Influence of Lower Extremity Muscle Size and Quality on Stair-Climb Performance in Career Firefighters

The purpose of this study was to examine the influence of lower extremity muscular size and quality on stair-climb performance (SCP) in career firefighters. Forty-six male career firefighters (age = 37.0 ± 7.2 years; stature = 180.2 ± 6.9 cm; body mass = 108.0 ± 19.8 kg) volunteered for this study. Panoramic ultrasound images of the vastus lateralis and rectus femoris were obtained to determine cross-sectional area (CSA) and echo intensity (EI) of each muscle. The CSA of each muscle was then summed together and normalized to body mass (CSA/BM [QCSA]). Additionally, EI was averaged across both muscles (QEI). Participants then performed a timed and weighted SCP assessment where they ascended and descended 26 stairs 4 times as quickly as possible while wearing a weighted vest (22.73 kg) to simulate the weight of their self-contained breathing apparatus and turnout gear. Bivariate correlations and stepwise regression analyses were used to examine the relationships among variables and the relative contributions of QCSA and QEI to SCP. Partial correlations were used to examine the relationship between QCSA and SCP and QEI and SCP while controlling for age and body mass index (BMI). The results indicated that QCSA and QEI were significantly related to SCP before (r = -0.492, p = 0.001; r = 0.363, p = 0.013, respectively) and after accounting for age and BMI (r = -0.324, p = 0.032; r = 0.413, p = 0.005, respectively). Both QCSA and QEI contributed significantly to the prediction of SCP (r = 0.560, p < 0.001). These findings indicate that lower extremity muscle size and quality are important contributors to critical firefighting tasks, which have been shown to be improved with resistance training

Cognitive Fatigue Influences Time-On-Task during Bodyweight Resistance Training Exercise

Prior investigations have shown measurable performance impairments on continuous physical performance tasks when preceded by a cognitively fatiguing task. However, the effect of cognitive fatigue on bodyweight resistance training exercise task performance is unknown. In the current investigation 18 amateur athletes completed a full body exercise task preceded by either a cognitive fatiguing or control intervention. In a randomized repeated measure design, each participant completed the same exercise task preceded by a 52 minute cognitively fatiguing intervention (vigilance) or control intervention (video). Data collection sessions were separated by 1 week. Participants rated the fatigue intervention as being significantly more mentally demanding than the control intervention (p .05). There was no statistical difference for heart rate or metabolic expenditure as a function of fatigue intervention during exercise. Cognitively fatigued athletes have decreased time-on-task in bodyweight resistance training exercise tasks

Heart Rate Variability Spectral Parameters Across the Menstrual Cycle

Heart Rate Variability Spectral Parameters Across the Menstrual Cycle Andrew Tweedell1 (Undergraduate), Matthew Tenan1 (MA, ATC) Anthony Hackney2 (PhD, DSc, FACSM), Matthew Brothers1 (PhD), Lisa Griffin1 (PhD) 1 Neuromuscular Physiology Laboratory, Department of Kinesiology, University of Texas at Austin; 2 Applied Physiology Laboratory, Department of Exercise and Sport Science, University of North Carolina at Chapel Hill Heart rate variability (HRV) is a measure of autonomic nervous system function. Absolute low frequency (LF) and high frequency (HF) and the ratio of these components (LH/HF) are used as measures of HRV. Gonadotropin hormones may affect autonomic nervous system function; however, no difference in HRV across the menstrual cycle has been found while participants were in a supine position and breathing spontaneously. The purpose of this study was to investigate different heart rate variability power spectral components across the menstrual cycle while participants were seated and breathing spontaneously. Method: Five women volunteered for this experiment. A month prior to testing, they measured basal body temperature daily to map the timing of the menstrual cycle phases. Participants were tested once in each cycle phase. A three lead electrode arrangement recorded electrocardiographic data, sampled at 1000 Hz. A piezoelectric force transducer was placed around the participant’s chest to concurrently record breathing rate. The test consisted of a 15 minute rest period at an upright seated position. Following the rest period, 5 minutes of ECG activity was recorded while the participant remained in a seated position and breathed spontaneously. A power spectral density analysis was performed on the R-R interval variations by the fast Fourier transformation method using ECGlab. The spectrum was reduced to absolute and normalized LF, HF and LH/HF. Results: There were no significant differences in the absolute LF (p=0.7944), normalized LF (p=0.4449), absolute HF (p=0.4805), normalized HF (p=0.3994), or the LF/HF ratio (p=0.6315) across the five phases of the menstrual cycle. The breathing rate also showed no significant differences (p=0.2116). Conclusions: HRV did not change across the cycle when collected in a seated position. However, slow breathing rates (\u3c10 breaths per\u3eminute) can increase low frequency power. Participant breathing rate for this study ranged from 6.6 - 23.6 breaths per minute. It is possible that the slow breathing rates caused a false increase in LF and LF/HF ratio. While confirming previous reports of HRV changes across the menstrual cycle, follow-up research should investigate seated HRV changes across the menstrual cycle in a controlled breathing condition

Relationships Between Neuromuscular Function and Functional Balance Performance in Firefighters

