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Abstract

To compare the effects of three 7-week interval training programs varying in work period duration but matched for effort in trained recreational cyclists. Thirty-five cyclists (29 male, 6 female, VO(2peak) 52 ± 6 mL kg/min) were randomized to four training groups with equivalent training the previous 2 months (∼6 h/wk, ∼1.5 int. session/wk). Low only (n=8) trained 4-6 sessions/wk at a low-intensity. Three groups (n=9 each) trained 2 sessions/wk × 7 wk: 4 × 4 min, 4 × 8 min, or 4 × 16 min, plus 2-3 weekly low-intensity bouts. Interval sessions were prescribed at the maximal tolerable intensity. Interval training was performed at 88 ± 2, 90 ± 2, and 94 ± 2% of HR(peak) and 4.9, 9.6, and 13.2 mmol/L blood lactate in 4 × 16, 4 × 8, and 4 × 4 min groups, respectively (both P<0.001). 4 × 8min training induced greater overall gains in VO(2) peak, power@VO(2) peak, and power@4 mM bLa- (Mean ± 95%CI): 11.4 (8.0-14.9), vs 4.2 (0.4-8.0), 5.6 (2.1-9.1), and 5.5% (2.0-9.0) in Low, 4 × 16, and 4 × 4 min groups, respectively (P<0.02 for 4 × 8 min vs all other groups). Interval training intensity and accumulated duration interact to influence the adaptive response. Accumulating 32 min of work at 90% HR max induces greater adaptive gains than accumulating 16 min of work at ∼95% HR max despite lower RPE.
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... Depending on the purpose of the training session, these variables can be manipulated to give countless different combinations. Studies of well-trained endurance athletes indicate that the best training effect, i.e., increased stroke volume and oxygen delivery, is achieved at intensities between 85%-95% of HRmax [13][14][15], although studies have also demonstrated positive effects of training at higher intensities [12,16]. ...
... The optimal interval duration for well-trained endurance athletes appears to be 4-10 min [13][14][15], with a beneficial effect observed at a total effective duration of approximately 15 min [5,13]. However, durations of 30-45 min appear to elicit the best effect [14,15]. ...
... The optimal interval duration for well-trained endurance athletes appears to be 4-10 min [13][14][15], with a beneficial effect observed at a total effective duration of approximately 15 min [5,13]. However, durations of 30-45 min appear to elicit the best effect [14,15]. ...
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Purpose: To determine the impact of interval training frequency in elite endurance athletes. It was hypothesized that two longer sessions would elicit greater performance improvements and physiological adaptation than four shorter sessions at the same intensity. Methods: Elite cross-country skiers and biathletes were randomly assigned to either a high-frequency group (HF group) (5 M, 1 F, age 22 (19-26), VO2max 67.8 (65.5-70.2) mL/kg/min) doing four short interval sessions per week or a low-frequency group (LF group) (8 M, 1 F, age 22 (18-23), VO2max 70.7 (67.0-73.9) mL/kg/min) doing two longer interval sessions. All interval sessions were performed at ~85% of maximum heart rate, and groups were matched for total weekly training volume. Pre- and post-intervention, athletes completed an 8 km rollerski time-trial, maximal oxygen uptake (VO2max) test, and an incremental, submaximal exercise test. Results: The LF group had a statistically significant improved time-trial performance following the intervention (p = 0.04), with no statistically significant changes in the HF group. Similarly, percentage utilization of VO2max at anaerobic threshold (p = 0.04) and exercise economy (p = 0.01) were statistically significantly improved following the intervention in the LF group only. No statistically significant changes in VO2max were observed in either group. Conclusions: Two longer interval sessions appear superior to four shorter sessions per week in promoting endurance adaptations and performance improvements in elite endurance athletes. Despite matched training volume and exercise intensity, the larger, more concentrated exercise stimulus in the LF group appears to induce more favorable adaptations. The longer time between training sessions in the LF group may also have allowed athletes to recover more effectively and better "absorb" the training. These findings are in line with the "best practice" observed by many of the world's best endurance athletes.
