The Physiological Capacity of the World’s Highest Ranked Female Cross-country Skiers

Sandbakk, Øyvind; Hegge, Ann Magdalen; Losnegard, Thomas; Skattebo, Øyvind; Tønnessen, Espen; Holmberg, Hans‐Christer

doi:10.1249/mss.0000000000000862

Cited by 83 publications

(147 citation statements)

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“…This is due to the polynomial relationship between speed and submaximal metabolic rate, which also results in GE to be speed-dependent (i.e., non-linear) (see Figures 2-4). Therefore, none of the more conventional methods for estimating the anaerobic capacity (i.e., the linear MAOD methods or both GE methods) can be recommended for DP, which has also been indicated in previous publications (Sandbakk et al, 2016;Andersson et al, 2017) -but not systematically analyzed. Therefore, when evaluating the anaerobic capacity in DP, the non-linear relationships for speed vs. metabolic rate and the nonlinear speed-dependency of GE suggests the use of alternative methods, such as the second-degree polynomial method (i.e., 8-Y POL ) described in the current study, or, alternatively, the method described by Beneke et al (2002).…”

Section: Different Effect Of Computational Methods In Dp and Ds Sub-tementioning

confidence: 99%

The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Andersson

Noordhof

Lögdal

2020

Front. Sports Act. Living

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Anaerobic capacity is an important performance-determining variable of sprint cross-country skiing. Nevertheless, to date, no study has directly compared the anaerobic capacity, determined using the maximal accumulated oxygen deficit (MAOD) method and gross efficiency (GE) method, while using different skiing sub-techniques.Purpose: To compare the anaerobic capacity assessed using two different MAOD approaches (including and excluding a measured y-intercept) and the GE method during double poling (DP) and diagonal stride (DS) cross-country skiing.Methods: After an initial familiarization trial, 16 well-trained male cross-country skiers performed, in each sub-technique on separate occasions, a submaximal protocol consisting of eight 4-min bouts at intensities between ∼47-78% ofVO 2peak followed by a 4-min roller-skiing time trial, with the order of sub-technique being randomized. Linear and polynomial speed-metabolic rate relationships were constructed for both sub-techniques, while using a measured y-intercept (8+Y LIN and 8+Y POL ) or not (8-Y LIN and 8-Y POL ), to determine the anaerobic capacity using the MAOD method. The average GE (GE AVG ) of all eight submaximal exercise bouts or the GE of the last submaximal exercise bout (GE LAST ) were used to calculate the anaerobic capacity using the GE method. Repeated measures ANOVA were used to test differences in anaerobic capacity between methods/approaches.Results: A significant interaction was found between computational method and skiing sub-technique (P < 0.001, η 2 = 0.51) for the anaerobic capacity estimates. The different methodologies resulted in significantly different anaerobic capacity values in DP (P < 0.001, η 2 = 0.74) and in DS (P = 0.016, η 2 = 0.27). The 8-Y POL model resulted in the smallest standard error of the estimate (SEE, 0.24 W·kg −1 ) of the MAOD methods in DP, while the 8-Y LIN resulted in a smaller SEE value than the 8+Y LIN model (0.17 vs. 0.33 W·kg −1 ) in DS. The 8-Y LIN and GE LAST resulted in the closest agreement in anaerobic capacity values in DS (typical error 2.1 mL O 2 eq·kg −1 ). Andersson et al. Estimating Anaerobic Energy ProductionConclusions: It is discouraged to use the same method to estimate the anaerobic capacity in DP and DS sub-techniques. In DP, a polynomial MAOD method (8-Y POL ) seems to be the preferred method, whereas the 8-Y LIN , GE AVG , and GE LAST can all be used for DS, but not interchangeable, with GE LAST being the least time-consuming method.

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Section: Different Effect Of Computational Methods In Dp and Ds Sub-tementioning

confidence: 99%

The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Andersson

Noordhof

Lögdal

2020

Front. Sports Act. Living

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“…In a recent review, it was summarized that most retrospective studies on well-trained athletes report on PYR TID, although some world-class athletes performed POL TID during certain phases of the season (Stöggl and Sperlich, 2015). Sandbakk et al (2016), Tønnessen et al (2014), and Guellich et al (2009) reported a general focus on HVLIT TID in the preparation period, which became more polarized in the competition period of world/national class cross-country skiers, biathletes, and young world-class rowers.…”

