International Journal of Sports Physiology and Performance, 2015, 10, 593 -599 http://dx.doi.org/10.1123/ijspp.2014-0292 © 2015 Human Kinetics, Inc.
ORIGINAL INVESTIGATION
Physiological Characteristics of Well-Trained Junior Sprint Kayak Athletes Thiago Oliveira Borges, Ben Dascombe, Nicola Bullock, and Aaron J. Coutts This study aimed to profile the physiological characteristics of junior sprint kayak athletes (n = 21, VO2max 4.1 ± 0.7 L/min, training experience 2.7 ± 1.2 y) and to establish the relationship between physiological variables (VO2max, VO2 kinetics, muscleoxygen kinetics, paddling efficiency) and sprint kayak performance. VO2max, power at VO2max, power:weight ratio, paddling efficiency, VO2 at lactate threshold, and whole-body and muscle oxygen kinetics were determined on a kayak ergometer in the laboratory. Separately, on-water time trials (TT) were completed over 200 m and 1000 m. Large to nearly perfect (–.5 to –.9) inverse relationships were found between the physiological variables and on-water TT performance across both distances. Paddling efficiency and lactate threshold shared moderate to very large correlations (–.4 to –.7) with 200- and 1000-m performance. In addition, trivial to large correlations (–.11 to –.5) were observed between muscle-oxygenation parameters, muscle and whole-body oxygen kinetics, and performance. Multiple regression showed that 88% of the unadjusted variance for the 200-m TT performance was explained by VO2max, peripheral muscle deoxygenation, and maximal aerobic power (P < .001), whereas 85% of the unadjusted variance in 1000-m TT performance was explained by VO2max and deoxyhemoglobin (P < .001). The current findings show that well-trained junior sprint kayak athletes possess a high level of relative aerobic fitness and highlight the importance of the peripheral muscle metabolism for sprint kayak performance, particularly in 200-m races, where finalists and nonfinalists are separated by very small margins. Such data highlight the relative aerobic-fitness variables that can be used as benchmarks for talent-identification programs or monitoring longitudinal athlete development. However, such approaches need further investigation. Keywords: aerobic fitness, oxygen kinetics, muscle oxygenation The current Olympic sprint kayak program comprises 1000-m (men’s race time range 205–215 s), 500-m (women, 115–120 s), and 200-m (men 34–35 s and women 39–41 s) races. It has been estimated on a kayak ergometer that the 1000-m event requires ~82% of total energy to be provided through oxidative pathways, whereas only ~65% and ~37% are required for the 500-m and 200-m races, respectively.1 Aerobic metabolism in sprint kayak performance has been well documented, with large to very large correlations reported between maximum oxygen uptake (VO2max) and 1000- and 500-m performance,2,3 while only trivial to moderate relationships have been observed with 200-m performance.4,5 Other important measures of oxidative metabolism, such as whole-body oxygen kinetics (VO2 kinetics), muscle oxygen (MO2) kinetics, and muscle-oxygenation parameters, have yet to be reported in sprint kayak athletes. The VO2 kinetics represent the rate of systemic metabolic adaptation to exercise6 and have been shown to be faster in higherlevel athletes in similar racing sports such as rowing7 and cycling.8,9 Together, this information suggests that athletes who can rapidly adjust to increases in exercise intensity may benefit from the efficient supply of energy from the aerobic system and a reduced anaerobic contribution, limiting the accumulation of metabolic byproducts. Moreover, faster VO2 kinetics appear to benefit aerobic and Oliveira Borges and Coutts are with UTS: Health, University of Technology, Sydney, Australia. Dascombe is with the Applied Sports Science and Exercise Testing Laboratory, University of Newcastle, Newcastle, Australia. Bullock is with the Physiology Dept, Australian Institute of Sport, Gold Coast, QLD, Australia. Address author correspondence to Thiago Oliveira Borges at [email protected].
anaerobic performance, as a faster VO2 kinetics response enables better repeated-sprint performance.10 Nonetheless, there is still a need to improve our understanding of the physiological factors that may contribute to sprint kayak performance. At present, there is a relatively poor understanding of the physiological characteristics of developing junior sprint kayak athletes and their relationships with performance. Furthermore, there is limited literature examining the physiological factors associated with 200-m performance. Therefore, the aims of this study were to profile the physiological characteristics of developing junior sprint kayak athletes and establish the relationship between these physiological variables (ie, VO2 kinetics, MO2 kinetics, muscleoxygenation parameters, paddling efficiency, VO2max, maximal heart rate and lactate, lactate thresholds) and performance in the longer (1000-m) and shorter (200-m) sprint kayak time trials (TT).
Methods Experimental Design Anthropometric, physiological, and performance characteristics of well-trained junior sprint kayak athletes were assessed during a 7-day period. Anthropometric characteristics, aerobic-fitness parameters, and gross efficiency were assessed on day 1, and VO2 on-kinetics (phase II fast kinetics: τ) and muscle-oxygenation parameters were assessed on days 3 and 5. These variables were determined in the laboratory, whereas on-water 200-m and 1000-m TT performance was determined on day 7 at the athletes’ normal training venue. A comprehensive list of physiological variables assessed is presented herein. 593
594 Oliveira Borges et al
Participants Twenty-one (13 male, 8 female) well-trained junior sprint kayak athletes (17.0 ± 1.2 y, sum of 7 skinfolds 83.3 ± 31.3 mm, body mass 72.4 ± 8.3 kg, stature 176.1 ± 8.8 cm, training experience 2.7 ± 1.2 y, typical training volume 11.3 ± 3.0 h/wk) took part in this study, which had been approved by local university’s ethics committee (UTS HREC 2011-162).
