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Acute effects of fatigue on internal and external load variables determining cyclists' power profile
Abstract
The aim of the present study was to determine whether fatigue affects internal and external load variables determining power profile in cyclists. Ten cyclists performed outdoor power profile tests (lasting 1-, 5 and 20-min) on two consecutive days, subject either to a fatigued condition or not. Fatigue was induced by undertaking an effort (10-min at 95% of average power output obtained in a 20-min effort followed by 1-min maximum effort) until the power output decreased by 20% compared to the 1-min power output. Fatigued condition decreased power output (p < 0.05, 1-min: 9.0 ± 3.8%; 5-min: 5.9 ± 2.5%; 20-min: 4.1 ± 1.9%) and cadence in all test durations, without differences in torque. Lactate decreased in longer efforts when a fatigue protocol had previously been conducted (e.g., 20-min: 8.6 ± 3.0 vs. 10.9 ± 2.7, p < 0.05). Regression models (r2 ≥ 0.95, p < 0.001) indicated that a lower variation in load variables of 20-min in fatigued condition compared with the non-fatigued state resulted in a lower decrease in critical power after the fatigue protocol. The results suggest that fatigued condition on power was more evident in shorter efforts and seemed to rely more on a decrease in cadence than on torque.
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This paper aims to study the evolution of CO2 concentrations and emissions on a conventional farm with weaned piglets between 6.9 and 17.0 kg live weight based on setpoint temperature, outdoor temperature, and ventilation flow. The experimental trial was conducted during one transition cycle. Generally, the ventilation flow increased with the reduction in setpoint temperature throughout the cycle, which caused a reduction in CO2 concentration and an increase in emissions. The mean CO2 concentration was 3.12 g m–3. Emissions of CO2 had a mean value of 2.21 mg s−1 per animal, which is equivalent to 0.195 mg s−1 kg−1. A potential function was used to describe the interaction between 10 min values of ventilation flow and CO2 concentrations, whereas a linear function was used to describe the interaction between 10 min values of ventilation flow and CO2 emissions, with r values of 0.82 and 0.85, respectively. Using such equations allowed for simple and direct quantification of emissions. Furthermore, two prediction models for CO2 emissions were developed using two neural networks (for 10 min and 60 min predictions), which reached r values of 0.63 and 0.56. These results are limited mainly by the size of the training period, as well as by the differences between the behavior of the series in the training stage and the testing stage.
Purpose:
The present study aimed to determine the influence of fatigue on the record power profile of professional male cyclists. We also assessed whether fatigue could differently affect cyclists of 2 competition categories.
Methods:
We analyzed the record power profile in 112 professional cyclists (n = 46 and n = 66 in the ProTeam [PT] and WorldTour [WT] category, respectively; age 29 [6] y, 8 [5] y experience in the professional category) during 2013-2021 (8 [5] seasons/cyclist). We analyzed their mean maximal power (MMP) values for efforts lasting 10 seconds to 120 minutes with no fatigue (after 0 kJ·kg-1) and with increasing levels of fatigue (after 15, 25, 35, and 45 kJ·kg-1).
Results:
A significant (P < .001) and progressive deterioration of all MMP values was observed from the lowest levels of fatigue assessed (ie, -1.6% to -3.0% decline after 15 kJ·kg-1, and -6.0% to -9.7% after 45 kJ·kg-1). Compared with WT, PT cyclists showed a greater decay of MMP values under fatigue conditions (P < .001), and these differences increased with accumulating levels of fatigue (decay of -1.8 to -2.9% [WT] with reference to 0 kJ·kg-1 vs -1.1% to -4.4% [PT] after 15 kJ·kg-1 and of -4.7% to -8.8% [WT] vs -7.6% to -11.6% [PT] after 45 kJ·kg-1). No consistent differences were found between WT and PT cyclists in MMP values assessed in nonfatigue conditions (after 0 kJ·kg-1), but WT cyclists attained significantly higher MMP values with accumulating levels of fatigue, particularly for long-duration efforts (≥5 min).
Conclusions:
Our findings highlight the importance of considering fatigue when assessing the record power profile of endurance athletes and support the ability to attenuate fatigue-induced decline in MMP values as a determinant of endurance performance.
Throughout the sport-science and sports-medicine literature, the term “elite” subjects might be one of the most overused and ill-defined terms. Currently, there is no common perspective or terminology to characterize the caliber and training status of an individual or cohort. This paper presents a 6-tiered Participant Classification Framework whereby all individuals across a spectrum of exercise backgrounds and athletic abilities can be classified. The Participant Classification Framework uses training volume and performance metrics to classify a participant to one of the following: Tier 0: Sedentary; Tier 1: Recreationally Active; Tier 2: Trained/Developmental; Tier 3: Highly Trained/National Level; Tier 4: Elite/International Level; or Tier 5: World Class. We suggest the Participant Classification Framework can be used to classify participants both prospectively (as part of study participant recruitment) and retrospectively (during systematic reviews and/or meta-analyses). Discussion around how the Participant Classification Framework can be tailored toward different sports, athletes, and/or events has occurred, and sport-specific examples provided. Additional nuances such as depth of sport participation, nationality differences, and gender parity within a sport are all discussed. Finally, chronological age with reference to the junior and masters athlete, as well as the Paralympic athlete, and their inclusion within the Participant Classification Framework has also been considered. It is our intention that this framework be widely implemented to systematically classify participants in research featuring exercise, sport, performance, health, and/or fitness outcomes going forward, providing the much-needed uniformity to classification practices.
Prolonged exercise induces cardiovascular drift, which is characterized by decreasing mean arterial pressure (MAP), stroke volume and heart rate increase. Cardiovascular drift has been debated for a long time. Although the exact mechanisms underlying cardiovascular drift are still unknown, two theories have been proposed. The first is that increased skin blood flow displaces blood volume from central circulation to the periphery, which reduces stroke volume. According to this theory, the rise in heart rate is presumably responding to the drop in stroke volume and MAP. The alternative theory is that an increase in heart rate is due to an increase in sympathetic nervous activity causing reducing time at diastole, and therefore stroke volume. It may be difficult to determine a single robust factor accounting for cardiovascular drift, due to the broad range of circumstances. The primary focus of this review is to elucidate our understanding of cardiovascular drift during prolonged exercise through nitric oxide and force-frequency relationship. We highlight for the very first time that cardiovascular drift (in some conditions and within a specific time period) may be considered as a protective strategy against potential damage that could be induced by the intense and prolonged contraction of the myocardium.
