Cadence Paradox in Cycling-Part 2: Theory and Simulation of Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

PURPOSE: To develop and evaluate a theory on the frequent observation that cyclists prefer cadences (RPMs) higher than those considered most economical at submaximal exercise intensities via modeling and simulation of its mathematical description. METHODS: The theory combines the parabolic power-to-velocity (v) relationship, where v is defined by crank length, RPM-dependent ankle velocity, and gear ratio, RPM effects on the maximal lactate steady state (MLSS), and lactate-dependent carbohydrate oxidation (CHO). It was tested against recent experimental results of 12 healthy male recreational cyclists determining the v-dependent peak oxygen uptake (VO2PEAKv), MLSS (MLSSv), corresponding power output (PMLSSv), oxygen uptake at PMLSSv (VO2MLSSv), and CHOMLSSv-management at 100 versus 50 per minute, respectively. Maximum RPM (RPMMAX) attained at minimized pedal torque was measured. RPM-specific maximum sprint power output (PMAXv) was estimated at RPMs of 100 and 50, respectively. RESULTS: Modeling identified that MLSSv and PMLSSv related to PMAXv (IPMLSSv) promote CHO and that VO2MLSSv related to VO2PEAKv inhibits CHO. It shows that cycling at higher RPM reduces IPMLSSv. It suggests that high cycling RPMs minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX. CONCLUSIONS: The present theory-guided modeling approach is exclusively based on data routinely measured in high-performance testing. It implies a higher performance reserve above IPMLSSv at higher RPM. Cyclists may prefer high cycling RPMs because they appear to minimize differences in the reliance on CHO at MLSSv between athletes with high versus low RPMMAX.

Cadence Paradox in Cycling-Part 1: Maximal Lactate Steady State and Carbohydrate Utilization Dependent on Cycling Cadence.

PURPOSE: To assess (1) whether and how a higher maximal lactate steady state (MLSS) at higher cycling cadence (RPM) comes along with higher absolute and/or fractional carbohydrate combustion (CHOMLSS), respectively, and (2) whether there is an interrelation between potential RPM-dependent MLSS effects and the maximally achievable RPM (RPMMAX). METHODS: Twelve healthy males performed incremental load tests to determine peak power, peak oxygen uptake, and 30-minute MLSS tests at 50 and 100 per minute, respectively, to assess RPM-dependent MLSS, corresponding power output, CHOMLSS responses, and 6-second sprints to measure RPMMAX. RESULTS: Peak power, peak carbon dioxide production, and power output at MLSS were lower (P = .000, ω2 = 0.922; P = .044, ω2 > 0.275; and P = .016, ω2 = 0.373) at 100 per minute than at 50 per minute. With 6.0 (1.5) versus 3.8 (1.2) mmol·L-1, MLSS was higher (P = .000, ω2 = 0.771) at 100 per minute than at 50 per minute. No corresponding RPM-dependent differences were found in oxygen uptake at MLSS, carbon dioxide production at MLSS, respiratory exchange ratio at MLSS, CHOMLSS, or fraction of oxygen uptake used for CHO at MLSS, respectively. There was no correlation between the RPM-dependent difference in MLSS and RPMMAX. CONCLUSIONS: The present study extends the previous finding of a consistently higher MLSS at higher RPM by indicating (1) that at fully established MLSS conditions, respiration and CHOMLSS management do not differ significantly between 100 per minute and 50 per minute, and (2) that linear correlation models did not identify linear interdependencies between RPM-dependent MLSS conditions and RPMMAX.

Reliability of the 3-Component Model of Aerobic, Anaerobic Lactic, and Anaerobic Alactic Energy Distribution (PCr-LA-O2) for Energetic Profiling of Continuous and Intermittent Exercise.

