Participants
Eleven healthy participants (nine males, two females; mean ± SD: age 26.3 ± 6.0 years; height 1.75 ± 0.08 m; body mass 68.7 ± 9.7 kg) provided written informed consent to participate in the study, which was approved by the ethics committee of the University of Kent (Prop 122_2016_17), and which adhered to the Declaration of Helsinki (except for the inclusion of the protocol in a publicly accessible database). Participants were instructed to arrive at the laboratory in a rested state (having performed no strenuous exercise in the preceding 24 h) and to have consumed neither any food nor caffeinated beverages in the 3 h prior to arrival. Participants visited the laboratory at the same time of day (± 2 h).
Experimental design
Participants visited the laboratory on four occasions, with a minimum of 48 h between each visit. During their first visit, participants were familiarised with all testing equipment and procedures, and the settings for the dynamometer and stimulator were recorded. During the next three visits, participants performed, in a randomised order, intermittent isometric contractions at 30°, 60° and 90° of knee flexion (short, medium and long muscle lengths, respectively) to task failure or for 30 min, whichever occurred sooner. In each trial, torque output was sampled continuously to allow the quantification of complexity, muscle activity was measured from the vastus lateralis and vastus medialis electromyogram (EMG), knee-extensor metabolic rate was assessed using near-infrared spectroscopy (NIRS) and arterial occlusion, and maximal voluntary contractions (MVCs) with supramaximal femoral nerve stimulation were used to quantify global (i.e. loss of maximal voluntary torque), central and peripheral fatigue.
Dynamometry
During all visits, participants sat in the chair of a Cybex isokinetic dynamometer (HUMAC Norm; CSMi, Massachusetts, USA), initialised and calibrated according to the manufacturer’s instructions. Their right leg was attached to the lever arm of the dynamometer, with the seating position adjusted to ensure that the lateral epicondyle of the femur was in line with the axis of rotation of the lever arm. Participants sat with a relative hip angle of 85° and a relative knee angle of either 30°, 60° or 90°, with full extension being 0°. The lower leg was securely attached to the lever arm above the malleoli with a padded Velcro strap, whilst straps secured firmly across both shoulders and the waist prevented any extraneous movement and the use of the hip extensors during the isometric contractions. The seating position for each joint angle was recorded during the first visit and replicated during each subsequent visit.
Femoral nerve stimulation
Electrical stimulation of the femoral nerve was used to assess neuromuscular fatigue processes, as described previously in Pethick et al. (2015). A carbon rubber electrode with adhesive gel (100 × 50 mm; Phoenix Healthcare Products Ltd., Nottingham, UK) acted as the anode and was placed lateral to the ischial tuberosity, on the posterior aspect of the leg. The position of the cathode was determined using a motor point pen (Compex; DJO Global, Guildford, UK), and based on the location in the femoral triangle giving the largest twitch and greatest peak-to-peak amplitude of the compound muscle action potential (M-wave) following single stimulation at 100 mA, using a constant-current variable voltage stimulator (Digitimer, DS7AH, Welwyn Garden City, UK). Following determination of the precise cathode location, an Ag/AgCl electrode coated in conductive gel (32 × 32 mm; Nessler Medizintechnik, Innsbruck, Austria) was placed over the femoral nerve.
The appropriate stimulator current was then established by incrementally increasing the current, in steps of 20 mA, until knee extensor torque and the M-wave response to single twitches had plateaued. This was confirmed with stimulation delivered during a contraction at 50% MVC to ensure that a maximal M-wave during an isometric contraction was also evident. Once this was obtained, the stimulator current was increased to 130% of the current producing a maximal M-wave. In all subsequent trials, doublet stimulation (two 200 µs pulses with 10 ms interpulse interval) was used.
Surface EMG
The EMG of the vastus lateralis and vastus medialis were sampled using Ag/AgCl electrodes (32 × 32 mm; Nessler Medizintechnik, Innsbruck, Austria). Prior to attachment of the electrodes, the skin of the participants was shaved, abraded and cleaned with an alcohol swab over the belly of the muscle to reduce impedance. The electrodes were placed on the prepared skin over the belly of the muscle, parallel to the approximate alignment of the muscle fibres. A reference electrode was placed on prepared skin medial to the tibial tuberosity. The raw EMG signals were sampled at 1 kHz, amplified (gain 1000; Biopac MP150; Biopac Systems Inc., California, USA) and band-pass filtered (10–500 Hz; Biopac MP150; Biopac Systems Inc., California, USA).
