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Background: It is unknown whether there are differences in maximal oxygen uptake ( V O 2max ) response when prescribing intensity relative to traditional (TRAD) anchors or to physiological thresholds (THR)., Objectives: The present meta-analysis sought to compare: (a) mean change in V O 2max , (b) proportion of individuals increasing V O 2max beyond a minimum important difference (MID) and (c) response variability in V O 2max between TRAD and THR., Methods: Electronic databases were searched, yielding data for 1544 individuals from 42 studies. Two datasets were created, comprising studies with a control group ('controlled' studies), and without a control group ('non-controlled' studies). A Bayesian approach with multi-level distributional models was used to separately analyse V O 2max change scores from the two datasets and inferences were made using Bayes factors (BF). The MID was predefined as one metabolic equivalent (MET; 3.5 mL kg -1  min -1 )., Results: In controlled studies, mean V O 2max change was greater in the THR group compared with TRAD (4.1 versus 1.8 mL kg -1  min -1 , BF > 100), with 64% of individuals in the THR group experiencing an increase in V O 2max  > MID, compared with 16% of individuals taking part in TRAD. Evidence indicated no difference in standard deviation of change between THR and TRAD (1.5 versus 1.7 mL kg -1  min -1 , BF = 0.55), and greater variation in exercise groups relative to non-exercising controls (1.9 versus 1.3 mL kg -1  min -1 , BF = 12.4). In non-controlled studies, mean V O 2max change was greater in the THR group versus the TRAD group (4.4 versus 3.4 mL kg -1  min -1 , BF = 35.1), with no difference in standard deviation of change (3.0 versus 3.2 mL kg -1  min -1 , BF = 0.41)., Conclusion: Prescribing exercise intensity using THR approaches elicited superior mean changes in V O 2max and increased the likelihood of increasing V O 2max beyond the MID compared with TRAD. Researchers designing future exercise training studies should thus consider the use of THR approaches to prescribe exercise intensity where possible. Analysis comparing interventions with controls suggested the existence of intervention response heterogeneity; however, evidence was not obtained for a difference in response variability between THR and TRAD. Future primary research should be conducted with adequate power to investigate the scope of inter-individual differences in V O 2max trainability, and if meaningful, the causative factors., Competing Interests: Declarations Funding No funding and financial assistance was used for this review. Conflicts of Interest Authors Samuel J. R. Meyler, Paul A. Swinton, Lindsay Bottoms, Lance C. Dalleck, Ben Hunter, Mark A. Sarzynski, David Wellsted, Camilla J. Williams and Daniel Muniz-Pumares declare that they have no conflicts of interest relevant to the content of this review. Availability of Data and Material Datasets analysed in the current review are available upon reasonable request but are subject to permission from original authors. Ethics Approval Each study received ethical approval from their respective institutions, conformed to the guidelines of the Declaration of Helsinki and obtained written informed consent from each participant prior to commencing data collection. Author Contributions All authors: (1) made substantial contributions to the conception or design of the work or to the acquisition, analysis, or interpretation of data and (2) drafted the work or revised it critically for important intellectual content. All authors read and approved the final version of the manuscript., (© 2024. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Purpose: Training characteristics such as duration, frequency, and intensity can be manipulated to optimize endurance performance, with an enduring interest in the role of training-intensity distribution to enhance training adaptations. Training intensity is typically separated into 3 zones, which align with the moderate-, heavy-, and severe-intensity domains. While estimates of the heavy- and severe-intensity boundary, that is, the critical speed (CS), can be derived from habitual training, determining the moderate-heavy boundary or first threshold (T1) requires testing, which can be costly and time-consuming. Therefore, the aim of this review was to examine the percentage at which T1 occurs relative to CS., Results: A systematic literature search yielded 26 studies with 527 participants, grouped by mean CS into low (11.5 km·h-1; 95% CI, 11.2-11.8), medium (13.4 km·h-1; 95% CI, 11.2-11.8), and high (16.0 km·h-1; 95% CI, 15.7-16.3) groups. Across all studies, T1 occurred at 82.3% of CS (95% CI, 81.1-83.6). In the medium- and high-CS groups, T1 occurred at a higher fraction of CS (83.2% CS, 95% CI, 81.3-85.1, and 84.2% CS, 95% CI, 82.3-86.1, respectively) relative to the low-CS group (80.6% CS, 95% CI, 78.0-83.2)., Conclusions: The study highlights some uncertainty in the fraction of T1 relative to CS, influenced by inconsistent approaches in determining both boundaries. However, our findings serve as a foundation for remote analysis and prescription of exercise intensity, although testing is recommended for more precise applications.