Emerging trends in technological innovations, data analysis and practical applications have facilitated the measurement of cycling power output in the field, leading to improvements in training prescription, performance testing and race analysis. This review aimed to critically reflect on power profiling strategies in association with the power-duration relationship in cycling, to provide an updated view for applied researchers and practitioners. The authors elaborate on measuring power output followed by an outline of the methodological approaches to power profiling. Moreover, the deriving a power-duration relationship section presents existing concepts of power-duration models alongside exercise intensity domains. Combining laboratory and field testing discusses how traditional laboratory and field testing can be combined to inform and individualize the power profiling approach. Deriving the parameters of power-duration modelling suggests how these measures can be obtained from laboratory and field testing, including criteria for ensuring a high ecological validity (e.g. rider specialization, race demands). It is recommended that field testing should always be conducted in accordance with pre-established guidelines from the existing literature (e.g. set number of prediction trials, inter-trial recovery, road gradient and data analysis). It is also recommended to avoid single effort prediction trials, such as functional threshold power. Power-duration parameter estimates can be derived from the 2 parameter linear or non-linear critical power model: P ( t ) = W ′/ t + CP ( W ′—work capacity above CP; t —time). Structured field testing should be included to obtain an accurate fingerprint of a cyclist’s power profile.
Durability and repeatability (i.e., the ability to sustain high power output values under fatigue and to endure repeated high-intensity efforts, respectively) are emerging as cycling performance determinants. We aimed to analyze whether these markers differ between professional cyclists of two competition levels (WorldTour [WT] and Proteam [PT]) during a Grand Tour. We studied 8 WT and 7 PT cyclists who competed in ‘La Vuelta 2020’. Durability was assessed with the mean maximal power (MMP) values attained between 5 sec–30 min after different levels of mechanical work done (0–35 kJ·kg-1). Repeatability was assessed as the ability to repeat efforts >95% MMP. Although no differences were found for durability during the whole race (p=0.209), a significant interaction effect was found in separate analyses by week (p=0.011). Thus, during the first week and in the ‘fresh’ state (0 kJ·kg-1), WT cyclists solely attained significantly higher MMP values for 30-min efforts. However, these differences enlarged with accumulating levels of fatigue (e.g., significantly higher MMP values in WT cyclists for 30-sec, 1-min, 5-min, 20-min and 30-min efforts after 35 kJ·kg-1). On the other hand, no between-group differences were found in repeatability for the whole race (p=0.777) or in separate analyses by week (p=0.808). In summary, the present results support the role of durability (but not of repeatability) as a performance indicator during professional cycling races.
Purpose: To analyze the relationship between critical power (CP) and different lactate threshold (LT2) markers in cyclists.
Methods: Seventeen male recreational cyclists [33 ± 5 years, peak power output (PO) = 4.5 ± 0.7 W/kg] were included in the study. The PO associated with four different fixed (onset of blood lactate accumulation) and individualized (Dmax exp , Dmax pol , and LT Δ1 ) LT2 markers was determined during a maximal incremental cycling test, and CP was calculated from three trials of 1-, 5-, and 20-min duration. The relationship and agreement between each LT2 marker and CP were then analyzed.
Results: Strong correlations ( r = 0.81–0.98 for all markers) and trivial-to-small non-significant differences (Hedges’ g = 0.01–0.17, bias = 1–9 W, and p > 0.05) were found between all LT2 markers and CP with the exception of Dmax exp , which showed the strongest correlation but was slightly higher than the CP (Hedges’ g = 0.43, bias = 20 W, and p < 0.001). Wide limits of agreement (LoA) were, however, found for all LT2 markers compared with CP (from ±22 W for Dmax exp to ±52 W for Dmax pol ), and unclear to most likely practically meaningful differences (PO differences between markers >1%, albeit <5%) were found between markers attending to magnitude-based inferences.
Conclusion: LT2 markers show a strong association and overall trivial-to-small differences with CP. Nevertheless, given the wide LoA and the likelihood of potentially meaningful differences between these endurance-related markers, caution should be employed when using them interchangeably.
Introduction: This study evaluates the power profile of a top-5 result achieved within World Tour cycling races of varying types, namely: flat sprint finish (FLAT), semi-mountain race with a sprint finish (SMsprint), semi-mountain race with uphill finish (SMuphill) and mountain races (MT).
Methods: Power output data from 33 professional cyclists were collected between 2012-2019. This large dataset was filtered so that it only included top-5 finishes in World Tour races (18 participants, 177 races). Each of these top-5 finishes were subsequently classified as FLAT, SMuphill, SMsprint and MT based on set criteria. Maximal mean power output (MMP) for a wide range of durations (5sec to 60min), expressed in both absolute (W) and relative terms (W∙kg-1), were assessed for each race type.
Result: Short-duration power outputs (<60sec), both in relative and absolute terms, are of higher importance to be successful in FLAT and SMsprint races. Longer-duration power outputs (≥3min), are of higher importance to be successful in SMuphill and MT. In addition, relative power outputs of >10min seem to be a key determining factor for success in MT. These race-type specific MMPs of importance (i.e. short-duration MMPs for sprint finishes, longer duration MMPs for races with more elevation gain) are performed at a wide range (80-97%) of the cyclist’s personal best MMP. Conclusions: This study shows that the relative importance of certain points on the power-duration spectrum varies with different race types and provides insight into benchmarks for achieving a result within a World Tour cycling race.
This study aimed to examine the validity and reliability of the recently developed Assioma Favero pedals under laboratory cycling conditions. In total, 12 well-trained male cyclists and triath- letes (VO2max = 65.7 ± 8.7 mL·kg−1·min−1) completed five cycling tests including graded exercises tests (GXT) at different cadences (70–100 revolutions per minute, rpm), workloads (100–650 Watts, W), pedaling positions (seated and standing), vibration stress (20–40 Hz), and an 8-s maximal sprint. Tests were completed using a calibrated direct drive indoor trainer for the standing, seated, and vibration GXTs, and a friction belt cycle ergometer for the high-workload step protocol. Power output (PO) and cadence were collected from three different brand, new pedal units against the gold-standard SRM crankset. The three units of the Assioma Favero exhibited very high within-test reliability and an extremely high agreement between 100 and 250 W, compared to the gold standard (Standard Error of Measurement, SEM from 2.3–6.4 W). Greater PO produced a significant underestimating trend (p < 0.05, Effect size, ES ≥ 0.22), with pedals showing systematically lower PO than SRM (1–3%) but producing low bias for all GXT tests and conditions (1.5–7.4 W). Furthermore, vibrations ≥ 30 Hz significantly increased the differences up to 4% (p < 0.05, ES ≥ 0.24), whereas peak and mean PO differed importantly between devices during the sprints (p < 0.03, ES ≥ 0.39). These results demon- strate that the Assioma Favero power meter pedals provide trustworthy PO readings from 100 to 650 W, in either seated or standing positions, with vibrations between 20 and 40 Hz at cadences of 70, 85, and 100 rpm, or even at a free chosen cadence.
