Physiological Evidence That the Critical Torque Is a Phase Transition, Not a Threshold

Pethick, Jamie; Winter, Samantha L.

doi:10.1249/mss.0000000000002389

“…Moreover, the shape of the relationship between complexity and contraction intensity differed with joint angle; at a flexed angle (100°), there was no relationship between contraction intensity and ApEn, whilst at an extended angle (40°), there was a quadratic trend, taking the form of a shallow U -shape. These findings contrast with the linear relationship previously observed during knee extension contractions performed at 90° of flexion (Pethick et al 2016 , 2020 ). The mediating effect of joint angle on the relationship between contraction intensity and complexity therefore requires further work.…”

Section: Introductioncontrasting

confidence: 92%

Fatigue-induced changes in knee-extensor torque complexity and muscle metabolic rate are dependent on joint angle

Pethick

¹

,

Winter

²

2021

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Purpose Joint angle is a significant determinant of neuromuscular and metabolic function. We tested the hypothesis that previously reported correlations between knee-extensor torque complexity and metabolic rate ($${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 ) would be conserved at reduced joint angles (i.e. shorter muscle lengths). Methods Eleven participants performed intermittent isometric knee-extensor contractions at 50% maximum voluntary torque for 30 min or until task failure (whichever occurred sooner) at joint angles of 30º, 60º and 90º of flexion (0º = extension). Torque and surface EMG were sampled continuously. Complexity and fractal scaling of torque were quantified using approximate entropy (ApEn) and detrended fluctuation analysis (DFA) α. $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 was determined using near-infrared spectroscopy. Results Time to task failure/end increased as joint angle decreased (P < 0.001). Over time, complexity decreased at 90º and 60º (decreased ApEn, increased DFA α, both P < 0.001), but not 30º. $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 increased at all joint angles (P < 0.001), though the magnitude of this increase was lower at 30º compared to 60º and 90º (both P < 0.01). There were significant correlations between torque complexity and $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 at 90º (ApEn, r = − 0.60, P = 0.049) and 60º (ApEn, r = − 0.64, P = 0.035; DFA α, ρ = 0.68, P = 0.015). Conclusion The lack of correlation between $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 and complexity at 30º was likely due to low relative task demands, given the similar kinetics of $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 and torque complexity. An inverse correlation between $${\text{m}\dot{\text{V}}\text{O}}_{{2}}$$ m V ˙ O 2 and knee-extensor torque complexity occurs during high-intensity contractions at intermediate, but not short, muscle lengths.

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“…These findings are reinforced by non-invasive 31 P-magnetic resonance spectroscopy assessment of the skeletal muscle metabolic responses, which demonstrate striking differences in the profiles of PCr, inorganic phosphate and pH for exercise performed just above, compared to just below, CP (Jones et al 2008 ). These differences in the rates of substrate utilisation and metabolite accumulation likely underpin observations that the rate and nature of neuromuscular fatigue development also differ according to the intensity of the exercise task relative to CP (Black et al 2017 ; Burnley et al 2012 ; Dinyer et al 2020 ; Pethick et al 2020 ). Finally, it is pertinent to note that simultaneous assessment of the responses of muscle [lactate] and blood [lactate] during heavy-intensity and severe-intensity exercise reveal that the former may be stable while the latter rises (Jones et al 2019b ), suggesting differences in the dynamics of lactate accumulation in the muscle and blood compartments.…”

Section: Discussionmentioning

confidence: 98%

“…day-to-day variability), surrounding the estimates (Black et al 2015 ; Mattioni Maturana et al 2018 ). For this reason, asking participants to exercise to the limit of tolerance exactly at the computed CP or CS can result in wide variability in both the physiological responses and the time to the limit of tolerance due to some participants being below and others being above the CP or CS (Pethick et al 2020 ; see Jones et al 2019a for review). Indeed, the notion of exercising at the CP (or CS) is vacuous because the asymptote of the power–time relationship represents the power that lies exactly between those powers at which W´ is utilised and those powers at which it is not; that is, it defines the threshold separating the heavy-intensity and severe-intensity domains and the inherent steady-state or non-steady-state physiological behaviour that defines those domains; and therefore, it is erroneous to define CP as the highest power at which steady-state responses are observed.…”

Section: Discussionmentioning

confidence: 99%

“…It should be acknowledged that, while the group mean CS was estimated to be 16.4 km/h, when the 95% CI is taken into account, the ‘actual’ CS could have occurred anywhere between 16.0 and 16.7 km/h. We took appropriate measures to minimise errors arising from biological factors and mathematical modelling, but it is important to note that it is not possible to entirely eliminate the error margin surrounding any physiological threshold estimate (Pethick et al 2020 ). It should also be appreciated that this range of speeds within which the CS resides represents an error of only ± 4–5 s per km (~ 2%).…”

Section: Discussionmentioning

confidence: 99%

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Steady-state $$\dot{V}{\text{O}}_{2}$$ above MLSS: evidence that critical speed better represents maximal metabolic steady state in well-trained runners