Muscle oxygen consumption
Muscle oxygen consumption (\({\text{m}\dot{\text{V}}\text{O}}_{{2}}\)) from the vastus lateralis was obtained using a continuous-wave NIRS device (Oxymon Mk III; Artinis Medical Systems, The Netherlands), calibrated according to the manufacturer’s instructions before each trial. The NIRS device generated light at three wavelengths (905, 850 and 770 nm) corresponding to the absorption wavelengths of oxyhaemoglobin (O2Hb) and deoxyhaemoglobin (HHb). An area at the level of the largest circumference of the vastus lateralis was shaved, abraded and cleaned with an alcohol swab. The NIRS optode was then placed at this location and secured with Velcro straps and biadhesive tape, such that the optode could not move during contractions. A blood pressure cuff (Hokanson E20 cuff inflator; D.E. Hokanson Inc., Bellevue, USA) was placed proximal to the NIRS optode and was used to occlude blood flow. NIRS data were collected at 10 Hz. Adipose tissue thickness at the site of measurement was assessed, as per the recommendations of Ferrari et al. (2011), using skinfold callipers. However, as demonstrated in Ryan et al. (2012), an ischaemic calibration eliminates any effect of adipose tissue thickness and scales the NIRS signals according to the maximal physiological range.
Protocol
All visits followed a similar pattern of data acquisition to Pethick et al. (2019), though each visit was conducted at a different knee joint angle: either 30°, 60° or 90° of knee flexion. Visits began with the instrumentation of the participants and the (re-)establishment of the correct dynamometer seating position and supramaximal stimulation response. Participants then performed a series of brief (3 s) MVCs to establish the maximum torque at that joint angle. These MVCs were separated by a minimum of 60 s rest and continued until the peak torque in three consecutive contractions were within 5% of each other. Participants were given a countdown, followed by very strong verbal encouragement to maximise torque. The first MVC was used to establish the fresh maximal EMG signal, against which the subsequent EMG signals were normalised (“Data analysis”). The second and third MVCs were performed with femoral nerve stimulation delivered during and after the contraction. The stimulation during the contraction was delivered at a plateau in torque, to test the maximality of the contraction and provide the resting voluntary activation. The stimulation after the contraction was delivered at rest, 2 s after the contraction, to establish the fresh potentiated doublet torque. All subsequent contractions with femoral nerve stimulation were conducted in this manner.
Ten minutes after the establishment of maximal torque, the resting \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) of the vastus lateralis was assessed based on the decrease in muscle oxygenation which accompanies an arterial occlusion (Ryan et al. 2012, 2013). It must be noted that this method does not provide \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) in absolute terms; rather, it gives a measure of relative \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) in units of %·s−1, where % is an estimate of tissue O2 saturation. For this, a blood pressure cuff was rapidly inflated to a pressure of 300 mmHg using a Hokanson AG101 (D.E. Hokanson Inc., Bellevue, USA). Four resting measurements were made using 10 s of arterial occlusion, each separated by 60 s. The resting mV̇O2 was calculated using linear regression with the first 8 s of each occlusion (“Data analysis”). Participants then rested for 10 min before performing one of the experimental trials.
Experimental trials
Participants performed a series of targeted intermittent isometric knee extension contractions at 25, 50, 75 and 100% MVC, to establish the relationship between complexity and contraction intensity at each joint angle. The target torques were determined from the highest instantaneous torque obtained during the pre-test MVCs. Participants performed three contractions at each intensity, with contractions held for 6 s and separated by 4 s rest. The intensities were performed in a randomised order, with 2 min rest between each intensity. Participants were instructed to match their instantaneous torque with a target bar superimposed on a display in front of them and were required to continue matching this torque for as much of the 6 s contraction as possible.
After participants had performed contractions at all four intensities, they rested for a further 10 min before performing an intermittent isometric fatigue test at 50% MVC. As with the targeted contractions, this torque was determined from the highest instantaneous torque obtained during the pre-test MVCs and the contractions were held for 6 s and separated by 4 s rest (Pethick et al. 2015, 2019). These contractions continued for 30 min or until task failure, whichever occurred sooner. Task failure was defined as the point at which the participants failed to reach the target torque on three consecutive occasions, despite strong verbal encouragement. Participants were not informed of the elapsed time during the trials but were informed of each “missed” contraction. Immediately at task failure, after the third missed contraction, participants were instructed to produce an MVC, which was accompanied by femoral nerve stimulation.
