A mechanomyographic fatigue threshold test for cycling

Grey Zone: A Gap Between Heavy and Severe Exercise Domain

ABSTRACT

Ozkaya, O, Balci, GA, As, H, Cabuk, R, and Norouzi, M. Grey zone: A gap between heavy and severe exercise domain. J Strength Cond Res 36(1): 113-120, 2022-The aim of this study was to determine a critical threshold (CT) interpreted as "the highest exercise intensity where V̇o2 can be stabilized before reaching 95% of V̇o2max (V̇o2peak)" and compare it with commonly used anaerobic threshold indices. Ten well-trained male cyclists volunteered for this study. Ventilatory threshold (VT) was determined from incremental tests. Multisession constant-load trials were performed to reveal V̇o2max. Mathematically modeled critical power (CP) was estimated through the best individual fit parameter method. Maximal lactate steady state (MLSS) was detected by 30-minute constant-load exercises. The individual CT load of each cyclist was tested by constant-load exercises to exhaustion with +15 W intervals until minimal power output to elicit V̇o2peak. The results showed that work rate corresponding to CT (329.5 ± 41.5 W) was significantly greater than that of the MLSS (269.5 ± 38.5 W; p = 0.000), VT (279.6 ± 33 W; p = 0.000), and CP (306.3 ± 39.4 W; p = 0.000), and CP overestimated both VT and MLSS (p = 0.000). There was no significant V̇o2 difference between the 10th and 30th minute of MLSS and MLSS + 15 W exercise (0.36-0.13 ml·min-1·kg-1; p = 0.621). Exercising V̇o2 response of MLSS + 15 W could not exceed the level of 95% V̇o2max (57.02 ± 3.87 ml·min-1·kg-1 and 87.2 ± 3.1% of V̇o2max; p = 0.000), whereas V̇o2 responses greater than 95% of V̇o2max were always attained during exercises performed at CT + 15 W (64.52 ± 4.37 ml·min-1·kg-1 and 98.6 ± 1% of V̇o2max; p > 0.05). In conclusion, this study indicates that there is a "grey zone" between heavy and severe exercise domain. This information may play a key role in enhancing athletic performance by improving the quality of training programs.

A new technique to analyse threshold-intensities based on time dependent change-points in the ratio of minute ventilation and end-tidal partial pressure of carbon-dioxide production

The aim of this study was to test the utility and effectiveness of an alternative computational approach to threshold-intensities based on time dependent change-points in minute ventilation divided by end-tidal partial pressure of CO₂ (V_E/P_ETCO₂) to reveal whether respiratory compensation point (RCP) is a third ventilatory threshold, or not. Ten recreationally active young adults and ten well-trained athletes volunteered to take part in this study. Following incremental ramp tests, gas exchange threshold (GET) and respiratory compensation point (RCP) were respectively evaluated by the slopes of VCO₂-VO₂ and V_E-VCO₂ using the Innocor system automatically. Respiratory threshold (RT) was analysed based on time dependent change-points in the V_E/P_ETCO₂ using binary segmentation algorithm. Additionally, those intersections were analysed independently by two experienced investigators using a visual identification technique in a double-blind design. According to the results, in the recreationally active group, there were the first (GET₁) and the second (GET₂) gas exchange thresholds which were identical with the RT₁ (139 W; 1.9 L⋅min^-1 of VO₂; 1.73 L⋅min^-1 of VCO₂; 49.9 L⋅min^-1 of V_E versus 139 W; 1.88 L⋅min^-1; 1.7 L⋅min^-1; 49 L⋅min^-1, respectively) and RT₂ (186 W; 2.39 L⋅min^-1 of VO₂; 2.44 L⋅min^-1 of VCO₂; 66 L⋅min^-1 of V_E versus 187 W; 2.41 L⋅min^-1; 2.49 L⋅min^-1; 65.7 L⋅min^-1, respectively). However, there were three threshold intensities which were determined by GET₁, GET₂, and RCP in well-trained athletes. Additionally, RT₁, RT₂, and RT₃ were determined as valid surrogates of the GET₁ (194 W; 2.56 L⋅min^-1 of VO₂; 1.99 L⋅min^-1 of VCO₂; 57.5 L⋅min^-1 of V_E versus 192 W; 2.61 L⋅min^-1; 1.99 Lmin^-1; 57.7 L⋅min^-1, respectively), GET₂ (267 W; 3.6 L⋅min^-1 of VO₂; 3.29 L⋅min^-1 of VCO₂; 94.5 L⋅min^-1 of V_E versus 266 W; 3.58 L⋅min^-1; 3.26 L⋅min^-1; 93.4 L⋅min^-1, respectively), and RCP (324 W; 4.05 L⋅min^-1 of VO₂; 4.13 L⋅min^-1 of VCO₂; 124 L⋅min^-1 of V_E versus 322 W; 4.02 L⋅min^-1; 4.07 L⋅min^-1; 122 L⋅min^-1, respectively) in well-trained athletes. There were high levels of agreements between the power outputs determined by traditional techniques and newly proposed change-points in RT. All markers were strongly correlated (p < 0.001). It was shown that RT technique can provide an accurate threshold determination. Furthermore, the RCP was observed as a third threshold-intensity for well-trained athletes but not for recreationally active young adults.

