Is the Functional Threshold Power Interchangeable With the Maximal Lactate Steady State in Trained Cyclists?

Is the 5-Minute Time-Trial Cycling Test a Valid Predictor of Maximal Oxygen Uptake? An External Cross-Validation Study

PURPOSE

This study aimed to cross-validate a recently proposed equation for the prediction of maximal oxygen uptake (V˙O2max) in cycling exercise by using the average power output normalized by the body mass from a 5-minute time trial (RPO5-min) as the independent variable. Further, the study aimed to update the predictive equation using Bayesian informative prior distributions and meta-analysis.

METHODS

On different days, 49 male cyclists performed an incremental graded exercise test until exhaustion and a 5-minute time trial on a stationary cycle ergometer. We compared the actual V˙O2max with the predicted value obtained from the RPO5-min, using a modified Bayesian Bland-Altman agreement analysis. In addition, this study updated the data on the linear regression between V˙O2max and RPO5-min, by incorporating information from a previous study as a Bayesian informative prior distribution or via meta-analysis.

RESULTS

On average, the predicted V˙O2max using RPO5-min underestimated the actual V˙O2max by -6.6 mL·kg-1·min-1 (95% credible interval, -8.6 to -4.7 mL·kg-1·min-1). The lower and upper 95% limits of agreement were -17.2 (-22.7 to -12.3) and 3.8 (-1.0 to 9.5) mL·kg-1·min-1, respectively. When the current study's data were analyzed using the previously published data as a Bayesian informative prior distribution, the accuracy of predicting sample means was found to be better when compared with the data combined via meta-analyses.

CONCLUSIONS

The proposed equation presented systematic bias in our sample, in which the prediction underestimated the actual V˙O2max. We provide an updated equation using the previous one as the prior distribution, which could be generalized to a greater audience of cyclists.

Running Functional Threshold versus Critical Power: Same Concept but Different Values

The aims of this study were (i) to estimate the functional threshold power (FTP) and critical power (CP) from single shorter time trials (TTs) (i. e. 10, 20 and 30 minutes) and (ii) to assess their location in the power-duration curve. Fifteen highly trained athletes randomly performed ten TTs (i. e. 1, 2, 3, 4, 5, 10, 20, 30, 50 and 60 minutes). FTP was determined as the mean power output developed in the 60-min TT, while CP was estimated in the running power meter platform according to the manufacturer's recommendations. The linear regression analysis revealed an acceptable FTP estimate for the 10, 20 and 30-min TTs (SEE≤12.27 W) corresponding to a correction factor of 85, 90 and 95%, respectively. An acceptable CP estimate was only observed for the 20-min TT (SEE=6.67 W) corresponding to a correction factor of 95%. The CP was located at the 30-min power output (1.0 [-5.1 to 7.1] W), which was over FTP (14 [7.0 to 21] W). Therefore, athletes and practitioners concerned with determining FTP and CP through a feasible testing protocol are encouraged to perform a 20-min TT and apply a correction factor of 90 and 95%, respectively.

An Update Of The Allen & Coggan Equation To Predict 60-Min Power Output In Cyclists Of Different Performance Levels

The Allen & Coggan protocol suggests that 95% of the power output during a 20-min time trial is a valid surrogate for 60-min maximal power. The validity of this concept has not been studied previously in cyclists with different performance levels. As a result, we classified 120 cyclists in our study as recreationally trained, trained, well trained or professional, based on their maximal oxygen consumption. Participants performed a functional threshold power testing protocol based on a 20-min time trial and a 60-min time trial, separated by a 72-hour rest. Sixty-minute maximal power was successfully modeled with 20-min maximal power and performance group using 2/3 of the dataset (R2=0.77, 95% CrI [0.74, 0.79]) with different coefficients for each group: Professional: PO60min=PO20min × 0.96; well trained: PO60min=PO20min × 0.95; trained: PO60min=PO20min × 0.92 and recreationally trained: PO60min=PO20min × 0.88. The predictions of the original equation and our model were assessed using the remaining third of the data. The predictive performance of the updated equation was better (original: R2=0.51, mean absolute error=27 W, mean bias=-12 W; updated: R2=0.54, mean absolute error=25 W, mean bias=-7 W).