Cycling power meters enable monitoring external loads and performance changes. We aimed to determine the concurrent validity of the novel Favero Assioma Duo (FAD) pedal power meter compared with the crank-based SRM system (considered as gold standard). Thirty-three well-trained male cyclists were assessed at different power output (PO) levels (100-500 W and all-out 15-s sprints), pedaling cadences (75-100 rpm) and cycling positions (seating and standing) to compare the FAD device vs SRM. No significant differences were found between devices for cadence nor for PO during all-out efforts (p > 0.05), although significant but small differences were found for efforts at lower PO values (p < 0.05 for 100-500 W, mean bias 3-8 W). A strong agreement was observed between both devices for mean cadence (ICC > 0.87) and PO values (ICC > 0.81) recorded in essentially all conditions and for peak cadence (ICC > 0.98) and peak PO (ICC > 0.99) during all-out efforts. The coefficient of variation for PO values was consistently lower than 3%. In conclusion, the FAD pedal-based power meter can be considered an overall valid system to record PO and cadence during cycling, although it might present a small bias compared with power meters placed on other locations such as SRM.
Background:
A variety of intensity, load, and performance measures (eg, "power profile") have been used to characterize the demands of professional cycling races with differing stage types. An increased understanding of the characteristics of these races could provide valuable insight for practitioners toward the design of training strategies to optimally prepare for these demands. However, current reviews within this area are outdated and do not include a recent influx of new articles describing the demands of professional cycling races.
Purpose:
To provide an updated overview of the intensity and load demands and power profile of professional cycling races. Typically adopted measures are introduced and their results summarized.
Conclusion:
There is a clear trend in the research that stage type significantly influences the intensity, load, and power profile of races with more elevation gain typically resulting in a higher intensity and load and longer-duration power outputs (ie, >10 min). Flat and semimountainous stages are characterized by higher maximal mean power outputs over shorter durations (ie, <2 min). Furthermore, single-day races tend to have a higher (daily) intensity and load compared with stages within multiday races. Nevertheless, while the presented mean (grouped) data provide some indications on the demands of these races and differences between varying competition elements, a limited amount of research is available describing the "race-winning efforts" in these races, and this is proposed as an important area for future research. Finally, practitioners should consider the limitations of each metric individually, and a multivariable approach to analyzing races is advocated.
This study analyses the influence of race category and result on the demands of professional cycling races. In total, 2920 race files were collected from 20 male professional cyclists, within a variety of race categories: Single-day (1.WT) and multi-day (2.WT) World Tour races, single-day (1.HC) and multi-day (2.HC ) Hors Catégorie races and single-day (1.1) and multi-day (2.1) category 1 races. Additionally, the five cycling "monuments" were analysed separately. Maximal mean power outputs (MMP) were measured across a broad range of durations. Volume and load were large to very largely (d = 1.30 – 4.80) higher in monuments compared to other single-day race categories. Trivial to small differences were observed for most intensity measures between different single-day race categories, with only RPE and sRPE·km⁻¹ being moderately (d = 0.70 – 1.50) higher in the monuments. Distance and duration were small to moderately (d = 0.20 – 0.80) higher in 2.WT races compared to 2.HC and 2.1 multi-day race categories with only small differences in terms of load and intensity. Generally, higher ranked races (i.e. Monuments, 2.WT and GT) tend to present with lower shorter-duration MMPs (e.g. 5 to 120 sec) compared to races of “lower rank” (with less differences and/or mixed results being present over longer durations), potentially caused by a “blunting” effect of the higher race duration and load of higher ranked races on short duration MMPs. MMP were small to largely higher over shorter durations (<5min) for a top-10 result compared to no top-10, within the same category.
Mountain bike cross-country Olympic has an intermittent performance profile, underlining the importance of short-term but high cycling power output. Previous findings indicate that power output during sprint tests differs between laboratory and field-based conditions and that cycling cadence rises with increasing workload. The aim was therefore to examine power output and cadence in short-term efforts under laboratory and field conditions. Twenty-three competitive athletes (17.9±3.7 years) performed a laboratory power profile test and a simulated race within one week. Power output and cadence during the power profile test were compared to corresponding short-term efforts during the race over durations of 10–300s (TT10–300). Differences were TT10+8%, TT30+7%, TT60–15% and TT300–12% for power output and+10%,+8%,+19%,+21% for cadence respectively. Compared to the race, we found higher power output during the power profile test for the shorter efforts but lower for TT60 and TT300. Confirming previous results, cadence was higher during the power profile test compared to the respective intervals of the race and increased with increasing workload or shorter time trial duration. Future research should take into account that compared to the field, a higher cadence is used in laboratory settings to produce similar power output.
In vitro studies have shown that alterations in redox state can cause a range of opposing effects on the properties of the contractile apparatus in skeletal muscle fibers. To test whether and how redox changes occurring in vivo affect the contractile properties, vastus lateralis muscle fibers from seven healthy young adults were examined at rest (PRE) and following (POST) high-intensity intermittent cycling exercise. Individual mechanically-skinned muscle fibers were exposed to heavily buffered solutions at progressively higher free [Ca ²⁺ ] to determine their force-Ca ²⁺ relationship. Following acute exercise, Ca ²⁺ sensitivity was significantly decreased in type I fibers (by 0.06 pCa unit) but not in type II fibers (0.01 pCa unit). Specific force decreased after the exercise in type II fibers (-18%), but was unchanged in type I fibers. Treatment with the reducing agent dithiothreitol (DTT) caused a small decrease in Ca ²⁺ -sensitivity in type II fibers at PRE (by ~0.014 pCa units) and a significantly larger decrease at POST (~0.035 pCa units), indicating that the exercise had increased S‑glutathionylation of fast troponin I. DTT treatment also increased specific force (by ~4%) but only at POST. In contrast, DTT treatment had no effect on either parameter in type I fibers at either PRE or POST. In type I fibers, the decreased Ca ²⁺ ‑sensitivity was not due to reversible oxidative changes and may have contributed to a decrease in power production during vigorous exercises. In type II fibers, exercise-induced redox changes help counter the decline in Ca ²⁺ -sensitivity while causing a small decline in maximum force.
This study aims to describe the intensity and load demands of different stage types within a cycling Grand Tour. Nine professional cyclists, whom are all part of the same World-Tour professional cycling team, participated in this investigation. Competition data were collected during the 2016 Giro d’Italia. Stages within the Grand Tour were classified into four categories: flat stages (FLAT), semi-mountainous stages (SMT), mountain stages (MT) and individual time trials (TT). Exercise intensity, measured with different heart rate and power output based variables, was highest in the TT compared to other stage types. During TT’s the main proportion of time was spent at the high-intensity zone, whilst the main proportion of time was spent at low intensity for the mass start stage types (FLAT, SMT, MT). Exercise load, quantified using Training Stress Score and Training Impulse, was highest in the mass start stage types with exercise load being highest in MT (329, 359 AU) followed by SMT (280, 311 AU) and FLAT (217, 298 AU). Substantial between-stage type differences were observed in maximal mean power outputs over different durations. FLAT and SMT were characterised by higher short-duration maximal power outputs (5–30 s for FLAT, 30 s–2 min for SMT) whilst TT and MT are characterised by high longer duration maximal power outputs (>10 min). The results of this study contribute to the growing body of evidence on the physical demands of stage types within a cycling Grand Tour.