... It has been suggested that matching on energy consumption artificially constrains the training in a manner not representative of how athletes may perform their trainings in real life. 16 Despite improved endurance performance after HIT interventions in endurance athletes, there are often lack of improvements in some or all of the traditional main determinants of endurance performance such as V O2max, [17][18][19] work economy [19][20][21] and fractional utilization of V O2max. 15,20 Other potential contributors to performance improvements include increased skeletal muscle buffering capacity 22 and increased ability to perform with high blood lactate concentration ([La -]), manifested by increased [La -] during time trials. ...
... At least two of the three weekly HIT sessions were supervised. (6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) 17.8 ± 0.6 17.6 ± 0.6 ...
... While the traditional approach may clearly have its scientific advantages this approach may also create a training situation not compliant with real world training, and indeed it has been suggested that perceived effort-matched assessment is closer to how athletes typical perform their HIT training sessions. 16 With this is mind we decided to apply perceived effort and volumematching of the groups. The approach was successful as demonstrated by similar RPE scores after all work intervals in the two groups. ...
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The purpose of this study was to compare the effects of 3 weeks with three weekly sessions (ie, nine sessions in total) of short intervals (SI; n = 9; 3 series with 13 × 30-second work intervals interspersed with 15-second recovery and 3-minutes recovery between series) against effort-matched (rate of perceived effort based) long intervals (LI; n = 9; 4 series of 5-minute work intervals with 2.5-minutes recovery between series) on performance parameters in elite cyclists ( V ˙ O 2max 73 ± 4 mL min-1 kg-1 ). There were no differences between groups in total volume and intensity distribution of training during the intervention period. SI achieved a larger (P < .05) relative improvement in peak aerobic power output than LI (3.7 ± 4.3% vs -0.3 ± 2.8%, respectively), fractional utilization of V ˙ O 2max at 4 mmol L-1 [La- ] (3.0 ± 5.8 percent points vs -3.5 ± 2.7 percent points, respectively), and larger relative increase in power output at 4 mmol L-1 [La- ] (2.0 ± 6.7% vs -2.8 ± 3.4, respectively), while there was no group difference in change of V ˙ O 2max . Improvements in performance measured as mean power output during 20-minute cycling test were greater (P < .01) in SI compared with LI (4.7 ± 4.4% vs -1.4 ± 2.2%, respectively). Mean effect size of the improvement in the above variables revealed a small to large effect of SI training vs LI training. The data thus demonstrate that the present SI protocol induces superior training adaptations compared with the present LI protocol in elite cyclists.
... Furthermore, studies showed, that compared to moderate exercise intensities, more intensive training (e.g., HIIE) leads to a higher improvement in CRF (17)(18)(19). On the other hand, studies with coronary artery disease patients showed that HIIE does not seem to be superior with regard to influencing health-relevant variables such as body weight, blood pressure, or resting heart rate (20, 21). ...
Article
Purpose: We aimed to investigate differences between high-intensity interval exercise [HIIE; included high-intensity interval training (HIIT) and sprint interval training (SIT)] and moderate-intensity continuous training (MICT) on physical fitness, body composition, blood pressure, blood lipids, insulin and glucose metabolism, inflammation, and endothelial function. Methods: Differences between HIIE and MICT were summarized using a random-effects meta-analysis on the effect size (Cohen's d). A meta-regression was conducted using subgroups: population, age, training duration, men ratio, exercise type, baseline values (clinical relevant ranges), and type of HIIE. Studies were included if at least one of the following outcomes were reported: maximal oxygen uptake (V[Combining Dot Above]O2max), flow-mediated dilation (FMD), body mass index (BMI), body mass, % body fat, systolic and diastolic blood pressure, high-density lipoprotein (HDL), low-density lipoprotein (LDL), triglycerides, total cholesterol, C-reactive protein (CRP), fasting glucose and insulin, glycated hemoglobin (HbA1c), and insulin resistance (HOMA-IR). A total of 55 studies were included. Results: Overall, HIIE was superior to MICT in improving V[Combining Dot Above]O2max (d=0.40, p<0.001), and FMD (d=0.54, p<0.05). Oppositely, MICT was superior to HIIE in improving HbA1c (d=-0.27, p<0.05). No differences were observed in BMI (d=-0.02), body mass (d=-0.05), % body fat (d=0.04), systolic (d=-0.04) and diastolic blood pressure (d=0.03), HDL (d=-0.05), LDL (d=0.08), triglycerides (d=0.03), total cholesterol (d=0.14), CRP (d=-0.11), fasting insulin (d=0.02), fasting glucose (d=0.02), and HOMA-IR (d=-0.04). Moderator analyses indicated the difference between HIIE and MICT was affected by different subgroups. Conclusion: Overall, HIIE showed to be more effective in improving cardiovascular health and cardiorespiratory fitness, while MICT was superior in improving long-term glucose metabolism. In the process of personalized training counseling, health-enhancing effects of exercise training may be improved by considering the individual risk profiles.