Functional Threshold Power Field Test Exceeds Laboratory Performance in Junior Road Cyclists

Vinetti, G, Rossi, H, Bruseghini, P, Corti, M, Ferretti, G, Piva, S, Taboni, A, and Fagoni, N. The functional threshold power field test exceeds laboratory performance in junior road cyclists. J Strength Cond Res 37(9): 1815–1820, 2023—The functional threshold power (FTP) field test is appealing for junior cyclists, but it was never investigated in this age category, and even in adults, there are few data on FTP collected in field conditions. Nine male junior road cyclists (16.9 ± 0.8 years) performed laboratory determination of maximal aerobic power (MAP), 4-mM lactate threshold (P4mM), critical power (CP), and the curvature constant (W ′), plus a field determination of FTP as 95% of the average power output during a 20-minute time trial in an uphill road. The level of significance was set at p < 0.05. Outdoor FTP (269 ± 34 W) was significantly higher than CP (236 ± 24 W) and P4mM (233 ± 23 W). The V ˙ O 2 peak of the field FTP test (66.9 ± 4.4 ml·kg−1·min−1) was significantly higher than the V ˙ O 2 peak assessed in the laboratory (62.7 ± 3.7 ml·kg−1·min−1). Functional threshold power was correlated, in descending order, with MAP (r = 0.95), P4mM (r = 0.94), outdoor and indoor V ˙ O 2 peak (r = 0.93 and 0.93, respectively), CP (r = 0.84), and W ′ (r = 0.66). It follows that in junior road cyclists, the FTP field test was feasible and related primarily to aerobic endurance parameters and secondarily, but notably, to W ′. However, the FTP field test significantly exceeded all laboratory performance tests. When translating laboratory results to outdoor uphill conditions, coaches and sport scientists should consider this discrepancy, which may be particularly enhanced in this cycling age category.

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds

BACKGROUND

Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a 'lower' and a 'higher' threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking.

OBJECTIVE

This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept.

METHODS

A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized.

RESULTS

Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of - 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): - 10.9 to 12.8 bpm) and 2.7 bpm (LoA: - 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was - 4 bpm (LoA: - 17.9 to 9.9 bpm) and 2.5 bpm (LoA: - 12.1 to 17.1 bpm) between HRVThigh and LThigh.

CONCLUSION

HRVTlow seems to be a promising approach for the determination of a 'lower' threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

Functional threshold power is not a valid marker of the maximal metabolic steady state

Functional Threshold Power (FTP) has been considered a valid alternative to other performance markers that represent the upper boundary of the heavy intensity domain. However, such a claim has not been empirically examined from a physiological perspective.This study examined the blood lactate and VO₂ response when exercising at and 15 W above the FTP (FTP_+15W). Thirteen cyclists participated in the study. The VO₂ was recorded continuously throughout FTP and FTP_+15W, with blood lactate measured before the test, every 10 minutes and at task failure. Data were subsequently analysed using two-way ANOVA. The time to task failure at FTP and FTP_+15W were 33.7 ± 7.6 and 22.0 ± 5.7 minutes (p < 0.001), respectively. The VO_2peak was not attained when exercising at FTP_+15W (VO_2peak: 3.61 ± 0.81 vs FTP_+15W 3.33 ± 0.68 L·min^-1, p < 0.001). The VO₂ stabilised during both intensities. However, the end test blood lactate corresponding to FTP and FTP_+15W was significantly different (6.7 ± 2.1 mM vs 9.2 ± 2.9 mM; p < 0.05). The VO₂ response corresponding to FTP and FTP_+15W suggests that FTP should not be considered a threshold marker between heavy and severe intensity.

Improved Estimation of Exercise Intensity Thresholds by Combining Dual Non-Invasive Biomarker Concepts: Correlation Properties of Heart Rate Variability and Respiratory Frequency

Identifying exercise intensity boundaries has been shown to be important during endurance training for performance enhancement and rehabilitation. Unfortunately, even though surrogate markers show promise when assessed on a group level, substantial deviation from gold standards can be present in each individual. The aim of this study was to evaluate whether combining two surrogate intensity markers improved this agreement. Electrocardiogram (ECG) and gas exchange data were obtained from 21 participants who performed an incremental cycling ramp to exhaustion and evaluated for first (VT1) and second (VT2) ventilatory thresholds, heart rate (HR) variability (HRV), and ECG derived respiratory frequency (EDR). HRV thresholds (HRVT) were based on the non-linear index a1 of a Detrended Fluctuation Analysis (DFA a1) and EDR thresholds (EDRT) upon the second derivative of the sixth-order polynomial of EDR over time. The average of HRVT and EDRT HR was set as the combined threshold (Combo). Mean VT1 was reached at a HR of 141 ± 15, HRVT1 at 152 ± 14 (p < 0.001), EDRT1 at 133 ± 12 (p < 0.001), and Combo1 at 140 ± 13 (p = 0.36) bpm with Pearson's r of 0.83, 0.78, and 0.84, respectively, for comparisons to VT1. A Bland-Altman analysis showed mean biases of 8.3 ± 7.9, -8.3 ± 9.5, and -1.7 ± 8.3 bpm, respectively. A mean VT2 was reached at a HR of 165 ± 13, HRVT2 at 167 ± 10 (p = 0.89), EDRT2 at 164 ± 14 (p = 0.36), and Combo2 at 164 ± 13 (p = 0.59) bpm with Pearson's r of 0.58, 0.95, and 0.94, respectively, for comparisons to VT2. A Bland-Altman analysis showed mean biases of -0.3 ± 8.9, -1.0 ± 4.6, and -0.6 ± 4.6 bpm, respectively. Both the DFA a1 and EDR intensity thresholds based on HR taken individually had moderate agreement to targets derived through gas exchange measurements. By combining both non-invasive approaches, there was improved correlation, reduced bias, and limits of agreement to the respective corresponding HRs at VT1 and VT2.

