Speed is not an appropriate parameter for the classification of team sport activity comprising continual changes in speed and direction; however, critical metabolic power derived from variable-speed activity seems useful for this purpose.
Purpose: To compare methods of monitoring and prescribing on-water exercise intensity (heart rate [HR], stroke rate [SR], and power output [PO]) during sprint kayak training. Methods: Twelve well-trained flat-water sprint kayak athletes completed a preliminary on-water 7 × 4-min graded exercise test and a 1000-m time trial to delineate individual training zones for PO, HR, and SR into a 5-zone model (T1–T5). Subsequently, athletes completed 2 repeated trials of an on-water training session, where intensity was prescribed based on individual PO zones. Times quantified for T1–T5 during the training session were then compared between PO, HR, and SR. Results: Total time spent in T1 was higher for HR (P < .01) compared with PO. Time spent in T2 was lower for HR (P < .001) and SR (P < .001) compared with PO. Time spent in T3 was not different between PO, SR, and HR (P > .05). Time spent in T4 was higher for HR (P < .001) and SR (P < .001) compared with PO. Time spent in T5 was higher for SR (P = .03) compared with PO. Differences were found between the prescribed and actual time spent in T1–T5 when using PO (P < .001). Conclusions: The measures of HR and SR misrepresented time quantified for T1–T5 as prescribed by PO. The stochastic nature of PO during on-water training may explain the discrepancies between prescribed and actual time quantified for power across these zones. For optimized prescription and monitoring of athlete training loads, coaches should consider the discrepancies between different measures of intensity and how they may influence intensity distribution.
Purpose: To determine the reliability and validity of a power-prescribed on-water (OW) graded exercise test (GXT) for flat-water sprint kayak athletes. Methods: Nine well-trained sprint kayak athletes performed 3 GXTs in a repeated-measures design. The initial GXT was performed on a stationary kayak ergometer in the laboratory (LAB). The subsequent 2 GXTs were performed OW (OW1 and OW2) in an individual kayak. Power output (PWR), stroke rate, blood lactate, heart rate, oxygen consumption, and rating of perceived exertion were measured throughout each test. Results: Both PWR and oxygen consumption showed excellent test–retest reliability between OW1 and OW2 for all 7 stages (intraclass correlation coefficient > .90). The mean results from the 2 OW GXTs (OWAVE) were then compared with LAB, and no differences in oxygen consumption across stages were evident (P ≥ .159). PWR was higher for OWAVE than for LAB in all stages (P ≤ .021) except stage 7 (P = .070). Conversely, stroke rate was lower for OWAVE than for LAB in all stages (P < .010) except stage 2 (P = .120). Conclusions: The OW GXT appears to be a reliable test in well-trained sprint kayak athletes. Given the differences in PWR and stroke rate between the LAB and OW tests, an OW GXT may provide more specific outcomes for OW training.
Quantification of external training load for sprint kayak athletes can be challenging due to the influence of the water flow on boat velocity in a flowing river environment. Therefore, this study examined the utility of novel measures of power output (PO) and its relationship to measures of relative boat speed when training on a flowing river. Twelve (8 males, 4 female) well-trained sprint kayak athletes completed 4 separate on-water sessions comprising one timetrial session (2 × 1000-m maximal efforts) and three repeated sprint kayak training sessions (5 x split 1000-m [2 × 500-m up and down the river] submaximal efforts) in their individual (K1) kayak. For each session, a Kayak Power Meter recorded athletes' PO, and a SpeedCoach device recorded relative land-speed via a Global Positioning System (GPS) (S GPS ), and relative waterspeed via an impeller mounted under the boat hull (S IMP ). Non-linear least squares regression were used to evaluate the curvilinear relationship between PO and speed (S GPS and S IMP ) data. The exponents of velocity in the PO-S IMP relationship (2.87 females, 2.94 males) were closer to theoretical values (3.00) and showed greater model accuracy (root mean squared error (RMSE) = 20-26 W) than the PO-S GPS relationships (speed exponents = 1.58-2.02, RMSE = 31-40 W). Overall, PO measures could better account for the influence of water flow compared to traditional S GPS measures, and therefore, may be more suitable for quantifying athletes' external load in their training environment. Highlights:. Since traditional S GPS and time-to-completion measures do not adjust for the water flow, these measures appear limited for prescribing and monitoring sprint kayak training within flowing river environments. . The prescription of paddling PO across a wide spectrum of relative PO values elicited similar internal and external athlete responses, regardless of the direction travelled on a flowing river (i.e. upstream or downstream). . The relationship between PO and S IMP during on-water sprint kayaking appears similar to those observed in rowing, where every percent change in boat speed measured relative to water (S IMP ) requires a 2.87 and 2.94-fold percent change in paddling PO in female and male sprint kayak athletes, respectively. . Continued evaluation of the PO-speed relationship for individual athletes may provide further insight into modelling performance and training targets for sprint kayak athletes.
This study examined the utility of novel measures of power output (PO) compared to traditional measures of heart rate (HR) and stroke rate (SR) for quantifying high-intensity sprint kayak training. Twelve well-trained, male and female sprint kayakers (21.3 ± 6.8 y) completed an on-water graded exercise test (GXT) and a 200-, 500-and 1000-m time-trial for the delineation of individualised training zones (T) for HR (5-zone model, T1-T5), SR and PO (8-zone model, T1-T8). Subsequently, athletes completed two repeat trials of a high-intensity interval (HIIT) and a sprint interval (SIT) training session, where intensity was prescribed using individualised PO-zones. Time-in-zone (minutes) using PO, SR and HR was then compared for both HIIT and SIT. Compared to PO, time-in-zone using HR was higher for T1 in HIIT and SIT (P < 0.001, d ≥ 0.90) and lower for T5 in HIIT (P < 0.001, d = 1.76). Average and peak HR were not different between HIIT (160 ± 9 and 173 ± 11 bpm, respectively) and SIT (157 ± 13 and 174 ± 10 bpm, respectively) (P ≥ 0.274). In HIIT, timein-zone using SR was higher for T4 (P < 0.001, d = 0.85) and was lower for T5 (P = 0.005, d = 0.43) and T6 (P < 0.001, d = 0.94) compared to PO. In SIT, time-in-zone using SR was lower for T7 (P = 0.001, d = 0.66) and was higher for T8 (P = 0.004, d = 0.70), compared to PO. Heart rate measures were unable to differentiate training demands across different high-intensity sessions, and could therefore misrepresent the training load in such instances. Furthermore, SR may not provide a sensitive measure for detecting changes in intensity due to fatigue, whereas PO may be more suitable.
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