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Adjustment Effects of Maximum Intensity Tolerance During Whole-Body Electromyostimulation Training
(2019)
Intensity regulation during whole-body electromyostimulation (WB-EMS) training is mostly controlled by subjective scales such as CR-10 Borg scale. To determine objective training intensities derived from a maximum as it is used in conventional strength training using the one-repetition-maximum (1-RM), a comparable maximum in WB-EMS is necessary. Therefore, the aim of this study was to examine, if there is an individual maximum intensity tolerance plateau after multiple consecutive EMS application sessions. A total of 52 subjects (24.1 ± 3.2 years; 76.8 ± 11.1 kg; 1.77 ± 0.09 m) participated in the longitudinal, observational study (38 males, 14 females). Each participant carried out four consecutive maximal EMS applications (T1–T4) separated by 1 week. All muscle groups were stimulated successively until their individual maximum and combined to a whole-body stimulation index to carry out a possible statement for the development of the maximum intensity tolerance of the whole body. There was a significant main effect between the measurement times for all participants (p < 0.001; ????2 = 0.39) as well as gender specific for males (p = 0.001; ????2 = 0.18) and females (p < 0.001; ????2 = 0.57). There were no interaction effects of gender × measurement time (p = 0.394). The maximum intensity tolerance increased significantly from T1 to T2 (p = 0.001) and T2 to T3 (p < 0.001). There was no significant difference between T3 and T4 (p = 1.0). These results indicate that there is an adjustment of the individual maximum intensity tolerance to a WB-EMS training after three consecutive tests. Therefore, there is a need of several habituation units comparable to the identification of the individual 1-RM in conventional strength training. Further research should focus on an objective intensity-specific regulation of the WB-EMS based on the individual maximum intensity tolerance to characterize different training areas and therefore generate specific adaptations to a WB-EMS training compared to conventional strength training methods.
The importance of well trained and stable neck flexors and extensors as well as trunk muscles for intentional headers in soccer is increasingly discussed. The neck flexors and extensors should ensure a coupling of trunk and head at the time of ball contact to increase the physical mass hitting the ball and reduce head acceleration. The aim of the study was to analyze the influence of a 6-week strength training program (neck flexors, neck extensors) on the acceleration of the head during standing, jumping and running headers as well as after fatigue of the trunk muscles on a pendulum header. A total of 33 active male soccer players (20.3 ± 3.6 years, 1.81 ± 0.07 m, 75.5 ± 8.3 kg) participated and formed two training intervention groups (IG1: independent adult team, IG2: independent youth team) and one control group (CG: players from different teams). The training intervention consisted of three exercises for the neck flexors and extensors. The training effects were verified by means of the isometric maximum voluntary contraction (IMVC) measured by a telemetric Noraxon DTS force sensor. The head acceleration during ball contact was determined using a telemetric Noraxon DTS 3D accelerometer. There was no significant change of the IMVC over time between the groups (F=2.265, p=.121). Head acceleration was not reduced significantly for standing (IG1 0.4 ± 2.0, IG2 0.1 ± 1.4, CG -0.4 ± 1.2; F = 0.796, p = 0.460), jumping (IG1-0.7 ± 1.4, IG2-0.2 ± 0.9, CG 0.1 ± 1.2; F = 1.272, p = 0.295) and running (IG1-1.0 ± 1.9, IG2-0.2 ± 1.4, CG -0.1 ± 1.6; F = 1.050, p = 0.362) headers as well as after fatigue of the trunk musculature for post-jumping (IG1-0.2 ± 2.1, IG2-0.6 ± 1.4; CG -0.6 ± 1.3; F = 0.184, p = 0.833) and post-running (IG1-0.3 ± 1.6, IG2-0.7 ± 1.2, CG 0.0 ± 1.4; F = 0.695, p = 0.507) headers over time between IG1, IG2 and CG. A 6-week strength training of the neck flexors and neck extensors could not show the presumed preventive benefit. Both the effects of a training intervention and the consequences of an effective intervention for the acceleration of the head while heading seem to be more complex than previously assumed and presumably only come into effect in case of strong impacts.
