A 30-Min Rest Protocol Does Not Affect W', Critical Power, and Systemic Response

Muscle Oximetry in Sports Science: An Updated Systematic Review

BACKGROUND

In the last 5 years since our last systematic review, a significant number of articles have been published on the technical aspects of muscle near-infrared spectroscopy (NIRS), the interpretation of the signals and the benefits of using the NIRS technique to measure the physiological status of muscles and to determine the workload of working muscles.

OBJECTIVES

Considering the consistent number of studies on the application of muscle oximetry in sports science published over the last 5 years, the objectives of this updated systematic review were to highlight the applications of muscle oximetry in the assessment of skeletal muscle oxidative performance in sports activities and to emphasize how this technology has been applied to exercise and training over the last 5 years. In addition, some recent instrumental developments will be briefly summarized.

METHODS

Preferred Reporting Items for Systematic Reviews guidelines were followed in a systematic fashion to search, appraise and synthesize existing literature on this topic. Electronic databases such as Scopus, MEDLINE/PubMed and SPORTDiscus were searched from March 2017 up to March 2023. Potential inclusions were screened against eligibility criteria relating to recreationally trained to elite athletes, with or without training programmes, who must have assessed physiological variables monitored by commercial oximeters or NIRS instrumentation.

RESULTS

Of the identified records, 191 studies regrouping 3435 participants, met the eligibility criteria. This systematic review highlighted a number of key findings in 37 domains of sport activities. Overall, NIRS information can be used as a meaningful marker of skeletal muscle oxidative capacity and can become one of the primary monitoring tools in practice in conjunction with, or in comparison with, heart rate or mechanical power indices in diverse exercise contexts and across different types of training and interventions.

CONCLUSIONS

Although the feasibility and success of the use of muscle oximetry in sports science is well documented, there is still a need for further instrumental development to overcome current instrumental limitations. Longitudinal studies are urgently needed to strengthen the benefits of using muscle oximetry in sports science.

Vascular dysfunction and the age-related decline in critical power

Ageing results in lower exercise tolerance, manifested as decreased critical power (CP). We examined whether the age-related decrease in CP occurs independently of changes in muscle mass and whether it is related to impaired vascular function. Ten older (63.1 ± 2.5 years) and 10 younger (24.4 ± 4.0 years) physically active volunteers participated. Physical activity was measured with accelerometry. Leg muscle mass was quantified with dual X-ray absorptiometry. The CP and maximum power during a graded exercise test (PGXT ) of single-leg knee-extension exercise were determined over the course of four visits. During a fifth visit, vascular function of the leg was assessed with passive leg movement (PLM) hyperaemia and leg blood flow and vascular conductance during knee-extension exercise at 10 W, 20 W, slightly below CP (90% CP) and PGXT . Despite not differing in leg lean mass (P = 0.901) and physical activity (e.g., steps per day, P = 0.735), older subjects had ∼30% lower mass-specific CP (old = 3.20 ± 0.94 W kg-1 vs. young = 4.60 ± 0.87 W kg-1 ; P < 0.001). The PLM-induced hyperaemia and leg blood flow and/or conductance were blunted in the old at 20 W, 90% CP and PGXT (P < 0.05). When normalized for leg muscle mass, CP was strongly correlated with PLM-induced hyperaemia (R2  = 0.52; P < 0.001) and vascular conductance during knee-extension exercise at 20 W (R2  = 0.34; P = 0.014) and 90% CP (R2  = 0.39; P = 0.004). In conclusion, the age-related decline in CP is not only an issue of muscle quantity, but also of impaired muscle quality that corresponds to impaired vascular function.