PURPOSE: To assess the test-retest reliability of the continuous (PCr-LA-O2) and intermittent (PCr-LA-O2int) version of the 3-component model of energy distribution in an applied setting. METHODS: Sixteen male handball players (age 23 [3] y, height 185 [7] cm, weight 85 [14] kg) completed the 30-15 Intermittent Fitness Test (30-15IFT) twice. Performance was assessed by peak speed (speed of the last successfully completed stage of the 30-15IFT [VIFT], in kilometers per hour) and time to exhaustion (in seconds). Oxygen uptake (in milliliters per kilogram per minute) and blood lactate concentrations (in millimoles per liter) were obtained before, during, and until 15 minutes after exercise. Total metabolic energy (in joules per kilogram), total metabolic power (in watts per kilogram), and energy shares (in joules per kilogram and percentage) of the aerobic (energy contribution of the aerobic system [WAERint]), anaerobic lactic, and anaerobic alactic (anaerobic alactic energy [WPCrint]) systems were calculated using both model versions, respectively. RESULTS: Test-retest reliability was very good for VIFT (limits of agreement [LoA]: -1.13 to 0.63 km·h-1, coefficient of variation [CV%] 1.68), time to exhaustion (LoA: -101 to 38 s, CV% 2.92), peak oxygen uptake (LoA: -2.68 to 4.04 mL·min-1·kg-1, CV% 1.48), and peak heart rate (-6.9 to 7.7 beats·min-1, CV% 1.1), but moderate for change in blood lactate concentration (LoA: -3.84 to 4.07 mmol·L-1, CV% 11.43). Reliability of the modeled total energy and its fractions were high for total metabolic energy (LoA: -1489 to 1177 J·kg-1, CV% 2.88), total metabolic power (LoA: -2.0 to 1.9 W·kg-1, CV% 3.58), contribution of aerobic (LoA: -1673 to 1283 J·kg-1, CV% 3.62), WAERint (LoA: -1760 to 2160 J·kg-1, CV% 6.04), and moderate for anaerobic alactic (LoA: -368 to 439 J·kg-1, CV% 14.85), WPCrint (LoA: -1707 to 988 J·kg-1, CV% 9.98), and energy share of anaerobic lactic concentration (LoA: -229 to 235 J·kg-1, CV% 11.43). CONCLUSION: Considering the inherent fluctuations of the underlying energetics, the reliabilities of both versions of the 3-component model of energy distribution are acceptable for applied settings.

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]O2max at MLSS (%[Formula: see text]O2_MLSS) are decisive for endurance performance. We tested the hypotheses that (1) %[Formula: see text]O2_MLSS is positively correlated with the ability to sustain a high fraction of [Formula: see text]O2max for a given competition duration (%[Formula: see text]O2_TT); (2) %[Formula: see text]O2_MLSS improves the prediction of the average power output of a time trial (PO_TT) in addition to [Formula: see text]O2max and gross efficiency (GE); (3) PO_MLSS improves the prediction of PO_TT in addition to [Formula: see text]O2max 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]O2max, and 20-min time trials for %[Formula: see text]O2_TT and PO_TT. Hypothesis 1 was tested via bivariate and partial correlations between %[Formula: see text]O2_MLSS and %[Formula: see text]O2_TT. Multiple regression models with [Formula: see text]O2max, GE, ∆PO_LEmin, and %[Formula: see text]O2_MLSS (Hypothesis 2) or PO_MLSS instead of %[Formula: see text]O2_MLSS (Hypothesis 3), respectively, as predictors, and PO_TT as the dependent variable were used to test the hypotheses. RESULTS: %[Formula: see text]O2_MLSS was not correlated with %[Formula: see text]O2_TT (r = 0.17, p = 0.583). Neither %[Formula: see text]O2_MLSS (p = 0.424) nor PO_MLSS (p = 0.208) did improve the prediction of PO_TT in addition to [Formula: see text]O2max and GE. CONCLUSION: These results challenge the assumption that PO_MLSS or %[Formula: see text]O2_MLSS are independent predictors of supra-MLSS PO_TT and %[Formula: see text]O2_TT.

COVID-19 Lockdown: A Global Study Investigating the Effect of Athletes' Sport Classification and Sex on Training Practices.