... The LT 1 has been used to determine the boundary between L1 and L2 [16,18], and zones 1 and 2 [17,21,154], and is assumed to demarcate the moderate and heavy exercise domains [4,146]. The methods typically associated with determining LT 1 are the visual inspection point, the log-log LT, or an increase in blood lactate of 0.5 mmol . ...
Article
Prescribing the frequency, duration, or volume of training is simple as these factors can be altered by manipulating the number of exercise sessions per week, the duration of each session, or the total work performed in a given time frame (e.g., per week). However, prescribing exercise intensity is complex and controversy exists regarding the reliability and validity of the methods used to determine and prescribe intensity. This controversy arises from the absence of an agreed framework for assessing the construct validity of different methods used to determine exercise intensity. In this review, we have evaluated the construct validity of different methods for prescribing exercise intensity based on their ability to provoke homeostatic disturbances (e.g., changes in oxygen uptake kinetics and blood lactate) consistent with the moderate, heavy, and severe domains of exercise. Methods for prescribing exercise intensity include a percentage of anchor measurements, such as maximal oxygen uptake (\({\dot{\text{V}}\text{O}}_{{{\text{2max}}}}\)), peak oxygen uptake (\({\dot{\text{V}}\text{O}}_{{{\text{2peak}}}}\)), maximum heart rate (HRmax), and maximum work rate (i.e., power or velocity—\({\dot{\text{W}}}_{{\max}}\) or \({\dot{\text{V}}}_{{\max}}\), respectively), derived from a graded exercise test (GXT). However, despite their common use, it is apparent that prescribing exercise intensity based on a fixed percentage of these maximal anchors has little merit for eliciting distinct or domain-specific homeostatic perturbations. Some have advocated using submaximal anchors, including the ventilatory threshold (VT), the gas exchange threshold (GET), the respiratory compensation point (RCP), the first and second lactate threshold (LT1 and LT2), the maximal lactate steady state (MLSS), critical power (CP), and critical speed (CS). There is some evidence to support the validity of LT1, GET, and VT to delineate the moderate and heavy domains of exercise. However, there is little evidence to support the validity of most commonly used methods, with exception of CP and CS, to delineate the heavy and severe domains of exercise. As acute responses to exercise are not always predictive of chronic adaptations, training studies are required to verify whether different methods to prescribe exercise will affect adaptations to training. Better ways to prescribe exercise intensity should help sport scientists, researchers, clinicians, and coaches to design more effective training programs to achieve greater improvements in health and athletic performance.
... However, as the competition season approached, the total volume decreased while the intensity gradually increased to maximal effort [12]. Vittori's speed endurance concept has later been adopted by other acknowledged sprint coaches [11,[13][14][15][16]. Available evidence in endurance and strength training also demonstrates that high but sub-maximal intensity loading effectively stimulates adaptation through the interaction between high intensity and larger accumulated work that can be achieved before the onset of fatigue, compared with maximal efforts [90,108]. While most practitioners argue that 92-95% intensity is required [11,[13][14][15][16], the lowest effective sprinting intensity for stimulating adaptation is so far not established in the research literature. ...
