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Overtraining Syndrome as a Complex Systems Phenomenon
The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments r...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013019/ https://www.ncbi.nlm.nih.gov/pubmed/36925581 http://dx.doi.org/10.3389/fnetp.2021.794392 |
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author | Armstrong, Lawrence E. Bergeron, Michael F. Lee, Elaine C. Mershon, James E. Armstrong, Elizabeth M. |
author_facet | Armstrong, Lawrence E. Bergeron, Michael F. Lee, Elaine C. Mershon, James E. Armstrong, Elizabeth M. |
author_sort | Armstrong, Lawrence E. |
collection | PubMed |
description | The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS. |
format | Online Article Text |
id | pubmed-10013019 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100130192023-03-15 Overtraining Syndrome as a Complex Systems Phenomenon Armstrong, Lawrence E. Bergeron, Michael F. Lee, Elaine C. Mershon, James E. Armstrong, Elizabeth M. Front Netw Physiol Network Physiology The phenomenon of reduced athletic performance following sustained, intense training (Overtraining Syndrome, and OTS) was first recognized more than 90 years ago. Although hundreds of scientific publications have focused on OTS, a definitive diagnosis, reliable biomarkers, and effective treatments remain unknown. The present review considers existing models of OTS, acknowledges the individualized and sport-specific nature of signs/symptoms, describes potential interacting predisposing factors, and proposes that OTS will be most effectively characterized and evaluated via the underlying complex biological systems. Complex systems in nature are not aptly characterized or successfully analyzed using the classic scientific method (i.e., simplifying complex problems into single variables in a search for cause-and-effect) because they result from myriad (often non-linear) concomitant interactions of multiple determinants. Thus, this review 1) proposes that OTS be viewed from the perspectives of complex systems and network physiology, 2) advocates for and recommends that techniques such as trans-omic analyses and machine learning be widely employed, and 3) proposes evidence-based areas for future OTS investigations, including concomitant multi-domain analyses incorporating brain neural networks, dysfunction of hypothalamic-pituitary-adrenal responses to training stress, the intestinal microbiota, immune factors, and low energy availability. Such an inclusive and modern approach will measurably help in prevention and management of OTS. Frontiers Media S.A. 2022-01-18 /pmc/articles/PMC10013019/ /pubmed/36925581 http://dx.doi.org/10.3389/fnetp.2021.794392 Text en Copyright © 2022 Armstrong, Bergeron, Lee, Mershon and Armstrong. 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 | Network Physiology Armstrong, Lawrence E. Bergeron, Michael F. Lee, Elaine C. Mershon, James E. Armstrong, Elizabeth M. Overtraining Syndrome as a Complex Systems Phenomenon |
title | Overtraining Syndrome as a Complex Systems Phenomenon |
title_full | Overtraining Syndrome as a Complex Systems Phenomenon |
title_fullStr | Overtraining Syndrome as a Complex Systems Phenomenon |
title_full_unstemmed | Overtraining Syndrome as a Complex Systems Phenomenon |
title_short | Overtraining Syndrome as a Complex Systems Phenomenon |
title_sort | overtraining syndrome as a complex systems phenomenon |
topic | Network Physiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013019/ https://www.ncbi.nlm.nih.gov/pubmed/36925581 http://dx.doi.org/10.3389/fnetp.2021.794392 |
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