PURPOSE: To investigate differences in athletes' knowledge, beliefs, and training practices during COVID-19 lockdowns with reference to sport classification and sex. This work extends an initial descriptive evaluation focusing on athlete classification. METHODS: Athletes (12,526; 66% male; 142 countries) completed an online survey (May-July 2020) assessing knowledge, beliefs, and practices toward training. Sports were classified as team sports (45%), endurance (20%), power/technical (10%), combat (9%), aquatic (6%), recreational (4%), racquet (3%), precision (2%), parasports (1%), and others (1%). Further analysis by sex was performed. RESULTS: During lockdown, athletes practiced body-weight-based exercises routinely (67% females and 64% males), ranging from 50% (precision) to 78% (parasports). More sport-specific technical skills were performed in combat, parasports, and precision (∼50%) than other sports (∼35%). Most athletes (range: 50% [parasports] to 75% [endurance]) performed cardiorespiratory training (trivial sex differences). Compared to prelockdown, perceived training intensity was reduced by 29% to 41%, depending on sport (largest decline: ∼38% in team sports, unaffected by sex). Some athletes (range: 7%-49%) maintained their training intensity for strength, endurance, speed, plyometric, change-of-direction, and technical training. Athletes who previously trained ≥5 sessions per week reduced their volume (range: 18%-28%) during lockdown. The proportion of athletes (81%) training ≥60 min/session reduced by 31% to 43% during lockdown. Males and females had comparable moderate levels of training knowledge (56% vs 58%) and beliefs/attitudes (54% vs 56%). CONCLUSIONS: Changes in athletes' training practices were sport-specific, with few or no sex differences. Team-based sports were generally more susceptible to changes than individual sports. Policy makers should provide athletes with specific training arrangements and educational resources to facilitate remote and/or home-based training during lockdown-type events.

Energetics of Floor Gymnastics: Aerobic and Anaerobic Share in Male and Female Sub-elite Gymnasts.

BACKGROUND: Artistic gymnastics is a popular Olympic discipline where female athletes compete in four and male athletes in six events with floor exercise having the longest competition duration in Women's and Men's artistic gymnastics (WAG, MAG). To date no valid information on the energetics of floor gymnastics is available although this may be important for specific conditioning programming. This study evaluated the metabolic profile of a simulated floor competition in sub-elite gymnasts. METHODS: 17 (9 male, 8 female) sub-elite gymnasts aged 22.5 ± 2.6y took part in a floor-training-competition where oxygen uptake was measured during and until 15 min post-exercise. Additionally, resting and peak blood lactate concentration after exercise were obtained. The PCr-LA-O2 method was used to calculate the metabolic energy and the relative aerobic (WAER), anaerobic alactic (WPCr) and anaerobic lactic (WBLC) energy contribution. Further, the athletes completed a 30 s Bosco-jumping test, a countermovement jump and a drop jump. RESULTS: The competition scores were 9.2 (CI:8.9-9.3) in WAG and 10.6 (CI:10.4-10.9) in MAG. The metabolic profile of the floor routine was mainly aerobic (58.9%, CI: 56.0-61.8%) followed by the anaerobic alactic (24.2%, CI: 21.3-27.1%) and anaerobic lactic shares (16.9%, CI:14.9-18.8%). While sex had a significant (p = .010, d = 1.207) large effect on energy contribution, this was not the case for competition duration (p = .728, d = 0.061). Relative energy contribution of WAG and MAG differed in WAER (64.0 ± 4.7% vs. 54.4 ± 6.8%, p = .004, d = 1.739) but not in WPCr (21.3 ± 6.1% vs. 26.7 ± 8.0%, p = .144, d = 0.801) and WBLC (14.7 ± 5.4% vs. 18.9 ± 4.2%, p = .085, d = 0.954). Further no correlation between any energy share and performance was found but between WPCr and training experience (r = .680, p = .044) and WBLC and competition level (r = .668, p = .049). CONCLUSION: The results show a predominant aerobic energy contribution and a considerable anaerobic contribution with no significant difference between anaerobic shares. Consequently, gymnastic specific aerobic training should not be neglected, while a different aerobic share in WAG and MAG strengthens sex-specific conditioning. All in all, the specific metabolic share must secure adequate energy provision, while relative proportions of the two anaerobic pathways seem to depend on training and competition history.

Training During the COVID-19 Lockdown: Knowledge, Beliefs, and Practices of 12,526 Athletes from 142 Countries and Six Continents.