Validation of a non-linear index of heart rate variability to determine aerobic and anaerobic thresholds during incremental cycling exercise in women

Studies highlight the usage of non-linear time series analysis of heart rate variability (HRV) using the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA-alpha1) during exercise to determine aerobic and anaerobic thresholds. The present study aims to further verify this approach in women. Gas exchange and HRV data were collected from 26 female participants with different activity levels. Oxygen uptake (VO₂) and heart rate (HR) at first (VT1) and second ventilatory thresholds (VT2) were compared with DFA-alpha1-based thresholds 0.75 (HRVT1) and 0.50 (HRVT2). Results: VO₂ at VT1 and VT2 were 25.2 ml/kg/min (± 2.8) and 31.5 ml/kg/min (± 3.6) compared with 26.5 ml/kg/min (± 4.0) and 31.9 ml/kg/min (± 4.5) for HRVT1 and HRVT2, respectively (ICC_3,1 = 0.77, 0.84; r = 0.81, 0.86, p < 0.001). The mean HR at VT1 was 147 bpm (± 15.6) and 167 bpm (± 12.7) for VT2, compared with 152 bpm (± 15.5) and 166 bpm (± 13.2) for HRVT1 and HRVT2, respectively (ICC_3,1 = 0.87, 0.90; r = 0.87, 0.90, p < 0.001). Bland-Altman analysis for VT1 vs. HRVT1 showed a mean difference of - 1.3 ml/kg/min (± 2.4; LoA: 3.3, - 6.0 ml/kg/min) for VO₂ and of - 4.7 bpm (± 7.8; LoA: 10.6, - 20.0 bpm) for HR. VT2 vs. HRVT2 showed a mean difference of - 0.4 ml/kg/min (± 2.3; LoA: 4.1, - 4.9 ml/kg/min) for VO₂ and 0.5 bpm (± 5.7; LoA: 11.8, - 10.8 bpm) for HR. DFA-alpha1-based thresholds showed good agreement with traditionally used thresholds and could be used as an alternative approach for marking organismic transition zones for intensity distribution in women.

A proposal to identify the maximal metabolic steady state by muscle oxygenation and VO2max levels in trained cyclists

Purpose

Near-infrared spectroscopy (NIRS) sensors measure muscle oxygen saturation (SmO2) as a performance factor in endurance athletes. The objective of this study is to delimit metabolic thresholds relative to maximal metabolic steady state (MMSS) using SmO2 in cyclists.

Methods

Forty-eight cyclists performed a graded incremental test (GTX) (100 W-warm-up followed by 30 W min) until exhaustion. SmO2 was measured with a portable NIRS placed on the vastus lateralis. Subjects were classified by VO2max levels with a scale from 2 to 5: L2 = 45–54.9, L3 = 55–64.9, L4 = 65–71, L5 =  > 71, which represent recreationally trained, trained, well-trained, and professional, respectively. Then, metabolic thresholds were determined: Fatmax zone, functional threshold power (FTP), respiratory compensation point (RCP), and maximal aerobic power (MAP). In addition, power output%, heart rate%, VO2%, carbohydrate and fat consumption to cutoff SmO2 point relative to MMSS were obtained.

Results

A greater SmO2 decrease was found in cyclists with > 55 VO2max (L3, L4 and L5) vs. cyclists (L2) in the MMSS. Likewise, after passing FTP and RCP, performance is dependent on better muscle oxygen extraction. Furthermore, the MMSS was defined at 27% SmO2, where a non-steady state begins during exercise in trained cyclists.

Conclusion

A new indicator has been provided for trained cyclists, < 27% SmO2 as a cut-off to define the MMSS Zone. This is the intensity for which the athlete can sustain 1 h of exercise under quasi-steady state conditions without fatiguing.

Is the maximal lactate steady state concept really relevant to predict endurance performance?

PURPOSE

There is no convincing evidence for the idea that a high power output at the maximal lactate steady state (PO_{_MLSS}) and a high fraction of [Formula: see text]O_2max at MLSS (%[Formula: see text]O_{2_MLSS}) are decisive for endurance performance. We tested the hypotheses that (1) %[Formula: see text]O_{2_MLSS} is positively correlated with the ability to sustain a high fraction of [Formula: see text]O_2max for a given competition duration (%[Formula: see text]O_{2_TT}); (2) %[Formula: see text]O_{2_MLSS} improves the prediction of the average power output of a time trial (PO_{_TT}) in addition to [Formula: see text]O_2max and gross efficiency (GE); (3) PO_{_MLSS} improves the prediction of PO_{_TT} in addition to [Formula: see text]O_2max and GE.