Performance and Physiological Assessments
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Aerobic Fitness. Measures of aerobic fitness (VO2max, power at
VO2max [MAP], and lactate threshold [LT2]) were determined using the Australian Institute of Sport (AIS) national sprint kayak step test.11 Briefly, five 4-minute submaximal stages were performed at fixed intensities depending on the gender, age, and performance ability of the athlete. The last 4-minute stage was performed maximally at perceived TT intensity. A 60-second period was allowed between workloads for sampling blood lactate concentration ([La]) and recording average power and ratings of perceived of exertion. Gas exchange was measured continuously breath by breath using a MedGraphics System (CPX Ultima, MedGraphics Corp, St Paul, MN, USA) with the highest consecutive 1 minute reported as VO2max. The system was calibrated before each test session using known O2 and CO2 concentrations, according to the manufacturer’s specifications. The flowmeter was calibrated using a 3-L syringe. LT2 was determined by the D-max method (ICC .77–.93, P < .01) as described elsewhere.12 All laboratory tests were performed on a dynamically calibrated Weba kayak ergometer (Weba Sport und Med-Artikel GmbH, Vienna, Austria), on which the power output varied according to the tension applied throughout each stroke, mimicking the actual movement of the on-water stroke (typical error of estimate –7.2%). Near-Infrared Spectroscopy. Blood flow and muscle oxygenation
of the latissimus dorsi were measured using a portable near-infrared spectroscopy (NIRS) device (Portamon, Artinis Medical Systems, Zetten, the Netherlands) during each testing session. A 2-wavelength continuous-wave system that uses the modified Beer-Lambert law and spatially resolved spectroscopy methods simultaneously measured changes in tissue oxyhemoglobin (O 2Hb), deoxyhemoglobin (HHb), and total hemoglobin (tHb) by using their chromophoric properties at 750 and 860 nm. The difference between the HHb and HbO2, representing the average saturation of the latissimus dorsi, was calculated as the tissue-saturation index (TSI). The scores representing the muscle-oxygenation parameters are the average recorded during the maximal stage of the step test. An arbitrary value of 3.83 was used for the differential path length factor.13 The NIRS device was placed at the midpoint between the inferior border of the scapula and posterior axillar fold and oriented parallel to the muscle fibers. A translucid waterproof adhesive (OpSite Flexgrid, Smith&Nephew, Australia) was fixed between the NIRS device and the skin to prevent the sweat from causing any problem with light transmission/absorption. The device was fixed to the skin by a dark adhesive tape and covered with a tight-fitting black sport crop top to ensure that no ambient light penetrated and to help keep the device on site. The NIRS data were continuously recorded at a 10-Hz rate, and the data were later averaged to 1-second values using bespoke software (LabVIEW, National Instruments, USA), to allow synchronization with VO2 data. The NIRS-derived data were averaged at the last minute of each submaximal stage of the step test and are reported as either changes in micromolar units (ΔO2Hb, ΔHHb, ΔtHb) or percentage (TSI) of the baseline scores. Similar to the VO2max test protocol, the 2 consecutive maximal
30-second values for each parameter were averaged and considered maximal effort. Oxygen-Kinetics Assessment. A series of 3 square-wave transitions from rest to exercise was performed on the kayak ergometer in the laboratory. The athletes paddled for 2 minutes at a very low intensity (20 W) followed by a rapid increase to a predetermined paddling intensity for 6 minutes. The intensities corresponded to power outputs: individual LT2 (~80% of VO2, moderate domain), the intensity midway between individual LT2 and VO2max (50%Δ, heavy domain), and the intensity at 80% of the difference between LT2 and VO2max (80%Δ, severe domain). According to previous methods, a 6-minute rest was allowed between the moderate and heavy domains, while a 10-minute break was provided between the heavy and severe domains to enable metabolic rate to return to resting levels.7,14These procedures were done on day 3 and repeated on day 5. Modeling of O2 Kinetics and Muscle-Oxygenation Data. After a thorough examination of the VO2 data, any data point found more than 4 SD away from the mean response was deleted. The breath-bybreath data were interpolated to second-by-second values, and the response of the VO2 for each domain was time-aligned and averaged. For the MO2 kinetics, the main parameter of choice was the TSI. The raw data recorded at 10 Hz were initially averaged to 1 Hz and then time-aligned. A nonlinear least-squares-regression technique was used to model the time course of the VO2 response and TSI. A singlecomponent exponential equation (Equations 1 and 2) was used to model the moderate response, while for the heavy and severe domains, a double-component equation (Equations 3 and 4) was applied.6,8 The Solver function in Microsoft Excel (Microsoft Corp, Redmond, WA, USA) was used to determine the best fit of the parameters:
! 2 moderate domain − VO ! 2 (t ) VO ! 2 ( b ) + Ap ⋅ ⎡1− e −(t−TDp ) t p ⎤ = VO ⎣⎢ ⎦⎥ TSI moderate domain − TSI ( t ) − t−TD t = TSI ( b ) − Ap ⋅ ⎡⎢1− e ( p ) p ⎤⎥ ⎣ ⎦
(Eq 1)
(Eq 2)
! 2 heavyandsevere domain − VO ! 2 (t ) VO
(Eq 3)
(Eq 4)
! 2 ( b ) + Ap ⋅ ⎡1− e −(t−TDp ) t p ⎤ + As ⋅ ⎡1− e −(t−TDs ) t s ⎤ = VO ⎢⎣ ⎥⎦ ⎣ ⎦ TSI heavyandsevere domain − TSI ( t ) − t−TD t = TSI ( b ) + Ap ⋅ ⎡⎢1− e ( p ) p ⎤⎥ + As · ⎡⎣1− e −(t−TDs ) t s ⎤⎦ ⎣ ⎦
where VO2(t) and TSI(t) are the VO2 and TSI at a given time, VO2(b) and TSI(b) are the baseline values across the last 2 minutes of “unloaded” paddling, Ap and As are the asymptotic amplitudes for the primary and slow exponential components, τp and τs are the time constants for each component, and TDp and TDs are the time delays for the primary and slow components. Even though a 2-component model was used to fit both the heavyand severe-intensity VO2 and TSI responses, only the time constant for the fast component of the VO2 and TSI kinetics was reported. Energy Expenditure and Gross Efficiency. Energy expenditure
during the maximal stage of the AIS step test was calculated as the sum of the net VO2max (represented by the gain of the VO2max minus rest VO2) and net [La] (calculated from the [La]max gain minus rest [La]—assumed 1 mmol/L). The net [La] was then converted into O2
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equivalent using energy equivalent for VO2 as 20.9 kJ/L.15 Paddling gross efficiency was calculated as the ratio between power output and energy expenditure. The power output was converted into energy assuming the energy equivalent for VO2 as 20.9 kJ/L.15–18 Anthropometry. A calibrated skinfold caliper (Harpenden, Baty Intl, UK) with 0.2-mm precision was used for skinfold measurements. The sum of 7 skinfolds was measured according to International Society for the Advancement of Kinanthropometry standards.19 Body mass was determined by a Tanita digital scale (BC-590BT, Tanita Corp of America, Arlington Heights, IL, USA) and stature by a wooden stadiometer with 0.1-cm precision.
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Sprint Kayak Performance. On-water sprint kayak performance
was determined during TTs at the athletes’ regular training venue. The athletes used their own standard K1 sprint kayak and were individually assessed to avoid pacing or wash influence from other paddlers.20 The time was recorded for each effort by using 2 synchronized stopwatches (Interval2000, Nielsen-Kellerman, Boothwyn, PA, USA). The TTs were performed with a tail wind of ~2.5 m/s at an ambient temperature of ~20.0°C. After a standard warm-up that consisted of 3 minutes of paddling at ~85% of HRmax followed by two 15-second accelerations interspersed with 45 seconds rest and 2 standing starts of 24 strokes with 45 seconds rest between, ending with 3 minutes of paddling at 85% HRmax, the athlete positioned the boat at the start line and paddled in the shortest time possible for the 200- and 1000-m. After the 200-m trial, the athletes performed a moderate 25-minute active paddling recovery (~70% HRmax), which was then followed by the 1000-m TT. It was assumed that the 200-m effort had little impact on the 1000-m performance, as all the junior athletes had followed a similar training program and were not specialists in these distances.
Statistics The data are presented as mean ± SD. All data were initially assessed for normality and log-transformed where required. Initially, product– moment Pearson correlation was used to determine the relationship between all the physiological, anthropometrical, and performance variables. The correlation coefficients were also used to represent the effect size, where .1, .3, .5, .7, .9, and 1 were considered trivial, small,
moderate, large, very large, nearly perfect, and perfect, respectively, as described elsewhere.21 Hierarchical multiple-regression analyses were carried out to determine the best predictors for performance in sprint kayak racing. Multiple-regression outcomes were represented as the coefficient of estimates with 90% confidence intervals. To test for differences between models, 1-way ANOVA was applied. The cross-validation of the models was done by calculating the adjusted R2. Significance level was set at P < .05. All statistical procedures were conducted using R statistics software,22 car,23 and QuantPsyc packages for R.24
Results The mean 200-m and 1000-m TT performance times were 44.6 ±4.5 seconds and 279.3 ±22.2 seconds, respectively. Aerobic-fitness measures and their relationships with on-water 200-m and 1000-m TT performance are shown in Table 1. There were large to nearly perfect relationships of relative and absolute VO2max with MAP in both TT performances, with slightly stronger relationships observed with the 1000-m. Moreover, there were also moderate to large relationships between the power:weight ratio, lactate threshold, energy expenditure, and gross paddling efficiency and performance in both TTs. Table 2 shows the muscle-oxygenation parameters and the fastcomponent on-kinetics for muscle and whole body, as well as their relationship with 200- and 1000-m on-water performance. Trivial to large correlations were found of the muscle-oxygenation parameters, VO2 kinetics, and MO2 kinetics with on-water performance over both distances (Table 2). Both the HHb and TSI responses showed moderate to large correlations with on-water TT performances for both distances. Multiple regression showed that 88% of the unadjusted variance for the 200-m TT performance was explained by absolute VO2max, HHb, and MAP (F3,17 = 40.6, P < .001). The coefficient estimates for this model were –7.89 (90% CI –10.16 to –5.62), 0.08 (90% CI 0.03–0.14), and –0.25 (90% CI –0.35 to –0.14) for absolute VO2max, MAP, and HHb, respectively. Similarly, multiple regression showed that 85% of the unadjusted variance in 1000-m kayak TT performance was explained by absolute VO2max and HHb (F3,17= 34.8, P < .001). The coefficient estimates for absolute VO2max were –27.40 (90% CI –39.50 to –15.30); MAP, 0.05 (90% CI –0.24 to 0.35); and HHb, –0.78 (90% CI –1.35 to –0.21). See Figure 1.