Purpose: This study aimed to compare estimations of critical speed (CS) and work completed above CS (D'), and their analogies for running power (critical power [CP] and W'), derived from raw data obtained from habitual training (HAB) and intentional maximal efforts in the form of time trials (TTs) and 3-minute all-out tests (3MTs) in recreational runners. The test-retest reliability of the 3MT was further analyzed., Methods: Twenty-three recreational runners (4 female) used a foot pod to record speed, altitude, and power output for 8 consecutive weeks. CS and D', and CP and W', were calculated from the best 3-, 7-, and 12-minute segments recorded in the first 6 weeks of their HAB and in random order in weeks 7 and 8 from 3 TTs (3, 7, and 12 min) and three 3MTs (to assess test-retest reliability)., Results: There was no difference between estimations of CS or CP derived from HAB, TT, and 3MT (3.44 [0.63], 3.42 [0.53], and 3.76 [0.57] m · s-1 and 281 [41], 290 [45], and 305 [54] W, respectively), and strong agreement between HAB and TT for CS (r = .669) and CP (r = .916). Limited agreement existed between estimates of D'/W'. Moderate reliability of D'/W' was demonstrated between the first and second 3MTs, whereas excellent reliability was demonstrated for CS/CP., Conclusion: These data suggest that estimations of CS/CP can be derived remotely, from either HAB, TT, or 3MT, although the lower agreement between D'/W' warrants caution when using these measures interchangeably.

The aim of this scoping review was to evaluate research approaches that quantify changes to non-linear movement dynamics during running in response to fatigue, different speeds, and fitness levels. PubMed and Scopus were used to identify appropriate research articles. After the selection of eligible studies, study details and participant characteristics were extracted and tabulated to identify methodologies and findings. Twenty-seven articles were included in the final analysis. To evaluate non-linearities in the time series, a range of approaches were identified including motion capture, accelerometery, and foot switches. Common methods of analysis included measures of fractal scaling, entropy, and local dynamic stability. Conflicting findings were evident when studies examined non-linear features in fatigued states when compared to non-fatigued. More pronounced alterations to movement dynamics are evident when running speed is changed markedly. Greater fitness levels resulted in more stable and predictable running patterns. The mechanisms by which these changes are underpinned require further examination. These could include the physiological demand of running, biomechanical constraints of the runner, and the attentional demands of the task. Moreover, the practical implications are yet to be elucidated. This review has identified gaps in the literature which should be addressed for further understanding of the field.

Aim: This study characterised the decoupling of internal-to-external workload in marathon running and investigated whether decoupling magnitude and onset could improve predictions of marathon performance., Methods: The decoupling of internal-to-external workload was calculated in 82,303 marathon runners (13,125 female). Internal workload was determined as a percentage of maximum heart rate, and external workload as speed relative to estimated critical speed (CS). Decoupling magnitude (i.e., decoupling in the 35-40 km segment relative to the 5-10 km segment) was classified as low (< 1.1), moderate (≥ 1.1 but < 1.2) or high (≥ 1.2). Decoupling onset was calculated when decoupling exceeded 1.025., Results: The overall internal-to-external workload decoupling experienced was 1.16 ± 0.22, first detected 25.2 ± 9.9 km into marathon running. The low decoupling group (34.5% of runners) completed the marathon at a faster relative speed (88 ± 6% CS), had better marathon performance (217.3 ± 33.1 min), and first experienced decoupling later in the marathon (33.4 ± 9.0 km) compared to those in the moderate (32.7% of runners, 86 ± 6% CS, 224.9 ± 31.7 min, and 22.6 ± 7.7 km), and high decoupling groups (32.8% runners, 82 ± 7% CS, 238.5 ± 30.7 min, and 19.1 ± 6.8 km; all p < 0.01). Compared to females, males' decoupling magnitude was greater (1.17 ± 0.22 vs. 1.12 ± 0.16; p < 0.01) and occurred earlier (25.0 ± 9.8 vs. 26.3 ± 10.6 km; p < 0.01). Marathon performance was associated with the magnitude and onset of decoupling, and when included in marathon performance models utilising CS and the curvature constant, prediction error was reduced from 6.45 to 5.16%., Conclusion: Durability characteristics, assessed as internal-to-external workload ratio, show considerable inter-individual variability, and both its magnitude and onset are associated with marathon performance., (© 2022. The Author(s).)

