Development of an On-Water Graded Exercise Test for Flat-Water Sprint Kayak Athletes

Winchcombe, Chelsie E.; Binnie, Martyn J.; Doyle, Matthew; Hogan, Cruz; Peeling, Peter

doi:10.1123/ijspp.2018-0717

Cited by 11 publications

(22 citation statements)

References 16 publications

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“…Indeed, this extensive experience was considered important for achieving optimal reliability and validity. In fact, in connection with previous investigations employing a protocol that differed only slightly from our own with respect to the number, duration, and intensity of incremental steps, the reliability and validity of measurements of HR, oxygen consumption (VO 2 ), blood lactate and stroke rate were all reported to be acceptable to excellent (Carrasco Páez et al, 2010 ; Winchcombe et al, 2019 ; Matzka et al, 2021 ).…”

Section: Methodssupporting

confidence: 64%

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

Matzka

Leppich

Holmberg

et al. 2022

Front. Sports Act. Living

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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 [<80% of peak oxygen consumption (VO2peak)], Z2 (81–87% VO2peak) and Z3 (>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.

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Section: Methodssupporting

confidence: 64%

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

Matzka

Leppich

Holmberg

et al. 2022

Front. Sports Act. Living

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“…Training in different zones strengthens the connected processes to provide energy, allowing the athlete to perform better during the related load conditions. While previous groups concluded that there are differences between onergometer and on-water kayaking with this regard Hunter, Cochrane, & Sachlikidis, 2008), ergometer training is still a widely used tool during winter and to study the different aspects of kayaking (Jones & Peeling, 2014;Michael, Rooney, & Smith, 2008;Winchcombe et al, 2019). In our exercise stresskayak ergometer setting by using the vita maxima and vita sub-maximal test load profiles, we were able to eliminate anomalies resulting from individual differences in the metabolic background, thus producing physical stress equal for all participants.…”

Section: Discussionmentioning

confidence: 99%

“…Several earlier reports showed the feasibility and reproducibility of various measurements during training (Fleming, Donne, Fletcher, & Mahony, 2012;Tay & Kong, 2018;, in laboratory environments and on-water (Bjerkefors et al, 2019;Steeves et al, 2019;Winchcombe, Binnie, Doyle, Hogan, & Peeling, 2019). However, sophisticated parallel data collecting, processing, and feedback systems still need to be established for kayakers.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Approach for Kayaking Performance Analysis and Improvement

Nagy

Komka

Szathmáry

et al. 2020

International Journal of Computer Science in Sport

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Artificial Intelligence (AI) invades fields where sophisticated analytics has not been applied before. Modality refers to how something happens or is experienced. Multimodal datasets are beneficial for solving complex research problems with AI methods. Kayaking technique optimization has been challenging, as there seems to be no gold standard for effective paddling techniques since there are outstanding athletes with profoundly different physical capabilities and kayaking styles.Multimodal analysis can help find the most effective paddling techniques for training and competition based on individuals’ abilities.We describe the characteristics of the output power of kayak athletes and Electromyogram (EMG) measurements collected from the most critical muscles, and the relationship between these modalities. We propose metrics (weighted arithmetic mean difference and variability of power output and stroke duration) suitable for discerning athletes based on how efficiently and correctly they perform particular training tasks. Additionally, the described methods (asymmetry, coactivation, muscle intensity-output power) help athletes and coaches in assessing their performance and compare it with others based on their EMG activities.As the next step, we will apply machine-learning approaches on the synchronized dataset we collect with the described methods to reveal desirable EMG and stroke patterns.

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“…The time for each change in direction was extracted using the GPS-data. Previous investigations involving on-water incremental step testing in sprint kayakers showed acceptable to excellent reliability and validity of measurements of HR, oxygen uptake, blood lactate and stroke rate [18,19] and strong correlations with performance [17].…”

Section: Incremental Testingmentioning

confidence: 99%

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

2022

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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 (TIDBla-HR) or velocity (TIDBla-V) based on blood lactate (Bla) concentrations (Z1 ≤ 2.5 mmol L−1 Bla, Z2 = 2.5–4.0 mmol L−1 Bla, Z3 ≥ 4.0 mmol L−1 Bla) of an incremental test or the 1000-m race pace (TIDRace): Z1 ≤ 85% of race pace, Z2 = 86–95% and Z3 ≥ 95%. Results TIDBla-V (Z1: 68%, Z2: 14%, Z3: 18%) differed from TIDBla-HR (Z1: 91%, Z2: 6%, Z3: 3%) in each zone (all p < 0.01). TIDRace (Z1: 73%, Z2: 20%, Z3: 7%) differed to Z3 in TIDBla-V (p < 0.01) and all three TIDBla-HR zones (all p < 0.01). Individual analysis revealed ranges of Z1, Z2, Z3 fractions for TIDBla-HR of 85–98%, 2–11% and 0.1–6%. For TIDBla-V, the individual ranges were 41–82% (Z1), 6–30% (Z2) and 8–30% (Z3) and for TIDRace 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. TIDRace determination shows low interindividual variation compared to the physiologically based TIDBla-HR and TIDBla-V. Depending on the aim of the analysis TIDRace, TIDBla-HR and TIDBla-V have advantages as well as drawbacks and may be implemented in conjunction to maximize adaptation.

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Development of an On-Water Graded Exercise Test for Flat-Water Sprint Kayak Athletes

Cited by 11 publications

References 16 publications

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

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

Multimodal Approach for Kayaking Performance Analysis and Improvement

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

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