Table 1 Mean ± SD and Correlation Coefficients of Physiological, Energetic, and Performance Characteristics of Well-Trained Junior Sprint Kayak Athletes Mean ± SD Maximum oxygen uptake (mL ·
kg–1
·
min–1)
Maximum oxygen uptake (L/min) Peak blood lactate concentration (mmol/L) Maximum heart rate (beats/min)
200-m TT (s)
1000-m TT (s)
46.6 ± 4.5
279.3 ± 22.2
R
R
57.0 ± 8.0
–.76
–.84
4.1 ± 0.7
–.86
–.90
9.0 ± 2.3
–.23
–.26
197.1 ± 7.7
.03
–.12
Power-to-weight ratio (kg/W)
2.4 ± 0.4
–.49
–.66
Maximal aerobic power (W)
169.3 ± 29.9
–.74
–.84
Lactate threshold 1 (W)
108.9 ± 18.4
–.65
–.68
Lactate threshold 2 (W)
129.4 ± 22.8
–.74
–.75
Energy expenditure (mL · kg–1 · min–1)
61.7 ± 10.4
–.42
–.59
Gross efficiency (%)
13.1 ± 1.6
–.48
–.34
Abbreviation: TT, time trial. IJSPP Vol. 10, No. 5, 2015
Table 2 Mean ± SD and Correlation Coefficients of Whole-Body and Muscle Oxygen Kinetics, Muscle-Deoxygenation Parameters, and Performance Characteristics of Well-Trained Junior Sprint Kayak Athletes Mean ± SD
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Deoxyhemoglobin (μM)
200-m time trial (s)
1000-m time trial (s)
46.6 ± 4.5
279.3 ± 22.2
R
R
–.54
–.49
12.1 ± 6.8
Oxyhemoglobin (μM)
0.6 ± 6.5
.17
.26
Total hemoglobin (μM)
12.8 ± 7.3
–.36
–.23
Tissue-saturation index (%)
55.3 ± 16.1
.42
.49
Oxygen uptake τ moderate (s)
36.5 ± 5.4
–.20
–.03
Oxygen uptake τ heavy (s)
24.1 ± 5.2
–.11
.01
Oxygen uptake τ severe (s)
24.3 ± 5.0
–.17
–.05
Tissue-saturation index τ moderate (s)
9.8 ± 3.7
.07
.19
Tissue-saturation index τ heavy (s)
14.8 ± 6.6
.25
.30
Tissue-saturation index τ severe (s)
13.4 ± 7.2
–.12
–.20
Figure 1 — Modeling of (left) oxygen-uptake (VO2) kinetics and (right) muscle-oxygenation kinetics in a participant in the 3 intensity domains: (upper) moderate, (middle) heavy, and (lower) severe. Abbreviation: TSI, tissue-saturation index.
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Discussion This study profiled the physiological characteristics of well-trained developing junior sprint kayak athletes and established the relationships between physiological variables and performance in 200-m and 1000-m sprint kayak TT performance. The main results were that aerobic-fitness characteristics demonstrated stronger relationships with 1000-m than with 200-m paddling performance, whereas MO2 kinetics in the heavy domain were related to both 200-m and 1000-m sprint kayak performance. The current results demonstrated that well-trained junior sprint kayak athletes possess a lower absolute maximal aerobic fitness and poorer gross efficiency than older, more experienced athletes. However, when expressed relative to body mass, the maximal aerobic-fitness characteristics were similar to or even higher than values previously reported in senior sprint kayakers. For example, older paddlers (~25.4 y) ranging from club to elite level are reported to be ~11% heavier and possess ~15% higher absolute VO2max, 25% higher MAP, and 26% higher [La]max.3,4,25,26 However, the difference in relative VO2max was reduced to 4% ± 8%.3,4,26 Similarly, despite absolute power output at LT2 being ~14% higher in mature elite paddlers, the current younger paddlers had a similar LT2 when expressed as a percentage of MAP (older ~74% ± 9% MAP vs younger 77% ± 8%).4,5,11,15,25 Despite the poorer absolute maximal aerobic-fitness levels, the younger paddlers in the current study had a higher level of gross efficiency (~30%) than well-trained older sprint kayak athletes.15 This may be explained by the lower muscle mass in younger athletes and their lower production of anaerobic energy.2 Collectively, these results suggest that the younger paddlers in this study were well trained, although it seems that further sustained training is required to meet the maximal aerobic fitness required for elite-level performance at the open level. Similar to other studies, strong relationships were observed between maximal aerobic fitness and TT performance.3–5 The very large to nearly perfect correlations between VO2max and MAP for the 200-m and the 1000-m distances agree with Fry and Morton3 over the 1000-m but contrast with van Someren and Howatson,4 who reported only trivial correlations between VO2max, MAP, and on-water 1000-m race performance. Furthermore, van Someren and Howatson4 also found trivial and large correlations between on-water 200-m race performance, VO2max, and MAP. One possible explanation for the differences was that TT performance was assessed in controlled and not race conditions, limiting the influence of other paddlers on pacing strategies and overall performance. In any case, the current study demonstrated that maximal aerobicfitness indices relate to both 1000-m and 200-m sprint kayak performance, with stronger relationships for the longer-distance event. Based on the current findings, coaches should prescribe training programs aimed at developing aerobic fitness and muscle strength, as the power produced at VO2max level not only presented very large correlations with on-water performance in both distances but was also powerful in predicting performance, as evidenced through the multiple-regression results. Comparable to maximal aerobic-fitness variables, strong relationships were present between submaximal measures of aerobic fitness such as LT2 and gross efficiency with both 1000-m and 200-m TT performance. Indeed, these measures are typically more sensitive to changes in training27 and may reflect how athletes are adapting to training. Other studies have previously shown large to very large correlations of LT2 with the 1000-m and the 500-m
on-water performance in sprint kayak.2,4 Our results corroborate these findings for the 1000-m, while this is the first study to demonstrate a very large correlation with on-water 200-m performance. One reasonable explanation may be the training background and the age of the athletes from these different studies. It should be acknowledged that the athletes in the current study were well-trained junior sprint kayak athletes whose training may have been designed toward more generalized development, compared with that of older athletes whose training was likely focused on specific race demands. Collectively, this information supports the suggestion that junior sprint kayak paddlers should maintain a sustained training program over time to further develop physiological attributes that will be related to performance into the open-age, higher-level competition. Furthermore, these data from the relative fitness variables in the current study may be used by coaches and sport scientists as a reference or even as benchmarks for talent-identification programs since these athletes were of a high fitness level. Nonetheless, since the cross-sectional design used in the current study does not assess this hypothesis, future prospective studies that examine the temporal changes in these variables are required. This is the first study to assess the relationships between VO2 kinetics, MO2 kinetics, and on-water sprint kayak performance over 200 m and 1000 m. The athletes in the current study presented a VO2 kinetics phase II time constant (τphaseII) that was 17% slower for the heavy domain when compared with 1500-m international runners.28 Moreover, Ingham et al7 reported τphaseII values for the moderate domain of 19.4 ± 5.6 and 13.9 ± 4.0 seconds, and in the heavy domain they were 22.4 ± 3.7 and 18.7 ± 2.1 seconds for club and elite rowers, respectively. Furthermore, trained cyclists (VO2max 66.6 ± 2.5 mL · kg–1 · min–1) presented τphaseII of 11.7 ± 2.5 and 15.2 ± 2.0 seconds while their untrained counterparts (42.9 ± 5.1 mL · kg–1 · min–1) showed τphaseII of 21.5 ± 6.6 and 23.5 ± 2.8 seconds in the moderate and heavy domains, respectively.8 These results may reflect discrepancies between exercise mode and the magnitude of muscle mass recruited. For example, the upper body predominantly possesses fast-twitch fibers compared with lower limbs,29 and past data have shown that fast-twitch fibers are linked to a slower τphaseII.30 Taken together, this information strengthens the evidence that the VO2 kinetics response depends on exercise mode and muscle-fiber