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Despite a voluminous body of research devoted to sprint training, our understanding of the training process leading to world-class sprint performance is limited. The objective of this review is to integrate scientific and best practice literature regarding the training and development of elite sprint performance. Sprint performance is heavily dependent upon genetic traits, and the annual within-athlete performance differences are lower than the typical variation, the smallest worthwhile change and the influence of external conditions such as wind, monitoring methodologies, etc. Still, key underlying determinants (e.g., power, technique and sprint-specific endurance) are trainable. In this review, we describe how well-known training principles (progression, specificity, variation/periodization and individualization) and varying training methods (e.g., sprinting/running, technical training, strength/power, plyometric training) are used in a sprint-training context. Indeed, there is a considerable gap between science and best practice in how training principles and methods are applied. While the vast majority of sprint-related studies are performed on young team-sport athletes and focus on brief sprints with maximal intensity and short recoveries, elite sprinters perform sprinting/running over a broad range of distances and with varying intensity and recovery periods. Within best practice there is a stronger link between choice of training component (i.e., modality, duration, intensity, recovery, session rate) and the intended purpose of the training session compared to the “one-size-fits-all” approach in scientific literature. This review provides a point of departure for scientists and practitioners regarding the training and development of elite sprint performance and can serve as a position statement for outlining state-of-the-art sprint training recommendations and for generation of new hypotheses to be tested in future research.
... Interval exercise is commonly subdivided into HIIT and SIT. HIIT is often carried out at intensities >80% of the maximal heart rate and usually involves bouts of 1.5-16 min interspersed with 2-3 min of rest [96,97]; SIT is usually performed as all-out exercise bouts lasting 6 to 90 s interspersed with periods of rest of 1 to >5 times the duration of the exercise [98]. MCIT is performed in a continuous manner and at lower intensities than internal exercise is. ...
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Skeletal muscles require the proper production and distribution of energy to sustain their work. To ensure this requirement is met, mitochondria form large networks within skeletal muscle cells, and during exercise, they can enhance their functions. In the present review, we discuss recent findings on exercise-induced mitochondrial adaptations. We emphasize the importance of mitochondrial biogenesis, morphological changes, and increases in respiratory supercomplex formation as mechanisms triggered by exercise that may increase the function of skeletal muscles. Finally, we highlight the possible effects of nutraceutical compounds on mitochondrial performance during exercise and outline the use of exercise as a therapeutic tool in noncommunicable disease prevention. The resulting picture shows that the modulation of mitochondrial activity by exercise is not only fundamental for physical performance but also a key point for whole-organism well-being.
... Participants were instructed to perform each HIT session at their maximal sustainable intensity (isoeffort). 15 RPE for the previous series (Borg 6-20 scale) was reported during the 2min recovery periods. Power output during the recovery periods was 50% of the power output used during work intervals. ...
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Intensive training periods may negatively influence immune function, but the immunological consequences of specific high-intensity-training (HIT) prescriptions are not well defined. Purpose: To explore whether 3 different HIT prescriptions influence multiple health-related biomarkers and whether biomarker responses to HIT were associated with upper-respiratory-illness (URI) risk. Methods: Twenty-five male cyclists and triathletes were randomized to 3 HIT groups and completed 12 HIT sessions over 4 wk. Peak oxygen consumption (V˙O2peak) was determined using an incremental cycling protocol, while resting serum biomarkers (cortisol, testosterone, 25[OH]D, and ferritin), salivary immunoglobulin-A (s-IgA), and energy availability (EA) were assessed before and after the training intervention. Participants self-reported upper-respiratory symptoms during the intervention, and episodes of URI were identified retrospectively. Results: Fourteen athletes reported URIs, but there were no differences in incidence, duration, or severity between groups. Increased risk of URI was associated with higher s-IgA secretion rates (odds ratio = 0.90, 90% confidence interval 0.83-0.97). Lower preintervention cortisol and higher EA predicted a 4% increase in URI duration. Participants with higher V˙O2peak reported higher total symptom scores (incidence rate ratio = 1.07, 90% confidence interval 1.01-1.13). Conclusions: Although multiple biomarkers were weakly associated with risk of URI, the direction of associations between s-IgA, cortisol, EA, and URI risk were inverse to previous observations and physiological rationale. There was a cluster of URIs in the first week of the training intervention, but no samples were collected at this time point. Future studies should incorporate more-frequent sample time points, especially around the onset of new training regimens, and include athletes with suspected or known nutritional deficiencies.
