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Purpose: To test the validity and reliability of field critical power (CP). Method: Laboratory CP tests comprised three exhaustive trials at intensities of 80, 100 and 105 % maximal aerobic power and CP results were compared with those determined from the field. Experiment 1: cyclists performed three CP field tests which comprised maximal efforts of 12, 7 and 3 min with a 30 min recovery between efforts. Experiment 2: cyclists performed 3 × 3, 3 × 7 and 3 × 12 min individual maximal efforts in a randomised order in the field. Experiment 3: the highest 3, 7 and 12 min power outputs were extracted from field training and racing data. Results: Standard error of the estimate of CP was 4.5, 5.8 and 5.2 % for experiments 1-3, respectively. Limits of agreement for CP were -26 to 29, 26 to 53 and -34 to 44 W for experiments 1-3, respectively. Mean coefficient of variation in field CP was 2.4, 6.5 and 3.5 % for experiments 1-3, respectively. Intraclass correlation coefficients of the three repeated trials for CP were 0.99, 0.96 and 0.99 for experiments 1-3, respectively. Conclusions: Results suggest field-testing using the different protocols from this research study, produce both valid and reliable CP values.
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... 2,3 Field-derived MMP values can also be used for inferring the power-duration curve. 4 The parameters derived from this curve-critical power (CP, ie, the theoretical highest sustainable rate of aerobic metabolism without a continuous loss of homeostasis) 5 and anaerobic work capacity (W′, ie, the amount of work that can be performed above CP) 6 -are potentially applicable to monitor performance and training loads. ...
... Some authors have suggested that MMP (and the derived CP and W′) can be considered practical surrogates of performance determined with specific tests, such as simulated time trials (TTs) designed ad hoc for assessing cyclists' maximal capabilities. 4,7 However, these findings have not been corroborated in other studies, at least when considering MMP data obtained during the preparatory period. 8 In this effect, several factors can influence MMP values including not only fatigue, 9 environmental conditions, 10,11 or race type, 12 but also the period of the season in question (eg, preparatory vs specific). ...
... Twenty-seven male professional road cyclists from the same team (age, 24 [4] y; body mass, 66.9 [5.2] kg; experience in the professional category, 4 [3] y) volunteered to participate in this study. At the time of enrollment, cyclists were free of any type of musculoskeletal injury that would hinder their participation in the study. ...
Purpose: To determine the validity of field-derived mean maximum power (MMP) values for monitoring maximal cycling endurance performance. Methods: Twenty-seven male professional cyclists performed 3 timed trials (TTs) of 1-, 5-, and 20-minute duration that were used as the gold standard reference. Field-based power output data (3336 files; 124 [25] per cyclist) were registered during the preparatory (60 d pre-TT, including training data only) and specific period of the season (60 d post-TT, including both training and competitions). Comparisons were made between TT performance (mean power output) and MMP values obtained for efforts of the same duration as TT (MMP of 1-, 5-, and 20-min duration). The authors also compared TT- and MMP-derived values of critical power (CP) and anaerobic work capacity. Results: A large correlation (P < .001, r > .65) was found between MMP and TT performance regardless of the effort duration or season period. However, considerable differences (P < .05, standard error of measurement [SEM] > 5%) were found between MMP and TT values for all effort durations in the preparatory period, as well as for the derived CP and anaerobic work capacity. Significant differences were also found between MMP and TT of 1 minute in the specific period, as well as for anaerobic work capacity, yet with no differences for MMP of 5- and 20-minute duration or the derived CP (P > .05, SEM < 5%). Conclusion: MMP values (for efforts ≥5 min) and the associated CP obtained from both training sessions and competitions can be considered overall accurate indicators of the cyclist's maximal capabilities, but specific tests might be necessary for shorter efforts or when considering training sessions only.
... In total, 242 participants were involved in the records (199 males, 43 females, mean ± SD: age = 29.1 ± 5.9 years). Ten of the studies included men and women [13][14][15][16][17][18][19][20][21][22], while the rest included only men [8,[23][24][25][26][27][28][29][30][31][32]. The inclusion criteria for participants were defined as trained and experienced athletes. ...