type. The investigation of muscle-oxygenation parameters can help clarify the specific physiological demands of sprint kayak. The MO2 kinetics represent how efficient an individual is at delivering and extracting oxygen within the working muscle at the onset of exercise. The current MO2 kinetics were similar to those reported by Grassi et al31 for moderate-domain exercise (8.5 ± 0.9 vs 9.8 ± 3.7 s) but somewhat slower across the heavy domain (~7 vs 14.8 ± 6.6 s). These differences likely reflect differences in the training status of the current group of youth sprint kayak athletes, as well as other factors such as muscle-fiber composition and exercise mode. The current study also profiled muscle-oxygenation parameters (HHb, O2Hb, tHb, and TSI) and examined the relationships of these with sprint kayak on-water performance. Our findings demonstrated that the muscles’ ability to extract oxygen (represented by the HHb and TSI responses) has a large relationship with sprint kayak performance. Our data (Table 2) differ from those of Dascombe et al,32 who reported higher values for ΔHHb (20.5 ± 1.1 μM) and lower for ΔO2Hb (–16.7 ± 0.8 μM), ΔtHb (3.9 ± 0.3 μM), and TSI (37.3%) in a group of ~23-year-old elite paddlers. These differences likely reflect the application of the NIRS device to different musculature used
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in kayaking (forearm, which requires isometric contraction to hold the paddle vs latissimus dorsi) and that the subjects of Dascombe et al32 were older and possessed similar VO2max levels, lower relative power output at LT2 level (64.6% vs 76.4% for the current study), and higher (37%) MAP than the current cohort. Taken together, our findings demonstrate that the ability to rapidly extract oxygen for energy production has stronger relationships with 200-m performance; 1000-m performance appears more reliant on measures of aerobic capacity and power output. These results are not surprising due to the demands of each distance. The multipleregression analysis supports these observations by demonstrating that the predictive power of the absolute VO2max and MAP increased by adding ΔHHb to the model for 200-m performance. These findings further demonstrate the importance of anaerobic parameters for 200-m performance in sprint kayak, as the correlation analysis demonstrated that athletes who could extract greater O2 from the muscle performed better in the 200-m TT (R = –.54). This finding suggests that targeted training programs that focus on developing these characteristics may be required if athletes are to specialize in either the 200-m or the 1000-m event. A limitation of this study was that controlled laboratory-based TTs over the 200-m and 1000-m were not accomplished, which limits further insights on the basis of these findings and actual physiological response of well-trained sprint kayak athletes. Future research may look at laboratory-based demands or even on-water performance, using portable metabolic measurement devices.
Conclusion In conclusion, well-trained junior sprint kayak athletes possess lower absolute maximal aerobic fitness, despite similar relative aerobic fitness, than their older counterparts. Furthermore, the current data support the relationship between maximal aerobic capacities and performance, as well as the importance of the ΔHHb for performance during 200-m sprint kayak. Future studies should examine best methods for developing absolute aerobicfitness variables including maximal indicators such as VO2max and MAP and submaximal variables like the thresholds associated with the balance between lactate production and consumption (LT2), as well as looking at the best training strategy to enhance these variables. Acknowledgments The authors are grateful for the financial support provided by the University of Technology, Sydney (UTS); the Australian government, Department of Innovation, Industry, Science and Research: the Australian Institute of Sport; and Australian Canoeing. We also would like to thank all the athletes and coaches who participated in this study.
References 1. Byrnes WC, Kearney JT. Aerobic and anaerobic contributions during simulated canoe/kayak sprint events. Med Sci Sports Exerc. 1997;29(Suppl 5):S220. doi:10.1097/00005768-199705001-01254 2. Bishop D. Physiological predictors of flat-water kayak performance in women. Eur J Appl Physiol. 2000;82(1–2):91–97. PubMed doi:10.1007/s004210050656 3. Fry RW, Morton AR. Physiological and kinanthropometric attributes of elite flatwater kayakists. Med Sci Sports Exerc. 1991;23(11):1297– 1301. PubMed doi:10.1249/00005768-199111000-00016
4. van Someren KA, Howatson G. Prediction of flatwater kayaking performance. Int J Sports Physiol Perform. 2008;3(2):207–218. PubMed 5. van Someren KA, Palmer GS. Prediction of 200-m sprint kayaking performance. Can J Appl Physiol. 2003;28(4):505–517. PubMed doi:10.1139/h03-039 6. Burnley M, Jones AM. Oxygen uptake kinetics as a determinant of sports performance. Eur J Sport Sci. 2007;7(2):63–79. doi:10.1080/17461390701456148 7. Ingham SA, Carter H, Whyte GP, Doust JH. Comparison of the oxygen uptake kinetics of club and Olympic champion rowers. Med Sci Sports Exerc. 2007;39(5):865–871. PubMed doi:10.1249/ mss.0b013e31803350c7 8. Koppo K, Bouckaert J, Jones AM. Effects of training status and exercise intensity on phase II VO 2 kinetics. Med Sci Sports Exerc. 2004;36(2):225–232. PubMed doi:10.1249/01. MSS.0000113473.48220.20 9. Koppo K, Whipp BJ, Jones AM, Aeyels D, Bouckaert J. Overshoot in VO2 following the onset of moderate-intensity cycle exercise in trained cyclists. Eur J Appl Physiol. 2004;93(3):366–373. PubMed doi:10.1007/s00421-004-1229-8 10. Dupont G, Millet GP, Guinhouya C, Berthoin S. Relationship between oxygen uptake kinetics and performance in repeated running sprints. Eur J Appl Physiol. 2005;95(1):27–34. PubMed doi:10.1007/s00421005-1382-8 11. Bullock N, Woolford S, Peeling P, Bonetti D. Physiological protocols for the assessment of athletes in specific sports—sprint kayak athletes. In: Tanner RK, Gore CJ, eds. Physiological Tests for Elite Athletes. 2nd ed. Champaign, IL: Human Kinetics; 2013:421–433. 12. Zhou S, Weston S. Reliability of using the D-max method to define physiological responses to incremental exercise testing. Physiol Meas. 1997;18(2):145–154. PubMed doi:10.1088/0967-3334/18/2/005 13. Buchheit M, Laursen PB, Ahmaidi S. Effect of prior exercise on pulmonary O2 uptake and estimated muscle capillary blood flow kinetics during moderate-intensity field running in men. J Appl Physiol. 2009;107(2):460–470. PubMed doi:10.1152/japplphysiol.91625.2008 14. Burnley M, Jones AM, Carter H, Doust JH. Effects of prior heavy exercise on phase II pulmonary oxygen uptake kinetics during heavy exercise. J Appl Physiol. 2000;89(4):1387–1396. PubMed 15. Gomes BB, Mourão L, Massart A, et al. Gross efficiency and energy expenditure in kayak ergometer exercise. Int J Sports Med. 2012;33(8):654–660. PubMed doi:10.1055/s-0032-1301907 16. Di Prampero PE. Energetics of muscular exercise. In: Adrian RH, Helmerich E, Holzer H, et al., eds. Reviews of Physiology, Biochemistry and Pharmacology. Vol 89. New York-Berlin: Springer-Verlag; 1981:143-222. 17. di Prampero PE. The energy cost of human locomotion on land and in water. Int J Sports Med. 1986;7(2):55–72. PubMed doi:10.1055/s-2008-1025736 18. Zamparo P, Capelli C, Guerrini G. Energetics of kayaking at submaximal and maximal speeds. Eur J Appl Physiol Occup Physiol. 1999;80(6):542–548. PubMed doi:10.1007/s004210050632 19. Marfell-Jones TOM, Stewart A, Carter L. International Standards for Anthropometric Assessment. Underdale, Australia: International Society for the Advancement of Kinanthropometry; 2006. 20. Pérez-Landaluce J, Rodriguez-Alonso M, Fernandez-Garcia B, Bustillo-Fernandez E, Terrados N. Importance of wash riding in kayaking training and competition. Med Sci Sports Exerc. 1998;30(12):1721–1724. PubMed doi:10.1097/00005768-19981200000010 21. Hopkins WG. A scale of magnitudes for effect statistics. A New View of Statistics. 2002. http://www.sportsci.org/resource/stats/effectmag. html. Accessed October 10, 2012.