Thesis
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Variation between individuals in response to a stimulus is a well-established phenomenon. This thesis discusses the drivers of this inter-individual response, identifying three major determinants; genetic, environmental, and epigenetic variation between individuals. Focusing on genetic variation, the thesis explores how this information may be useful in elite sport, aiming to answer the question “Is there utility to genetic information in elite sport?” The current literature was critically analysed, with a finding that the majority of exercise genomics research explains what has happened previously, as opposed to assisting practitioners in modifying athlete preparation and enhancing performance. An exploration of the potential ways in which genetic information may be useful in elite sport then follows, including that of inter- individual variation in response to caffeine supplementation, the use of genetic information to assist in reducing hamstring injuries, and whether genetic information may help identify future elite athletes. These themes are then explored via empirical work. In the first study, an internet-based questionnaire assessed the frequency of genetic testing in elite athletes, finding that around 10% had undertaken such a test. The second study determined that a panel of five genetic variants could predict the magnitude of improvements in Yo-Yo test improvements following a standardised training programme in youth soccer players. The third study demonstrated the effectiveness of a panel of seven genetic variants in predicting the magnitude of neuromuscular fatigue in youth soccer players. The fourth and final study recruited five current or former elite athletes, including an Olympic Champion, and created the most comprehensive Total Genotype Score in the published literature to date, to determine whether their scores deviated significantly from a control population of over 500 non-athletes. The genetic panels were unable to adequately discriminate the elite performers from non-athletes, suggesting that, at this time, genetic testing holds no utility in the identification of future elite performers. The wider utilisation of genetic information as a public health tool is discussed, and a framework for the implementation of genetic information in sport is also proposed. In summary, this thesis suggests that there is great potential for the use of genetic information to assist practitioners in the athlete management process in elite sport, and demonstrates the efficacy of some commercially available panels, whilst cautioning against the use of such information as a talent identification tool. The major limitation of the current thesis is the low sample sizes of many of the experimental chapters, a common issue in exercise genetics research. Future work should aim to further explore the implementation of genetic information in elite sporting environments.
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This study examined the relative contribution of exercise duration and intensity to team-sport athlete’s training load. Male, professional rugby league (n = 10) and union (n = 22) players were monitored over 6- and 52-week training periods, respectively. Whole-session (load) and per-minute (intensity) metrics were monitored (league: session rating of perceived exertion training load [sRPE-TL], individualised training impulse, total distance, BodyLoad™; union: sRPE-TL, total distance, high-speed running distance, PlayerLoad™). Separate principal component analyses were conducted on the load and intensity measures to consolidate raw data into principal components (PC, k = 4). The first load PC captured 70% and 74% of the total variance in the rugby league and rugby union datasets, respectively. Multiple linear regression subsequently revealed that session duration explained 73% and 57% of the variance in first load PC, respectively, while the four intensity PCs explained an additional 24% and 34%, respectively. Across two professional rugby training programmes, the majority of the variability in training load measures was explained by session duration (~60–70%), while a smaller proportion was explained by session intensity (~30%). When modelling the training load, training intensity and duration should be disaggregated to better account for their between-session variability.