... Four studies described moderate and recreationally trained subjects, which is somewhat in conflict with these criteria [13,14,17,25], but all participants were capable of completing the test and all studies contain important information. The other studies described subjects as elite/competitive, club-level, trained, or a combination [8,15,19,20,[22][23][24]26,[28][29][30][31][32]. Three incorporated various fitness levels that included competitive and recreationally trained individuals [16,18,21]. ...
... The original test requires a large number of trials performed on separate days, but there is also the option of using the traditional protocol with testing within one day with different recovery times ranging from 30 min up to 3 h [23][24][25]27,30]. This may help make the test more readily available and usable in practice. ...
Article
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Critical power represents an important parameter of aerobic function and is the highest average effort that can be sustained for a period of time without fatigue. Critical power is determined mainly in the laboratory. Many different approaches have been applied in testing methods, and it is a difficult task to determine which testing protocol it the most suitable. This review aims to evaluate all possible tests on bicycle ergometers or bicycles used to estimate critical power and to compare them. A literature search was conducted in four databases (PubMed, Scopus, SPORTDiscus, and Web of Science) published from 2012 to 2022 and followed the PRISMA guidelines to process the review. Twenty-one articles met the eligibility criteria: records with trained or experienced endurance athletes (adults > 18), bicycle ergometer, a description of the testing protocol, and comparison of the tests. We found that the most widely used tests were the 3-min all-out tests set in a linear mode and the traditional protocol time to exhaustion. Some other alternatives could have been used but were not as regular. To summarize, the testing methods offered two main approaches in the laboratory (time to exhaustion test andthe 3-min all-out test with different protocols) and approach in the field, which is not yet completely standardized.
... Traditionally, performing three to five prediction trials between 2 and 15 min of duration (Karsten et al. 2015;Maturana et al. 2018;Muniz-Pumares et al. 2019) allows CP and W′ to be derived through weighted least square or geometric mean linear and nonlinear regression analysis (Vinetti et al. 2017;Vinetti et al. 2020). Prediction trials shorter than 2 min do not ensure the attainment of V O 2max (i.e. they fall outside the severe intensity domain) (Hill and Smith 1994;Maturana et al. 2018;Muniz-Pumares et al. 2019;Nimmerichter et al. 2020), while prediction trials longer than 15 min are not recommended due to the influence of glycogen depletion and psychological factors (i.e. ...
... Prediction trials shorter than 2 min do not ensure the attainment of V O 2max (i.e. they fall outside the severe intensity domain) (Hill and Smith 1994;Maturana et al. 2018;Muniz-Pumares et al. 2019;Nimmerichter et al. 2020), while prediction trials longer than 15 min are not recommended due to the influence of glycogen depletion and psychological factors (i.e. motivation) (Karsten et al. 2015;Maturana et al. 2018). To avoid any skewness during the mathematical modelling and reduce errors in the calculation of CP and W′ the shortest prediction trial should last between 2 and 5 min and the longest prediction trial between 12 and 15 min (Karsten et al. 2015;Maturana et al. 2018;Muniz-Pumares et al. 2019). ...
... motivation) (Karsten et al. 2015;Maturana et al. 2018). To avoid any skewness during the mathematical modelling and reduce errors in the calculation of CP and W′ the shortest prediction trial should last between 2 and 5 min and the longest prediction trial between 12 and 15 min (Karsten et al. 2015;Maturana et al. 2018;Muniz-Pumares et al. 2019). Inter-trial recovery between prediction trials should be set to a minimum of 30 min during a single visit or 24 h during multiple days (Karsten et al. 2017). ...
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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.
... The estimation requires data from a sufficiently large number of maximal efforts (i.e. efforts to exhaustion) over different distances or durations with a time to exhaustion between 2 and 15 minutes (Karsten et al., 2015). ...