Effects of Curcumin and Fenugreek Soluble Fiber on the Physical Working Capacity at the Fatigue Threshold, Peak Oxygen Consumption, and Time to Exhaustion

Herrick, LP, Goh, J, Menke, W, Campbell, MS, Fleenor, BS, Abel, MG, and Bergstrom, HC. Effects of curcumin and fenugreek soluble fiber on the physical working capacity at the fatigue threshold, peak oxygen consumption, and time to exhaustion. J Strength Cond Res 34(12): 3346-3355, 2020-The purpose of this study was to examine the effects of curcumin in combination with fenugreek soluble fiber (CUR + FEN) or fenugreek soluble fiber alone (FEN) on the neuromuscular fatigue threshold (PWCFT), peak oxygen consumption (V˙o2peak), and time to exhaustion (Tlim) on a graded exercise test (GXT), in untrained subjects. The PWCFT estimates the highest power output that can be maintained without evidence of neuromuscular fatigue. Forty-seven untrained, college-aged subjects were randomly assigned to one of 3 supplementation groups; placebo (PLA, n = 15), CUR + FEN (500 mg·d, n = 18), or FEN (300 mg·d, n = 14). The subjects completed a maximal GXT on a cycle ergometer to determine the PWCFT, V˙o2peak, and Tlim before (PRE) and after (POST) 28 days of daily supplementation. Surface electromyographic signals were recorded from a bipolar electrode arrangement on the vastus lateralis of the right leg during each test. Separate one-way analysis of covariances were used to determine if there were between-group differences for adjusted POST-PWCFT, POST-V˙o2peak, and POST-Tlim values, covaried for the respective PRE-test scores. The adjusted POST-PWCFT for the CUR + FEN group (mean ± SD: 196 ± 58 W) was greater (p = 0.016) than the PLA group (168 ± 49 W) but the FEN group (185 ± 32 W) was not different from the CUR + FEN or PLA groups (p > 0.05). There were no differences for adjusted POST-V˙o2peak (p = 0.612) or POST-Tlim (p = 0.508) among the groups. These findings suggested curcumin combined with fenugreek soluble fiber might delay neuromuscular fatigue.

Wet, volatile, and dry biomarkers of exercise-induced muscle fatigue

Background

The physiological background of exercise-induced muscle fatigue(EIMUF) is only poorly understood. Thus, monitoring of EIMUF by a single or multiple biomarkers(BMs) is under debate.

After a systematic literature review 91 papers were included.

Results

EIMUF is mainly due to depletion of substrates, increased oxidative stress, muscle membrane depolarisation following potassium depletion, muscle hyperthermia, muscle damage, impaired oxygen supply to the muscle, activation of an inflammatory response, or impaired calcium-handling. Dehydration, hyperammonemia, mitochondrial biogenesis, and genetic responses are also discussed. Since EIMUF is dependent on age, sex, degree of fatigue, type, intensity, and duration of exercise, energy supply during exercise, climate, training status (physical fitness), and health status, BMs currently available for monitoring EIMUF have limited reliability. Generally, wet, volatile, and dry BMs are differentiated. Among dry BMs of EIMUF the most promising include power output measures, electrophysiological measures, cardiologic measures, and questionnaires. Among wet BMs of EIMUF those most applicable include markers of ATP-metabolism, of oxidative stress, muscle damage, and inflammation. VO₂-kinetics are used as a volatile BM.

Conclusions

Though the physiology of EIMUF remains to be fully elucidated, some promising BMs have been recently introduced, which together with other BMs, could be useful in monitoring EIMUF. The combination of biomarkers seems to be more efficient than a single biomarker to monitor EIMUF. However, it is essential that efficacy, reliability, and applicability of each BM candidate is validated in appropriate studies.

Mechanomyography and muscle function assessment: a review of current state and prospects

Previous studies have explored to saturation the efficacy of the conventional signal (such as electromyogram) for muscle function assessment and found its clinical impact limited. Increasing demand for reliable muscle function assessment modalities continues to prompt further investigation into other complementary alternatives. Application of mechanomyographic signal to quantify muscle performance has been proposed due to its inherent mechanical nature and ability to assess muscle function non-invasively while preserving muscular neurophysiologic information. Mechanomyogram is gaining accelerated applications in evaluating the properties of muscle under voluntary and evoked muscle contraction with prospects in clinical practices. As a complementary modality and the mechanical counterpart to electromyogram; mechanomyogram has gained significant acceptance in analysis of isometric and dynamic muscle actions. Substantial studies have also documented the effectiveness of mechanomyographic signal to assess muscle performance but none involved comprehensive appraisal of the state of the art applications with highlights on the future prospect and potential integration into the clinical practices. Motivated by the dearth of such critical review, we assessed the literature to investigate its principle of acquisition, current applications, challenges and future directions. Based on our findings, the importance of rigorous scientific and clinical validation of the signal is highlighted. It is also evident that as a robust complement to electromyogram, mechanomyographic signal may possess unprecedented potentials and further investigation will be enlightening.

An examination of neuromuscular and metabolic fatigue thresholds

This study examined the relationships among the physical working capacity at the fatigue threshold (PWCFT), the power outputs associated with the gas exchange threshold (PGET) and the respiratory compensation point (PRCP), and critical power (CP) to identify possible physiological mechanisms underlying the onset of neuromuscular fatigue. Ten participants (mean ± SD age: 20 ± 1 years) performed a maximal incremental cycle ergometer test to determine the PWCFT, PGET, and PRCP. CP was determined from the 3 min all-out test. The PWCFT (197 ± 55 W), PRCP (212 ± 50 W), and CP (208 ± 63 W) were significantly greater than the PGET (168 ± 40 W), but there were no significant differences among the PWCFT, PRCP, and CP. All thresholds were significantly inter-4 (r = 0.794-0.958). The 17% greater estimates for the PWCFT than PGET were likely related to differences in the physiological mechanisms that underlie these fatigue thresholds, while the non-significant difference and high correlation between the PWCFT and the PRCP suggested that hyperkalemia may underlie both thresholds. Furthermore, it is possible that the 5% lower estimate of the PWCFT than CP could more accurately reflect the demarcation of the heavy from severe exercise intensity domains.