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Purpose: Maximal oxygen uptake (V˙O2max) is a key determinant of endurance performance. Therefore, devising high-intensity interval training (HIIT) that maximizes stress of the oxygen-transport and -utilization systems may be important to stimulate further adaptation in athletes. The authors compared physiological and perceptual responses elicited by work intervals matched for duration and mean power output but differing in power-output distribution. Methods: Fourteen cyclists (V˙O2max 69.2 [6.6] mL·kg-1·min-1) completed 3 laboratory visits for a performance assessment and 2 HIIT sessions using either varied-intensity or constant-intensity work intervals. Results: Cyclists spent more time at >90%V˙O2max during HIIT with varied-intensity work intervals (410 [207] vs 286 [162] s, P = .02), but there were no differences between sessions in heart-rate- or perceptual-based training-load metrics (all P ≥ .1). When considering individual work intervals, minute ventilation (V˙E) was higher in the varied-intensity mode (F = 8.42, P = .01), but not respiratory frequency, tidal volume, blood lactate concentration [La], ratings of perceived exertion, or cadence (all F ≤ 3.50, ≥ .08). Absolute changes (Δ) between HIIT sessions were calculated per work interval, and Δ total oxygen uptake was moderately associated with ΔV˙E (r = .36, P = .002). Conclusions: In comparison with an HIIT session with constant-intensity work intervals, well-trained cyclists sustain higher fractions of V˙O2max when work intervals involved power-output variations. This effect is partially mediated by an increased oxygen cost of hyperpnea and not associated with a higher [La], perceived exertion, or training-load metrics.
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Endurance training involves manipulation of intensity, duration, and frequency of training sessions. The relative impact of short, high-intensity training versus longer, slower distance training has been studied and debated for decades among athletes, coaches, and scientists. Currently, the popularity pendulum has swung towards high-intensity interval training. Many fitness experts, as well as some scientists, now argue that brief, high-intensity interval work is the only form of training necessary for performance optimization. Research on the impact of interval and continuous training with untrained to moderately trained subjects does not support the current interval craze, but the evidence does suggest that short intense training bouts and longer continuous exercise sessions should both be a part of effective endurance training. Elite endurance athletes perform 80 % or more of their training at intensities clearly below their lactate threshold and use high-intensity training surprisingly sparingly. Studies involving intensification of training in already well-trained athletes have shown equivocal results at best. The available evidence suggests that combining large volumes of low-intensity training with careful use of high-intensity interval training throughout the annual training cycle is the best-practice model for development of endurance performance. KEYWORDS: lactate threshold, maximal oxygen uptake, VO2max, periodization.
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Successful endurance training involves the manipulation of training intensity, duration, and frequency, with the implicit goals of maximizing performance, minimizing risk of negative training outcomes, and timing peak fitness and performances to be achieved when they matter most. Numerous descriptive studies of the training characteristics of nationally or internationally competitive endurance athletes training 10 to 13 times per week seem to converge on a typical intensity distribution in which about 80% of training sessions are performed at low intensity (2 mM blood lactate), with about 20% dominated by periods of high-intensity work, such as interval training at approx. 90% VO2max. Endurance athletes appear to self-organize toward a high-volume training approach with careful application of high-intensity training incorporated throughout the training cycle. Training intensification studies performed on already well-trained athletes do not provide any convincing evidence that a greater emphasis on high-intensity interval training in this highly trained athlete population gives long-term performance gains. The predominance of low-intensity, long-duration training, in combination with fewer, highly intensive bouts may be complementary in terms of optimizing adaptive signaling and technical mastery at an acceptable level of stress.
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The pattern of energy expenditure during sustained high-intensity exercise is influenced by several variables. Data from athletic populations suggest that a pre-exercise conceptual model, or template, is a central variable relative to controlling energy expenditure. The aim of this study was to make systematic observations regarding how the performance template develops in fit individuals who have limited specific experience with sustained high-intensity exercise (eg, time trials). The study was conducted in four parts and involved measuring performance (time and power output) during: (A) six 3 km cycle time trials, (B) three 2 km rowing time trials, (C) four 2 km rowing time trials with a training period between trials 2 and 3, and (D) three 10 km cycle time trials. All time trials were self-paced with feedback to the subjects regarding previous performances and momentary pace. In all four series of time trials there was a progressive pattern of improved performance averaging 6% over the first three trials and 10% over six trials. In all studies improvement was associated with increased power output during the early and middle portions of the time trial and a progressively greater terminal rating of perceived exertion. Despite the change in the pattern of energy expenditure, the subjects did not achieve the pattern usually displayed by athletes during comparable events. This study concludes that the pattern of learning the performance template is primarily related to increased confidence that the trial can be completed without unreasonable levels of exertion or injury, but that the process takes more than six trials to be complete.
