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Enregistrement W4403405113 · doi:10.3389/fphys.2024.1502939

Editorial: Muscle oxygenation and vascular adaptations in sports performance and rehabilitation

2024· editorial· en· W4403405113 sur OpenAlexaff
Rodrígo Sepúlveda, Daniel Rojas‐Valverde, José A. Parraça, François Billaut, Stéphane Perrey, Aldo A. Vasquez-Bonilla

Notice bibliographique

RevueFrontiers in Physiology · 2024
Typeeditorial
Langueen
DomaineMedicine
ThématiqueMusculoskeletal pain and rehabilitation
Établissements canadiensUniversité Laval
Organismes subventionnairesnon disponible
Mots-clésRehabilitationMedicinePhysical medicine and rehabilitationOxygenationPhysical therapyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Technological advancements in sports and health have enabled the indirect (and non-invasive) measurement of metabolism, blood flow, and oxygenation at the muscle level, during exercise. This has significantly enhanced our understanding of clinical physiology, as applied to physical activity.The use of near-infrared spectroscopy (NIRS, Perrey et al., 2024) to assess muscle oxygenation, alongside thermography to measure skin surface temperature (Tsk, Sillero-Quintana et al., 2021), has shown potential as tools for identifying physiological adaptations relevant to sport performance and health. However, scientific gaps remain regarding the use of NIRS sensor and Tsk data to ascertain metabolic and vascular enhancements derived from physical exercise. Furthermore, a significant milestone in sports science is the application of vascular methods in the functional rehabilitation of pathological conditions and injury sports. This editorial presents four studies using NIRS and Tsk in performance, health, and physical rehabilitation contexts.The first study, Arnold et al., examined the pattern and reliability of post-exercise reoxygenation using NIRS (Moxy Monitor, Fortiori Design LLC., Hutchinson, MN, United States). This was performed during an incremental cycling test across four muscle sites: the locomotor muscles vastus lateralis (VL) and rectus femoris (RF), as well as the accessory muscles lumbar paraspinal (PS) and lateral deltoid (DL). The results showed slower reoxygenation kinetics in response to increased workload in the VL, RF, and PS, but not in the DL. As expected, the VL, the primary muscle involved in cycling, demonstrated faster reoxygenation (Shibuya & Tanaka, 2003) and exhibited the greatest reliability than the accessory muscles. These findings show the utility of NIRS in the VL from a reoxygenation kinetics perspective (Maliszewski et al., 2024), suggesting that physiologists can indirectly assess the muscle oxidative capacity to recover from exercise and detect subtle adaptations in athletes (Billaut & Buchheit, 2013).The second study, conducted by Tandirerung et al. , used NIRS (Portamon, Artinis Medical System, Netherlands) with a short exercise and arterial occlusion protocol typically employed to measure mitochondrial capacity in endurance athletes and cardiovascular diseases indirectly (Jones et al., 2017). The novelty of this study lies in its focus on reproducibility in non-athletic adults aged 18 to 60, as skeletal muscle function declines with age and across various disease phenotypes, potentially leading to reduced physical performance, frailty, and loss of independence (Gomes et al., 2017).The results demonstrated good reliability for a "short-rapid" protocol, which involved performing rapid dynamic plantar flexions against a resistance band as many times as possible within 10 seconds. Following this exercise, short transient arterial occlusions were applied, lasting 5 to 8 seconds over a 3-minute period (5 seconds in the first minute and 8 seconds in the second and third minutes) to track muscle oxygen consumption recovery and estimate oxidative capacity from the recovery time constant (τ) (Southern et al., 2014). The method proposed in this study offers insights into tracking pathophysiological alterations in skeletal muscle function, which is necessary for understanding disease mechanisms, progression, and response to intervention (Coen et al., 2019).The findings are related to impaired blood flow transport capacity, reflected in NIRS values.The third study, conducted by Rubio-Zarapus et al., , explores the use of NIRS to investigate adaptations to two types of training (neuromodulation and high-intensity interval training) in fibromyalgia, which is recognized as a chronic disorder characterized by widespread musculoskeletal pain, premature fatigue, and cognitive impairment (Antunes & Marques, 2022).Muscle oxygen saturation (SmO2) at rest appears to be associated with improved strength performance and reduced pain in this population (Villafaina et al., 2023). However, evaluating only resting SmO2 without considering the SmO2 decrease as a target variable limits the understanding of blood flow redistribution and skeletal muscle metabolism. NIRS can be further applied to assess SmO2 dynamics during exercise, as impaired muscle oxygen utilization in individuals with fibromyalgia may affect daily activities such as walking and impact their quality of life (Shang et al., 2012). The study shows that monitoring SmO2 decreases with NIRS sensors could serve as a valuable, low-cost, non-invasive method to guide strength training and physical therapy in this population (Melian et al., 2021).The fourth study, conducted by Trovato et al., assessed thermography-derived knee Tsk to evaluate the effects of static and dynamic warm-ups, as well as a 90-degree change of direction exercise, on the temperature response. Thermal responses are related to blood flow dynamics, with vasodilation leading to an increase and vasoconstriction causing a decrease in Tsk (Brengelmann et al., 1977).The circulatory system's capacity, through blood vessels, to facilitate blood flow and deliver oxygen and nutrients to tissues and organs, such as skeletal muscles, makes monitoring Tsk valuable for insights in injury rehabilitation processes (Gómez-Carmona et al., 2020;Lamers et al., 2022).Despite some limitations-such as the lack of control over exercise intensity, a recreational sample group, and a generalized warm-up focused on the quadriceps-this study marks a significant step forward in sports science. It demonstrates how thermography can identify changes in the knee tissues based on temperature variation, showing that better physical preparation can be achieved through specific warm-up strategies. Coaches can use this information to customize warm-up protocols that optimize knee temperature to prepare athletes more effectively for subsequent performance and potentially reducing injury risks. Additionally, these findings could be helpful for researchers studying knee thermal responses after various intensity exercises and provide a better recovery tracking system. In summary, this research topic highlights the expanding role and diversification of NIRS and thermography in advancing our understanding of muscle oxygenation and vascular responses in the sport performance and rehabilitation contexts. By providing non-invasive, reliable, and costeffective methods to assess physiological changes, these technologies offer valuable insights for optimizing performance, guiding rehabilitation, and improving health outcomes in athletic and clinical populations. However, further research is needed to address current limitations and expand the applicability of these tools across diverse populations and training modalities. In the coming years, it is suggested to develop a position stand to unify the criteria for the use of these technologies to address the effects on performance and rehabilitation.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,014
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,017
Score d'incertitude au seuil0,056

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,014
Méta-épidémiologie (sens strict)0,0050,001
Méta-épidémiologie (sens large)0,0050,003
Bibliométrie0,0040,001
Études des sciences et des technologies0,0030,003
Communication savante0,0050,004
Science ouverte0,0040,001
Intégrité de la recherche0,0140,014
Charge utile insuffisante (le modèle a refusé de juger)0,0170,013

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,003
Tête enseignante GPT0,237
Écart entre enseignants0,234 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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