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Enregistrement W2332655997 · doi:10.1519/jsc.0000000000001249

Greater Electromyographic Responses Do Not Imply Greater Motor Unit Recruitment and ‘Hypertrophic Potential’ Cannot Be Inferred

2015· article· en· W2332655997 sur OpenAlexaff
Andrew D. Vigotsky, Chris Beardsley, Bret Contreras, James Steele, Dan Ogborn, Stuart M. Phillips

Notice bibliographique

RevueThe Journal of Strength and Conditioning Research · 2015
Typearticle
Langueen
DomaineEngineering
ThématiqueMuscle activation and electromyography studies
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMotor unitElectromyographyMistakePhysical medicine and rehabilitationMotor unit recruitmentMedicinePsychologyAnatomy

Résumé

récupéré en direct d'OpenAlex

To the Editor: We read with interest the study by Looney et al. (13), investigating the effects of load on electromyographic (EMG) amplitude and rating of perceived exertion (RPE) during squats taken to muscular failure. There are numerous interesting takeaways from this study, including the similar RPE outcomes of different loads when sets are taken to failure; however, we demur with the authors' interpretation of the findings. In the title and the body of the article, the term motor unit (MU) recruitment is used synonymously with EMG amplitude. This is an incorrect assumption, but regrettably a common mistake in sports and exercise science. We find this mistake being made especially when dealing with fatiguing and dynamic conditions, such as those investigated by Looney et al. (13). In fact, Enoka and Duchateau (7) recently described how numerous studies have misinterpreted surface EMG signals by inferring specific MU recruitment. More than 2 decades previously, De Luca (4) stated, “To its detriment, electromyography is too easy to use and consequently too easy to abuse.” Looney et al. (13) state that MU firing rate decreases with fatigue (10,15) and consequently that the increase in EMG amplitude is caused by increased MU recruitment (19–21) and has applied that same logic to the subsequent interpretation of the findings, as the authors repeatedly state that the greater EMG amplitude observed in the heavier conditions is indicative of greater MU recruitment. Regrettably, the interpretation of EMG is not so straightforward. Moreover, different quadriceps muscles may use different neural strategies to maintain force generation during repeated concentric contractions (6), which makes the findings of Looney et al. (13) particularly difficult to interpret. Although EMG amplitude is influenced by MU recruitment, MU recruitment cannot be inferred from changes in surface EMG amplitude. The recruitment threshold of high threshold MUs is reduced during sustained, fatiguing contractions (1), and the subsequent recruitment of these MUs assists in the maintenance force production. However, MU cycling may momentarily derecruit fatigued MUs to reduce fatigue (22). This means that, in scenarios that require less force output, such as low-load conditions, there may be lower simultaneous MU recruitment compared with high-load conditions. Ultimately, a comparable complement of the MU population of a particular muscle may be recruited, but not simultaneously as in high-load conditions. This would explain the observation of reduced peak EMG amplitude in low-load training, as reported by Looney et al. (13). These factors, including the reduced recruitment threshold of high threshold MUs, in addition to MU cycling during fatiguing contractions, may also explain other recent work showing differences in peak amplitude measured during surface EMG for high-load and low-load conditions (12,16). Electromyographic amplitude during fatiguing conditions can be extraordinarily misleading, as EMG measures consist not only of multiple neural components (MU recruitment, rate coding, and possibly MU synchronization) but also of multiple peripheral constituents: muscle fiber propagation velocity and intracellular action potentials (5). Intracellular action potentials are of particular interest during fatiguing conditions, as the ensuing increase in length of intracellular action potentials may augment surface EMG signals, despite a decrease in intracellular action potential magnitude. These inherent limitations make it impossible to discern MU recruitment from increases in EMG amplitude during fatiguing, dynamic conditions (2,5,8,9). It may be true that greater loads induce greater MU recruitment, but to measure this, more advanced methods are needed, such as spike-triggered averaging (3) or initial wavelet analysis followed by principal component classification of major frequency properties and optimization to tune wavelets to these frequencies (11). In addition to our concerns regarding the confusion of EMG amplitude with MU recruitment, we note that inferring chronic adaptations from acute, mechanistic variables is very difficult. Looney et al. (13) suggest that their findings support the use of heavier loads for hypertrophy. Such a conclusion is unwarranted, as the literature does not currently differentiate between the long-term effects of heavy and light loads on increases in muscular size (18). Data from Mitchell et al. (14) also demonstrated comparable growth of type I and II fibers after 10 weeks of strength training at either low (30% 1 repetition maximum [1RM]) or high-loads (80% 1RM). If the differential EMG amplitude between high and low-load training observed by Looney et al. (13) and others (12,16) is representative of greater recruitment of presumably high threshold MUs, then one would expect a differential hypertrophic response between low and high threshold MUs, which is presently not supported. In fact, from an evidence-based perspective, Schoenfeld et al. (18), in their meta-analysis, showed no difference between studies that have used lighter or heavier loads to induce hypertrophy. A recent study by the same author confirmed that this was true even in well trained participants (17). Thus, longitudinal trials are clearly needed to elucidate these mechanisms, in addition to comparing individual loading with combined loading schemes. The findings of Looney et al. (13) provide more data that unequal EMG amplitudes are obtained during fatiguing contractions with low-load and high-load conditions and the novel finding that both conditions elicit similar RPE. What these data do not provide, however, is evidence that heavier load contractions recruit more MUs and that this can be inferred to result in greater hypertrophy. We hope that our letter helps put these findings into a clearer perspective.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,746
Score d'incertitude au seuil0,400

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,097
Tête enseignante GPT0,323
Écart entre enseignants0,226 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations47
Publié2015
Routes d'admission1
Résumé présentoui

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