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28. Ferri A, Adamo S, La Torre A, Marzorati M, Bishop DJ, Miserocchi G. Determinants of performance in 1,500-m runners. Eur J Appl Physiol. 2012;112(8):3033–3043. PubMed doi:10.1007/s00421-011-2251-2 29. Mygind E. Fibre characteristics and enzyme levels of arm and leg muscles in elite cross‐country skiers. Scand J Med Sci Sports. 1995;5(2):76–80. PubMed doi:10.1111/j.1600-0838.1995.tb00016.x 30. Pringle JSM, Doust JH, Carter H, Tolfrey K, Campbell IT, Jones AM. Oxygen uptake kinetics during moderate, heavy and severe intensity ‘submaximal’ exercise in humans: the influence of muscle fibre type and capillarisation. Eur J Appl Physiol. 2003;89(3-4):289–300. PubMed doi:10.1007/s00421-003-0799-1 31. Grassi B, Pogliaghi S, Rampichini S, et al. Muscle oxygenation and pulmonary gas exchange kinetics during cycling exercise ontransitions in humans. J Appl Physiol. 2003;95(1):149–158. PubMed 32. Dascombe B, Laursen PB, Nosaka K, Polglaze T. No effect of upper body compression garments in elite flat-water kayakers. Eur J Sport Sci. 2013;13(4):341–349. PubMed doi:10.1080/17461391.2011.606842
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22. R: A Language and Environment for Statistical Computing [computer program]. Vienna, Austria: The R Core Team; 2013. 23. An {R} Companion to Applied Regression [computer program]. 2nd ed. Thousand Oaks CA: Sage; 2011. 24. Fletcher TD. QuantPsyc: Quantitative Psychology Tools. R package version 1.5 [computer program]. Vienna, Austria; 2012. 25. García-Pallarés J, Garcia-Fernandez M, Sanchez-Medina L, Izquierdo M. Performance changes in world-class kayakers following two different training periodization models. Eur J Appl Physiol. 2010;110(1):99–107. PubMed 26. García-Pallarés J, Sanchez-Medina L, Carrasco L, Diaz A, Izquierdo M. Endurance and neuromuscular changes in world-class level kayakers during a periodized training cycle. Eur J Appl Physiol. 2009;106(4):629–638. PubMed doi:10.1007/s00421-009-1061-2 27. Jones AM, Carter H. The effect of endurance training on parameters of aerobic fitness. Sports Med. 2000;29(6):373–386. PubMed doi:10.2165/00007256-200029060-00001
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ORIGINAL INVESTIGATION
Physiological Characteristics of Well-Trained Junior Sprint Kayak Athletes Thiago Oliveira Borges, Ben Dascombe, Nicola Bullock, and Aaron J. Coutts This study aimed to profile the physiological characteristics of junior sprint kayak athletes (n = 21, VO2max 4.1 ± 0.7 L/min, training experience 2.7 ± 1.2 y) and to establish the relationship between physiological variables (VO2max, VO2 kinetics, muscleoxygen kinetics, paddling efficiency) and sprint kayak performance. VO2max, power at VO2max, power:weight ratio, paddling efficiency, VO2 at lactate threshold, and whole-body and muscle oxygen kinetics were determined on a kayak ergometer in the laboratory. Separately, on-water time trials (TT) were completed over 200 m and 1000 m. Large to nearly perfect (–.5 to –.9) inverse relationships were found between the physiological variables and on-water TT performance across both distances. Paddling efficiency and lactate threshold shared moderate to very large correlations (–.4 to –.7) with 200- and 1000-m performance. In addition, trivial to large correlations (–.11 to –.5) were observed between muscle-oxygenation parameters, muscle and whole-body oxygen kinetics, and performance. Multiple regression showed that 88% of the unadjusted variance for the 200-m TT performance was explained by VO2max, peripheral muscle deoxygenation, and maximal aerobic power (P < .001), whereas 85% of the unadjusted variance in 1000-m TT performance was explained by VO2max and deoxyhemoglobin (P < .001). The current findings show that well-trained junior sprint kayak athletes possess a high level of relative aerobic fitness and highlight the importance of the peripheral muscle metabolism for sprint kayak performance, particularly in 200-m races, where finalists and nonfinalists are separated by very small margins. Such data highlight the relative aerobic-fitness variables that can be used as benchmarks for talent-identification programs or monitoring longitudinal athlete development. However, such approaches need further investigation. Keywords: aerobic fitness, oxygen kinetics, muscle oxygenation The current Olympic sprint kayak program comprises 1000-m (men’s race time range 205–215 s), 500-m (women, 115–120 s), and 200-m (men 34–35 s and women 39–41 s) races. It has been estimated on a kayak ergometer that the 1000-m event requires ~82% of total energy to be provided through oxidative pathways, whereas only ~65% and ~37% are required for the 500-m and 200-m races, respectively.1 Aerobic metabolism in sprint kayak performance has been well documented, with large to very large correlations reported between maximum oxygen uptake (VO2max) and 1000- and 500-m performance,2,3 while only trivial to moderate relationships have been observed with 200-m performance.4,5 Other important measures of oxidative metabolism, such as whole-body oxygen kinetics (VO2 kinetics), muscle oxygen (MO2) kinetics, and muscle-oxygenation parameters, have yet to be reported in sprint kayak athletes. The VO2 kinetics represent the rate of systemic metabolic adaptation to exercise6 and have been shown to be faster in higherlevel athletes in similar racing sports such as rowing7 and cycling.8,9 Together, this information suggests that athletes who can rapidly adjust to increases in exercise intensity may benefit from the efficient supply of energy from the aerobic system and a reduced anaerobic contribution, limiting the accumulation of metabolic byproducts. Moreover, faster VO2 kinetics appear to benefit aerobic and Oliveira Borges and Coutts are with UTS: Health, University of Technology, Sydney, Australia. Dascombe is with the Applied Sports Science and Exercise Testing Laboratory, University of Newcastle, Newcastle, Australia. Bullock is with the Physiology Dept, Australian Institute of Sport, Gold Coast, QLD, Australia. Address author correspondence to Thiago Oliveira Borges at [email protected].
anaerobic performance, as a faster VO2 kinetics response enables better repeated-sprint performance.10 Nonetheless, there is still a need to improve our understanding of the physiological factors that may contribute to sprint kayak performance. At present, there is a relatively poor understanding of the physiological characteristics of developing junior sprint kayak athletes and their relationships with performance. Furthermore, there is limited literature examining the physiological factors associated with 200-m performance. Therefore, the aims of this study were to profile the physiological characteristics of developing junior sprint kayak athletes and establish the relationship between these physiological variables (ie, VO2 kinetics, MO2 kinetics, muscleoxygenation parameters, paddling efficiency, VO2max, maximal heart rate and lactate, lactate thresholds) and performance in the longer (1000-m) and shorter (200-m) sprint kayak time trials (TT).