Section: Introductionmentioning

confidence: 99%

Training Intensity Distribution and Changes in Performance and Physiology of a 2nd Place Finisher Team of the Race across America Over a 6 Month Preparation Period

et al. 2016

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Aim: To monitor the training intensity distribution (TID) and the development of physiological and performance parameters.Methods: During their preparation period for the RAAM, 4 athletes (plus 1 additional backup racer) performed 3 testing sessions; one before, one after 3, and one after 6 months of training. VO2max, maximal rate of lactate accumulation (dLa/dtmax), critical power, power output at lactate minimum (MLSSP), peak and mean power output during a sprint test, heart rate recovery, isometric strength, jumping height, and body composition were determined. All training sessions were recorded with a power meter. The endurance TID was analyzed based on the time in zone approach, according to a classical 3-zone model, including all power data of training sessions, and a power specific 3-zone model, where time with power output below 50% of MLSSP was not considered.Results: The TID using the classical 3-zone model reflected a pyramidal TID (zone 1: 63 ± 16, zone 2: 28 ± 13 and zone 3: 9 ± 4%). The power specific 3-zone model resulted in a threshold-based TID (zone 1: 48 ± 13, zone 2: 39 ± 10, zone 3: 13 ± 4%). VO2max increased by 7.1 ± 5.3% (P = 0.06). dLa/dtmax decreased by 16.3 ± 8.1% (P = 0.03). Power output at lactate minimum and critical power increased by 10.3 ± 4.1 and 16.8 ± 6.2% (P = 0.01), respectively. No changes were found for strength parameters and jumps.Conclusion: The present study underlines that a threshold oriented TID results in only moderate increases in physiological parameters. The amount of training below 50% of MLSSp (~28% of total training time) is remarkably high. Researchers, trainers, and athletes should pay attention to the different ways of interpreting training power data, to gain realistic insights into the TID and the corresponding improvements in performance and physiological parameters.

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“…In endurance sports, these include the duration and intensity of individual training sessions, the frequency of training sessions, and the organizational pattern of these stimulus variables over time. Recent descriptive studies of some of the world_s best endurance athletes have shown that successful athletes in cycling (14,25,35), running (1,2), and cross-country skiing (21,22,33) perform a high volume of low-intensity training (LIT) (defined as work eliciting a stable blood lactate concentration [la j ] of less than approximately 2 mmolIL j1 ) in addition to much smaller but substantial proportions of both moderate-intensity training (MIT) (2-4 mmolIL j1 blood lactate) and high-intensity training (HIT) (training above maximum lactate steady-state intensity [94 mmolIL j1 blood lactate]) throughout the preparation period. The majority of descriptive studies present a ''pyramidal'' training intensity distribution (TID), with high volume of LIT, substantial MIT, and less HIT, whereas a few studies suggest athletes to adopt a ''polarized'' TID (reduced volume of MIT, somewhat higher HIT), which have been proposed to give superior endurance adaptations (27,29).…”

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confidence: 99%

The Effect of Different High-Intensity Periodization Models on Endurance Adaptations

Sylta

Tønnessen

Hammarström

et al. 2016

Medicine &Amp; Science in Sports &Amp; Exercise

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. Purpose: This study aimed to compare the effects of three different high-intensity training (HIT) models, balanced for total load but differing in training plan progression, on endurance adaptations. Methods: Sixty-three cyclists (peak oxygen uptake (V O 2peak ) 61.3 T 5.8 mLIkg j1 Imin j1 ) were randomized to three training groups and instructed to follow a 12-wk training program consisting of 24 interval sessions, a high volume of low-intensity training, and laboratory testing. The increasing HIT group (n = 23) performed interval training as 4 Â 16 min in weeks 1-4, 4 Â 8 min in weeks 5-8, and 4 Â 4 min in weeks 9-12. The decreasing HIT group (n = 20) performed interval sessions in the opposite mesocycle order as the increasing HIT group, and the mixed HIT group (n = 20) performed the interval prescriptions in a mixed distribution in all mesocycles. Interval sessions were prescribed as maximal session efforts and executed at mean values 4.7, 9.2, and 12.7 mmolIL j1 blood lactate in 4 Â 16-, 4 Â 8-, and 4 Â 4-min sessions, respectively (P G 0.001). Pre-and postintervention, cyclists were tested for mean power during a 40-min all-out trial, peak power output during incremental testing to exhaustion, V O 2peak , and power at 4 mmolIL j1 lactate. Results: All groups improved 5%-10% in mean power during a 40-min all-out trial, peak power output, and V O 2peak postintervention (P G 0.05), but no adaptation differences emerged among the three training groups (P 9 0.05). Further, an individual response analysis indicated similar likelihood of large, moderate, or nonresponses, respectively, in response to each training group (P 9 0.05). Conclusions: This study suggests that organizing different interval sessions in a specific periodized mesocycle order or in a mixed distribution during a 12-wk training period has little or no effect on training adaptation when the overall training load is the same.

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The Physiological Capacity of the World’s Highest Ranked Female Cross-country Skiers

Abstract: Supplemental digital content is available in the text.

Cited by 83 publications

References 32 publications

The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

The Anaerobic Capacity of Cross-Country Skiers: The Effect of Computational Method and Skiing Sub-technique

Training Intensity Distribution and Changes in Performance and Physiology of a 2nd Place Finisher Team of the Race across America Over a 6 Month Preparation Period

The Effect of Different High-Intensity Periodization Models on Endurance Adaptations

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