An improved methodology for estimating critical power from mean maximal power output data

The aim of this study was to determine if Critical Power (CP) and W' can be estimated from mean maximal power output (MMP) data collected in cycling races. Data were collected from 13 under 23 professional cyclists (mean ± SD; age, 19.5 ± 1.1 y; body mass, 66.3 ± 5.0 kg; height, 180.0 ± 5.0 cm; CP, 5.7 ± 0.3 W · kg-1). Participants conducted a CP test in the field to determine CPTest and W'Test. MMP data were then collected in races for the subsequent 90 days. CP and W' were estimated from MMP values in two ways, using fixed MMP durations, 2, 5 and 12 min (CPFixed and W'Fixed), and via a novel filtering of second-by-second MMP data (CPFiltered and W'Filtered). CPFixed and CPFiltered were not significantly different from CPTest (Mean Difference (MD) 5 W and 7 W, respectively, p > 0.05). W'Fixed and W'Filtered were not significantly different from W'Test (MD 2.68 kJ and 0.89 kJ, respectively, p > 0.05). CPFixed and CPFiltered correlated significantly with CPTest (r = 0.872 and 0.922, respectively, p < 0.0001 for both). Neither W'Fixed nor W'Filtered correlated significantly with W'Test (p > 0.05). Both CPFixed and CPFiltered provide valid estimates of CPTest.; however, CPFiltered provides a better estimate.

Biomechanical adaptations during exhaustive runs at 90 to 120% of peak aerobic speed

The aim of this study was to examine how running biomechanics (spatiotemporal and kinetic variables) adapt with exhaustion during treadmill runs at 90, 100, 110, and 120% of the peak aerobic speed (PS) of a maximal incremental aerobic test. Thirteen male runners performed a maximal incremental aerobic test on an instrumented treadmill to determine their PS. Biomechanical variables were evaluated at the start, mid, and end of each run until volitional exhaustion. The change of running biomechanics with fatigue was similar among the four tested speeds. Duty factor and contact and propulsion times increased with exhaustion (P ≤ 0.004; F ≥ 10.32) while flight time decreased (P = 0.02; F = 6.67) and stride frequency stayed unchanged (P = 0.97; F = 0.00). A decrease in vertical and propulsive peak forces were obtained with exhaustion (P ≤ 0.002; F ≥ 11.52). There was no change in the impact peak with exhaustion (P = 0.41; F = 1.05). For runners showing impact peaks, the number of impact peaks increased (P ≤ 0.04; [Formula: see text] ≥ 6.40) together with the vertical loading rate (P = 0.005; F = 9.61). No changes in total, external, and internal positive mechanical work was reported with exhaustion (P ≥ 0.12; F ≤ 2.32). Results suggest a tendency towards a "smoother" vertical and horizontal running pattern with exhaustion. A smoother running pattern refers to the development of protective adjustments, leading to a reduction of the load applied to the musculoskeletal system at each running step. This transition seemed continuous between the start and end of the running trials and could be adopted by the runners to decrease the muscle force level during the propulsion phase. Despite these changes with exhaustion, there were no changes in either gesture speed (no alteration of stride frequency) or positive mechanical work, advocating that runners unconsciously organize themselves to maintain a constant whole-body mechanical work output.

The exercise power-duration relationship is equally reproducible in eumenorrheic female and male humans

This study aims to investigate the effect of the menstrual cycle (MC) on exercise performance across the power-duration relationship (PDR). We hypothesized females would exhibit greater variability in the PDR across the MC than males across a similar timespan, with critical power (CP) and work-prime (W') being lower during the early follicular phase than the late follicular and midluteal phases. Seven eumenorrheic, endurance-trained female adults performed multiple constant-load-to-task-failure and maximum-power tests at three timepoints across the MC (early follicular, late follicular, and midluteal phases). Ten endurance-trained male adults performed the same tests approximately 10 days apart. No differences across the PDR were observed between MC phases (CP: 186.74 ± 31.00 W, P = 0.955, CV = 0.81 ± 0.65%) (W': 7,961.81 ± 2,537.68 J, P = 0.476, CV = 10.48 ± 3.06%). CP was similar for male and female subjects (11.82 ± 1.42 W·kg^-1 vs. 11.56 ± 1.51 W·kg^-1, respectively) when controlling for leg lean mass. However, W' was larger (P = 0.047) for male subjects (617.28 ± 130.10 J·kg^-1) than female subjects (490.03 ± 136.70 J·kg^-1) when controlling for leg lean mass. MC phase does not need to be controlled when conducting aerobic endurance performance research on eumenorrheic female subjects without menstrual dysfunction. Nevertheless, several sex differences in the power-duration relationship exist, even after normalizing for body composition. Therefore, previous studies describing the physiology of exercise performance in male subjects may not perfectly describe that of female subjects.NEW & NOTEWORTHY Females are often excluded from exercise performance research due to experimental challenges in controlling for the menstrual cycle (MC), causing uncertainty regarding how the MC impacts female performance. The present study examined the influences that biological sex and the MC have on the power-duration relationship (PDR) by comparing critical power (CP), Work-prime (W'), and maximum power output (P_MAX) in males and females. Our data provide evidence that the MC does not influence the PDR and that females exhibit similar reproducibility as males. Thus, when conducting aerobic endurance exercise research on eumenorrheic females without menstrual dysfunction, the phase of the MC does not need to be controlled. Although differences in body composition account for some differences between the sexes, sex differences in W' and P_MAX persisted even after normalizing for different metrics of body composition. These data highlight the necessity and feasibility of examining sex differences in performance, as previously generated male-only data within the literature may not apply to female subjects.