... Rather, data points to the left and right of a kink are likely sub-maximal efforts. Indeed, 3, 7 and 12 minutes are often-recommended test durations for the hyperbolic model(Karsten et al., 2015). Note also that these data were "mined" from training history so that multiple points may correspond to the same activity and then cannot all represent maximal efforts. ...
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The power–duration relationship describes the time to exhaustion for exercise at different intensities. It is generally believed to be a "fundamental bioenergetic property of living systems" that this relationship is hyperbolic. Indeed, the hyperbolic (a.k.a. critical-power ) model which formalises this belief is viewed as the "gold standard" for assessing exercise capacity, e.g. in cycling, running, rowing, and swimming. However, the hyperbolic model is now the focus of two heated debates in the literature because: (a) it unrealistically represents efforts that are short (< 2 minutes) or long (> 15 minutes); (b) it contradicts widely-used performance predictors such as the so-called functional threshold power (FTP) in cycling. We contribute to both debates by demonstrating that the power–duration relationship is more adequately represented by an alternative, power-law model. In particular, we show that the often observed good fit of the hyperbolic model between 2 and 15 minutes should not be taken as proof that the power–duration relationship is hyperbolic. Rather, in this range, a hyperbolic function just happens to approximate a power law fairly well. We also prove a mathematical result which suggests that the power-law model is a safer tool for pace selection than the hyperbolic model. Finally, we use the power-law model to shed light on popular performance predictors in cycling, running and rowing such as FTP and Jack Daniels' "VDOT" calculator .
... Garmin Vector, Garmin International Inc., Olathe, KS, USA; Powertap, CycleOps, Madisson, USA; SRM Powermeter, Schoberer Rad-und Messtechnik, Julich, Germany) [9][10][11] enable the determination of the power-duration relationship in field conditions [12]. It has been shown that field derived CP estimates may be considered as valid and reliable compared with laboratory estimates, whereas the reliability (and hence validity) of field derived W´ estimates is still debated [13,14]. However, previous research has shown that road gradient may partially affect biomechanical and physiological parameters like crank kinetics (e. g. crank inertial load, crank torque profile) [15][16][17], lower limb joint kinetics (e. g. joint moments, joint mechanical work) [18,19], lower limb neuromuscular activation (e. g. intensity and timing of EMG activity) [20][21][22] and gross efficiency [15,23] during cycling in a seated position. ...
... A similar effect has been shown in a recent study where the ingestion of caffeine increased lower limb EMG activity and, subsequently, peripheral fatigue and the amount of work performed above CP during a 4 km time-trial [49]. However, since recent research questioned the reliability of field derived W´ estimates, especially when a single-visit protocol with recovery periods of 30 min is used [14], caution must be taken when interpreting these results. ...
Article
The purpose of this study was to investigate the effects of flat and uphill cycling on critical power and the work available above critical power. Thirteen well-trained endurance athletes performed three prediction trials of 10-, 4- and 1-min in both flat (0.6%) and uphill (9.8%) cycling conditions on two separate days. Critical power and the work available above critical power were estimated using various mathematical models. The best individual fit was used for further statistical analyses. Paired t-tests and Bland-Altman plots with 95% limits of agreement were applied to compare power output and parameter estimates between cycling conditions. Power output during the 10- and 4-min prediction trial and power output at critical power were not significantly affected by test conditions (all at p>0.05), but the limits of agreement between flat and uphill cycling power output and critical power estimates are too large to consider both conditions as equivalent. However, power output during the 1-min prediction trial and the work available above critical power were significantly higher during uphill compared to flat cycling (p<0.05). The results of this investigation indicate that gradient affects cycling time-trial performance, power output at critical power, and the amount of work available above critical power.
... The ICC value between the T10 test and T10 retest (r = 0.93; 90% CI: 0.89-0.96) indicates high reliability and is also similar to those reported by others (Karsten et al., 2015;Triska, Karsten, Heidegger et al., 2017;Wright et al., 2017). ...