METHODS

Twenty-one recreationally active participants performed stepwise incremental tests on the first and final testing day to measure GE and check for potential test-related training effects in terms of changes in the minimal lactate equivalent power output (∆PO_LEmin), 30-min constant load tests to determine MLSS, a ramp test and verification bout for [Formula: see text]O_2max, and 20-min time trials for %[Formula: see text]O_{2_TT} and PO_{_TT}. Hypothesis 1 was tested via bivariate and partial correlations between %[Formula: see text]O_{2_MLSS} and %[Formula: see text]O_{2_TT}. Multiple regression models with [Formula: see text]O_2max, GE, ∆PO_LEmin, and %[Formula: see text]O_{2_MLSS} (Hypothesis 2) or PO_{_MLSS} instead of %[Formula: see text]O_{2_MLSS} (Hypothesis 3), respectively, as predictors, and PO_{_TT} as the dependent variable were used to test the hypotheses.

RESULTS

%[Formula: see text]O_{2_MLSS} was not correlated with %[Formula: see text]O_{2_TT} (r = 0.17, p = 0.583). Neither %[Formula: see text]O_{2_MLSS} (p = 0.424) nor PO_{_MLSS} (p = 0.208) did improve the prediction of PO_{_TT} in addition to [Formula: see text]O_2max and GE.

CONCLUSION

These results challenge the assumption that PO_{_MLSS} or %[Formula: see text]O_{2_MLSS} are independent predictors of supra-MLSS PO_{_TT} and %[Formula: see text]O_{2_TT}.

Fractal Correlation Properties of Heart Rate Variability as a Biomarker for Intensity Distribution and Training Prescription in Endurance Exercise: An Update

While established methods for determining physiologic exercise thresholds and intensity distribution such as gas exchange or lactate testing are appropriate for the laboratory setting, they are not easily obtainable for most participants. Data over the past two years has indicated that the short-term scaling exponent alpha1 of Detrended Fluctuation Analysis (DFA a1), a heart rate variability (HRV) index representing the degree of fractal correlation properties of the cardiac beat sequence, shows promise as an alternative for exercise load assessment. Unlike conventional HRV indexes, it possesses a dynamic range throughout all intensity zones and does not require prior calibration with an incremental exercise test. A DFA a1 value of 0.75, reflecting values midway between well correlated fractal patterns and uncorrelated behavior, has been shown to be associated with the aerobic threshold in elite, recreational and cardiac disease populations and termed the heart rate variability threshold (HRVT). Further loss of fractal correlation properties indicative of random beat patterns, signifying an autonomic state of unsustainability (DFA a1 of 0.5), may be associated with that of the anaerobic threshold. There is minimal bias in DFA a1 induced by common artifact correction methods at levels below 3% and negligible change in HRVT even at levels of 6%. DFA a1 has also shown value for exercise load management in situations where standard intensity targets can be skewed such as eccentric cycling. Currently, several web sites and smartphone apps have been developed to track DFA a1 in retrospect or in real-time, making field assessment of physiologic exercise thresholds and internal load assessment practical. Although of value when viewed in isolation, DFA a1 tracking in combination with non-autonomic markers such as power/pace, open intriguing possibilities regarding athlete durability, identification of endurance exercise fatigue and optimization of daily training guidance.

Constant power threshold—predicting maximal lactate steady state in recreational cyclists

Introduction

Prolonged time trials proved capable of precisely estimating anaerobic threshold. However, time trial studies in recreational cyclists are missing. The aim of the present study was to evaluate accuracy and viability of constant power threshold, which is the highest power output constantly maintainable over time, for estimating maximal lactate steady state in recreational athletes.

Methods

A total of 25 recreational athletes participated in the study of whom 22 (11 female, 11 male) conducted all constant load time trials required for determining constant power threshold 30 min and 45 min, which is the highest power output constantly maintainable over 30 min and 45 min, respectively. Maximal lactate steady state was assessed subsequently from blood samples taken every 5 min during the time trials.

Results

Constant power threshold over 45 min (175.5 ± 49.6 W) almost matched power output at maximal lactate steady state (176.4 ± 50.5 W), whereas constant power threshold over 30 min (181.4 ± 51.4 W) was marginally higher (P = 0.007, d = 0.74). Interrelations between maximal lactate steady state and constant power threshold 30 min and constant power threshold 45 min were very close (R2 = 0.99, SEE = 8.9 W, Percentage SEE (%SEE) = 5.1%, P < 0.001 and R2 = 0.99, SEE = 10.0 W, %SEE = 5.7%, P < 0.001, respectively).

Conclusions

Determination of constant power threshold is a straining but viable and precise alternative for recreational cyclists to estimate power output at maximal lactate steady state and thus maximal sustainable oxidative metabolic rate.