Modeling the Relationship between Load and Repetitions to Failure in Resistance Training: A Bayesian Analysis

PURPOSE

To identify the relationship between load and the number of repetitions performed to momentary failure in the pin press exercise, the present study compared different statistical model types and structures using a Bayesian approach.

METHODS

Thirty resistance-trained men and women were tested on two separate occasions. During the first visit, participants underwent assessment of their one-repetition maximum (1-RM) in the pin press exercise. On the second visit, they performed sets to momentary failure at 90%, 80% and 70% of their 1-RM in a fixed order during a single session. The relationship between relative load and repetitions performed to failure was fitted using linear regression, exponential regression and the critical load model. Each model was fitted according to the Bayesian framework in two ways: using an across-subjects pooled data structure and using a multilevel structure. Models were compared based on the variance explained (R²) and leave-one-out cross-validation information criterion (LOOIC).

RESULTS

Multilevel models, which incorporate higher-level commonalities into individual relationships, demonstrated a substantially better fit (R²: 0.97-0.98) and better predictive accuracy compared to generalized pooled-data models (R²: 0.89-0.93). The multilevel 2-parameter exponential regression emerged as the best representation of data in terms of model fit, predictive accuracy and model simplicity.

CONCLUSION

The relationship between load and repetitions performed to failure follows an individually expressed exponential trend in the pin press exercise. To accurately predict the load that is associated with a certain repetition maximum, the relationship should therefore be modeled on a subject-specific level.

Functional Threshold Power Estimated from a 20-minute Time-trial Test is Warm-up-dependent

This study investigated the influence of different warm-up protocols on functional threshold power. Twenty-one trained cyclists (˙VO₂max=60.2±6.8 ml·kg^-1·min^-1) performed an incremental test and four 20-min time trials preceded by different warm-up protocols. Two warm-up protocols lasted 45 min, with a 5-min time trial performed either 15 min (Traditional) or 25 min (Reverse) before the 20-min time trial. The other two warm-up protocols lasted 25 min (High Revolutions-per minute) and 10 min (Self-selected), including three fast accelerations and self-selected intensity, respectively. The power outputs achieved during the 20-min time trial preceded by the Traditional and Reverse warm-up protocols were significantly lower than the High Revolutions-per-minute and Self-selected protocols (256±30; 257±30; 270±30; 270±30 W, respectively). Participants chose a conservative pacing strategy at the onset (negative) for the Traditional and Reverse but implemented a fast-start strategy (U-shaped) for the High revolutions-per-minute and Self-selected warm-up protocols. In conclusion, 20-min time-trial performance and pacing are affected by different warm-ups. Consequently, the resultant functional threshold power may be different depending on whether the original protocol with a 5-min time trial is followed or not.

Critical speed estimated by statistically appropriate fitting procedures

PURPOSE

Intensity domains are recommended when prescribing exercise. The distinction between heavy and severe domains is made by the critical speed (CS), therefore requiring a mathematically accurate estimation of CS. The different model variants (distance versus time, running speed versus time, time versus running speed, and distance versus running speed) are mathematically equivalent. Nevertheless, error minimization along the correct axis is important to estimate CS and the distance that can be run above CS (d'). We hypothesized that comparing statistically appropriate fitting procedures, which minimize the error along the axis corresponding to the properly identified dependent variable, should provide similar estimations of CS and d' but that different estimations should be obtained when comparing statistically appropriate and inappropriate fitting procedure.