New findings:
What is the central question of this study? Is the magnitude of neuromuscular fatigue dependent upon exercise intensity above critical power (CP) when W' (the curvature constant of the power-duration relationship) is depleted? What is the main finding and its importance? The magnitude of neuromuscular fatigue is the same following two bouts of supra-CP cycling (3 vs. 12 min) when controlling for W' depletion, but is larger for individuals of greater anaerobic capacity following the shorter, and smaller for individuals of greater aerobic capacity following the longer exercise. These findings provide new insight into the mechanisms underpinning exercise above CP.
Abstract:
The aim of the present study was to test whether the development of neuromuscular fatigue within the severe intensity domain could be linked to the depletion of the curvature constant (W') of the power-duration relationship. Twelve recreationally active men completed tests to determine V̇O2peak , Critical Power (CP) and W', followed by two randomly assigned constant-load supra-CP trials set to fully deplete W' in 3 (P-3) and 12 min (P-12). Pre- to post-exercise changes in maximal voluntary contraction (MVC), potentiated quadriceps twitch force evoked by single (Qpot ) and paired high- (PS100) and low-frequency (PS10) stimulations and voluntary activation (VA) were determined. Cycling above CP reduced MVC (P-3: -20 ± 10% vs. P-12: -15 ± 7%), measures associated with peripheral fatigue (Qpot : -35 ± 13% vs. -31 ± 14%; PS10: -38 ± 13% vs. -37 ± 17%; PS100: -18 ± 9% vs. -13 ± 8% for P-3 and P-12, respectively) and VA (P-3: -12 ± 3% vs. P-12: -13 ± 3%) (P < 0.05), with no significant difference between trials (P > 0.05). Changes in MVC and evoked twitch forces were inversely correlated with CP and V̇O2peak following P-12, while W' was significantly correlated with changes in Qpot and PS10 following P-3 (P < 0.05). Therefore, the magnitude of neuromuscular fatigue does not depend on exercise intensity when W' is fully exhausted during severe intensity exercise, yet exploration of inter-individual variations suggests that mechanisms underpinning exercise tolerance within this domain differ between short- vs. long-duration exercise. This article is protected by copyright. All rights reserved.
Objectives: To evaluate the physiological requirements imposed by the current mountain biking Cross-Country Olympic (XCO) format.
Methods: Sixteen Cross-Country cyclists competing at national or international level participated in this study. All participants completed a simulated and a real official race on a cycling-accredited race track. Oxygen consumption (O2) and heart rate (HR) values expressed as %O2max and %HRmax, respectively, were divided into three physiological intensity zones. The first zone (Z1) was the physiological region below VT1, the second zone (Z2) corresponded to a region between VT1 and VT2, and the third zone (Z3) was located between VT2 and VO2max. For power output, an additional fourth zone was considered above maximal aerobic power (MAP).
Results: When competing in the current XCO format, 37.0 ± 17.9% of the race is performed above the second ventilatory threshold at a mean intensity of 87% O2max and 25% of the race was spent above MAP. This contribution varied between laps, with a very high intensity during the first lap and more aerobic subsequent laps. The durations of most of the periods beyond MAP oscillated between 5 and 30 s. Between these short, repeated bursts, low-intensity periods of exercise were recorded.
Conclusion: The current XCO race format is an acyclical and intermittent exercise comparable to high-intensity team sports. Moreover, our results highlight the relevance of O2 values when analyzing XCO performance, they should be combined with commonly used HR and/or power output data.
Once thought to be a waste product of anaerobic metabolism, lactate is now known to form continuously under aerobic conditions. Shuttling between producer and consumer cells fulfills at least three purposes for lactate: (1) a major energy source, (2) the major gluconeogenic precursor, and (3) a signaling molecule. "Lactate shuttle" (LS) concepts describe the roles of lactate in delivery of oxidative and gluconeogenic substrates as well as in cell signaling. In medicine, it has long been recognized that the elevation of blood lactate correlates with illness or injury severity. However, with lactate shuttle theory in mind, some clinicians are now appreciating lactatemia as a "strain" and not a "stress" biomarker. In fact, clinical studies are utilizing lactate to treat pro-inflammatory conditions and to deliver optimal fuel for working muscles in sports medicine. The above, as well as historic and recent studies of lactate metabolism and shuttling, are discussed in the following review.
The purpose of this investigation was to evaluate the effects of repeated, high- (HT: 70% MVIC) versus low-torque (LT: 30% MVIC) isometric exercise performed to failure on motor unit (MU) recruitment and firing behavior of the vastus lateralis. Eighteen resistance trained males (23.1 ± 3.8 yrs) completed familiarization, followed by separate experimental sessions in which they completed either HT or LT exercise to failure in random order. LT exercise resulted in a greater time to task failure and a more dramatic decline in the muscle’s force capacity, but the total work completed was similar for HT and LT exercise. An examination of the firing trains from 4,670 MUs recorded during exercise revealed that firing rates generally increased during HT and LT exercise, but were higher during HT than LT exercise. Further, recruitment thresholds (RT) did not significantly change during HT exercise, while the RT of the smallest MUs increased and the RT for the moderate to large MUs decreased during LT exercise. Both HT and LT exercise resulted in the recruitment of additional higher-threshold MUs in order to maintain torque production. However, throughout exercise, HT required the recruitment of larger MUs than did LT exercise. In a few cases, however, MUs were recruited by individuals during LT exercise that were similar in size and original (pre) RT to those detected during HT exercise. Thus, the ability to achieve full MU recruitment during LT exercise may be dependent on the subject. Consequently, our data emphasize the task- and subject-dependency of muscle fatigue.
The curvilinear relationship between power output and the time for which it can be sustained is a fundamental and well-known feature of high-intensity exercise performance. This relationship ‘levels off’ at a ‘critical power’ (CP) that separates power outputs that can be sustained with stable values of, for example, muscle phosphocreatine, blood lactate, and pulmonary oxygen uptake (\( \dot{V}{\text{O}}_{2} \)), from power outputs where these variables change continuously with time until their respective minimum and maximum values are reached and exercise intolerance occurs. The amount of work that can be done during exercise above CP (the so-called W′) is constant but may be utilized at different rates depending on the proximity of the exercise power output to CP. Traditionally, this two-parameter CP model has been employed to provide insights into physiological responses, fatigue mechanisms, and performance capacity during continuous constant power output exercise in discrete exercise intensity domains. However, many team sports (e.g., basketball, football, hockey, rugby) involve frequent changes in exercise intensity and, even in endurance sports (e.g., cycling, running), intensity may vary considerably with environmental/course conditions and pacing strategy. In recent years, the appeal of the CP concept has been broadened through its application to intermittent high-intensity exercise. With the assumptions that W′ is utilized during work intervals above CP and reconstituted during recovery intervals below CP, it can be shown that performance during intermittent exercise is related to four factors: the intensity and duration of the work intervals and the intensity and duration of the recovery intervals. However, while the utilization of W′ may be assumed to be linear, studies indicate that the reconstitution of W′ may be curvilinear with kinetics that are highly variable between individuals. This has led to the development of a new CP model for intermittent exercise in which the balance of W′ remaining (\( W_{\text{BAL}}^{\prime } \)) may be calculated with greater accuracy. Field trials of athletes performing stochastic exercise indicate that this \( W_{\text{BAL}}^{\prime } \) model can accurately predict the time at which W′ tends to zero and exhaustion is imminent. The \( W_{\text{BAL}}^{\prime } \) model potentially has important applications in the real-time monitoring of athlete fatigue progression in endurance and team sports, which may inform tactics and influence pacing strategy.