New Findings: What is the topic of this review? Biological and methodological factors associated with the variable changes in cardiorespiratory fitness in response to endurance training. What advances does it highlight? Several biological and methodological factors exist that each contribute, to a given extent, to response variability. Notably, prescribing exercise intensity relative to physiological thresholds reportedly increases cardiorespiratory fitness response rates compared to when prescribed relative to maximum physiological values. As threshold-based approaches elicit more homogeneous acute physiological responses among individuals, when repeated over time, these uniform responses may manifest as more homogeneous chronic adaptations thereby reducing response variability., Abstract: Changes in cardiorespiratory fitness (CRF) in response to endurance training (ET) exhibit large variations, possibly due to a multitude of biological and methodological factors. It is acknowledged that ∼20% of individuals may not achieve meaningful increases in CRF in response to ET. Genetics, the most potent biological contributor, has been shown to explain ∼50% of response variability, whilst age, sex and baseline CRF appear to explain a smaller proportion. Methodological factors represent the characteristics of the ET itself, including the type, volume and intensity of exercise, as well as the method used to prescribe and control exercise intensity. Notably, methodological factors are modifiable and, upon manipulation, alter response rates to ET, eliciting increases in CRF regardless of an individual's biological predisposition. Particularly, prescribing exercise intensity relative to a physiological threshold (e.g., ventilatory threshold) is shown to increase CRF response rates compared to when intensity is anchored relative to a maximum physiological value (e.g., maximum heart rate). It is, however, uncertain whether the increased response rates are primarily attributable to reduced response variability, greater mean changes in CRF or both. Future research is warranted to elucidate whether more homogeneous chronic adaptations manifest over time among individuals, as a result of exposure to more homogeneous exercise stimuli elicited by threshold-based practices., (© 2021 The Authors. Experimental Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.)

Purpose: Altered movement complexity, indicative of system dysfunction, has been demonstrated with increased running velocity and neuromuscular fatigue. The critical velocity (CV) denotes a metabolic and neuromuscular fatigue threshold. It remains unclear whether changes to complexity during running are coupled with the exercise intensity domain in which it is performed. The purpose of this study was to examine whether movement variability and complexity differ exclusively above the CV intensity during running., Methods: Ten endurance-trained participants ran at 95%, 100%, 105% and 115% CV for 20 min or to task failure, whichever occurred first. Movement at the hip, knee, and ankle were sampled throughout using 3D motion analysis. Complexity of kinematics in the first and last 30 s were quantified using sample entropy (SampEn) and detrended fluctuation analysis (DFA-α). Variability was determined using standard deviation (SD)., Results: SampEn decreased during all trials in knee flexion/extension and it increased in hip internal/external rotation, whilst DFA-α increased in knee internal/external rotation. SD of ankle plantar/dorsiflexion and inversion/eversion, knee internal/external rotation, and hip flexion/extension and abduction/adduction increased during trials. Hip flexion/extension SampEn values were lowest below CV. DFA-α was lower at higher velocities compared to velocities below CV in ankle plantar/dorsiflexion, hip flexion/extension, hip adduction/abduction, hip internal/external rotation. In hip flexion/extension SD was highest at 115% CV., Conclusions: Changes to kinematic complexity over time are consistent between heavy and severe intensity domains. The findings suggest running above CV results in increased movement complexity and variability, particularly at the hip, during treadmill running.