Methods Experimental Design Anthropometric, physiological, and performance characteristics of well-trained junior sprint kayak athletes were assessed during a 7-day period. Anthropometric characteristics, aerobic-fitness parameters, and gross efficiency were assessed on day 1, and VO2 on-kinetics (phase II fast kinetics: τ) and muscle-oxygenation parameters were assessed on days 3 and 5. These variables were determined in the laboratory, whereas on-water 200-m and 1000-m TT performance was determined on day 7 at the athletes’ normal training venue. A comprehensive list of physiological variables assessed is presented herein. 593
594 Oliveira Borges et al
Participants Twenty-one (13 male, 8 female) well-trained junior sprint kayak athletes (17.0 ± 1.2 y, sum of 7 skinfolds 83.3 ± 31.3 mm, body mass 72.4 ± 8.3 kg, stature 176.1 ± 8.8 cm, training experience 2.7 ± 1.2 y, typical training volume 11.3 ± 3.0 h/wk) took part in this study, which had been approved by local university’s ethics committee (UTS HREC 2011-162).
Performance and Physiological Assessments
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Aerobic Fitness. Measures of aerobic fitness (VO2max, power at
VO2max [MAP], and lactate threshold [LT2]) were determined using the Australian Institute of Sport (AIS) national sprint kayak step test.11 Briefly, five 4-minute submaximal stages were performed at fixed intensities depending on the gender, age, and performance ability of the athlete. The last 4-minute stage was performed maximally at perceived TT intensity. A 60-second period was allowed between workloads for sampling blood lactate concentration ([La]) and recording average power and ratings of perceived of exertion. Gas exchange was measured continuously breath by breath using a MedGraphics System (CPX Ultima, MedGraphics Corp, St Paul, MN, USA) with the highest consecutive 1 minute reported as VO2max. The system was calibrated before each test session using known O2 and CO2 concentrations, according to the manufacturer’s specifications. The flowmeter was calibrated using a 3-L syringe. LT2 was determined by the D-max method (ICC .77–.93, P < .01) as described elsewhere.12 All laboratory tests were performed on a dynamically calibrated Weba kayak ergometer (Weba Sport und Med-Artikel GmbH, Vienna, Austria), on which the power output varied according to the tension applied throughout each stroke, mimicking the actual movement of the on-water stroke (typical error of estimate –7.2%). Near-Infrared Spectroscopy. Blood flow and muscle oxygenation
of the latissimus dorsi were measured using a portable near-infrared spectroscopy (NIRS) device (Portamon, Artinis Medical Systems, Zetten, the Netherlands) during each testing session. A 2-wavelength continuous-wave system that uses the modified Beer-Lambert law and spatially resolved spectroscopy methods simultaneously measured changes in tissue oxyhemoglobin (O 2Hb), deoxyhemoglobin (HHb), and total hemoglobin (tHb) by using their chromophoric properties at 750 and 860 nm. The difference between the HHb and HbO2, representing the average saturation of the latissimus dorsi, was calculated as the tissue-saturation index (TSI). The scores representing the muscle-oxygenation parameters are the average recorded during the maximal stage of the step test. An arbitrary value of 3.83 was used for the differential path length factor.13 The NIRS device was placed at the midpoint between the inferior border of the scapula and posterior axillar fold and oriented parallel to the muscle fibers. A translucid waterproof adhesive (OpSite Flexgrid, Smith&Nephew, Australia) was fixed between the NIRS device and the skin to prevent the sweat from causing any problem with light transmission/absorption. The device was fixed to the skin by a dark adhesive tape and covered with a tight-fitting black sport crop top to ensure that no ambient light penetrated and to help keep the device on site. The NIRS data were continuously recorded at a 10-Hz rate, and the data were later averaged to 1-second values using bespoke software (LabVIEW, National Instruments, USA), to allow synchronization with VO2 data. The NIRS-derived data were averaged at the last minute of each submaximal stage of the step test and are reported as either changes in micromolar units (ΔO2Hb, ΔHHb, ΔtHb) or percentage (TSI) of the baseline scores. Similar to the VO2max test protocol, the 2 consecutive maximal
30-second values for each parameter were averaged and considered maximal effort. Oxygen-Kinetics Assessment. A series of 3 square-wave transitions from rest to exercise was performed on the kayak ergometer in the laboratory. The athletes paddled for 2 minutes at a very low intensity (20 W) followed by a rapid increase to a predetermined paddling intensity for 6 minutes. The intensities corresponded to power outputs: individual LT2 (~80% of VO2, moderate domain), the intensity midway between individual LT2 and VO2max (50%Δ, heavy domain), and the intensity at 80% of the difference between LT2 and VO2max (80%Δ, severe domain). According to previous methods, a 6-minute rest was allowed between the moderate and heavy domains, while a 10-minute break was provided between the heavy and severe domains to enable metabolic rate to return to resting levels.7,14These procedures were done on day 3 and repeated on day 5. Modeling of O2 Kinetics and Muscle-Oxygenation Data. After a thorough examination of the VO2 data, any data point found more than 4 SD away from the mean response was deleted. The breath-bybreath data were interpolated to second-by-second values, and the response of the VO2 for each domain was time-aligned and averaged. For the MO2 kinetics, the main parameter of choice was the TSI. The raw data recorded at 10 Hz were initially averaged to 1 Hz and then time-aligned. A nonlinear least-squares-regression technique was used to model the time course of the VO2 response and TSI. A singlecomponent exponential equation (Equations 1 and 2) was used to model the moderate response, while for the heavy and severe domains, a double-component equation (Equations 3 and 4) was applied.6,8 The Solver function in Microsoft Excel (Microsoft Corp, Redmond, WA, USA) was used to determine the best fit of the parameters:
! 2 moderate domain − VO ! 2 (t ) VO ! 2 ( b ) + Ap ⋅ ⎡1− e −(t−TDp ) t p ⎤ = VO ⎣⎢ ⎦⎥ TSI moderate domain − TSI ( t ) − t−TD t = TSI ( b ) − Ap ⋅ ⎡⎢1− e ( p ) p ⎤⎥ ⎣ ⎦
(Eq 1)
(Eq 2)
! 2 heavyandsevere domain − VO ! 2 (t ) VO
(Eq 3)
(Eq 4)
! 2 ( b ) + Ap ⋅ ⎡1− e −(t−TDp ) t p ⎤ + As ⋅ ⎡1− e −(t−TDs ) t s ⎤ = VO ⎢⎣ ⎥⎦ ⎣ ⎦ TSI heavyandsevere domain − TSI ( t ) − t−TD t = TSI ( b ) + Ap ⋅ ⎡⎢1− e ( p ) p ⎤⎥ + As · ⎡⎣1− e −(t−TDs ) t s ⎤⎦ ⎣ ⎦
where VO2(t) and TSI(t) are the VO2 and TSI at a given time, VO2(b) and TSI(b) are the baseline values across the last 2 minutes of “unloaded” paddling, Ap and As are the asymptotic amplitudes for the primary and slow exponential components, τp and τs are the time constants for each component, and TDp and TDs are the time delays for the primary and slow components. Even though a 2-component model was used to fit both the heavyand severe-intensity VO2 and TSI responses, only the time constant for the fast component of the VO2 and TSI kinetics was reported. Energy Expenditure and Gross Efficiency. Energy expenditure
during the maximal stage of the AIS step test was calculated as the sum of the net VO2max (represented by the gain of the VO2max minus rest VO2) and net [La] (calculated from the [La]max gain minus rest [La]—assumed 1 mmol/L). The net [La] was then converted into O2
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equivalent using energy equivalent for VO2 as 20.9 kJ/L.15 Paddling gross efficiency was calculated as the ratio between power output and energy expenditure. The power output was converted into energy assuming the energy equivalent for VO2 as 20.9 kJ/L.15–18 Anthropometry. A calibrated skinfold caliper (Harpenden, Baty Intl, UK) with 0.2-mm precision was used for skinfold measurements. The sum of 7 skinfolds was measured according to International Society for the Advancement of Kinanthropometry standards.19 Body mass was determined by a Tanita digital scale (BC-590BT, Tanita Corp of America, Arlington Heights, IL, USA) and stature by a wooden stadiometer with 0.1-cm precision.