Methods:
9 moderately trained cyclists performed an incremental test to exhaustion to establish the power output associated with the maximum oxygen uptake (p[Formula: see text]max), and 3 protocols requiring time-to-exhaustion trials at a constant work-rate performed at 80%, 100% and 105% of p[Formula: see text]max. Design: Protocol A utilised 24-h inter-trial recovery (CP24/W'24), protocol B utilised 3-h inter-trial recovery (CP3/W'3), and protocol C used 30-min inter-trial recovery period (CP0.5/W'0.5). CP and W' were calculated using the inverse time (1/t) versus power (P) relation (P = W'(1/t) + CP).
Results:
95% Limits of Agreement between protocol A and B were -9 to 15 W; -7.4 to 7.8 kJ (CP/W') and between protocol A and protocol C they were -27 to 22 W; -7.2 to 15.1 kJ (CP/W'). Compared to criterion protocol A, the average prediction error of protocol B was 2.5% (CP) and 25.6% (W'), whilst for protocol C it was 3.7% (CP) and 32.9% (W').
Conclusion:
3-h and 30-min inter-trial recovery time protocols provide valid methods of determining CP but not W' in cycling.
The hyperbolic form of the power-duration relationship is rigorous and highly conserved across species, forms of exercise and individual muscles/muscle groups. For modalities such as cycling, the relationship resolves to two parameters, the asymptote for power (critical power, CP) and the so-called W' (work doable above CP), which together predict the tolerable duration of exercise above CP. Crucially, the CP concept integrates sentinel physiological profiles - respiratory, metabolic and contractile - within a coherent framework that has great scientific and practical utility. Rather than calibrating equivalent exercise intensities relative to metabolically distant parameters such as the lactate threshold or V˙O2 max, setting the exercise intensity relative to CP unifies the profile of systemic and intramuscular responses and, if greater than CP, predicts the tolerable duration of exercise until W' is expended, V˙O2 max is attained, and intolerance is manifested. CP may be regarded as a 'fatigue threshold' in the sense that it separates exercise intensity domains within which the physiological responses to exercise can (<CP) or cannot (>CP) be stabilized. The CP concept therefore enables important insights into 1) the principal loci of fatigue development (central vs. peripheral) at different intensities of exercise, and 2) mechanisms of cardiovascular and metabolic control and their modulation by factors such as O2 delivery. Practically, the CP concept has great potential application in optimizing athletic training programs and performance as well as improving the life quality for individuals enduring chronic disease.
The aims of this study were to: (1) determine the validity and reliability of the Nova Biomedical Lactate Plus portable analyzer, and quantify any fixed or proportional bias; (2) determine the effect of any bias on the determination of the lactate threshold and (3) determine the effect that blood sampling methods have on validity and reliability.
In this method comparison study we compared blood lactate concentration measured using the Lactate Plus portable analyzer to lactate concentration measured by a reference analyzer, the YSI 2300.
University campus in the USA.
Fifteen active men and women performed a discontinuous graded exercise test to volitional exhaustion on a motorised treadmill. Blood samples were taken via finger prick and collected in microcapillary tubes for analysis by the reference instrument at the end of each stage. Duplicate samples for the portable analyzer were either taken directly from the finger or from the micro capillary tubes. PRIMARY OUTCOME MEASUREMENTS: Ordinary least products regressions were used to assess validity, reliability and bias in the portable analyzer. Lactate threshold was determined by visual inspection.
Though measurements from both instruments were correlated (r=0.91), the differences between instruments had large variability (SD=1.45 mM/l) when blood was sampled directly from finger. This variability was reduced by ∼95% when both instruments measured blood collected in the capillary tubes. As the proportional and fixed bias between instruments was small, there was no difference in estimates of the lactate threshold between instruments. Reliability for the portable instrument was strong (r=0.99, p<0.05) with no proportional bias (slope=1.02) and small fixed bias (-0.19 mM/l).
The Lactate Plus analyzer provides accurate and reproducible measurements of blood lactate concentration that can be used to estimate workloads corresponding to blood lactate transitions or any absolute lactate concentrations.
This study tested the effects of low-cadence (60 rev min−1) uphill (Int60) or high-cadence (100 rev min−1) level-ground (Int100) interval training on power output (PO) during 20-min uphill (TTup) and flat (TTflat) time-trials. Eighteen male cyclists (\( \dot{V}{\text{O}}_{2\max } \): 58.6 ± 5.4 mL min−1 kg−1) were randomly assigned to Int60, Int100 or a control group (Con). The interval training comprised two training sessions per week over 4 weeks, which consisted of six bouts of 5 min at the PO corresponding to the respiratory compensation point (RCP). For the control group, no interval training was conducted. A two-factor ANOVA revealed significant increases on performance measures obtained from a laboratory-graded exercise test (GXT) (P
max: 2.8 ± 3.0%; p < 0.01; PO and \( \dot{V}{\text{O}}_{2} \) at RCP: 3.6 ± 6.3% and 4.7 ± 8.2%, respectively; p < 0.05; and \( \dot{V}{\text{O}}_{2} \) at ventilatory threshold: 4.9 ± 5.6%; p < 0.01), with no significant group effects. Significant interactions between group and uphill and flat time-trial, pre- versus post-training on PO were observed (p < 0.05). Int60 increased PO during both TTup (4.4 ± 5.3%) and TTflat (1.5 ± 4.5%). The changes were −1.3 ± 3.6, 2.6 ± 6.0% for Int100 and 4.0 ± 4.6%, −3.5 ± 5.4% for Con during TTup and TTflat, respectively. PO was significantly higher during TTup than TTflat (4.4 ± 6.0; 6.3 ± 5.6%; pre and post-training, respectively; p < 0.001). These findings suggest that higher forces during the low-cadence intervals are potentially beneficial to improve performance. In contrast to the GXT, the time-trials are ecologically valid to detect specific performance adaptations.