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The elegant concept of a hyperbolic relationship between power, velocity, or torque and time to exhaustion has rightfully captivated the imagination and inspired extensive research for over half a century. Theoretically, the relationship’s asymptote along the time axis (critical power, velocity, or torque) indicates the exercise intensity that could be maintained for extended durations, or the “heavy–severe exercise boundary”. Much more than a critical mass of the extensive accumulated evidence, however, has persistently shown the determined intensity of critical power and its variants as being too high to maintain for extended periods. The extensive scientific research devoted to the topic has almost exclusively centered around its relationships with various endurance parameters and performances, as well as the identification of procedural problems and how to mitigate them. The prevalent underlying premise has been that the observed discrepancies are mainly due to experimental ‘noise’ and procedural inconsistencies. Consequently, little or no effort has been directed at other perspectives such as trying to elucidate physiological reasons that possibly underly and account for those discrepancies. This review, therefore, will attempt to offer a new such perspective and point out the discrepancies’ likely root causes.
Since its publication in 2012, the W' balance model has become an important tool in the scientific armamentarium for understanding and predicting human physiology and performance during high-intensity intermittent exercise. Indeed, publications featuring the model are accumulating, and it has been adapted for popular use both in desktop computer software and on wrist-worn devices. Despite the model's intuitive appeal, it has achieved mixed results thus far, in part due to a lack of clarity in its basis and calculation. Purpose: This review examines the theoretical basis, assumptions, calculation methods, and the strengths and limitations of the integral and differential forms of the W' balance model. In particular, the authors emphasize that the formulations are based on distinct assumptions about the depletion and reconstitution of W' during intermittent exercise; understanding the distinctions between the 2 forms will enable practitioners to correctly implement the models and interpret their results. The authors then discuss foundational issues affecting the validity and utility of the model, followed by evaluating potential modifications and suggesting avenues for further research. Conclusions: The W' balance model has served as a valuable conceptual and computational tool. Improved versions may better predict performance and further advance the physiology of high-intensity intermittent exercise.
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While several studies have been conducted on the impacts of logging on forest recovery in tropical forests, most of these studies have largely focused on short term forest recovery and without perspectives on how different logging intensities could influence the nature and extent of forest recovery in the long term. This study therefore aims to explore how varying site conditions, resulting from the creation of different disturbance types (skid trails and felling gaps) following selective logging at two logging intensities influenced long-term floristic composition eighteen years after logging. The study was conducted in the permanent sample plots located in a 134-ha compartment in the Pra-Anum Forest Reserve. Data collection was conducted on 160 recording plots distributed on three disturbance sites and an unlogged area, within two blocks of logging intensity of 26.32 m³/ha and 52.63 m³/ha (representing the volume of trees harvested per hectare). Results showed that the main skid trail had significantly lower species abundance and richness than the other disturbance types and the unlogged area. There was also a significant difference in species spatial distribution across all the four sites. These distribution patterns revealed differences in plant dominance at disturbance gradients, which indicate their success at competing for available resources within their niche space. Furthermore, the number of species belonging to particular ecological guilds classified from the different skid trails and unlogged areas as well logging intensities showed no significant variation. However, most of the species sampled across the disturbance regimes and unlogged area were observed to be shade tolerant. Logging disturbance and logging intensity did not influence the distribution of trees of national conservation importance (based on tree distribution, ecology, local abundance, interaction with ecosystem parameters and economic importance which have been used as basis conservation prioritization). However, disturbance type and higher logging intensities favoured tree species of minimal timber and national conservation importance, relative to the unlogged area. Given the increasing demand for timber products for both domestic and international markets, which is driving higher harvesting intensities legally and illegally, this study will inform forest management decision making on the long term dynamics of tree species recovery following logging.