Introduction: Critical speed (CS) represents the highest intensity at which a physiological steady state may be reached. The aim of this study was to evaluate whether estimations of CS obtained from raw training data can predict performance and pacing in marathons., Methods: We investigated running activities logged into an online fitness platform by >25,000 recreational athletes before big-city marathons. Each activity contained time, distance, and elevation every 100 m. We computed grade-adjusted pacing and the fastest pace recorded for a set of target distances (400, 800, 1000, 1500, 3000, and 5000 m). CS was determined as the slope of the distance-time relationship using all combinations of, at least, three target distances., Results: The relationship between distance and time was linear, irrespective of the target distances used (pooled mean ± SD: R = 0.9999 ± 0.0001). The estimated values of CS from all models were not different (3.74 ± 0.08 m·s), and all models correlated with marathon performance (R = 0.672 ± 0.036, error = 8.01% ± 0.51%). CS from the model including 400, 800, and 5000 m best predicted performance (R = 0.695, error = 7.67%) and was used in further analysis. Runners completed the marathon at 84.8% ± 13.6% CS, with faster runners competing at speeds closer to CS (93.0% CS for 150 min marathon times vs 78.9% CS for 360 min marathon times). Runners who completed the first half of the marathon at >94% of their CS, and particularly faster than CS, were more likely to slowdown by more than 25% in the second half of race., Conclusion: This study suggests that estimations of CS from raw training data can successfully predict marathon performance and provide useful pacing information.

Purpose: To investigate the influence of torque factor and sprint duration on the effects of caffeine on sprint cycling performance., Methods: Using a counterbalanced, randomized, double-blind, placebo-controlled design, 13 men completed 9 trials. In trial 1, participants completed a series of 6-s sprints at increasing torque factors to determine the torque factor, for each individual, that elicited the highest (T optimal ) peak power output (PPO). The remaining trials involved all combinations of torque factor (0.8 N·m -1 ·kg -1 vs T optimal ), sprint duration (10 s vs 30 s), and supplementation (caffeine [5 mg·kg -1 ] vs placebo)., Results: There was a significant effect of torque factor on PPO, with higher values at T optimal (mean difference 168 W; 95% likely range 142-195 W). There was also a significant effect of sprint duration on PPO, with higher values in 10-s sprints (mean difference 52 W; 95% likely range 18-86 W). However, there was no effect of supplementation on PPO (P = .056). Nevertheless, there was a significant torque factor × sprint duration × supplement interaction (P = .036), with post hoc tests revealing that caffeine produced a higher PPO (mean difference 76 W; 95% likely range 19-133 W) when the sprint duration was 10 s and the torque factor was T optimal ., Conclusion: The results of this study show that when torque factor and sprint duration are optimized, to allow participants to express their highest PPO, there is a clear effect of caffeine on sprinting performance.

Muniz-Pumares, D, Karsten, B, Triska, C, and Glaister, M. Methodological approaches and related challenges associated with the determination of critical power and W'. J Strength Cond Res 33(2): 584-596, 2019-The relationship between exercise intensity and time to task failure (P-T relationship) is hyperbolic, and characterized by its asymptote (critical power [CP]) and curvature constant (W'). The determination of these parameters is of interest for researchers and practitioners, but the testing protocol for CP and W' determination has not yet been standardized. Conventionally, a series of constant work rate (CWR) tests to task failure have been used to construct the P-T relationship. However, the duration, number, and recovery between predictive CWR and the mathematical model (hyperbolic or derived linear models) are known to affect CP and W'. Moreover, repeating CWR may be deemed as a cumbersome and impractical protocol. Recently, CP and W' have been determined in field and laboratory settings using time trials, but the validity of these methods has raised concerns. Alternatively, a 3-minute all-out test (3MT) has been suggested, as it provides a simpler method for the determination of CP and W', whereby power output at the end of the test represents CP, and the amount of work performed above this end-test power equates to W'. However, the 3MT still requires an initial incremental test and may overestimate CP. The aim of this review is, therefore, to appraise current methods to estimate CP and W', providing guidelines and suggestions for future research where appropriate.

This study investigated (i) whether the accumulated oxygen deficit (AOD) and curvature constant of the power-duration relationship (W') are different during constant work-rate to exhaustion (CWR) and 3-min all-out (3MT) tests and (ii) the relationship between AOD and W' during CWR and 3MT. Twenty-one male cyclists (age: 40 ± 6 years; maximal oxygen uptake [V̇O 2max ]: 58 ± 7 ml · kg -1 · min -1 ) completed preliminary tests to determine the V̇O 2 -power output relationship and V̇O 2max . Subsequently, AOD and W' were determined as the difference between oxygen demand and oxygen uptake and work completed above critical power, respectively, in CWR and 3MT. There were no differences between tests for duration, work, or average power output (P ≥ 0.05). AOD was greater in the CWR test (4.18 ± 0.95 vs. 3.68 ± 0.98 L; P = 0.004), whereas W' was greater in 3MT (9.55 ± 4.00 vs. 11.37 ± 3.84 kJ; P = 0.010). AOD and W' were significantly correlated in both CWR (P < 0.001, r = 0.654) and 3MT (P < 0.001, r = 0.654). In conclusion, despite positive correlations between AOD and W' in CWR and 3MT, between-test differences in the magnitude of AOD and W', suggest that both measures have different underpinning mechanisms.