This study aimed to reveal the neural and muscular adjustments following a repeated-sprint (RS) running exercise. Sixteen subjects performed a series of neuromuscular tests before, immediately after and 30 min (passive recovery) post-RS exercise (12 x 40 m sprints interspaced by 30 s of passive recovery). Sprint times significantly lengthened over repetitions (+17% from the first to the last sprint; P < 0.05). After RS running exercise, maximal voluntary contraction torque of the plantar flexors (-11 +/- 7.3%), muscle activation (twitch interpolation) (-2.7 +/- 3.4%) and soleus maximal M-wave amplitude (-20 +/- 17%) were significantly (P < 0.05) reduced but returned close to baseline after 30 min. Both soleus EMG activity and maximal Hoffmann reflex normalized with respect to M-wave amplitude did not change during the whole experiment. From pre- to post-RS exercise, evoked twitch response was characterized by lower peak torque and maximal rate of torque development (-13 and -11%, respectively, P < 0.05), but was not different from baseline after recovery. Peak tetanus at 20 and 80 Hz were 17 and 8% lower (P < 0.05) in the fatigued state, respectively. Acute muscle fatigue induced by RS running exercise is mainly peripheral as the short-term (30 min) recovery pattern of plantar flexors contractile properties follows that of the voluntary force-generating capacity.
Laboratory tests of fitness variables have previously been shown to be valid predictors of cycling time-trial performance. However, due to the influence of drafting, tactics and the variability of power output in mass-start road races, comparisons between laboratory tests and competition performance are limited. The purpose of this study was to compare the power produced in the laboratory Power Profile (PP) test and Maximum Mean Power (MMP) analysis of competition data. Ten male cyclists (mean+/-SD: 20.8+/-1.5 y, 67.3+/-5.5 kg, V O (2 max) 72.7+/-5.1 mL x kg (-1) x min (-1)) completed a PP test within 14 days of competing in a series of road races. No differences were found between PP results and MMP analysis of competition data for durations of 60-600 s, total work or estimates of critical power and the fixed amount of work that can be completed above critical power (W'). Self-selected cadence was 15+/-7 rpm higher in the lab. These results indicate that the PP test is an ecologically valid assessment of power producing capacity over cycling specific durations. In combination with MMP analysis, this may be a useful tool for quantifying elements of cycling specific performance in competitive cyclists.
The study aimed to assess the reproducibility of power output during a 4 min (TT4) and a 20 min (TT20) time-trial and the relationship with performance markers obtained during a laboratory graded exercise test (GXT). Ventilatory and lactate thresholds during a GXT were measured in competitive male cyclists (n=15; (.)VO (2max) 67+/-5 ml x min (-1) x kg (-1); P (max) 440+/-38W). Two 4 min and 20 min time-trials were performed on flat roads. Power output was measured using a mobile power-meter (SRM). Strong intraclass-correlations for TT4 ( R=0.98; 95% CL: 0.92-0.99) and TT20 ( R=0.98; 95% CL: 0.95-0.99) were observed. TT4 showed a bias+/-random error of - 0.8+/-23W or - 0.2+/-5.5%. During TT20 the bias+/-random error was - 1.8+/-14W or 0.6+/-4.4%. Both time-trials were strongly correlated with performance measures from the GXT (p<0.001). Significant differences were observed between power output during TT4 and GXT measures (p<0.001). No significant differences were found between TT20 and power output at the second lactate-turn-point (LTP2) (p=0.98) and respiratory compensation point (RCP) (p=0.97). In conclusion, TT4 and TT20 mean power outputs are reliable predictors of aerobic endurance. TT20 was in agreement with power output at RCP and LTP2.
Performance tests are an integral component of assessment for competitive cyclists in practical and research settings. Cycle ergometry is the basis of most of these tests. Most cycle ergometers are stationary devices that measure power while a cyclist pedals against sliding friction (e.g. Monark), electromagnetic braking (e.g. Lode), or air resistance (e.g. Kingcycle). Mobile ergometers (e.g. SRM cranks) allow measurement of power through the drive train of the cyclist’s own bike in real or simulated competitions on the road, in a velodrome or in the laboratory. The manufacturers’ calibration of all ergometers is questionable; dynamic recalibration with a special rig is therefore desirable for comparison of cyclists tested on different ergometers.
For monitoring changes in performance of a cyclist, an ergometer should introduce negligible random error (variation) in its measurements; in this respect, SRM cranks appear to be the best ergometer, but more comparison studies of ergometers are needed. Random error in the cyclist’s performance should also be minimised by choice of an appropriate type of test. Tests based on physiological measures (e.g. maximum oxygen uptake, anaerobic threshold) and tests requiring self-selection of pace (e.g. constant-duration and constant-distance tests) usually produce random error of at least ~2 to 3%in the measure of power output. Random error as low as ~1% is possible for measures of power in ’all-out’ sprints, incremental tests, constant-power tests to exhaustion and probably also time trials in an indoor velodrome. Measures with such low error might be suitable for tracking the small changes in competitive performance that matter to elite cyclists.
Purpose:
To present normative data for the record power profile of male professional cyclists attending to team categories and riding typologies.
Methods:
Power output data registered from 4 professional teams during 8 years (N = 144 cyclists, 129,262 files, and 1062 total seasons [7 (5) per cyclist] corresponding to both training and competition sessions) were analyzed. Cyclists were categorized as ProTeam (n = 46) or WorldTour (n = 98) and as all-rounders (n = 65), time trialists (n = 11), climbers (n = 50), sprinters (n = 11), or general classification contenders (n = 7). The record power profile was computed as the highest maximum mean power (MMP) value attained for different durations (1 s to 240 min) in both relative (W·kg-1) and absolute units (W).
Results:
Significant differences between ProTeam and WorldTour were found for both relative (P = .002) and absolute MMP values (P = .006), with WT showing lower relative, but not absolute, MMP values at shorter durations (30-60 s). However, higher relative and absolute MMP values were recorded for very short- (1 s) and long-duration efforts (60 and 240 min for relative MMP values and ≥5 min for absolute ones). Differences were also found regarding cyclists' typologies for both relative and absolute MMP values (P < .001 for both), with sprinters presenting the highest relative and absolute MMP values for short-duration efforts (5-30 s) and general classification contenders presenting the highest relative MMP values for longer efforts (1-240 min).
Conclusions:
The present results--obtained from the largest cohort of professional cyclists assessed to date-could be used to assess cyclists' capabilities and indicate that the record power profile can differ between cyclists' categories and typologies.
Introduction: This study aimed to investigate if performance measures are related to success in professional cycling and to highlight the influence of work done on these performance measures and success.
Methods: Power output data from 26 professional cyclists, in total 85 seasons, collected between 2012-2019, were analysed. The cyclists were classified as ‘climber’ or ‘sprinter’ and into category.1 (CAT.1) (≥400PSCpoints [successful]) and CAT.2 (<400PSCpoints [less successful]), based on the number of procyclingstats-points collected for that particular season (PSCpoints). Maximal mean power output (MMP) for 20min, 5min, 1min and 10sec relative to bodyweight for every season were determined. To investigate the influence of prior work done on these MMPs, six different work done levels were determined which are based on a certain amount of completed kJ∙kg-1 (0, 10, 20, 30, 40 and 50kJ∙kg-1). Subsequently, the decline in MMP for each duration (if any) after these work done levels was evaluated.