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Sprint Kayak Performance. On-water sprint kayak performance
was determined during TTs at the athletes’ regular training venue. The athletes used their own standard K1 sprint kayak and were individually assessed to avoid pacing or wash influence from other paddlers.20 The time was recorded for each effort by using 2 synchronized stopwatches (Interval2000, Nielsen-Kellerman, Boothwyn, PA, USA). The TTs were performed with a tail wind of ~2.5 m/s at an ambient temperature of ~20.0°C. After a standard warm-up that consisted of 3 minutes of paddling at ~85% of HRmax followed by two 15-second accelerations interspersed with 45 seconds rest and 2 standing starts of 24 strokes with 45 seconds rest between, ending with 3 minutes of paddling at 85% HRmax, the athlete positioned the boat at the start line and paddled in the shortest time possible for the 200- and 1000-m. After the 200-m trial, the athletes performed a moderate 25-minute active paddling recovery (~70% HRmax), which was then followed by the 1000-m TT. It was assumed that the 200-m effort had little impact on the 1000-m performance, as all the junior athletes had followed a similar training program and were not specialists in these distances.
Statistics The data are presented as mean ± SD. All data were initially assessed for normality and log-transformed where required. Initially, product– moment Pearson correlation was used to determine the relationship between all the physiological, anthropometrical, and performance variables. The correlation coefficients were also used to represent the effect size, where .1, .3, .5, .7, .9, and 1 were considered trivial, small,
moderate, large, very large, nearly perfect, and perfect, respectively, as described elsewhere.21 Hierarchical multiple-regression analyses were carried out to determine the best predictors for performance in sprint kayak racing. Multiple-regression outcomes were represented as the coefficient of estimates with 90% confidence intervals. To test for differences between models, 1-way ANOVA was applied. The cross-validation of the models was done by calculating the adjusted R2. Significance level was set at P < .05. All statistical procedures were conducted using R statistics software,22 car,23 and QuantPsyc packages for R.24
Results The mean 200-m and 1000-m TT performance times were 44.6 ±4.5 seconds and 279.3 ±22.2 seconds, respectively. Aerobic-fitness measures and their relationships with on-water 200-m and 1000-m TT performance are shown in Table 1. There were large to nearly perfect relationships of relative and absolute VO2max with MAP in both TT performances, with slightly stronger relationships observed with the 1000-m. Moreover, there were also moderate to large relationships between the power:weight ratio, lactate threshold, energy expenditure, and gross paddling efficiency and performance in both TTs. Table 2 shows the muscle-oxygenation parameters and the fastcomponent on-kinetics for muscle and whole body, as well as their relationship with 200- and 1000-m on-water performance. Trivial to large correlations were found of the muscle-oxygenation parameters, VO2 kinetics, and MO2 kinetics with on-water performance over both distances (Table 2). Both the HHb and TSI responses showed moderate to large correlations with on-water TT performances for both distances. Multiple regression showed that 88% of the unadjusted variance for the 200-m TT performance was explained by absolute VO2max, HHb, and MAP (F3,17 = 40.6, P < .001). The coefficient estimates for this model were –7.89 (90% CI –10.16 to –5.62), 0.08 (90% CI 0.03–0.14), and –0.25 (90% CI –0.35 to –0.14) for absolute VO2max, MAP, and HHb, respectively. Similarly, multiple regression showed that 85% of the unadjusted variance in 1000-m kayak TT performance was explained by absolute VO2max and HHb (F3,17= 34.8, P < .001). The coefficient estimates for absolute VO2max were –27.40 (90% CI –39.50 to –15.30); MAP, 0.05 (90% CI –0.24 to 0.35); and HHb, –0.78 (90% CI –1.35 to –0.21). See Figure 1.
Table 1 Mean ± SD and Correlation Coefficients of Physiological, Energetic, and Performance Characteristics of Well-Trained Junior Sprint Kayak Athletes Mean ± SD Maximum oxygen uptake (mL ·
kg–1
·
min–1)
Maximum oxygen uptake (L/min) Peak blood lactate concentration (mmol/L) Maximum heart rate (beats/min)
200-m TT (s)
1000-m TT (s)
46.6 ± 4.5
279.3 ± 22.2
R
R
57.0 ± 8.0
–.76
–.84
4.1 ± 0.7
–.86
–.90
9.0 ± 2.3
–.23
–.26
197.1 ± 7.7
.03
–.12
Power-to-weight ratio (kg/W)
2.4 ± 0.4
–.49
–.66
Maximal aerobic power (W)
169.3 ± 29.9
–.74
–.84
Lactate threshold 1 (W)
108.9 ± 18.4
–.65
–.68
Lactate threshold 2 (W)
129.4 ± 22.8
–.74
–.75
Energy expenditure (mL · kg–1 · min–1)
61.7 ± 10.4
–.42
–.59
Gross efficiency (%)
13.1 ± 1.6
–.48
–.34
Abbreviation: TT, time trial. IJSPP Vol. 10, No. 5, 2015
Table 2 Mean ± SD and Correlation Coefficients of Whole-Body and Muscle Oxygen Kinetics, Muscle-Deoxygenation Parameters, and Performance Characteristics of Well-Trained Junior Sprint Kayak Athletes Mean ± SD
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Deoxyhemoglobin (μM)
200-m time trial (s)
1000-m time trial (s)
46.6 ± 4.5
279.3 ± 22.2
R
R
–.54
–.49
12.1 ± 6.8
Oxyhemoglobin (μM)
0.6 ± 6.5
.17
.26
Total hemoglobin (μM)
12.8 ± 7.3
–.36
–.23
Tissue-saturation index (%)
55.3 ± 16.1
.42
.49
Oxygen uptake τ moderate (s)
36.5 ± 5.4
–.20
–.03
Oxygen uptake τ heavy (s)
24.1 ± 5.2
–.11
.01
Oxygen uptake τ severe (s)
24.3 ± 5.0
–.17
–.05
Tissue-saturation index τ moderate (s)
9.8 ± 3.7
.07
.19
Tissue-saturation index τ heavy (s)
14.8 ± 6.6
.25
.30
Tissue-saturation index τ severe (s)
13.4 ± 7.2
–.12
–.20
Figure 1 — Modeling of (left) oxygen-uptake (VO2) kinetics and (right) muscle-oxygenation kinetics in a participant in the 3 intensity domains: (upper) moderate, (middle) heavy, and (lower) severe. Abbreviation: TSI, tissue-saturation index.