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.

Effects of a low-carbohydrate diet on body composition and performance in road cycling: a randomized, controlled trial

Introduction

Low-carbohydrate diets are frequently used to improve performance in endurance sports, often with contradictory results. This study aimed to assess whether a low-carbohydrate diet can outperform an isocaloric conventional diet for improving body composition and performance in a sample of twenty-six trained male road cyclists (previous experience in cyclosportive events, 7.6 ± 4.4 years; age, 26.9 ± 4.9 years; weekly training volume, 7.8 ± 2.9 hours; height, 176 ± 7 centimeters; body fat percentage, 9.7 ± 0.8 %; weight, 65.3 ± 2.3 kg). Detraining and pretreatment periods in which nutrition and training were standardized were followed by an eight-week long intervention in which cyclists consumed either a low-carbohydrate diet (15 % of calories from carbohydrates) or a conventional endurance sports diet while maintaining the same training volumes and intensities. Body composition was assessed through electrical impedance, and performance was evaluated through a twenty-minute time trial performed on a smart bike trainer. The results revealed an overall improvement over time in absolute and relative power, body mass, and body fat for both groups, whilst the improvement in absolute power was comparable. The improvements seen in relative power (p = 0.042), body mass (p = 0.006), and body fat (p = 0.01) were significantly higher in the low-carbohydrate group. We concluded that eight weeks of a low-carbohydrate diet significantly reduced body weight and body fat percentage, and improved 20-minute relative power values in a sample of road cyclists when compared to an isocaloric conventional diet.

What is known about the FTP20 test related to cycling? A scoping review

Functional Threshold Power (FTP) in cycling is increasingly used in exercise prescription, particularly with the rise in use of home trainers and virtual exercise platforms. FTP testing does not require biological sampling and is considered a more practical test than others. This scoping review investigated what is known about the 20-minute FTP (FTP²⁰) test. A three-step search strategy was used to identify studies in relevant databases (PubMed, CINAHL, SportDiscus, Google Scholar, Web of Science) and grey literature. Data were extracted and common themes identified which allowed for descriptive analysis and thematic summary. Fifteen studies were included. The primary focus fitted broadly into four themes: reliability, association with other physiological markers, other power-related concepts and performance prediction. The FTP²⁰ test was reported as a reliable test. Studies investigating the relationship of FTP²⁰ with other physiological markers and power-related concepts reported large limits of agreement suggesting parameters cannot be used interchangeably. Some findings indicate that FTP²⁰ may be useful in performance prediction. The majority of studies involved trained male cyclists. Overall, existing literature on the FTP²⁰ test is limited. Further investigation is needed to provide physiological justification for FTP²⁰ and inform use in exercise prescription in a range of populations.

Exercise Intensity and Pacing Pattern During a Cross-Country Olympic Mountain Bike Race

Objective: To examine the power profiles and pacing patterns in relation to critical power (CP) and maximal aerobic power (MAP) output during a cross-country Olympic (XCO) mountain bike race.

Methods: Five male and two female national competitive XCO cyclists completed a UCI Cat. 1 XCO race. The races were 19 km and 23 km and contained five (female) and six (male) laps, respectively. Power output (PO) during the race was measured with the cyclists’ personal power meters. On two laboratory tests using their own bikes and power meters, CP and work capacity above CP (W') were calculated using three time trials of 12, 7, and 3 min, while MAP was established based on a 3-step submaximal test and the maximal oxygen uptake from the 7-min time trial.

Results: Mean PO over the race duration (96 ± 7 min) corresponded to 76 ± 9% of CP and 63 ± 4% of MAP. 40 ± 8% of race time was spent with PO > CP, and the mean duration and magnitude of the bouts >CP was ~8 s and ~120% of CP. From the first to last lap, time >CP and accumulated W' per lap decreased with 9 ± 6% and 45 ± 17%, respectively. For single >CP bouts, mean magnitude and mean W' expended decreased by 25 ± 8% and 38 ± 15% from the first to the last lap, respectively. Number and duration of bouts did not change significantly between laps.

Conclusion: The highly variable pacing pattern in XCO implies the need for rapid changes in metabolic power output, as a result of numerous separate short-lived >CP actions which decrease in magnitude in later laps, but with little lap-to-lap variation in number and duration.

Detection of the Anaerobic Threshold in Endurance Sports: Validation of a New Method Using Correlation Properties of Heart Rate Variability

Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland-Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.