OBJECTIVE: Our objective was to explore the training-related knowledge, beliefs, and practices of athletes and the influence of lockdowns in response to the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: Athletes (n = 12,526, comprising 13% world class, 21% international, 36% national, 24% state, and 6% recreational) completed an online survey that was available from 17 May to 5 July 2020 and explored their training behaviors (training knowledge, beliefs/attitudes, and practices), including specific questions on their training intensity, frequency, and session duration before and during lockdown (March-June 2020). RESULTS: Overall, 85% of athletes wanted to "maintain training," and 79% disagreed with the statement that it is "okay to not train during lockdown," with a greater prevalence for both in higher-level athletes. In total, 60% of athletes considered "coaching by correspondence (remote coaching)" to be sufficient (highest amongst world-class athletes). During lockdown, < 40% were able to maintain sport-specific training (e.g., long endurance [39%], interval training [35%], weightlifting [33%], plyometric exercise [30%]) at pre-lockdown levels (higher among world-class, international, and national athletes), with most (83%) training for "general fitness and health maintenance" during lockdown. Athletes trained alone (80%) and focused on bodyweight (65%) and cardiovascular (59%) exercise/training during lockdown. Compared with before lockdown, most athletes reported reduced training frequency (from between five and seven sessions per week to four or fewer), shorter training sessions (from ≥ 60 to < 60 min), and lower sport-specific intensity (~ 38% reduction), irrespective of athlete classification. CONCLUSIONS: COVID-19-related lockdowns saw marked reductions in athletic training specificity, intensity, frequency, and duration, with notable within-sample differences (by athlete classification). Higher classification athletes had the strongest desire to "maintain" training and the greatest opposition to "not training" during lockdowns. These higher classification athletes retained training specificity to a greater degree than others, probably because of preferential access to limited training resources. More higher classification athletes considered "coaching by correspondence" as sufficient than did lower classification athletes. These lockdown-mediated changes in training were not conducive to maintenance or progression of athletes' physical capacities and were also likely detrimental to athletes' mental health. These data can be used by policy makers, athletes, and their multidisciplinary teams to modulate their practice, with a degree of individualization, in the current and continued pandemic-related scenario. Furthermore, the data may drive training-related educational resources for athletes and their multidisciplinary teams. Such upskilling would provide athletes with evidence to inform their training modifications in response to germane situations (e.g., COVID related, injury, and illness).

Metabolic Profiles of the 30-15 Intermittent Fitness Test and the Corresponding Continuous Version in Team-Sport Athletes-Elucidating the Role of Inter-Effort Recovery.

PURPOSE: To elucidate the role of inter-effort recovery in shuttle running by comparing the metabolic profiles of the 30-15 Intermittent Fitness Test (30-15IFT) and the corresponding continuous version (30-15IFT-CONT). METHODS: Sixteen state-level handball players (age = 23 [3] y, height = 185 [7] cm, weight = 85 [14] kg) completed the 30-15IFT and 30-15IFT-CONT, and speed at the last completed stage (in kilometers per hour) and time to exhaustion (in seconds) were assessed. Furthermore, oxygen uptake (in milliliters per kilogram per minute) and blood lactate were obtained preexercise, during exercise, and until 15 minutes postexercise. Metabolic energy (in kilojoules), metabolic power (in Watts per kilogram), and relative (in percentage) energy contribution of the aerobic (WAER, WAERint), anaerobic lactic (WBLC, WBLCint), and anaerobic alactic (WPCr, WPCrint) systems were calculated by PCr-La-O2 method for 30-15IFT-CONT and 30-15IFT. RESULTS: No difference in peak oxygen uptake was found between 30-15IFT and 30-15IFT-CONT (60.6 [6.6] vs 60.5 [5.1] mL·kg-1·min-1, P = .165, d = 0.20), whereas speed at the last completed stage was higher in 30-15IFT (18.3 [1.4] vs 16.1 [1.0] km·h-1, P < .001, d = 1.17). Metabolic energy was also higher in 30-15IFT (1224.2 [269.6] vs 772.8 [63.1] kJ, P < .001, d = 5.60), and metabolic profiles differed substantially for aerobic (30-15IFT = 67.2 [5.2] vs 30-15IFT-CONT = 85.2% [2.5%], P < .001, d = -4.01), anaerobic lactic (30-15IFT = 4.4 [1.4] vs 30-15IFT-CONT = 6.2% [1.8%], P < .001, d = -1.04), and anaerobic alactic (30-15IFT = 28.4 [4.7] vs 30-15IFT-CONT = 8.6% [2.1%], P < .001, d = 5.43) components. CONCLUSIONS: Both 30-15IFT and 30-15IFT-CONT are mainly fueled by aerobic energy, but their metabolic profiles differ substantially in both aerobic and anaerobic alactic energy contribution. Due to the presence of inter-effort recovery, intermittent shuttle runs rely to a higher extent on anaerobic alactic energy and a fast, aerobic replenishment of PCr during the short breaks between shuttles.