Nixon

¹

,

Kranen

²

,

Vanhatalo

³

et al. 2021

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The metabolic boundary separating the heavy-intensity and severe-intensity exercise domains is of scientific and practical interest but there is controversy concerning whether the maximal lactate steady state (MLSS) or critical power (synonymous with critical speed, CS) better represents this boundary. We measured the running speeds at MLSS and CS and investigated their ability to discriminate speeds at which $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 was stable over time from speeds at which a steady-state $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 could not be established. Ten well-trained male distance runners completed 9–12 constant-speed treadmill tests, including 3–5 runs of up to 30-min duration for the assessment of MLSS and at least 4 runs performed to the limit of tolerance for assessment of CS. The running speeds at CS and MLSS were significantly different (16.4 ± 1.3 vs. 15.2 ± 0.9 km/h, respectively; P < 0.001). Blood lactate concentration was higher and increased with time at a speed 0.5 km/h higher than MLSS compared to MLSS (P < 0.01); however, pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 did not change significantly between 10 and 30 min at either MLSS or MLSS + 0.5 km/h. In contrast, $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 increased significantly over time and reached $$\dot{V}{\text{O}}_{2\,\,\max }$$ V ˙ O 2 max at end-exercise at a speed ~ 0.4 km/h above CS (P < 0.05) but remained stable at a speed ~ 0.5 km/h below CS. The stability of $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 at a speed exceeding MLSS suggests that MLSS underestimates the maximal metabolic steady state. These results indicate that CS more closely represents the maximal metabolic steady state when the latter is appropriately defined according to the ability to stabilise pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 .

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“…Several studies on ageing and disease have demonstrated changes in complexity in the absence of changes in the magnitude of fluctuations (Fiogbé et al., 2021; Vaillancourt & Newell, 2000), suggesting that they may hold potential in detecting sub‐clinical changes in motor control. Furthermore, complexity measures have been demonstrated to be tightly coupled to the neuromuscular fatigue process (Pethick et al., 2018) and exhibit the same exercise intensity domain‐specific behaviours as measures such as

{\dot{V}}_{{normalO}_{2}}

, blood [lactate] and pH (Pethick et al., 2016; Pethick et al ., 2020; Poole et al., 2016). Taken together, such findings indicate that muscle force/torque complexity may provide a sensitive index of the state of the neuromuscular system, providing information in addition to, and in some instances beyond, traditional measures of signal variability.…”

Section: Future Research Directionsmentioning

confidence: 99%

Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

Pethick

¹

,

Winter

²

2021

Experimental Physiology

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New Findings What is the topic of this review?Physiological complexity in muscle force and torque fluctuations, specifically the quantification of complexity, how neuromuscular complexityis altered by perturbations and the potential mechanism underlying changes in neuromuscular complexity. What advances does it highlight?The necessity to calculate both magnitude‐ and complexity‐based measures for the thorough evaluation of force/torque fluctuations. Also the need for further research on neuromuscular complexity, particularly how it relates to the performance of functional activities (e.g. manual dexterity, balance, locomotion). Abstract Physiological time series produce inherently complex fluctuations. In the last 30 years, methods have been developed to characterise these fluctuations, and have revealed that they contain information about the function of the system producing them. Two broad classes of metrics are used: (1) those which quantify the regularity of the signal (e.g. entropy metrics); and (2) those which quantify the fractal properties of the signal (e.g. detrended fluctuation analysis). Using these techniques, it has been demonstrated that ageing results in a loss of complexity in the time series of a multitude of signals, including heart rate, respiration, gait and, crucially, muscle force or torque output. This suggests that as the body ages, physiological systems become less adaptable (i.e. the systems’ ability to respond rapidly to a changing external environment is diminished). More recently, it has been shown that neuromuscular fatigue causes a substantial loss of muscle torque complexity, a process that can be observed in a few minutes, rather than the decades it requires for the same system to degrade with ageing. The loss of torque complexity with neuromuscular fatigue appears to occur exclusively above the critical torque (at least for tasks lasting up to 30 min). The loss of torque complexity can be exacerbated with previous exercise of the same limb, and reduced by the administration of caffeine, suggesting both peripheral and central mechanisms contribute to this loss. The mechanisms underpinning the loss of complexity are not known but may be related to altered motor unit behaviour as the muscle fatigues.

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Physiological Evidence That the Critical Torque Is a Phase Transition, Not a Threshold

Cited by 42 publications

References 39 publications

Fatigue-induced changes in knee-extensor torque complexity and muscle metabolic rate are dependent on joint angle

Fatigue-induced changes in knee-extensor torque complexity and muscle metabolic rate are dependent on joint angle

Steady-state $$\dot{V}{\text{O}}_{2}$$ above MLSS: evidence that critical speed better represents maximal metabolic steady state in well-trained runners

Physiological complexity: influence of ageing, disease and neuromuscular fatigue on muscle force and torque fluctuations

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