The Importance of 'Durability' in the Physiological Profiling of Endurance Athletes

Profiling physiological attributes is an important role for applied exercise physiologists working with endurance athletes. These attributes are typically assessed in well-rested athletes. However, as has been demonstrated in the literature and supported by field data presented here, the attributes measured during routine physiological-profiling assessments are not static, but change over time during prolonged exercise. If not accounted for, shifts in these physiological attributes during prolonged exercise have implications for the accuracy of their use in intensity regulation during prolonged training sessions or competitions, quantifying training adaptations, training-load programming and monitoring, and the prediction of exercise performance. In this review, we argue that current models used in the routine physiological profiling of endurance athletes do not account for these shifts. Therefore, applied exercise physiologists working with endurance athletes would benefit from development of physiological-profiling models that account for shifts in physiological-profiling variables during prolonged exercise and quantify the 'durability' of individual athletes, here defined as the time of onset and magnitude of deterioration in physiological-profiling characteristics over time during prolonged exercise. We propose directions for future research and applied practice that may enable better understanding of athlete durability.

Relationship between functional threshold power, ventilatory threshold and respiratory compensation point in road cycling

BACKGROUND

The purpose of this study was to assess the relationship between power output and relative power output at the functional threshold power, ventilatory threshold and respiratory compensation point in road cyclists.

METHODS

Forty-six road cyclists (age 38 ± 9 years; height 177 ± 9 cm; body mass 71.4 ± 8.6 kg; body mass index 22.7 ± 2.2 kg·m-1; fat mass 7.8 ± 4%, VO2max 61.1 ± 9.1 ml·min-1·kg-1) performed a graded exercise test in which power output and relative power output at the ventilatory landmarks were identified. Functional threshold power was established as 95% of the power output during a 20-minute test.

RESULTS

Power output and relative power output at the functional threshold power were higher than at the ventilatory threshold (p < 0.001). There were very large to near perfect correlations for power output (95% CI for r from 0.71 to 0.9) and relative power output (95% CI for r from 0.79 to 0.93) at the functional threshold power and respiratory compensation point. Mean bias in power ouput and relative power output measured at RCP compared with FTP was not significant (mean bias 95% CI from -7 to 10 W and - 0.1 to 0.1 W/kg, respectively).

CONCLUSIONS

Power output and relative power output at the functional threshold power are higher than at the ventilatory threshold. Power output and relative power output at the functional threshold power and respiratory compensation point are strongly related, but caution is required when using both concepts indistinctly.

Predicting the individual lactate minimum speed by T10 and T30 in swimming

BACKGROUND

This study investigated the relationship between the lactate minimum (LACmin) and the 10- (T10) and 30-min (T30) continuous tests in swimmers.

METHODS

Twelve swimmers (78.1 ± 3.1% of world record) performed the LACmin (hyperlactatemia: 2 x 50 m all-out 8-min apart, incremental part: n x 300 m 30-s apart), T30 and T10 using the front-crawl stroke. Blood samples were collected after each stage of LACmin for lactate analysis. Swimmers were oriented to swim as fast and as constant as possible in T10 and T30.

RESULTS

Speeds in T10 (1.28 ± 0.10 m/s) and T30 (1.21 ± 0.09 m/s) were different from LACmin (1.24 ± 0.09 m/s). T10 and T30 speeds presented a nearly perfect relationship with LACmin and acceptable prediction errors (T10: r = 0.938, p < 0.001, 0.033 m/s; T30: r = 0.927, p < 0.001, 0.036 m/s, respectively).

CONCLUSIONS

T10 and T30 can be used as indirect tests for evaluating LACmin in swimming.

Can an Incremental Step Test Be Used for Maximal Lactate Steady State Determination in Swimming? Clues for Practice

We aimed to compare the velocity, physiological responses, and stroke mechanics between the lactate parameters determined in an incremental step test (IST) and maximal lactate steady state (MLSS). Fourteen well-trained male swimmers (16.8 ± 2.8 years) were timed for 400 m and 200 m (T₂₀₀). Afterwards, a 7 × 200-m front-crawl IST was performed. Swimming velocity, heart rate (HR), blood lactate concentration (BLC), stroke mechanics, and rate of perceived exertion (RPE) were measured throughout the IST and in the 30-min continuous test (CT) bouts for MLSS determination. Swimming velocities at lactate threshold determined with log-log methodology (1.34 ± 0.06 m∙s⁻¹) and Dmax methodology (1.40 ± 0.06 m∙s⁻¹); and also, the velocity at BLC of 4 mmol∙L⁻¹ (1.36 ± 0.07) were not significantly different from MLSSv, however, Bland–Altman analysis showed wide limits of agreement and the concordance correlation coefficient showed poor strength of agreement between the aforementioned parameters which precludes their interchangeable use. Stroke mechanics, HR, RPE, and BLC in MLSSv were not significantly different from the fourth repetition of IST (85% of T₂₀₀), which by itself can provide useful support to daily practice of well-trained swimmers. Nevertheless, the determination of MLSS_v, based on a CT, remains more accurate for exercise evaluation and prescription.