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Performance in intense exercise events, such as Olympic rowing, swimming, kayak, track running and track cycling events, involves energy contribution from aerobic and anaerobic sources. As aerobic energy supply dominates the total energy requirements after ∼75s of near maximal effort, and has the greatest potential for improvement with training, the majority of training for these events is generally aimed at increasing aerobic metabolic capacity. A short-term period (six to eight sessions over 2-4 weeks) of high-intensity interval training (consisting of repeated exercise bouts performed close to or well above the maximal oxygen uptake intensity, interspersed with low-intensity exercise or complete rest) can elicit increases in intense exercise performance of 2-4% in well-trained athletes. The influence of high-volume training is less discussed, but its importance should not be downplayed, as high-volume training also induces important metabolic adaptations. While the metabolic adaptations that occur with high-volume training and high-intensity training show considerable overlap, the molecular events that signal for these adaptations may be different. A polarized approach to training, whereby ∼75% of total training volume is performed at low intensities, and 10-15% is performed at very high intensities, has been suggested as an optimal training intensity distribution for elite athletes who perform intense exercise events.
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The purpose of this study was to determine the accuracy of the Velotron cycle ergometer and the SRM power meter using a dynamic calibration rig over a range of exercise protocols commonly applied in laboratory settings. These trials included two sustained constant power trials (250 W and 414 W), two incremental power trials and three high-intensity interval power trials. To further compare the two systems, 15 subjects performed three dynamic 30 km performance time trials. The Velotron and SRM displayed accurate measurements of power during both constant power trials (<1% error). However, during high-intensity interval trials the Velotron and SRM were found to be less accurate (3.0%, CI=1.6-4.5% and -2.6%, CI=-3.2--2.0% error, respectively). During the dynamic 30 km time trials, power measured by the Velotron was 3.7+/-1.9% (CI=2.9-4.8%) greater than that measured by the SRM. In conclusion, the accuracy of the Velotron cycle ergometer and the SRM power meter appears to be dependent on the type of test being performed. Furthermore, as each power monitoring system measures power at various positions (i.e. bottom bracket vs. rear wheel), caution should be taken when comparing power across the two systems, particularly when power is variable.
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To investigate the effect of task familiarisation on the spontaneous pattern of energy expenditure during a series of 2000 m cycling time trials (TTs). Nine trained males completed three 2000 m TTs on a Velotron cycling ergometer. To examine pacing strategy, the data were assigned to 250 m "bins," with the pattern of aerobic and anaerobic energy expenditure calculated from total work accomplished and gas-exchange data. There were no significant differences between trials in performance times (191.4 (SD 4.3), 189.4 (4.6), 190.1 (5.6) s), total aerobic (58.3 (2.7), 58.4 (3.1), 58.0 (3.4) kJ) and total anaerobic energy expenditure (16.4 (3.3), 17.3 (2.8), 16.5 (3.1) kJ). Pacing strategy in the second and third TT differed from the first TT in that a lower power output was adopted during the first 500 m, enabling a higher power output during the final 750 m of the TT. This adjustment in the pattern of energy expenditure was mediated by an alteration in the pattern of anaerobic energy expenditure, which paralleled changes in total energy expenditure. Furthermore, participants retained an anaerobic energy "reserve" enabling an end-spurt during the second and third trials. Small modifications to the pacing strategy are made following a single bout of exercise, primarily by altering the rate of anaerobic energy expenditure. This may have served to prevent critical metabolic disturbances. The alteration in pacing strategy following the first exercise bout is compatible with a complex intelligent regulatory system.