As in Pethick et al. (2019), after the fifth contraction of every minute of the fatigue test, \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) was assessed, instead of performing a targeted contraction. The blood pressure cuff was inflated to 300 mmHg for 5 s, with \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) calculated using linear regression over the course of this occlusion. This measure of \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) was performed instead of a targeted contraction. \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) was also assessed immediately prior to the MVC performed at task end/failure. Finally, 5 min after task end/failure, an ischaemia/hyperaemia calibration was performed to normalise the NIRS signals. The blood pressure cuff was inflated to 300 mmHg for 3–5 min (or until the NIRS signals plateaued). The plateau in HHb at the end of the occlusion was assumed to be the zero-point (the lowest functional level of HHb), with the peak response to hyperaemia upon cuff release being 100% oxygenation.
Data acquisition and participant interface
Data acquisition was performed as described in Pethick et al. (2019). The isokinetic dynamometer, stimulator and EMG were connected via BNC cables to a Biopac MP150 (Biopac Systems Inc., California, USA) and a CED Micro 1401-3 (Cambridge Electronic Design, Cambridge, UK) interfaced with a personal computer. These data were sampled at 1 kHz and collected in Spike2 (Version 7; Cambridge Electronic Design, Cambridge, UK). The NIRS data were sampled at 10 Hz and collected in OxySoft (Artinis Medical Systems, Netherlands).
A chart containing the instantaneous torque was projected onto a screen placed ~ 1 m in front of the participant. A scale consisting of a thin line (1 mm thick) was superimposed on the torque chart and acted as a target, so that participants were able to match their instantaneous torque output to the target torque during each visit.
Data analysis
All data were analysed using code written in MATLAB R2017a (The MathWorks, Massachusetts, USA). The data analysis focused on four specific areas: (1) basic measures of torque and EMG; (2) measures of central and peripheral fatigue; (3) the variability and complexity of torque output; and (4) measures of muscle oxygen consumption (\({\text{m}\dot{\text{V}}\text{O}}_{{2}}\)).
Torque and EMG
The mean and peak torque for each contraction in every trial were determined. The mean torque was calculated based on the steadiest 5 s of each contraction, with MATLAB code identifying the 5 s of each contraction with the lowest standard deviation (SD). The point of task failure in the fatigue test was determined as in Pethick et al. (2015). The mean torque produced during the first five contractions was calculated, with task failure deemed to occur when the mean torque recorded during three consecutive contractions was more than 5 N·m below the mean torque of the first five contractions, with the first of these contractions being considered the point of task failure.
The EMG outputs from the vastus lateralis and vastus medialis for each contraction were full-wave rectified during each 5 s window. The average rectified EMG (arEMG) was then calculated and normalised by expressing the arEMG as a fraction of the arEMG obtained during a 3 s MVC from the fresh muscle performed at the beginning of the trial.
Central and peripheral fatigue
Measures of central and peripheral fatigue were calculated from the stimuli delivered to the femoral nerve during and after the MVCs performed pre-test and at task failure. Peripheral fatigue was demonstrated by a fall in the potentiated doublet torque. Central fatigue was demonstrated by a decline in voluntary activation, as quantified using the twitch interpolation technique (Belanger and McComas 1981; Behm et al. 1996)
$${\text{Voluntary activation}} \left( \% \right) = 1 - ({\text{superimposed doublet}}/{\text{resting doublet}}) \times 100,$$
(1)
where the superimposed doublet was measured during the contraction of interest and the potentiated doublet was measured at rest 2 s after the contraction.
Variability and complexity
Measures of variability and complexity were calculated using the steadiest 5 s of each contraction (meaning 5000 data points were used), identified by MATLAB as the 5 s containing the lowest SD. The amount of variability in the torque output of each contraction was measured using the SD, which provides a measure of the absolute amount of variability in a time-series, and the coefficient of variation (CV), which provides a measure of the amount of variability in a time-series normalised to the mean of the time-series.