Results: Repeated-measures ANOVA revealed that work done affects the performance of climbers and sprinters negatively. However, CAT.1 climbers have a smaller decline in 20min and 5min MMP after high amounts of work done compared to CAT.2 climbers. Similarly, CAT.1 sprinters have a smaller decline in 10sec and 1min MMP after high amounts of work done compared to CAT.2 sprinters.
Conclusions: It seems that the ability to maintain high MMPs (corresponding with the specialization of a cyclist) after high amounts of work done (i.e. fatigue) is an important parameter for success in professional cyclists. These findings suggest that assessing changes in MMPs after different workloads might be highly relevant in professional cycling.
Purpose:
The aim of this study was to investigate changes in the power profile of U23 professional cyclists during a competitive season based on maximal mean power output (MMP) and derived critical power (CP) and work capacity above CP (W') obtained during training and racing.
Methods:
A total of 13 highly trained U23 professional cyclists (age = 21.1 [1.2] y, maximum oxygen consumption = 73.8 [1.9] mL·kg-1·min-1) participated in this study. The cycling season was split into pre-season and in-season. In-season was divided into early-, mid-, and late-season periods. During pre-season, a CP test was completed to derive CPtest and W'test. In addition, 2-, 5-, and 12-minute MMP during in-season were used to derive CPfield and W'field.
Results:
There were no significant differences in absolute 2-, 5-, and 12-minute MMP, CPfield, and W'field between in-season periods. Due to changes in body mass, relative 12-minute MMP was higher in late-season compared with early-season (P = .025), whereas relative CPfield was higher in mid- and late-season (P = .031 and P = .038, respectively) compared with early-season. There was a strong correlation (r = .77-.83) between CPtest and CPfield in early- and mid-season but not late-season. Bland-Altman plots and standard error of estimates showed good agreement between CPtest and in-season CPfield but not between W'test and W'field.
Conclusion:
These findings reveal that the power profile remains unchanged throughout the in-season, except for relative 12-minute MMP and CPfield in late-season. One pre-season and one in-season CP test are recommended to evaluate in-season CPfield and W'field.
PURPOSE: To examine the degree of neuromuscular fatigue development along with changes in muscle metabolism during two work-matched high-intensity intermittent exercise protocols in trained individuals.
METHODS: In a randomized, counter-balanced, crossover design, eleven endurance-trained men performed high-intensity intermittent cycle exercise protocols matched for total work and including either multiple short- (18×5 s; SS) or long-duration (6×20 s; LS) sprints. Neuromuscular fatigue was determined by pre- to post-exercise changes in maximal voluntary contraction force (MVC), voluntary activation level and contractile properties of the quadriceps muscle. Metabolites and pH were measured in vastus lateralis muscle biopsies taken before and after the first and last sprint of each exercise protocol.
RESULTS: Peak power output (11±2 vs. 16±8%, P<0.01), MVC (10±5 vs. 25±6%, P<0.05) and peak twitch force (34±5 vs. 67±5 %, P<0.01) declined to a lesser extent in SS than LS, while voluntary activation level decreased similarly in SS and LS (10±2 vs. 11±4%). Muscle [PCr] prior to the last sprint was 1.5-fold lower in SS than LS (P<0.001). Pre- to post-exercise intramuscular accumulation of lactate and H was two- and three-fold lower, respectively, in SS than LS (P<0.001), whereas muscle glycogen depletion was similar in SS and LS. Rate of muscle glycolysis was similar in SS and LS during the first sprint, but two-fold higher in SS than LS during the last sprint (P<0.05).
CONCLUSION: These findings indicate that, in endurance-trained individuals, multiple long-sprints induce larger impairments in performance along with greater degrees of peripheral fatigue compared to work-matched multiple short-sprints, with these differences being possibly attributed to more extensive intramuscular accumulation of lactate/H and to lower rates of glycolysis during multiple long-sprint exercise.
Background: Despite strong reservations regarding the validity of a number of heart rate variability (HRV) measures, these are still being used in recent studies. Aims: We aimed to compare the reactivity of ostensible sympathetic HRV markers (low and very low frequency [LF and VLF]) to that of electrodermal activity (EDA), an exclusively sympathetic marker, in response to cognitive and orthostatic stress, investigate the possibility of LF as a vagal-mediated marker of baroreflex modulation, and compare the ability of HRV markers of parasympathetic function (root mean square of successive differences [RMSSD] and high frequency [HF]) to quantify vagal reactivity to cognitive and orthostatic stress. Results: None of the purported sympathetic HRV markers displayed a reactivity that correlated with electrodermal reactivity. LF (ms ² ) reactivity correlated with the reactivity of both RMSSD and HF during baroreflex modulation. RMSSD and HF indexed the reactivity of the parasympathetic nervous system under conditions of normal breathing; however, RMSSD performed better as a marker of vagal activity when the task required breathing changes. Conclusions: Neither LF (in ms ² or normalized units [nu]) nor VLF represent cardiac sympathetic modulation of the heart. LF (ms ² ) may reflect vagally mediated baroreflex cardiac effects. HRV linear analysis therefore appears to be restricted to the determination of vagal influences on heart rate. With regard to HRV parasympathetic markers, this study supports the suggestion that HRV frequency domain analyses, such as HF, should not be used as an index of vagal activity in study tasks where verbal responses are required, as these responses may induce respiratory changes great enough to distort HF power.
This book introduces readers to the basic concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of the autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques.
Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data.
Training quantification is basic to evaluate an endurance athlete's responses to the training loads, ensure adequate stress/recovery balance and determine the relationship between training and performance. Quantifying both external and internal workload is important, because the external workload does not measure the biological stress imposed by the exercise sessions. Generally used quantification methods include retrospective questionnaires, diaries, direct observation and physiological monitoring, often based on the measurement of oxygen uptake, heart rate and blood lactate concentration. Other methods in use in endurance sports include speed measurement and the measurement of power output, made possible by recent technological advances, such as power meters in cycling and triathlon. Among subjective methods of quantification the RPE stands out because of its wide use. Concurrent assessments of the various quantification methods allow researchers and practitioners to evaluate stress/recovery balance, adjust individual training programmes and determine the relationships between external load, internal load and athletes' performance. This brief review summarizes the most relevant external and internal workload quantification methods in endurance sports, and provides practical examples of their implementation to adjust the training programmes of elite athletes in accordance to their individualized stress/recovery balance.
Mobile power meters provide a valid means of measuring cyclists’ power output in the field. These field measurements can be performed with very good accuracy and reliability making the power meter a useful tool for monitoring and evaluating training and race demands. This review presents power meter data from a Grand Tour cyclist’s training and racing and explores the inherent complications created by its stochastic nature. Simple summary methods cannot reflect a session’s variable distribution of power output or indicate its likely metabolic stress. Binning power output data, into training zones for example, provides information on the detail but not the length of efforts within a session. An alternative approach is to track changes in cyclists’ modelled training and racing performances. Both critical power and record power profiles have been used for monitoring training-induced changes in this manner. Due to the inadequacy of current methods, the review highlights the need for new methods to be established which quantify the effects of training loads and models their implications for performance.