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Discussion This study profiled the physiological characteristics of well-trained developing junior sprint kayak athletes and established the relationships between physiological variables and performance in 200-m and 1000-m sprint kayak TT performance. The main results were that aerobic-fitness characteristics demonstrated stronger relationships with 1000-m than with 200-m paddling performance, whereas MO2 kinetics in the heavy domain were related to both 200-m and 1000-m sprint kayak performance. The current results demonstrated that well-trained junior sprint kayak athletes possess a lower absolute maximal aerobic fitness and poorer gross efficiency than older, more experienced athletes. However, when expressed relative to body mass, the maximal aerobic-fitness characteristics were similar to or even higher than values previously reported in senior sprint kayakers. For example, older paddlers (~25.4 y) ranging from club to elite level are reported to be ~11% heavier and possess ~15% higher absolute VO2max, 25% higher MAP, and 26% higher [La]max.3,4,25,26 However, the difference in relative VO2max was reduced to 4% ± 8%.3,4,26 Similarly, despite absolute power output at LT2 being ~14% higher in mature elite paddlers, the current younger paddlers had a similar LT2 when expressed as a percentage of MAP (older ~74% ± 9% MAP vs younger 77% ± 8%).4,5,11,15,25 Despite the poorer absolute maximal aerobic-fitness levels, the younger paddlers in the current study had a higher level of gross efficiency (~30%) than well-trained older sprint kayak athletes.15 This may be explained by the lower muscle mass in younger athletes and their lower production of anaerobic energy.2 Collectively, these results suggest that the younger paddlers in this study were well trained, although it seems that further sustained training is required to meet the maximal aerobic fitness required for elite-level performance at the open level. Similar to other studies, strong relationships were observed between maximal aerobic fitness and TT performance.3–5 The very large to nearly perfect correlations between VO2max and MAP for the 200-m and the 1000-m distances agree with Fry and Morton3 over the 1000-m but contrast with van Someren and Howatson,4 who reported only trivial correlations between VO2max, MAP, and on-water 1000-m race performance. Furthermore, van Someren and Howatson4 also found trivial and large correlations between on-water 200-m race performance, VO2max, and MAP. One possible explanation for the differences was that TT performance was assessed in controlled and not race conditions, limiting the influence of other paddlers on pacing strategies and overall performance. In any case, the current study demonstrated that maximal aerobicfitness indices relate to both 1000-m and 200-m sprint kayak performance, with stronger relationships for the longer-distance event. Based on the current findings, coaches should prescribe training programs aimed at developing aerobic fitness and muscle strength, as the power produced at VO2max level not only presented very large correlations with on-water performance in both distances but was also powerful in predicting performance, as evidenced through the multiple-regression results. Comparable to maximal aerobic-fitness variables, strong relationships were present between submaximal measures of aerobic fitness such as LT2 and gross efficiency with both 1000-m and 200-m TT performance. Indeed, these measures are typically more sensitive to changes in training27 and may reflect how athletes are adapting to training. Other studies have previously shown large to very large correlations of LT2 with the 1000-m and the 500-m
on-water performance in sprint kayak.2,4 Our results corroborate these findings for the 1000-m, while this is the first study to demonstrate a very large correlation with on-water 200-m performance. One reasonable explanation may be the training background and the age of the athletes from these different studies. It should be acknowledged that the athletes in the current study were well-trained junior sprint kayak athletes whose training may have been designed toward more generalized development, compared with that of older athletes whose training was likely focused on specific race demands. Collectively, this information supports the suggestion that junior sprint kayak paddlers should maintain a sustained training program over time to further develop physiological attributes that will be related to performance into the open-age, higher-level competition. Furthermore, these data from the relative fitness variables in the current study may be used by coaches and sport scientists as a reference or even as benchmarks for talent-identification programs since these athletes were of a high fitness level. Nonetheless, since the cross-sectional design used in the current study does not assess this hypothesis, future prospective studies that examine the temporal changes in these variables are required. This is the first study to assess the relationships between VO2 kinetics, MO2 kinetics, and on-water sprint kayak performance over 200 m and 1000 m. The athletes in the current study presented a VO2 kinetics phase II time constant (τphaseII) that was 17% slower for the heavy domain when compared with 1500-m international runners.28 Moreover, Ingham et al7 reported τphaseII values for the moderate domain of 19.4 ± 5.6 and 13.9 ± 4.0 seconds, and in the heavy domain they were 22.4 ± 3.7 and 18.7 ± 2.1 seconds for club and elite rowers, respectively. Furthermore, trained cyclists (VO2max 66.6 ± 2.5 mL · kg–1 · min–1) presented τphaseII of 11.7 ± 2.5 and 15.2 ± 2.0 seconds while their untrained counterparts (42.9 ± 5.1 mL · kg–1 · min–1) showed τphaseII of 21.5 ± 6.6 and 23.5 ± 2.8 seconds in the moderate and heavy domains, respectively.8 These results may reflect discrepancies between exercise mode and the magnitude of muscle mass recruited. For example, the upper body predominantly possesses fast-twitch fibers compared with lower limbs,29 and past data have shown that fast-twitch fibers are linked to a slower τphaseII.30 Taken together, this information strengthens the evidence that the VO2 kinetics response depends on exercise mode and muscle-fiber type. The investigation of muscle-oxygenation parameters can help clarify the specific physiological demands of sprint kayak. The MO2 kinetics represent how efficient an individual is at delivering and extracting oxygen within the working muscle at the onset of exercise. The current MO2 kinetics were similar to those reported by Grassi et al31 for moderate-domain exercise (8.5 ± 0.9 vs 9.8 ± 3.7 s) but somewhat slower across the heavy domain (~7 vs 14.8 ± 6.6 s). These differences likely reflect differences in the training status of the current group of youth sprint kayak athletes, as well as other factors such as muscle-fiber composition and exercise mode. The current study also profiled muscle-oxygenation parameters (HHb, O2Hb, tHb, and TSI) and examined the relationships of these with sprint kayak on-water performance. Our findings demonstrated that the muscles’ ability to extract oxygen (represented by the HHb and TSI responses) has a large relationship with sprint kayak performance. Our data (Table 2) differ from those of Dascombe et al,32 who reported higher values for ΔHHb (20.5 ± 1.1 μM) and lower for ΔO2Hb (–16.7 ± 0.8 μM), ΔtHb (3.9 ± 0.3 μM), and TSI (37.3%) in a group of ~23-year-old elite paddlers. These differences likely reflect the application of the NIRS device to different musculature used
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in kayaking (forearm, which requires isometric contraction to hold the paddle vs latissimus dorsi) and that the subjects of Dascombe et al32 were older and possessed similar VO2max levels, lower relative power output at LT2 level (64.6% vs 76.4% for the current study), and higher (37%) MAP than the current cohort. Taken together, our findings demonstrate that the ability to rapidly extract oxygen for energy production has stronger relationships with 200-m performance; 1000-m performance appears more reliant on measures of aerobic capacity and power output. These results are not surprising due to the demands of each distance. The multipleregression analysis supports these observations by demonstrating that the predictive power of the absolute VO2max and MAP increased by adding ΔHHb to the model for 200-m performance. These findings further demonstrate the importance of anaerobic parameters for 200-m performance in sprint kayak, as the correlation analysis demonstrated that athletes who could extract greater O2 from the muscle performed better in the 200-m TT (R = –.54). This finding suggests that targeted training programs that focus on developing these characteristics may be required if athletes are to specialize in either the 200-m or the 1000-m event. A limitation of this study was that controlled laboratory-based TTs over the 200-m and 1000-m were not accomplished, which limits further insights on the basis of these findings and actual physiological response of well-trained sprint kayak athletes. Future research may look at laboratory-based demands or even on-water performance, using portable metabolic measurement devices.
Conclusion In conclusion, well-trained junior sprint kayak athletes possess lower absolute maximal aerobic fitness, despite similar relative aerobic fitness, than their older counterparts. Furthermore, the current data support the relationship between maximal aerobic capacities and performance, as well as the importance of the ΔHHb for performance during 200-m sprint kayak. Future studies should examine best methods for developing absolute aerobicfitness variables including maximal indicators such as VO2max and MAP and submaximal variables like the thresholds associated with the balance between lactate production and consumption (LT2), as well as looking at the best training strategy to enhance these variables. Acknowledgments The authors are grateful for the financial support provided by the University of Technology, Sydney (UTS); the Australian government, Department of Innovation, Industry, Science and Research: the Australian Institute of Sport; and Australian Canoeing. We also would like to thank all the athletes and coaches who participated in this study.
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