Heart rate and stroke rate misrepresent supramaximal sprint kayak training as quantified by power

Retrospective Analysis of Training Intensity Distribution Based on Race Pace Versus Physiological Benchmarks in Highly Trained Sprint Kayakers

Background

Research results on the training intensity distribution (TID) in endurance athletes are equivocal. This non-uniformity appears to be partially founded in the different quantification methods that are implemented. So far, TID research has solely focused on sports involving the lower-body muscles as prime movers (e.g. running). Sprint kayaking imposes high demands on the upper-body endurance capacity of the athlete. As there are structural and physiological differences between upper- and lower-body musculature, TID in kayaking should be different to lower-body dominant sports. Therefore, we aimed to compare the training intensity distribution during an 8-wk macrocycle in a group of highly trained sprint kayakers employing three different methods of training intensity quantification.

Methods

Heart rate (HR) and velocity during on-water training of nine highly trained German sprint kayakers were recorded during the final 8 weeks of a competition period leading to the national championships. The fractional analysis of TID was based on three zones (Z) derived from either HR (TID_Bla-HR) or velocity (TID_Bla-V) based on blood lactate (B_la) concentrations (Z1 ≤ 2.5 mmol L⁻¹ B_la, Z2 = 2.5–4.0 mmol L⁻¹ B_la, Z3 ≥ 4.0 mmol L⁻¹ B_la) of an incremental test or the 1000-m race pace (TID_Race): Z1 ≤ 85% of race pace, Z2 = 86–95% and Z3 ≥ 95%.

Results

TID_Bla-V (Z1: 68%, Z2: 14%, Z3: 18%) differed from TID_Bla-HR (Z1: 91%, Z2: 6%, Z3: 3%) in each zone (all p < 0.01). TID_Race (Z1: 73%, Z2: 20%, Z3: 7%) differed to Z3 in TID_Bla-V (p < 0.01) and all three TID_Bla-HR zones (all p < 0.01). Individual analysis revealed ranges of Z1, Z2, Z3 fractions for TID_Bla-HR of 85–98%, 2–11% and 0.1–6%. For TID_Bla-V, the individual ranges were 41–82% (Z1), 6–30% (Z2) and 8–30% (Z3) and for TID_Race 64–81% (Z1), 14–29% (Z2) and 4–10% (Z3).

Conclusion

The results show that the method of training intensity quantification substantially affects the fraction of TID in well-trained sprint kayakers. TID_Race determination shows low interindividual variation compared to the physiologically based TID_Bla-HR and TID_Bla-V. Depending on the aim of the analysis TID_Race, TID_Bla-HR and TID_Bla-V have advantages as well as drawbacks and may be implemented in conjunction to maximize adaptation.

Quantifying sprint kayak training on a flowing river: Exploring the utility of novel power measures and its relationship to measures of relative boat speed

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 time-trial 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 water-speed 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.

The Relationship Between the Distribution of Training Intensity and Performance of Kayak and Canoe Sprinters: A Retrospective Observational Analysis of One Season of Competition

Purpose: To evaluate retrospectively the training intensity distribution (TID) among highly trained canoe sprinters during a single season and to relate TID to changes in performance.

Methods: The heart rates during on-water training by 11 German sprint kayakers (7 women, 4 men) and one male canoeist were monitored during preparation periods (PP) 1 and 2, as well as during the period of competition (CP) (total monitoring period: 37 weeks). The zones of training intensity (Z) were defined as Z1 [&lt;80% of peak oxygen consumption (VO2peak)], Z2 (81–87% VO2peak) and Z3 (&gt;87% VO2peak), as determined by 4 × 1,500-m incremental testing on-water. Prior to and after each period, the time required to complete the last 1,500-m stage (all-out) of the incremental test (1,500-m time-trial), velocities associated with 2 and 4 mmol·L−1 blood lactate (v2[BLa], v4[BLa]) and VO2peak were determined.

Results: During each period, the mean TID for the entire group was pyramidal (PP1: 84/12/4%, PP2: 80/12/8% and CP: 91/5/4% for Z1, Z2, Z3) and total training time on-water increased from 5.0 ± 0.9 h (PP1) to 6.1 ± 0.9 h (PP2) and 6.5 ± 1.0 h (CP). The individual ranges for Z1, Z2 and Z3 were 61–96, 2–26 and 0–19%. During PP2 VO2peak (25.5 ± 11.4%) markedly increased compared to PP1 and CP and during PP1 v2[bla] (3.6 ± 3.4%) showed greater improvement compared to PP2, but not to CP. All variables related to performance improved as the season progressed, but no other effects were observed. With respect to time-trial performance, the time spent in Z1 (r = 0.66, p = 0.01) and total time in all three zones (r = 0.66, p = 0.01) showed positive correlations, while the time spent in Z2 (r = −0.57, p = 0.04) was negatively correlated.

Conclusions: This seasonal analysis of the effects of training revealed extensive inter-individual variability. Overall, TID was pyramidal during the entire period of observation, with a tendency toward improvement in VO2peak, v2[bla], v4[bla] and time-trial performance. During PP2, when the COVID-19 lockdown was in place, the proportion of time spent in Z3 doubled, while that spent in Z1 was lowered; the total time spent training on water increased; these changes may have accentuated the improvement in performance during this period. A further increase in total on-water training time during CP was made possible by reductions in the proportions of time spent in Z2 and Z3, so that more fractions of time was spent in Z1.