METHODS: This study investigated the effects of two different training interventions based on individualized load velocity profiles (LVP) on maximal bench press strength (i.e., 1RM), maximum throwing velocity (TV), and skeletal muscle mass (SKMM). Twenty-two university handball players were randomly assigned to Group 1 (low-movement speed training) or Group 2 (high-movement speed training). Group 1 exercised with a bar speed of 0.75-0.96 m/s, which corresponds to a resistance of approximately 60% 1RM, whereas Group 2 trained at 1.03-1.20 m/s, corresponding to a resistance of approximately 40% 1RM. Both groups exercised three times a week for five weeks, with strength and throwing tests performed at baseline and post-intervention.
RESULTS: A two-way repeated measures ANOVA was applied, and the results showed the interaction between group and time was not statistically significant for SKMM (p = 0.537), 1RM (p = 0.883), or TV (p = 0.774). However, both groups significantly improved after the five weeks of training: SKMM (3.1% and 3.5%, p
METHODOLOGY: Studies were identified by searching the SCOPUS, SPORTDiscus, PubMed, Web of Science, and CNKI databases up to May 13, 2024, using the following inclusion criteria: (a) healthy population; (b) comparison of LL-BFR vs HLR training; (c) pre- and post-training assessment of muscle strength (dynamic, isometric, and isokinetic), muscle power, jump, or speed performance; (d) PEDro scale score ≥4. The methodological quality of the included studies was assessed using the PEDro tool and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach, with meta-analyses conducted using the R program.
RESULTS: A total of 41 studies, involving 853 subjects, were included in the meta-analysis. Based on the PEDro scores and GRADE assessment, the overall quality of the included studies was assessed as moderate. LL-BFR training showed a slightly smaller effect on maximal strength compared to HLR training (ES = -0.19, 95% CI [-0.31 to -0.06], p < 0.01). There were no significant differences between LL-BFR and HLR training for muscle power (ES = -0.04, 95% CI [-0.33 to 0.24], p > 0.05), jump performance (ES = -0.08, 95% CI [-0.30 to 0.15], p > 0.05), and speed (ES = -0.28, 95% CI [-0.71 to 0.15], p > 0.05). Additionally, individual characteristics (i.e., age, gender, and training status) and training parameters (i.e., training duration, frequency, cuff pressure, and cuff width) did not significantly moderate the training effect.
CONCLUSIONS: LL-BFR training showed slightly less improvement in maximal strength compared to HLR training but demonstrated comparable effects on muscle power, jump performance, and speed in healthy individuals in healthy individuals. These findings suggest that LL-BFR may be a practical and effective alternative for individuals seeking performance improvements with lower training loads.
METHODOLOGY/PRINCIPAL FINDINGS: Five electronic databases were extensively searched for potentially eligible studies published between 2003 and 2012. Two authors independently assessed selected articles using an MS-Word based form created for this review. Several domains (name of muscle, study type, sensor type, subject's types, muscle contraction, measured parameters, frequency range, hardware and software, signal processing and statistical analysis, results, applications, authors' conclusions and recommendations for future work) were extracted for further analysis. From a total of 2184 citations 119 were selected for full-text evaluation and 36 studies of MFs were identified. The systematic results find sufficient evidence that MMG may be used for assessing muscle fatigue, strength, and balance. This review also provides reason to believe that MMG may be used to examine muscle actions during movements and for monitoring muscle activities under various types of exercise paradigms.
CONCLUSIONS/SIGNIFICANCE: Overall judging from the increasing number of articles in recent years, this review reports sufficient evidence that MMG is increasingly being used in different aspects of MF. Thus, MMG may be applied as a useful tool to examine diverse conditions of muscle activity. However, the existing studies which examined MMG for MFs were confined to a small sample size of healthy population. Therefore, future work is needed to investigate MMG, in examining MFs between a sufficient number of healthy subjects and neuromuscular patients.
METHODS: Twenty-five subjects performed isometric elbow extension until failure, and the rate of fatigue (ROF), time to fatigue (TTF) and normalized TTF (NTTF) were statistically analysed. Subsequently, the behaviour of root-mean-square (RMS), mean-power frequency (MPF) and median-power frequency (MDF) under pre-, onset- and post-fatigue conditions were compared.
RESULTS: The findings indicated that, among the heads, ROF was statistically significant at 30% and 45% MVC (P<0.05) but TTF and NTTF at all intensities was statistically insignificant (P>0.05). For every head, only TTF was statistically significant (P<0.05) at different intensities. MPF and MDF under pre-, onset- and post-fatigue conditions were statistically significant (P<0.05) among the heads at all intensities, whereas RMS showed no such behaviour.
CONCLUSION: The investigated parameters reveal that the three heads of TB act independently before fatigue onset and appear to work in union after fatigue. Synergist head pairs exhibit similar spectral and temporal behaviour in contrast to the non-synergist TB head pair. We find spectral parameters to be more specific predictors of fatigue.
METHODS: An initial search of the SCOPUS database using an appropriate set of keywords yielded 290 studies, and 59 potential studies were selected after all the records were screened using the eligibility criteria. This review on crosstalk revealed that signal contamination due to crosstalk remains a major challenge in the application of surface myography techniques. Various methods have been employed in previous studies to identify, quantify and reduce crosstalk in surface myographic signals.
RESULTS: Although correlation-based methods for crosstalk quantification are easy to use, there is a possibility that co-contraction could be interpreted as crosstalk. High-definition EMG has emerged as a new technique that has been successfully applied to reduce crosstalk.
CONCLUSIONS: The phenomenon of crosstalk needs to be investigated carefully because it depends on many factors related to muscle task and physiology. This review article not only provides a good summary of the literature on crosstalk in myographic signals but also discusses new directions related to techniques for crosstalk identification, quantification and reduction. The review also provides insights into muscle-related issues that impact crosstalk in myographic signals.
OBJECTIVE: The aim of this study was to compare the activity and relationship between surface EMG and static force from the BB muscle in terms of three sensor placement locations.
METHODS: Twenty-one right hand dominant male subjects (age 25.3±1.2 years) participated in the study. Surface EMG signals were detected from the subject's right BB muscle. The muscle activation during force was determined as the root mean square (RMS) electromyographic signal normalized to the peak RMS EMG signal of isometric contraction for 10 s. The statistical analysis included linear regression to examine the relationship between EMG amplitude and force of contraction [40-100% of maximal voluntary contraction (MVC)], repeated measures ANOVA to assess differences among the sensor placement locations, and coefficient of variation (CoV) for muscle activity variation.
RESULTS: The results demonstrated that when the sensor was placed on the muscle belly, the linear slope coefficient was significantly greater for EMG versus force testing (r2=0.62, P<0.05) than when placed on the lower part (r2=0.31, P>0.05) and upper part of the muscle belly (r2=0.29, P<0.05). In addition, the EMG signal activity on the muscle belly had less variability than the upper and lower parts (8.55% vs. 15.12% and 12.86%, respectively).
CONCLUSION: These findings indicate the importance of applying the surface EMG sensor at the appropriate locations that follow muscle fiber orientation of the BB muscle during static contraction. As a result, EMG signals of three different placements may help to understand the difference in the amplitude of the signals due to placement.