was to determine the normal week to week variability of the test scores, individual differences in variability, and the effects of training load on the scores. Methods: Forty-four elite Australian rules footballers from one club completed the weekly screening tests on day 2 or 3 post-main training (pre-season) or post-match (in-season) over a 10 month season. Ratings of perceived exertion and session duration for all training sessions were used to derive various measures of training load via both simple summations and exponentially weighted moving averages. Data were analyzed via linear and quadratic mixed modeling and interpreted using magnitude-based inference. Results: Substantial small to moderate variability was found for the tests at both season phases; for example over the in-season, the normal variability ±90% confidence limits were as follows: S&amp;R ±1.01 cm, ±0.12; DLT ±0.48 cm, ±0.06; AST ±7.4%, ±0.6%. Small individual differences in variability existed for the S&amp;R and AST (factor standard deviations between 1.31 and 1.66). All measures of training load had trivial effects on the screening scores. Conclusion: A change in a test score larger than the normal variability is required to be considered a true change. Athlete monitoring and flagging systems need to account for the individual differences in variability. The tests are not sensitive to internal training load when conducted 2 or 3 days post-training or post-match, and the scores should be interpreted cautiously when used as measures of recovery. 

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 is one of many papers by the authors continually using methods which have been stopped by the AIS. , 
 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. ,