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For high-intensity muscular exercise, the time-to-exhaustion (t) increases as a predictable and hyperbolic function of decreasing power (P) or velocity (V). This relationship is highly conserved across diverse species and different modes of exercise and is well described by two parameters: the 'critical power' (CP or CV), which is the asymptote for power or velocity, and the curvature constant (W') of the relationship such that t = W'/(P-CP). CP represents the highest rate of energy transduction (oxidative ATP production, V? O2) that can be sustained without continuously drawing on the energy store W' (composed in part of anaerobic energy sources and expressed in kilojoules). The limit of tolerance (time t) occurs when W' is depleted. The CP concept constitutes a practical framework in which to explore mechanisms of fatigue and help resolve crucial questions regarding the plasticity of exercise performance and muscular systems physiology. This brief review presents the practical and theoretical foundations for the CP concept, explores rigorous alternative mathematical approaches, and highlights exciting new evidence regarding its mechanistic bases and its broad applicability to human athletic performance.
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To compare critical speed measured from a single-visit field test of the distance-time relationship with the 'traditional' treadmill time to exhaustion multi-visit protocol. Ten male distance runners completed treadmill and field-tests in order to calculate critical speed (CS) and the maximum distance performed above CS (D'). The field-test involved 3 runs on a single visit to an outdoor athletics track over 3600 m, 2400 m and 1200 m. Two field-test protocols were evaluated using either a 30-min recovery or 60-min recovery between runs. The treadmill test involved runs to exhaustion at 100, 105 and 110% of velocity at VO2max, with 24-hours recovery between runs. There was no difference in CS measured with the treadmill, 30-min and 60-min recovery field tests, (P<0.05). CS from the treadmill test was highly correlated with CS from the 30 and 60-min field tests (r=0.89; r=0.82, P<0.05). However there was a difference and no correlation in D' between the treadmill test and the 30 and 60-min field tests (r=0.13; r=0.33, P>0.05). A typical error of the estimate of 0.14 m·s-1 (95% confidence limits: 0.09-0.26 m·s-1) was seen for CS and 88 m (95% confidence limits: 60-169 m) for D'. A coefficient of variation of 0.4% (95% confidence limits: 0.3-0.8%) was found for repeat tests of CS and 13% (95% confidence limits: 10-27%) for D'. The single-visit method provides a useful alternative for assessing CS in the field.
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The purpose of this study was to investigate the level of agreement between laboratory-based estimates of critical power (CP) and results taken from a novel field test. Subjects were fourteen trained cyclists (age 40±7 yrs; body mass 70.2±6.5 kg; V̇O2max 3.8±0.5 L · min-1). Laboratory-based CP was estimated from 3 constant work-rate tests at 80%, 100% and 105% of maximal aerobic power (MAP). Field-based CP was estimated from 3 all-out tests performed on an outdoor velodrome over fixed durations of 3, 7 and 12 min. Using the linear work limit (Wlim) vs. time limit (Tlim) relation for the estimation of CP1 values and the inverse time (1/t) vs. power (P) models for the estimation of CP2 values, field-based CP1 and CP2 values did not significantly differ from laboratory-based values (234±24.4 W vs. 234±25.5 W (CP1); P<0.001; limits of agreement [LOA], -10.98-10.8 W and 236±29.1 W vs. 235±24.1 W (CP2); P<0.001; [LOA], -13.88-17.3 W. Mean prediction errors for laboratory and field estimates were 2.2% (CP) and 27% (W'). Data suggest that employing all-out field tests lasting 3, 7 and 12 min has potential utility in the estimation of CP.
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Power output and heart rate were monitored for 11 months in one female (V(.)O(2max): 71.5 mL · kg⁻¹ · min⁻¹) and ten male (V(.)O(2max): 66.5 ± 7.1 mL · kg⁻¹ · min⁻¹) cyclists using SRM power-meters to quantify power output and heart rate distributions in an attempt to assess exercise intensity and to relate training variables to performance. In total, 1802 data sets were divided into workout categories according to training goals, and power output and heart rate intensity zones were calculated. The ratio of mean power output to respiratory compensation point power output was calculated as an intensity factor for each training session and for each interval during the training sessions. Variability of power output was calculated as a coefficient of variation. There was no difference in the distribution of power output and heart rate for the total season (P = 0.15). Significant differences were observed during high-intensity workouts (P < 0.001). Performance improvements across the season were related to low-cadence strength workouts (P < 0.05). The intensity factor for intervals was related to performance (P < 0.01). The variability in power output was inversely associated with performance (P < 0.01). Better performance by cyclists was characterized by lower variability in power output and higher exercise intensities during intervals.
