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Record W4403405113 · doi:10.3389/fphys.2024.1502939

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

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

Bibliographic record

VenueFrontiers in Physiology · 2024
Typeeditorial
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRehabilitationMedicinePhysical medicine and rehabilitationOxygenationPhysical therapyInternal medicine

Abstract

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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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.017
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.014
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0040.001
Science and technology studies0.0030.003
Scholarly communication0.0050.004
Open science0.0040.001
Research integrity0.0140.014
Insufficient payload (model declined to judge)0.0170.013

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.003
GPT teacher head0.237
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2024
Admission routes1
Has abstractyes

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