Purpose: The aim of the study was to determine the effect of supramaximal exercise intensity during constant work-rate cycling to exhaustion on the accumulated oxygen deficit (AOD) and to determine the test-retest reliability of AOD., Methods: Twenty-one trained male cyclists and triathletes (mean ± SD for age and maximal oxygen uptake [V̇O 2 max] were 41 ± 7 y and 4.53 ± 0.54 L/min, respectively) performed initial tests to determine the linear relationship between V̇O 2 and power output, and V̇O 2 max. In subsequent trials, AOD was determined from exhaustive square-wave cycling trials at 105%, 112.5% (in duplicate), 120%, and 127.5% V̇O 2 max., Results: Exercise intensity had an effect (P = .011) on the AOD (3.84 ± 1.11, 4.23 ± 0.96, 4.09 ± 0.87, and 3.93 ± 0.89 L at 105%, 112.5%, 120%, and 127.5% V̇O 2 max, respectively). Specifically, AOD at 112.5% V̇O 2 max was greater than at 105% V̇O 2 max (P = .033) and at 127.5% V̇O 2 max (P = .022), but there were no differences between the AOD at 112.5% and 120% V̇O 2 max. In 76% of the participants, the maximal AOD occurred at 112.5% or 120% V̇O 2 max. The reliability statistics of the AOD at 112.5% V̇O 2 max, determined as intraclass correlation coefficient and coefficient of variation, were .927 and 8.72%, respectively., Conclusions: The AOD, determined from square-wave cycling bouts to exhaustion, peaks at intensities of 112.5-120% V̇O 2 max. Moreover, the AOD at 112.5% V̇O 2 max exhibits an 8.72% test-retest reliability.

Background: The aim of this study was to investigate the relationship between oxygen uptake (V̇O2) and power output at intensities below and above the lactate threshold (LT) in cyclists; and to determine the reliability of supramaximal power outputs linearly projected from these relationships., Methods: Nine male cyclists (mean±standard deviation age: 41±8 years; mass: 77±6 kg, height: 1.79±0.05 m and V̇O2max: 54±7 mL∙kg-1∙min-1) completed two cycling trials each consisting of a step test (10×3 min stages at submaximal incremental intensities) followed by a maximal test to exhaustion. The lines of best fit for V̇O2 and power output were determined for: the entire step test; stages below and above the LT, and from rolling clusters of five consecutive stages. Lines were projected to determine a power output predicted to elicit 110% peak V̇O2., Results: There were strong linear correlations (r≥0.953; P<0.01) between V̇O2 and power output using the three approaches; with the slope, intercept, and projected values of these lines unaffected (P≥0.05) by intensity. The coefficient of variation of the predicted power output at 110% V̇O2max was 6.7% when using all ten submaximal stages., Conclusions: Cyclists exhibit a linear V̇O2 and power output relationship when determined using 3 min stages, which allows for prediction of a supramaximal intensity with acceptable reliability.