METHODS

Sixteen male runners performed a maximal incremental aerobic test and four exhaustive runs at 90, 100, 110, and 120% of their peak speed on a treadmill. Several fitting procedures (a combination of a two-parameter model variant and regression analysis: weighted least square) were used to estimate CS and d'.

RESULTS

Systematic biases (P < 0.001) were observed between each pair of fitting procedures for CS and d', even when comparing two statistically appropriate fitting procedures, though negligible, thus corroborating the hypothesis.

CONCLUSION

The differences suggest that a statistically appropriate fitting procedure should be chosen beforehand by the researcher. This is also important for coaches that need to prescribe training sessions to their athletes based on exercise intensity, and their choice should be maintained over the running seasons.

Effect of Mathematical Modeling and Fitting Procedures on the Assessment of Critical Speed and Its Relationship With Aerobic Fitness Parameters

An accurate estimation of critical speed (CS) is important to accurately define the boundary between heavy and severe intensity domains when prescribing exercise. Hence, our aim was to compare CS estimates obtained by statistically appropriate fitting procedures, i.e., regression analyses that correctly consider the dependent variables of the underlying models. A second aim was to determine the correlations between estimated CS and aerobic fitness parameters, i.e., ventilatory threshold, respiratory compensation point, and maximal rate of oxygen uptake. Sixteen male runners performed a maximal incremental aerobic test and four exhaustive runs at 90, 100, 110, and 120% of the peak speed of the incremental test on a treadmill. Then, two mathematically equivalent formulations (time as function of running speed and distance as function of running speed) of three different mathematical models (two-parameter, three-parameter, and three-parameter exponential) were employed to estimate CS, the distance that can be run above CS (d'), and if applicable, the maximal instantaneous running speed (s max ). A significant effect of the mathematical model was observed when estimating CS, d', and s max (P < 0.001), but there was no effect of the fitting procedure (P > 0.77). The three-parameter model had the best fit quality (smallest Akaike information criterion) of the CS estimates but the highest 90% confidence intervals and combined standard error of estimates (%SEE). The 90% CI and %SEE were similar when comparing the two fitting procedures for a given model. High and very high correlations were obtained between CS and aerobic fitness parameters for the three different models (r ≥ 0.77) as well as reasonably small SEE (SEE ≤ 6.8%). However, our results showed no further support for selecting the best mathematical model to estimate critical speed. Nonetheless, we suggest coaches choosing a mathematical model beforehand to define intensity domains and maintaining it over the running seasons.

Relationship Between the Critical Power Test and a 20-min Functional Threshold Power Test in Cycling

To investigate the agreement between critical power (CP) and functional threshold power (FTP), 17 trained cyclists and triathletes (mean ± SD: age 31 ± 9 years, body mass 80 ± 10 kg, maximal aerobic power 350 ± 56 W, peak oxygen consumption 51 ± 10 mL⋅min^-1⋅kg^-1) performed a maximal incremental ramp test, a single-visit CP test and a 20-min time trial (TT) test in randomized order on three different days. CP was determined using a time-trial (TT) protocol of three durations (12, 7, and 3 min) interspersed by 30 min passive rest. FTP was calculated as 95% of 20-min mean power achieved during the TT. Differences between means were examined using magnitude-based inferences and a paired-samples t-test. Effect sizes are reported as Cohen's d. Agreement between CP and FTP was assessed using the 95% limits of agreement (LoA) method and Pearson correlation coefficient. There was a 91.7% probability that CP (256 ± 50 W) was higher than FTP (249 ± 44 W). Indeed, CP was significantly higher compared to FTP (P = 0.041) which was associated with a trivial effect size (d = 0.04). The mean bias between CP and FTP was 7 ± 13 W and LoA were -19 to 33 W. Even though strong correlations exist between CP and FTP (r = 0.969; P < 0.001), the chance of meaningful differences in terms of performance (1% smallest worthwhile change), were greater than 90%. With relatively large ranges for LoA between variables, these values generally should not be used interchangeably. Caution should consequently be exercised when choosing between FTP and CP for the purposes of performance analysis.