The temporal structure, or complexity, of torque output was examined using multiple time domain analyses, as recommended by Goldberger et al. (2002b). The regularity of torque output was determined using approximate entropy (ApEn; Pincus 1991) and the temporal fractal scaling of torque was estimated using the detrended fluctuation analysis (DFA; Peng et al. 1994) α scaling exponent. Sample entropy (Richman and Moorman 2000) was also calculated, but as shown in Pethick et al. (2015), this measure does not differ from ApEn when 5000 data points are used in the calculation. The calculations of ApEn and DFA are detailed in Pethick et al. (2015). In brief, ApEn was calculated with the template length, m, set at 2 and the tolerance, r, set at 10% of the SD of torque output, and DFA was calculated across time scales (57 boxes ranging from 1250 to 4 data points). In four trials, a degree of crossover (Hu et al. 2001) was identified in the log–log plot of fluctuation size versus box size (as shown by an r < 0.95). To account for this, a least-squares linear regression was used to fit two lines to the plot, and two α exponents were quantified. The second of these (α2, representing longer, physiologic, timescales) was used in the DFA α analysis (Pethick et al. 2019).
Muscle oxygen consumption
\({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) was determined as in Pethick et al. (2019) using the method of Ryan et al. (2012; 2013), in which relative \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) is calculated as the slope of the change in O2Hb and HHb during arterial occlusion using simple linear regression. The resting \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) was based on the first 8 s (80 data points) of a 10 s arterial occlusion, whilst the exercising \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) measurements were based on a 5 s arterial occlusion (50 data points).
The NIRS data were corrected for blood volume changes as described in Ryan et al. (2012; 2013), using custom-written MATLAB code. A blood volume correction factor (β) was calculated for each data point during the arterial occlusions
$$\beta \left(t\right)= \frac{\left|{O}_{2}Hb(t)\right|}{\left(\left|{O}_{2}Hb(t)\right|+ \left|HHb(t)\right|\right)},$$
(2)
where β is the blood volume correction factor, t is time, O2Hb is the oxygenated haemoglobin/myoglobin signal, and HHb is the deoxygenated haemoglobin/myoglobin signal. Each data point was corrected using its corresponding β according to Eqs. 3 and 4
$$O_{2} Hb_{c} \left( t \right) = O_{2} Hb\left( t \right) - \left[ {tHb\left( t \right) \times \left( {1 - \beta } \right)} \right],$$
(3)
$$HHb_{c} \left( t \right) = HHb\left( t \right) - \left[ {tHb\left( t \right) \times \beta } \right],$$
(4)
where O2Hbc and HHbc are the corrected oxygenated and deoxygenated haemoglobin/myoglobin signals, respectively; tHb is the blood volume signal from the NIRS device; β is the blood volume correction factor; and t is time. The raw O2Hb signal in Eq. 3 is corrected by subtracting the proportion of the blood volume change attributed to O2Hb; whilst in Eq. 4, the raw HHb signal is corrected by subtracting the proportion of blood volume change attributed to HHb.
Statistics
All data are presented as means ± SD. All data were tested for normality using the Shapiro–Wilk test. For the fatigue tests, two-way analysis of variance (ANOVAs) with repeated measures were used to test for differences between conditions and time points, and for a condition x time interaction for MVC torque, arEMG, potentiated doublet torque, voluntary activation, variability, complexity and \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\). The variability, complexity, arEMG and \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) measures were analysed using means from the second minute, to account for the initial transient of the \({\dot{\text{V}}\text{O}}_{{2}}\) response (Burnley and Jones 2007) and the final minute before task end/failure. For the complexity–contraction intensity and variability–contraction intensity relationships, two-way ANOVAs with repeated measures were used to test for differences between conditions and contraction intensities, and for a condition x contraction intensity relationship for ApEn, DFA α, SD and CV. When main effects were observed, Bonferroni-adjusted 95% paired-samples confidence intervals were used to identify specific differences. The rates of change in all parameters during the fatigue test were analysed using Student’s paired-samples t tests. Correlations between rates of change in complexity and \({\text{m}\dot{\text{V}}\text{O}}_{{2}}\) were analysed using Pearson’s product–moment correlation (r) or, in the case of non-normally distributed data, Spearman’s rank-order correlation (ρ). Results were deemed statistically significant when P < 0.05.