The Oxygen Uptake Plateau-A Critical Review of the Frequently Misunderstood Phenomenon.

A flattening of the oxygen uptake-work rate relationship at severe exercise indicates the achievement of maximum oxygen uptake [Formula: see text]. Unfortunately, a distinct plateau [Formula: see text] at [Formula: see text]is not found in all participants. The aim of this investigation was to critically review the influence of research methods and physiological factors on the [Formula: see text] incidence. It is shown that many studies used inappropriate definitions or methodical approaches to check for the occurrence of a [Formula: see text]. In contrast to the widespread assumptions it is unclear whether there is higher [Formula: see text] incidence in (uphill) running compared to cycling exercise or in discontinuous compared to continuous incremental exercise tests. Furthermore, most studies that evaluated the validity of supramaximal verification phases, reported verification bout durations, which are too short to ensure that [Formula: see text] have been achieved by all participants. As a result, there is little evidence for a higher [Formula: see text] incidence and a corresponding advantage for the diagnoses of [Formula: see text] when incremental tests are supplemented by supramaximal verification bouts. Preliminary evidence suggests that the occurrence of a [Formula: see text] in continuous incremental tests is determined by physiological factors like anaerobic capacity, [Formula: see text]-kinetics and accumulation of metabolites in the submaximal intensity domain. Subsequent studies should take more attention to the use of valid [Formula: see text] definitions, which require a cut-off at ~ 50% of the submaximal [Formula: see text] increase and rather large sampling intervals. Furthermore, if verification bouts are used to verify the achievement of [Formula: see text]/[Formula: see text], it should be ensured that they can be sustained for sufficient durations.

The Metabolic Relevance of Type of Locomotion in Anaerobic Testing: Bosco Continuous Jumping Test Versus Wingate Anaerobic Test of the Same Duration.

PURPOSE: To evaluate the metabolic relevance of type of locomotion in anaerobic testing by analyzing and comparing the metabolic profile of the Bosco Continuous Jumping Test (CJ30) with the corresponding profile of the Wingate Anaerobic Test (WAnT). METHODS: A total of 11 well-trained, male team-sport athletes (age = 23.7 [2.2] y, height = 184.1 [2.8] cm, weight = 82.4 [6.4] kg) completed a CJ30 and WAnT each. During the WAnT, power data and revolutions per minute were recorded, and during the CJ30, jump height and jumping frequency were recorded. In addition, oxygen uptake and blood lactate concentration were assessed, and metabolic profiles were determined via the PCr-LA-O2 method. RESULTS: In the CJ30, metabolic energy was lower (109.3 [18.0] vs 143.0 [13.1] kJ, P < .001, d = -2.302), while peak power (24.8 [4.4] vs 11.8 [0.5] W·kg-1, P < .001, d = 3.59) and mean power (20.8 [3.6] vs 9.1 [0.5] W·kg-1, P < .001, d = 4.14) were higher than in the WAnT. The metabolic profiles of the CJ30 (aerobic energy = 20.00% [4.7%], anaerobic alactic energy [WPCr] = 45.6% [4.5%], anaerobic lactic energy = 34.4% [5.2%]) and the WAnT (aerobic energy = 16.0% [3.0%], anaerobic alactic WPCr = 34.5% [5.0%], anaerobic lactic energy = 49.5% [3.3%]) are highly anaerobic. Absolute energy contribution for the CJ30 and WAnT was equal in WPCr (49.9 [11.1] vs 50.2 [11.2] kJ), but anaerobic lactic energy (37.7 [7.7] vs 69.9 [5.3] kJ) and aerobic energy (20.6 [5.7] vs 23.0 [4.0] kJ) were higher in the WAnT. Mechanical efficiency was substantially higher in the CJ30 (37.9% [4.5%] vs 15.6% [1.0%], P < .001, d = 6.86), while the fatigue index was lower (18.5% [3.8%] vs 23.2% [3.1%], P < .001, d = -1.38) than in the WAnT. CONCLUSIONS: Although the anaerobic share in both tests is similar and predominant, the CJ30 primarily taxes the WPCr system, while the WAnT more strongly relies on the glycolytic pathway. Thus, the 2 tests should not be used interchangeably, and the type of locomotion seems crucial when choosing an anaerobic test for a specific sport.

