Faster intrinsic rate of torque development in elbow flexors than knee extensors: Effect of muscle architecture?

Cossich, Victor Rodrigues Amaral; Laett, Conrado Torres; Gavilão, Ubiratã Faleiro; Blazevich, Anthony J.; Oliveira, Carlos Gomes de

doi:10.1016/j.jelekin.2021.102570

Cited by 3 publications

(3 citation statements)

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“…They have a muscle-specific level of strength and quickness instead. This may be due to differences in morphological (muscle size and architecture) [ 45 ] and neural activation features [ 46 ] across muscles. Bishop and colleagues [ 9 ] highlighted the importance of reporting the agreement between asymmetry direction, as they noted that most previous studies did not mention the direction agreement among asymmetry metrics.…”

Section: Discussionmentioning

confidence: 99%

Strength Asymmetries Are Muscle-Specific and Metric-Dependent

Boccia

D’Emanuele

Brustio

et al. 2022

IJERPH

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We investigated if dominance affected upper limbs muscle function, and we calculated the level of agreement in asymmetry direction across various muscle-function metrics of two heterologous muscle groups. We recorded elbow flexors and extensors isometric strength of the dominant and non-dominant limb of 55 healthy adults. Participants performed a series of explosive contractions of maximal and submaximal amplitudes to record three metrics of muscle performance: maximal voluntary force (MVF), rate of force development (RFDpeak), and RFD-Scaling Factor (RFD-SF). At the population level, the MVF was the only muscle function that showed a difference between the dominant and non-dominant sides, being on average slightly (3–6%) higher on the non-dominant side. At the individual level, the direction agreement among heterologous muscles was poor for all metrics (Kappa values ≤ 0.15). When considering the homologous muscles, the direction agreement was moderate between MVF and RFDpeak (Kappa = 0.37) and low between MVF and RFD-SF (Kappa = 0.01). The asymmetries are muscle-specific and rarely favour the same side across different muscle-performance metrics. At the individual level, no one side is more performative than the other: each limb is favoured depending on muscle group and performance metric. The present findings can be used by practitioners that want to decrease the asymmetry levels as they should prescribe specific exercise training for each muscle.

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Section: Discussionmentioning

confidence: 99%

Strength Asymmetries Are Muscle-Specific and Metric-Dependent

Boccia

D’Emanuele

Brustio

et al. 2022

IJERPH

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“…To minimize the dampening effect occurring at the interface between the shin and the lever, and simultaneously to avoid pain, a standard soccer hard shin guard was used [1,22]. Our group has used this same procedure in previous investigations [1,10,16,22]. An accelerometer (EMG 830c ® , EMG System do Brasil, São José dos Campos, SP, Brazil) was placed on the shin guard to measure ACC signals in the x, y, and z axes (measured in g, Figure 1).…”

Section: Explosive Isometric Contractions (Strength and Acceleration ...mentioning

confidence: 99%

“…The former is mainly affected by mechanisms involving neural activation transmitted by motor neurons to muscles [1,2,[5][6][7], and the latter is strongly linked to maximal strength and is influenced by similar underlying mechanisms [4,8,9]. Consequently, a higher RTD indicates a faster and more efficient neuromuscular response, determined by motor unit activation rate (i.e., higher neural drive increases the explosive capacity) and muscle morphology (i.e., muscle thickness and muscle architecture) [1,[10][11][12]. RTD extends our capacity to evaluate the maximal force-generating capacity and plays a pivotal role in activities requiring quick muscle contractions and short-duration execution, such as jumping, sprinting, or weightlifting [13][14][15][16][17], as it reflects the muscles' capability to swiftly generate force during the very initial phase of muscle contraction [1,2].…”

Section: Introductionmentioning

confidence: 99%

AI-Enhanced Prediction of Peak Rate of Torque Development from Accelerometer Signals

Cossich,

Katz,

Laett

2024

Applied Sciences

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This study explores the use of accelerometer signals as the predictors of Rate of Torque Development (RTD) using an artificial neural network (ANN) prediction model. Sixteen physically active men participated (29 ± 5 years), performing explosive isometric contractions while acceleration (ACC) signals were measured. The dataset, comprising ACC signals and corresponding RTD values, was split into training and testing (70–30%) sets for ANN training. The trained model predicted the peak RTD values from the ACC signal inputs. The measured and predicted peak RTD values were compared, with no significant differences observed (p = 0.852). A strong linear fit (R² = 0.81), ICC = 0.94 (p < 0.001), and a mean bias of 30.8 Nm/s demonstrated almost perfect agreement between measures. The study demonstrates the feasibility of using accelerometer data to predict peak RTD, offering a portable and cost-effective method compared to traditional equipment. The ANN prediction model provides a reliable means of estimating RTD from ACC signals, potentially enhancing accessibility to RTD assessment in sports and rehabilitation settings. The findings support the use of ANN models for predicting RTD, highlighting the potential of AI in developing performance analysis tools.

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