Conference Paper
Introduction The majority of interval training studies are conducted on ergometers to control external variables and exercise intensity. The differences between laboratory and outdoor cycling have been discussed recently (Jobson et al., 2007) suggesting different physiological demands. With the use of mobile power meters, exercise intensity can be monitored in the field, which improves the ecological validity of the measurements. This study tested the effects of low-cadence (60 rpm) uphill (Int60) or high-cadence (100 rpm) level-ground (Int100) interval training on power output (PO) during 20-min uphill (TTup) and flat (TTflat) time-trials as well as on performance during a laboratory graded exercise test (GXT). Methods Eighteen male cyclists (VO2max: 58.6 ± 5.4 mL/min/kg) were randomly assigned to Int60, Int100 or a control group (Con). The interval training comprised of two training sessions per week over 4 weeks, which consisted of 6x5 min at the PO corresponding to the respiratory compensation point (RCP). For the control group, no interval training was conducted. During the interval training sessions and the time-trials, PO was measured with mobile power-meters (SRM). Results A two-factor ANOVA revealed significant increases on performance measures obtained from GXT (Pmax: 2.8 ± 3.0%; p<0.01; PO and VO2 at RCP: 3.6 ± 6.3% and 4.7 ± 8.2%, respectively; p<0.05), with no significant group effects. Significant interactions between group and uphill and flat time-trial, pre vs. 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). Discussion The performance improvements during TTup and TTflat have shown specific adaptations in response to the interval training sessions and indicate the ecological validity of the time-trials. The application of higher pedaling forces via low cadences provides a potentially higher training stimulus with a cross-over effect to flat time-trials. When evaluating power output data or prescribing training zones, it is important to note that trained cyclists are able to produce higher power outputs during uphill compared to flat time-trial conditions.
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The main purpose of the study was to investigate the relationships between the Lactate Threshold (LT), maximal oxygen uptake (KO2max), performance time, and Critical Aerobic Power (CAP) during a simulated 20 km cycling time trial (20 kmTT). CAP was operationally defined as the average oxygen uptake sustained during the 20 kmTT. The subjects were 11 experienced male cyclists (mean±SD age: 29±7.2 yr; VO2max: 4.51±0.11 (L · min−1). Each subject completed two 20 kmTT using their own racing bicycle on a custom designed, computerized roller system. Elapsed time for the best trial averaged 34.58 ±3.29 min. Test‐retest reliability estimates for elapsed time, average heart rate and VO2 during the two trials were 0.92, 0.98 and 0.98, respectively. Oxygen uptake during the 20 kmTT averaged 115% of VO2 at LT and 86% of FO2max· A significant correlation was found between CAP and performance time (r= —0.81, p ≤ 0.01). Significant correlations were found between CAP and VO2 at LT (r = 0.62, p ≤ 0.05) and CAP and VO2max (r = 0.97, p ≤ 0.05). Using stepwise regression, VO2max was the strongest predictor of CAP with no further contribution from VO2 at LT. It was concluded that CAP is a strong determinant of cycling performance for 30–40 min duration. In this study, CAP was dependent more on VO2max than on the LT.
Book
The purpose of physiological testing (J.D. MacDougall and H.A. Wenger) what do tests measure? (H.J. Green) testing strength and power (D.G. Sale) testing aerobic power (J.S. Thoden) testing anaerobic power and capacity (C. Bouchard, Albert W. Taylor, Jean-Aime Simoneau, and Serge Dulac) Kknanthropometry (WD. Ross and M.J. Marfell-Jones) testing flexibility (C.L. Hubley-Kozey) evaluating the health status of the athlete (R. Backus and D.C. Reid) modelling elite athletic performance (E.W. Banister).