Climbing-Specific Exercise Tests: Energy System Contributions and Relationships With Sport Performance.

PURPOSE: The aim of the study was to evaluate distinct performance indicators and energy system contributions in 3 different, new sport-specific finger flexor muscle exercise tests. METHODS: The tests included the maximal strength test, the all-out test (30 s) as well as the continuous and intermittent muscle endurance test at an intensity equaling 60% of maximal force, which were performed until target force could not be maintained. Gas exchange and blood lactate were measured in 13 experienced climbers during, as well as pre and post the test. The energy contribution (anaerobic alactic, anaerobic lactic, and aerobic) was determined for each test. RESULTS: The contribution of aerobic metabolism was highest during the intermittent test (59.9 ± 12.0%). During continuous exercise, this was 28.1 ± 15.6%, and in the all-out test, this was 19.4 ± 8.1%. The contribution of anaerobic alactic energy was 27.2 ± 10.0% (intermittent), 54.2 ± 18.3% (continuous), and 62.4 ± 11.3% (all-out), while anaerobic lactic contribution equaled 12.9 ± 6.4, 17.7 ± 8.9, and 18.2 ± 9.9%, respectively. CONCLUSION: The combined analysis of performance predictors and metabolic profiles of the climbing test battery indicated that not only maximal grip force, but also all-out isometric contractions are equally decisive physical performance indices of climbing performance. Maximal grip force reflects maximal anaerobic power, while all-out average force and force time integral of constant isometric contraction at 60% of maximal force are functional measures of anaerobic capacity. Aerobic energy demand for the intermittent exercise is dominated aerobic re-phosphorylation of high-energy phosphates. The force-time integral from the intermittent test was not decisive for climbing performance.

Energetic Profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2.

PURPOSE: To analyze the energetic profiles of the Yo-Yo Intermittent Recovery Tests 1 and 2 (YYIR1 and YYIR2). METHODS: Intermittent running distance (IR1D and IR2D), time to exhaustion (IR1T and IR2T), and total recovery time between shuttles (IR1R and IR2R) were measured in 10 well-trained male athletes (age 24.4 [2.0] y, height 182 [1] cm, weight 75.8 [7.9] kg). Respiratory gases and blood lactate (BLC) were obtained preexercise, during exercise, and until 15 min postexercise. Metabolic energy, average metabolic power , and energy share (percentage of aerobic [WAER], anaerobic lactic [WBLC], and anaerobic alactic energy system [WPCr]) were calculated using the PCr-La-O2 method. RESULTS: Peak oxygen consumption was possibly higher in YYIR2 (60.3 [5.1] mL·kg-1·min-1) than in YYIR1 (P = .116, 57.7 [4.5] mL·kg-1·min-1, d = -0.58). IR1D, IR1T, and IR1R were very likely higher than IR2D, IR2T, and IR2R, respectively (P < .001, 1876 [391] vs 672 [132] m, d = -2.83; P < .001, 916 [175] vs 304 [57] s, d = -3.03; and P < .001, 460 [100] vs 150 [40] s, d = -2.83). Metabolic energy was most likely lower in YYIR2 than in YYIR1 (P < .001, 493.5 [118.1] vs 984.8 [171.7] kJ, d = 3.24). Average metabolic power was most likely higher in YYIR2 than in YYIR1 (P < .001, 21.5 [1.7] vs 14.5 [2.2] W·kg-1, d = 3.54). When considering aerobic phosphocreatine restoration during breaks between shuttles, WAER (P = .693, 49% [10%] vs 48% [5%], d = -0.16) was similar, WPCr (P = .165, 47% [11%] vs 42% [6%], d = -0.54) possibly higher, and WBLC (P < .001, 4% [1%] vs 10% [3%], d = 1.95) almost certainly lower in YYIR1 than in YYIR2. CONCLUSIONS: WAER and WPCr are predominant in YYIR1 and YYIR2 with almost identical WAER. Higher IR1D and IR1T in YYIR1 result in higher metabolic energy but lower average metabolic power and slightly lower peak oxygen consumption. Higher IR1R allows for higher reliance on WPCr in YYIR1, while YYIR2 requires a higher fraction of WBLC.