Continuous forearm cooling attenuates gastrointestinal temperature increase during cycling

Hot environmental conditions can challenge thermoregulation resulting in exacerbated heat strain. This study evaluated the influence of continuous inner forearm cooling on gastrointestinal temperature (T_GI) and physiological responses to exercise in hot (30°C) and humid (relative humidity: 70%) conditions. Eleven trained cyclists (seven male age: 37±12 years; four female age: 41±15 years; mean±standard deviation) performed two experimental trials, cycling at 66% of their self-reported functional threshold power (average work rate over an hour of maximum effort cycling; 175±34W) for 45 minutes in an environmental chamber. One trial employed continuous inner forearm cooling (COOL) with 5°C water passing through aluminum heat exchangers, while the other had no cooling (CONTROL). Heat was removed from the body at an average rate of 30.3±6.6W during the COOL trial resulting in an attenuation of T_GI rise (CONTROL: 2.46±0.70, COOL: 2.03±0.63°C·h^-1; p=0.002). The change in heart rate from the 10th minute to the end of exercise, as an indicator of cardiovascular drift, was reduced (CONTROL: 20±7, COOL: 17±6beats·min^-1; p=0.050) and end-exercise thermal comfort was improved in the COOL trial with a trend for reduced rating of perceived exertion (p=0.055). Findings suggest that continuous cooling of the inner forearms can attenuate the rise of T_GI and help mitigate the risk of heat injury during exercise in hot and humid conditions.

The Application of Critical Power, the Work Capacity above Critical Power (W'), and its Reconstitution: A Narrative Review of Current Evidence and Implications for Cycling Training Prescription

The two-parameter critical power (CP) model is a robust mathematical interpretation of the power–duration relationship, with CP being the rate associated with the maximal aerobic steady state, and W′ the fixed amount of tolerable work above CP available without any recovery. The aim of this narrative review is to describe the CP concept and the methodologies used to assess it, and to summarize the research applying it to intermittent cycle training techniques. CP and W′ are traditionally assessed using a number of constant work rate cycling tests spread over several days. Alternatively, both the 3-min all-out and ramp all-out protocols provide valid measurements of CP and W′ from a single test, thereby enhancing their suitability to athletes and likely reducing errors associated with the assumptions of the CP model. As CP represents the physiological landmark that is the boundary between heavy and severe intensity domains, it presents several advantages over the de facto arbitrarily defined functional threshold power as the basis for cycle training prescription at intensities up to CP. For intensities above CP, precise prescription is not possible based solely on aerobic measures; however, the addition of the W′ parameter does facilitate the prescription of individualized training intensities and durations within the severe intensity domain. Modelling of W′ reconstitution extends this application, although more research is needed to identify the individual parameters that govern W′ reconstitution rates and their kinetics.

Time to Exhaustion at the Respiratory Compensation Point in Recreational Cyclists

The time to exhaustion (t_lim) at the respiratory compensation point (RCP) and whether a physiological steady state is observed at this workload remains unknown. Thus, this study analyzed t_lim at the power output eliciting the RCP (t_lim at RCP), the oxygen uptake (VO₂) response to this effort, and the influence of endurance fitness. Sixty male recreational cyclists (peak oxygen uptake [VO_2peak] 40–60 mL∙kg∙min⁻¹) performed an incremental test to determine the RCP, VO_2peak, and maximal aerobic power (MAP). They also performed constant-load tests to determine the t_lim at RCP and t_lim at MAP. Participants were divided based on their VO_2peak into a low-performance group (LP, n = 30) and a high-performance group (HP, n = 30). The t_lim at RCP averaged 20 min 32 s ± 5 min 42 s, with a high between-subject variability (coefficient of variation 28%) but with no differences between groups (p = 0.788, effect size = 0.06). No consistent relationships were found between the t_lim at RCP and the different fitness markers analyzed (RCP, power output (PO) at RCP, VO_2peak, MAP, or t_lim at MAP; all p > 0.05). VO₂ remained steady overall during the t_lim test, although a VO₂ slow component (i.e., an increase in VO₂ >200 mL·min⁻¹ from the third min to the end of the tests) was present in 33% and 40% of the participants in HP and LP, respectively. In summary, the PO at RCP could be maintained for about 20 min. However, there was a high between-subject variability in both the t_lim and in the VO₂ response to this effort that seemed to be independent of fitness level, which raises concerns on the suitability of this test for fitness assessment.

Functional Threshold Power: Relationship With Respiratory Compensation Point and Effects of Various Warm-Up Protocols

PURPOSE

The functional threshold power (FTP), which demarcates the transition from steady state to non-steady-state oxidative metabolism, is usually determined with a 20-minute cycling time trial that follows a standard ∼45-minute warm-up. This study aimed to determine if the standard warm-up inherent to FTP determination is actually necessary and how its modification or removal affects the relationship between FTP and the respiratory compensation point (RCP).

