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Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets

The design of high-intensity functional training (HIFT; e. g., CrossFit(®)) workouts and targeted physiological trait(s) vary on any given training day, week, or cycle. Daily workouts are typically comprised of different modality and exercise combinations that are prescribed across a wide range of i...

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Autores principales: Mangine, Gerald T., Seay, Tucker R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613943/
https://www.ncbi.nlm.nih.gov/pubmed/36311217
http://dx.doi.org/10.3389/fspor.2022.949429
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author Mangine, Gerald T.
Seay, Tucker R.
author_facet Mangine, Gerald T.
Seay, Tucker R.
author_sort Mangine, Gerald T.
collection PubMed
description The design of high-intensity functional training (HIFT; e. g., CrossFit(®)) workouts and targeted physiological trait(s) vary on any given training day, week, or cycle. Daily workouts are typically comprised of different modality and exercise combinations that are prescribed across a wide range of intensities and durations. The only consistent aspect appears to be the common instruction to maximize effort and workout density by either completing “as many repetitions as possible” within a time limit (e.g., AMRAP, Tabata) or a list of exercises as quickly as possible. However, because effort can vary within and across workouts, the impact on an athlete's physiology may also vary daily. Programming that fails to account for this variation or consider how targeted physiological systems interrelate may lead to overuse, maladaptation, or injury. Athletes may proactively monitor for negative training responses, but any observed response must be tied to a quantifiable workload before meaningful changes (to programming) are possible. Though traditional methods exist for quantifying the resistance training loads, gymnastic movements, and cardiorespiratory modalities (e.g., cycling running) that might appear in a typical HIFT workout, those methods are not uniform, and their meaning will vary based on a specific exercise's placement within a HIFT workout. To objectively quantify HIFT workloads, the calculation must overcome differences in measurement standards used for each modality, be able to account for a component's placement within the workout and be useful regardless of how a workout is commonly scored (e.g., repetitions completed vs. time-to-completion) so that comparisons between workouts are possible. This review paper discusses necessary considerations for quantifying various HIFT workout components and structures, and then details the advantages and shortcomings of different methods used in practice and the scientific literature. Methods typically used in practice range from being excessively tedious and not conducive for making comparisons within or across workouts, to being overly simplistic, based on faulty assumptions, and inaccurate. Meanwhile, only a few HIFT-related studies have attempted to report relevant workloads and have predominantly relied on converting component and workout performance into a rate (i.e., repetitions per minute or second). Repetition completion rate may be easily and accurately tracked and allows for intra- and inter-workout comparisons. Athletes, coaches, and sports scientists are encouraged to adopt this method and potentially pair it with technology (e.g., linear position transducers) to quantify HIFT workloads. Consistent adoption of such methods would enable more precise programming alterations, and it would allow fair comparisons to be made between existing and future research.
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spelling pubmed-96139432022-10-29 Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets Mangine, Gerald T. Seay, Tucker R. Front Sports Act Living Sports and Active Living The design of high-intensity functional training (HIFT; e. g., CrossFit(®)) workouts and targeted physiological trait(s) vary on any given training day, week, or cycle. Daily workouts are typically comprised of different modality and exercise combinations that are prescribed across a wide range of intensities and durations. The only consistent aspect appears to be the common instruction to maximize effort and workout density by either completing “as many repetitions as possible” within a time limit (e.g., AMRAP, Tabata) or a list of exercises as quickly as possible. However, because effort can vary within and across workouts, the impact on an athlete's physiology may also vary daily. Programming that fails to account for this variation or consider how targeted physiological systems interrelate may lead to overuse, maladaptation, or injury. Athletes may proactively monitor for negative training responses, but any observed response must be tied to a quantifiable workload before meaningful changes (to programming) are possible. Though traditional methods exist for quantifying the resistance training loads, gymnastic movements, and cardiorespiratory modalities (e.g., cycling running) that might appear in a typical HIFT workout, those methods are not uniform, and their meaning will vary based on a specific exercise's placement within a HIFT workout. To objectively quantify HIFT workloads, the calculation must overcome differences in measurement standards used for each modality, be able to account for a component's placement within the workout and be useful regardless of how a workout is commonly scored (e.g., repetitions completed vs. time-to-completion) so that comparisons between workouts are possible. This review paper discusses necessary considerations for quantifying various HIFT workout components and structures, and then details the advantages and shortcomings of different methods used in practice and the scientific literature. Methods typically used in practice range from being excessively tedious and not conducive for making comparisons within or across workouts, to being overly simplistic, based on faulty assumptions, and inaccurate. Meanwhile, only a few HIFT-related studies have attempted to report relevant workloads and have predominantly relied on converting component and workout performance into a rate (i.e., repetitions per minute or second). Repetition completion rate may be easily and accurately tracked and allows for intra- and inter-workout comparisons. Athletes, coaches, and sports scientists are encouraged to adopt this method and potentially pair it with technology (e.g., linear position transducers) to quantify HIFT workloads. Consistent adoption of such methods would enable more precise programming alterations, and it would allow fair comparisons to be made between existing and future research. Frontiers Media S.A. 2022-10-14 /pmc/articles/PMC9613943/ /pubmed/36311217 http://dx.doi.org/10.3389/fspor.2022.949429 Text en Copyright © 2022 Mangine and Seay. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Sports and Active Living
Mangine, Gerald T.
Seay, Tucker R.
Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title_full Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title_fullStr Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title_full_unstemmed Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title_short Quantifying CrossFit(®): Potential solutions for monitoring multimodal workloads and identifying training targets
title_sort quantifying crossfit(®): potential solutions for monitoring multimodal workloads and identifying training targets
topic Sports and Active Living
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613943/
https://www.ncbi.nlm.nih.gov/pubmed/36311217
http://dx.doi.org/10.3389/fspor.2022.949429
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