Key words: Heading, kinetics, head-neck-torso-alignment, neck musculature, repetitive head impacts, concussion
Muscular imbalances of the trunk muscles are held responsible for changes in body posture. At the same time, whole-body electromyostimulation (WB-EMS) has been established as a new training method that enables simultaneous stimulation of many muscle groups. This study was aiming to analyze if a 10 weeks WB-EMS training changes posture-relevant parameters and/or improves isometric strength of the trunk extensors and flexors, and if there are differences based on stimulation at 20 Hz and 85 Hz. Fifty eight untrained adult test persons were divided into three groups (control, CON; training with 20 Hz stimulation, TR20; training with 85 Hz, TR85). Anthropometric parameters, trunk extension and flexion forces and torques, and posture parameters were determined before (n = 58) and after (n = 53: CON: n = 15, TR20: n = 19, TR85: n = 19) a 10 weeks WB-EMS training program (15 applications, 9 exercises). Differences between the groups were calculated for pre- and post-tests using univariate ANOVA and between the test times using repeated (2 × 3) ANOVA. Comparisons of pairs were calculated post hoc based on Fisher (LSD). No differences between the groups were found for the posture parameters. The post hoc analysis of both trunk flexion and trunk extension forces and torques showed a significant difference between the groups TR85 and CON but no difference between the other group pairs. A 10 weeks whole-body electrostimulation training with a stimulation frequency of 85 Hz in contrast to training with a stimulation frequency of 20 Hz improves the trunk muscle strength of an untrained group but does not significantly change posture parameters.
Patients after total hip arthroplasty (THA) suffer from lingering musculoskeletal restrictions. Three-dimensional (3D) gait analysis in combination with machine-learning approaches is used to detect these impairments. In this work, features from the 3D gait kinematics, spatio temporal parameters (Set 1) and joint angles (Set 2), of an inertial sensor (IMU) system are proposed as an input for a support vector machine (SVM) model, to differentiate impaired and non-impaired gait. The features were divided into two subsets. The IMU-based features were validated against an optical motion capture (OMC) system by means of 20 patients after THA and a healthy control group of 24 subjects. Then the SVM model was trained on both subsets. The validation of the IMU system-based kinematic features revealed root mean squared errors in the joint kinematics from 0.24° to 1.25°. The validity of the spatio-temporal gait parameters (STP) revealed a similarly high accuracy. The SVM models based on IMU data showed an accuracy of 87.2% (Set 1) and 97.0% (Set 2). The current work presents valid IMU-based features, employed in an SVM model for the classification of the gait of patients after THA and a healthy control. The study reveals that the features of Set 2 are more significant concerning the classification problem. The present IMU system proves its potential to provide accurate features for the incorporation in a mobile gait-feedback system for patients after THA.
3D joint kinematics can provide important information about the quality of movements. Optical motion capture systems (OMC) are considered the gold standard in motion analysis. However, in recent years, inertial measurement units (IMU) have become a promising alternative. The aim of this study was to validate IMU-based 3D joint kinematics of the lower extremities during different movements. Twenty-eight healthy subjects participated in this study. They performed bilateral squats (SQ), single-leg squats (SLS) and countermovement jumps (CMJ). The IMU kinematics was calculated using a recently-described sensor-fusion algorithm. A marker based OMC system served as a reference. Only the technical error based on algorithm performance was considered, incorporating OMC data for the calibration, initialization, and a biomechanical model. To evaluate the validity of IMU-based 3D joint kinematics, root mean squared error (RMSE), range of motion error (ROME), Bland-Altman (BA) analysis as well as the coefficient of multiple correlation (CMC) were calculated. The evaluation was twofold. First, the IMU data was compared to OMC data based on marker clusters; and, second based on skin markers attached to anatomical landmarks. The first evaluation revealed means for RMSE and ROME for all joints and tasks below 3°. The more dynamic task, CMJ, revealed error measures approximately 1° higher than the remaining tasks. Mean CMC values ranged from 0.77 to 1 over all joint angles and all tasks. The second evaluation showed an increase in the RMSE of 2.28°– 2.58° on average for all joints and tasks. Hip flexion revealed the highest average RMSE in all tasks (4.87°– 8.27°). The present study revealed a valid IMU-based approach for the measurement of 3D joint kinematics in functional movements of varying demands. The high validity of the results encourages further development and the extension of the present approach into clinical settings.