The purpose of the present study was to examine the relationships between neuromuscular function and functional balance performance in firefighters. Fifty career firefighters (35.1±7.5yr) performed isometric leg extension and flexion muscle actions to examine peak torque (PT), and absolute (aTQ) and normalized (nTQ; %PT) rapid torque variables at 50, 100, 150, and 200ms. A performance index (PI) was determined from the functional balance assessment completion time. Partial correlations were used to examine the relationship between the PI and the maximal and rapid TQ variables for each muscle and the composite value, while controlling for demographic data related to the PI. Multiple regression analyses examined the relative contributions of the maximal and rapid aTQ variables, and demographic data on the PI. After controlling for age and %BF, the majority of the later aTQ and nTQ variables (100– 200ms) and PT were associated with the PI (r=−0.501–−0.315). Age, %BF, and aTQ100 explained 42– 50% of the variance in the PI. Lower rapid strength, increased age, and poorer body composition were related to worse performance during the functional balance assessment. Strategies to improve rapid strength and %BF, especially in aging firefighters may impact dynamic balance abilities in firefighters

Christianity as Public Religion::A Justification for using a Christian Sociological Approach for Studying the Social Scientific Aspects of Sport

The vast majority of social scientific studies of sport have been secular in nature and/or have tended to ignore the importance of studying the religious aspects of sport. In light of this, Shilling and Mellor (2014) have sought to encourage sociologists of sport not to divorce the ‘religious’ and the ‘sacred’ from their studies. In response to this call, the goal of the current essay is to explore how the conception of Christianity as ‘public religion’ can be utilised to help justify the use of a Christian sociological approach for studying the social scientific aspects of sport. After making a case for Christianity as public religion, we conclude that many of the sociological issues inherent in modern sport are an indirect result of its increasing secularisation and argue that this justifies the need for a Christian sociological approach. We encourage researchers to use the Bible, the tools of Christian theology and sociological concepts together, so to inform analyses of modern sport from a Christian perspective

High-density surface and intramuscular EMG recordings of the tibialis anterior muscle during isolated, dynamic contractions

Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and to developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for years, there is still a lack of adaptability to dynamic environments, due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126 electrode surface EMG grid. This data set can be used to i) validate methods for extracting neural signals from surface EMG, ii) develop models for predicting joint torque output, or iii) develop classifiers for human movement intent

High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions

Abstract Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for mapping neural input to mechanical output, there is a lack of adaptability to dynamic environments due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126-electrode surface EMG grid. This data set can be used to (i) validate methods for extracting neural signals from surface EMG, (ii) develop models for predicting torque output, or (iii) develop classifiers for movement intent

Analysis of statistical and standard algorithms for detecting muscle onset with surface electromyography.

The timing of muscle activity is a commonly applied analytic method to understand how the nervous system controls movement. This study systematically evaluates six classes of standard and statistical algorithms to determine muscle onset in both experimental surface electromyography (EMG) and simulated EMG with a known onset time. Eighteen participants had EMG collected from the biceps brachii and vastus lateralis while performing a biceps curl or knee extension, respectively. Three established methods and three statistical methods for EMG onset were evaluated. Linear envelope, Teager-Kaiser energy operator + linear envelope and sample entropy were the established methods evaluated while general time series mean/variance, sequential and batch processing of parametric and nonparametric tools, and Bayesian changepoint analysis were the statistical techniques used. Visual EMG onset (experimental data) and objective EMG onset (simulated data) were compared with algorithmic EMG onset via root mean square error and linear regression models for stepwise elimination of inferior algorithms. The top algorithms for both data types were analyzed for their mean agreement with the gold standard onset and evaluation of 95% confidence intervals. The top algorithms were all Bayesian changepoint analysis iterations where the parameter of the prior (p0) was zero. The best performing Bayesian algorithms were p0 = 0 and a posterior probability for onset determination at 60-90%. While existing algorithms performed reasonably, the Bayesian changepoint analysis methodology provides greater reliability and accuracy when determining the singular onset of EMG activity in a time series. Further research is needed to determine if this class of algorithms perform equally well when the time series has multiple bursts of muscle activity

Torque Estimation Using Neural Drive for a Concentric Contraction

© 2020 IEEE. The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. A challenge across these research areas is improving user-interface control. One established approach is using neural control interfaces derived from surface electromyography (sEMG). Although there has been some success with sEMG controlled prosthetics, the coarse nature of traditional sEMG processing has limited the development of fully functional prosthetics and wearable robotics. To solve this problem, blind source separation (BSS) techniques have been implemented to extract the user's movement intent from high-density sEMG (HDsEMG) measurements; however, current methods have only been well validated during static, low-level muscle contractions, and it is unclear how they will perform during movement. In this paper we present a neural drive based method for predicting output torque during a constant force, concentric contraction. This was achieved by modifying an existing HDsEMG decomposition algorithm to decompose 1 sec. overlapping windows. The neural drive profile was computed using both rate coding and kernel smoothing. Neither rate coding nor kernel smoothing performed as well as HDsEMG amplitude estimation, indicating that there are still significant limitations in adapting current methods to decompose dynamic contractions, and that sEMG amplitude estimation methods still remain highly reliable estimators