Skeletal muscle fatigue is characterized by the buildup of H(+) and inorganic phosphate (Pi), metabolites which are thought to cause fatigue by inhibiting muscle force, velocity, and power. While individual effects of elevated H(+) or Pi have been well characterized, the effects of simultaneously elevating the ions, as occurs during fatigue in vivo, are still poorly understood. To address this, we exposed slow and fast rat skinned muscle fibers to fatiguing levels of H(+) (pH 6.2) and Pi (30 mM) and determined the effects on contractile properties. At 30°C, elevated Pi and low pH depressed maximal shortening velocity (Vmax) by 15% (4.23 to 3.58 fl/s) in slow and 31% (6.24 vs. 4.55 fl/s) in fast fibers, values similar to depressions from low pH alone. Maximal isometric force dropped by 36% in slow (148 to 94 kN/m2) and 46% in fast fibers (148 to 80 kN/m(2)), declines substantially larger than what either ion exerted individually. The strong effect on force combined with the significant effect on velocity caused peak power to decline by over 60% in both fiber types. Force-stiffness ratios significantly decreased with pH 6.2 + 30 mM Pi in both fiber types, suggesting these ions reduced force by decreasing the force per bridge and/or increasing the number of low-force bridges. The data indicate the collective effects of elevating H(+) and Pi on maximal isometric force and peak power are stronger than what either ion exerts individually and suggest the ions act synergistically to reduce muscle function during fatigue.
Fiercer competition between athletes and a wider knowledge of optimal training regimens dramatically influence current training methods. A single training bout per day was previously considered sufficient, whereas today athletes regularly train twice a day or more. Consequently, the number of athletes who are overtraining and have insufficient rest is increasing.
Positive overtraining can be regarded as a natural process when the end result is adaptation and improved performance; the supercompensation principle — which includes the breakdown process (training) followed by the recovery process (rest) — is well known in sports. However, negative overtraining, causing maladaptation and other negative consequences such as staleness, can occur.
Physiological, psychological, biochemical and immunological symptoms must be considered, both independently and together, to fully understand the ’staleness’ syndrome. However, psychological testing may reveal early-warning signs more readily than the various physiological or immunological markers.
The time frame of training and recovery is also important since the consequences of negative overtraining comprise an overtraining-response continuum from short to long term effects. An athlete failing to recover within 72 hours has presumably negatively overtrained and is in an overreached state. For an elite athlete to refrain from training for >72 hours is extremely undesirable, highlighting the importance of a carefully monitored recovery process.
There are many methods used to measure the training process but few with which to match the recovery process against it. One such framework for this is referred to as the total quality recovery (TQR) process. By using a TQR scale, structured around the scale developed for ratings of perceived exertion (RPE), the recovery process can be monitored and matched against the breakdown (training) process (TQR versus RPE). The TQR scale emphasises both the athlete’s perception of recovery and the importance of active measures to improve the recovery process. Furthermore, directing attention to psychophysiological cues serves the same purpose as in RPE, i.e. increasing self-awareness.
This article reviews and conceptualises the whole overtraining process. In doing so, it (i) aims to differentiate between the types of stress affecting an athlete’s performance; (ii) identifies factors influencing an athlete’s ability to adapt to physical training; (iii) structures the recovery process. The TQR method to facilitate monitoring of the recovery process is then suggested and a conceptual model that incorporates all of the important parameters for performance gain (adaptation) and loss (maladaptation).
The purpose of this study was to analyze pedaling cadence, pedal forces, and muscle activation of triathletes during cycling to exhaustion. Fourteen triathletes were assessed at the power output level relative to their maximal oxygen uptake (355 +/- 23 W). Cadence, pedal forces, and muscle activation were analyzed during start, middle, and end test stages. Normal and tangential forces increased from the start to the end of the test (-288 +/- 33 to -352 +/- 42 N and -79 +/- 45 to -124 +/- 68 N, respectively) accompanied by a decrease in cadence (96 +/- 5 to 86 +/- 6 rpm). Muscle activation increased from the start to the middle and the end in the gluteus maximus (27 +/- 5.5% and 76 +/- 9.3%) and in the vastus lateralis (13 +/- 3.5% and 27 +/- 4.4%), similar increase was observed from the start to the end in the rectus femoris and the vastus medialis (50 +/- 9.3% and 20 +/- 5.7%, respectively). Greater normal force along with enhanced activation of knee and hip extensor muscles is linked with fatigue and declines in cadence of triathletes during cycling to exhaustion.
The purpose of this study was to assess the Record Power Profile (RPP) of cyclists, i. e., the relationship between different record Power Output (PO) and the corresponding durations through a whole race season. We hypothesized that PO of different effort durations could differ according to the cyclist's category and race performance profile. 17 cyclists (9 professionals and 8 elites) performed all trainings and competitions during 10 months with a mobile power meter device (SRM) mounted on their bike. The results show that the cyclists' RPP is a hyperbolic relationship between the different record PO and time durations. It significantly reflects the characteristics of different skills: (1) sprinters have the highest record PO within zone 5, (2) climbers present the highest record PO within zones 2-3 and, (3) climbers and flat specialists have higher zone 1 record PO than sprinters. These results suggest that the RPP represents "a signature" of the cyclists' physical capacity and that it allows the determination of different training intensities. The RPP appears as a new concept that is interesting for coaches and scientists in order to evaluate performance in cycling.
There is a great demand for perceptual effort ratings in order to better understand man at work. Such ratings are important complements to behavioral and physiological measurements of physical performance and work capacity. This is true for both theoretical analysis and application in medicine, human factors, and sports. Perceptual estimates, obtained by psychophysical ratio-scaling methods, are valid when describing general perceptual variation, but category methods are more useful in several applied situations when differences between individuals are described. A presentation is made of ratio-scaling methods, category methods, especially the Borg Scale for ratings of perceived exertion, and a new method that combines the category method with ratio properties. Some of the advantages and disadvantages of the different methods are discussed in both theoretical-psychophysical and psychophysiological frames of reference.
Monod and Scherrer (1965) showed that there was a linear relation between the maximal work and the maximal time over which the work was performed until the onset of local muscular exhaustion. This linear relation could be expressed by the equation: Wlim =a+bTlim, where maximal work (Wlim) was thought to result from the use of an energy reserve (a) and an energy reconstitution whose maximal rate was (b) We have extended this concept to total body work (bicycle ergometer). Eight male and eight female college students underwent exercise tests at 400, 350, 300,275 and 300,250,200,175 W respectively, to the onset of fatigue. The regression analysis revealed that the linearity of individual plots was found to be 0-982
Bayes factors, model choice and variable selection in linear models
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Garcia, G., Forte, A., & Vergara, C. (2020). Bayes factors, model choice and
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r-project.org/web/packages/BayesVarSel/BayesVarSel.pdf
Scale of magnitudes for effect statistics
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Hopkins, W. (2002). Scale of magnitudes for effect statistics. In A new view of
statistics. Internet Society for Sport Sciences. https://sportscience.
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