METHODS

A total of 15 male cyclists (age 35 [9] y, maximum oxygen uptake 66.4 [6.8] mL·kg-1·min-1) participated in this randomized, crossover study. Participants performed a ramp test for determination of RCP and maximum oxygen uptake. During subsequent visits, they performed a 20-minute time trial preceded by the "standard" warm-up that is typically performed before an FTP test (S-WU), a 10-minute warm-up at the power output (PO) corresponding to 60% of maximum oxygen uptake (60%-WU), or no warm-up (No-WU). FTP was computed as 95% of the mean PO attained during the time trial.

RESULTS

Although the FTP was correlated with the RCP independently of the warm-up (r = .89, .93, and .86 for No-WU, 60%-WU, and S-WU, respectively; all Ps < .001), the PO at RCP was higher than the FTP in all cases (bias ± 95% limits of agreement = 57 [24], 60 [23], and 57 [32] W for No-WU, 60%-WU, and S-WU, respectively; all Ps < .001 and effect size > 1.70).

CONCLUSIONS

The FTP is highly correlated with the RCP but corresponds to a significantly lower PO, being these results independent of the warm-up performed (or even with no warm-up).

Power Assessment in Road Cycling: A Narrative Review

Nowadays, the evaluation of physiological characteristics and training load quantification in road cycling is frequently performed through power meter data analyses, but the scientific evidence behind this tool is scarce and often contradictory. The aim of this paper is to review the literature related to power profiling, functional threshold testing, and performance assessment based on power meter data. A literature search was conducted following preferred reporting items for review statement (PRISMA) on the topic of {“cyclist” OR “cycling” AND “functional threshold” OR “power meter”}. The reviewed evidence provided important insights regarding power meter-based training: (a) functional threshold testing is closely related to laboratory markers of steady state; (b) the 20-min protocol represents the most researched option for functional threshold testing, although shorter durations may be used if verified on an individual basis; (c) power profiling obtained through the recovery of recorded power outputs allows the categorization and assessment of the cyclist’s fitness level; and (d) power meters represent an alternative to laboratory tests for the assessment of the relationship between power output and cadence. This review elucidates the increasing amount of studies related to power profiling, functional threshold testing, and performance assessment based on power meter data, highlighting the opportunity for the expanding knowledge that power meters have brought in the road cycling field.

Physiological Predictors of Competition Performance in CrossFit Athletes

The aim of this study was to determine the physiological variables that predict competition performance during a CrossFit competition. Fifteen male amateur CrossFit athletes (age, 35 ± 9 years; CrossFit experience, 40 ± 27 months) performed a series of laboratory-based tests (incremental load test for deep full squat and bench press; squat, countermovement and drop jump tests; and incremental running and Wingate tests) that were studied as potential predictors of CrossFit performance. Thereafter, they performed the five Workouts of the Day (WODs) corresponding to the CrossFit Games Open 2019, and we assessed the relationship between the laboratory-based markers and CrossFit performance with regression analyses. Overall CrossFit performance (i.e., final ranking considering the sum of all WODs, as assessed by number of repetitions, time spent in exercises or weight lifted) was significantly related to jump ability, mean and peak power output during the Wingate test, relative maximum strength for the deep full squat and the bench press, and maximum oxygen uptake (VO2max) and speed during the incremental test (all p < 0.05, r = 0.58-0.75). However, the relationship between CrossFit Performance and most laboratory markers varied depending on the analyzed WOD. Multiple linear regression analysis indicated that measures of lower-body muscle power (particularly jump ability) and VO2max explained together most of the variance (R2 = 81%, p < 0.001) in overall CrossFit performance. CrossFit performance is therefore associated with different power-, strength-, and aerobic-related markers.

Is the FTP Test a Reliable, Reproducible and Functional Assessment Tool in Highly-Trained Athletes?

The aim of the current study was to assess reliability of the Functional Threshold Power test (FTP) and the corresponding intensity sustainable for 1-hour in a "quasi-steady state". Highly-trained athletes (n = 19) completed four non-randomized tests over successive weeks on a Wattbike; a 3-min incremental test (GxT) to exhaustion, two 20-min FTP tests and a 60-min test at computed FTP (cFTP). Power at cFTP was calculated by reducing 20-min FTP data by 5% and was compared with power at Dmax and lactate threshold (TLac). Ventilatory and blood lactate (BLa) responses to cFTP were measured to determine whether cFTP was quasi-steady state. Agreement between consecutive FTP tests was quantified using a Bland-Altman plot with 95% limits of agreement (95% LoA) set at ± 20 W. Satisfactory agreement between FTP tests was detected (95% LoA = +13 and -17 W, bias +2 W). The 60-min effort at cFTP was successfully completed by 17 participants, and BLa and ventilatory data at cFTP were classified as quasi-steady state. A 5% increase in power above cFTP destabilized BLa data (p < 0.05) and prompted VO2 to increase to peak GxT rates. The FTP test is therefore deemed representative of the uppermost power a highly-trained athlete can maintain in a quasi-steady state for 60-min. Agreement between repeated 20-min FTP tests was judged acceptable.