Objectives: The aim of this study was to evaluate the effects of caffeine on physiological responses to submaximal exercise, with a focus on blood lactate concentration ([BLa])., Methods: Using a randomised, single-blind, crossover design; 16 endurance-trained, male cyclists (age: 38 ± 8 years; height: 1.80 ± 0.05 m; body mass: 76.6 ± 7.8 kg; [Formula: see text]: 4.3 ± 0.6 L∙min-1) completed four trials on an electromagnetically-braked cycle ergometer. Each trial consisted of a six-stage incremental test (3 minute stages) followed by 30 minutes of passive recovery. One hour before trials 2-4, participants ingested a capsule containing 5 mg∙kg-1 of either caffeine or placebo (maltodextrin). Trials 2 and 3 were designed to evaluate the effects of caffeine on various physiological responses during exercise and recovery. In contrast, Trial 4 was designed to evaluate the effects of caffeine on [BLa] during passive recovery from an end-exercise concentration of 4 mmol∙L-1., Results: Relative to placebo, caffeine increased [BLa] during exercise, independent of exercise intensity (mean difference: 0.33 ± 0.41 mmol∙L-1; 95% likely range: 0.11 to 0.55 mmol∙L-1), but did not affect the time-course of [BLa] during recovery (p = 0.604). Caffeine reduced ratings of perceived exertion (mean difference: 0.5 ± 0.7; 95% likely range: 0.1 to 0.9) and heart rate (mean difference: 3.6 ± 4.2 b∙min-1; 95% likely range: 1.3 to 5.8 b∙min-1) during exercise, with the effect on the latter dissipating as exercise intensity increased. Supplement × exercise intensity interactions were observed for respiratory exchange ratio (p = 0.004) and minute ventilation (p = 0.034)., Conclusions: The results of the present study illustrate the clear, though often subtle, effects of caffeine on physiological responses to submaximal exercise. Researchers should be aware of these responses, particularly when evaluating the physiological effects of various experimental interventions.

The aim of this study was to investigate the effects of caffeine supplementation on peak anaerobic power output (Wmax). Using a counterbalanced, randomised, double-blind, placebo-controlled design, 14 well-trained men completed three trials of a protocol consisting of a series of 6-s cycle ergometer sprints, separated by 5-min passive recovery periods. Sprints were performed at progressively increasing torque factors to determine the peak power/torque relationship and Wmax. Apart from Trial 1 (familiarisation), participants ingested a capsule containing 5 mg·kg(-1) of caffeine or placebo, one hour before each trial. The effects of caffeine on blood lactate were investigated using capillary samples taken after each sprint. The torque factor which produced Wmax was not significantly different (p ≥ 0.05) between the caffeine (1.15 ± 0.08 N·m·kg(-1)) and placebo (1.13 ± 0.10 N·m·kg(-1)) trials. There was, however, a significant effect (p < 0.05) of supplementation on Wmax, with caffeine producing a higher value (1885 ± 303 W) than placebo (1835 ± 290 W). Analysis of the blood lactate data revealed a significant (p < 0.05) torque factor × supplement interaction with values being significantly higher from the sixth sprint (torque factor 1.0 N·m·kg(-1)) onwards following caffeine supplementation. The results of this study confirm previous reports that caffeine supplementation significantly increases blood lactate and Wmax. These findings may explain why the majority of previous studies, which have used fixed-torque factors of around 0.75 N·m·kg(-1) and thereby failing to elicit Wmax, have failed to find an effect of caffeine on sprinting performance.

The aim of this study was to examine the acute supplementation effects of dietary nitrate, caffeine, and their combination on 20-km cycling time trial performance. Using a randomized, counterbalanced, double-blind Latin-square design, 14 competitive female cyclists (age: 31 ± 7 years; height: 1.69 ± 0.07 m; body mass: 61.6 ± 6.0 kg) completed four 20-km time trials on a racing bicycle fitted to a turbo trainer. Approximately 2.5 hours before each trial, subjects consumed a 70-ml dose of concentrated beetroot juice containing either 0.45 g of dietary nitrate or with the nitrate content removed (placebo). One hour before each trial, subjects consumed a capsule containing either 5 mg·kg of caffeine or maltodextrin (placebo). There was a significant effect of supplementation on power output (p = 0.001), with post hoc tests revealing higher power outputs in caffeine (205 ± 21 W) vs. nitrate (194 ± 22 W) and placebo (194 ± 25 W) trials only. Caffeine-induced improvements in power output corresponded with significantly higher measures of heart rate (caffeine: 166 ± 12 b·min vs. placebo: 159 ± 15 b·min; p = 0.02), blood lactate (caffeine: 6.54 ± 2.40 mmol·L vs. placebo: 4.50 ± 2.11 mmol·L; p < 0.001), and respiratory exchange ratio (caffeine: 0.95 ± 0.04 vs. placebo: 0.91 ± 0.05; p = 0.03). There were no effects (p ≥ 0.05) of supplementation on cycling cadence, rating of perceived exertion, (Equation is included in full-text article.), or integrated electromyographic activity. The results of this study support the well-established beneficial effects of caffeine supplementation on endurance performance. In contrast, acute supplementation with dietary nitrate seems to have no effect on endurance performance and adds nothing to the benefits afforded by caffeine supplementation.