Effect of intensive prior exercise on muscle fiber activation, oxygen uptake kinetics, and oxygen uptake plateau occurrence.

PURPOSE: We tested the hypothesis that the described increase in oxygen uptake ([Formula: see text])-plateau incidence following a heavy-severe prior exercise is caused by a steeper increase in [Formula: see text] and muscle fiber activation in the submaximal intensity domain. METHODS: Twenty-one male participants performed a standard ramp test, a [Formula: see text] verification bout, an unprimed ramp test with an individualized ramp slope and a primed ramp test with the same ramp slope, which was preceded by an intensive exercise at 50% of the difference between gas exchange threshold and maximum workload. Muscle fiber activation was recorded from vastus lateralis, vastus medialis, and gastrocnemius medialis using a surface electromyography (EMG) device in a subgroup of 11 participants. Linear regression analyses were used to calculate the [Formula: see text]-([Formula: see text]) and EMG-(∆RMS/∆P) ramp test kinetics. RESULTS: Twenty out of the 21 participants confirmed their [Formula: see text] in the verification bout. The [Formula: see text]-plateau incidence in these participants did not differ between the unprimed (n = 8) and primed (n = 7) ramp test (p = 0.500). The [Formula: see text] was lower in the primed compared to the unprimed ramp test (9.40 ± 0.66 vs. 10.31 ± 0.67 ml min-1 W-1, p < 0.001), whereas the ∆RMS/∆P did not differ between the ramp tests (0.62 ± 0.15 vs. 0.66 ± 0.14% W-1; p = 0.744). CONCLUSION: These findings do not support previous studies, which reported an increase in [Formula: see text]-plateau incidence as well as steeper increases in [Formula: see text] and muscle fiber activation in the submaximal intensity domain following a heavy-severe prior exercise.

Oxygen uptake plateau: calculation artifact or physiological reality?

PURPOSE: To test whether the oxygen uptake ([Formula: see text]) plateau at [Formula: see text] is simply a calculation artifact caused by the variability of [Formula: see text] or a clearly identifiable physiological event. METHODS: Forty-six male participants performed an incremental ramp and a [Formula: see text] verification test. Variability of the difference between adjacent sampling intervals (difference) and of the slope of the [Formula: see text]-workload relationship (slope) in the submaximal intensity domain were calculated. Workload defined sampling intervals used for the calculation of the difference and slope were systematically increased from 20 to 100 W until the expected risk of false plateau diagnoses based on the Gaussian distribution function was lower than 5%. Overall, more than 1500 differences and slopes were analyzed. Subsequently, frequencies of plateau diagnoses in the submaximal and maximal intensity domains were compared. RESULTS: Variability of the difference and slope decreased with increasing sampling interval (p < 0.001). At a sampling interval of 50 W, the predefined acceptable risk of false plateau diagnoses (≤ 5%) was achieved. At this sampling interval, the actual frequency (1.4%) of false-positive plateau diagnoses did not differ from the expected frequency in the submaximal intensity domain (1.6%; p = 0.491). In contrast, the actual frequency at maximal intensity (35.7%) was significantly higher compared to the submaximal intensity domain (p < 0.001) and even higher than the expected frequency of false-positive diagnoses (p < 0.001). CONCLUSION: The [Formula: see text] plateau at [Formula: see text] represents a physiological event and no calculation artifact caused by [Formula: see text] variability. However, detecting a [Formula: see text] plateau with sufficient certainty requires large sampling intervals.