Jos J. de Koning and Dionne A. Noordhof
Dionne A. Noordhof, Philip F. Skiba, and Jos J. de Koning
Anaerobic capacity/anaerobically attributable power is an important parameter for athletic performance, not only for short high-intensity activities but also for breakaway efforts and end spurts during endurance events. Unlike aerobic capacity, anaerobic capacity cannot be easily quantified. The 3 most commonly used methodologies to quantify anaerobic capacity are the maximal accumulated oxygen deficit method, the critical power concept, and the gross efficiency method. This review describes these methods, evaluates if they result in similar estimates of anaerobic capacity, and highlights how anaerobic capacity is used during sporting activities. All 3 methods have their own strengths and weaknesses and result in more or less similar estimates of anaerobic capacity but cannot be used interchangeably. The method of choice depends on the research question or practical goal.
Jacob Walther, Roy Mulder, Dionne A. Noordhof, Thomas A. Haugen, and Øyvind Sandbakk
Purpose: To quantify peak age and relative performance progression toward peak age in cross-country skiing according to event type, sex, and athlete performance level. Methods: International Ski Federation (FIS) points (performance expressed relative to the best athlete) of athletes born between 1981 and 1991, competing in junior world championships or finishing top 30 in world championships or Olympics, were downloaded from the FIS website. Individual performance trends were derived by fitting a quadratic curve to each athletes FIS point and age data. Results: Peak age was 26.2 (2.3) years in distance and 26.0 (1.7) years in sprint events. The sex difference in peak age in sprint events was ∼0.8 years (small, P = .001), while there was no significant sex difference in peak age in distance events (P = .668). Top performers displayed higher peak ages than other athletes in distance (mean difference, ±95% confidence limits = 1.6 y, ±0.6 y, moderate, P < .001) and sprint events (1.0, ±0.6 y, moderate, P < .001). FIS point improvement over the 5 years preceding peak age did not differ between event types (P = .325), while men improved more than women in both events (8.8, ±5.4%, small, P = .002 and 7.5, ±6.4%, small, P = .002). Performance level had a large effect on improvement in FIS points in both events (P < .001). Conclusion: This study provides novel insights on peak age and relative performance progression among world-class cross-country skiers and can assist practitioners, sport institutions, and federations with goal setting and evaluating strategies for achieving success.
Dionne A. Noordhof, Carl Foster, Marco J.M. Hoozemans, and Jos J. de Koning
A meaningful association between changes (Δ) in push-off angle or effectiveness (e) and changes in skating velocity (v) has been found during 5000-m races, although no significant association was found between changes in knee (θ0) and trunk angle (θ1) and Δv. It might be that speed skating event, sex, and performance level influence these associations.
To study the effect of skating event, sex, and performance level on the association between Δe and Δv and between Δθ0 and Δθ1 and Δv.
Video recordings were made from frontal (e) and sagittal views (θ0 and θ1) during 1500- and 5000-m men’s and women’s World Cup races. Radio-frequency identification tags provided data of v.
Skating event influenced the association between Δe and Δv, which resulted in a significant association between Δe and Δv for the 5000-m (β = –0.069, 95% confidence interval [–0.11, –0.030]) but not for the 1500-m (β = –0.011 [–0.032, 0.010]). The association between Δθ0 and Δθ1 and Δv was not significantly influenced by skating event. Sex and performance level did not substantially affect the association between Δe and Δv and between Δθ0 and Δθ1 and Δv.
Skating event significantly influenced the association between Δe and Δv; a 1° change in e results in a 0.011-m/s decrease in v during the 1500-m and a 0.069-m/s decrease in v during the 5000-m. Thus, it seems especially important to maintain a small e during the 5000-m.
Anna E. Voskamp, Senna van den Bos, Carl Foster, Jos J. de Koning, and Dionne A. Noordhof
Background: Gross efficiency (GE) declines during high-intensity exercise. Increasing extracellular buffer capacity might diminish the decline in GE and thereby improve performance. Purpose: To examine if sodium bicarbonate (NaHCO3) supplementation diminishes the decline in GE during a 2000-m cycling time trial. Methods: Sixteen male cyclists and 16 female cyclists completed 4 testing sessions including a maximal incremental test, a familiarization trial, and two 2000-m GE tests. The 2000-m GE tests were performed after ingestion of either NaHCO3 supplements (0.3 g/kg body mass) or placebo supplements (amylum solani, magnesium stearate, and sunflower oil capsules). The GE tests were conducted using a double-blind, randomized, crossover design. Power output, gas exchange, and time to complete the 2000-m time trials were recorded. Capillary blood samples were analyzed for blood bicarbonate, pH, and lactate concentration. Data were analyzed using magnitude-based inference. Results: The decrement in GE found after the 2000-m time trial was possibly smaller in the male and female groups after NaHCO3 than with placebo ingestion, with the effect in both groups combined being unclear. The effect on performance was likely trivial for males (placebo 164.2 [5.0] s, NaHCO3 164.3 [5.0] s; Δ0.1; ±0.6%), unclear for females (placebo 178.6 [4.8] s, NaHCO3 178.0 [4.3] s; Δ−0.3; ±0.5%), and very likely trivial when effects were combined. Blood bicarbonate, pH, and lactate concentration were substantially elevated from rest to pretest after NaHCO3 ingestion. Conclusions: NaHCO3 supplementation results in an unclear effect on the decrease in GE during high-intensity exercise and in a very likely trivial effect on performance.
Guro S. Solli, Silvana B. Sandbakk, Dionne A. Noordhof, Johanna K. Ihalainen, and Øyvind Sandbakk
Purpose: To investigate changes in self-reported physical fitness, performance, and side effects across the menstrual cycle (MC) phases among competitive endurance athletes and to describe their knowledge and communication with coaches about the MC. Methods: The responses of 140 participants (older than 18 y) competing in biathlon or cross-country skiing at the (inter)national level were analyzed. Data were collected via an online questionnaire addressing participants’ competitive level, training volume, MC history, physical fitness, and performance during the MC, MC-related side effects, and knowledge and communication with coaches about the MC and its effects on training and performance. Results: About 50% and 71% of participants reported improved and reduced fitness, respectively, during specific MC phases, while 42% and 49% reported improved and reduced performance, respectively. Most athletes reported their worst fitness (47%) and performance (30%) and the highest number of side effects during bleeding (P < .01; compared with all other phases). The phase following bleeding was considered the best phase for perceived fitness (24%, P < .01) and performance (18%, P < .01). Only 8% of participants reported having sufficient knowledge about the MC in relation to training, and 27% of participants communicated about it with their coach. Conclusions: A high proportion of athletes perceived distinct changes in fitness, performance, and side effects across the MC phases, with their worst perceived fitness and performance during the bleeding phase. Because most athletes indicate a lack of knowledge about the MC’s effect on training and performance and few communicate with coaches on the topic, the authors recommend that more time be devoted to educating athletes and coaches.
Roy C.M. Mulder, Dionne A. Noordhof, Katherine R. Malterer, Carl Foster, and Jos J. de Koning
Previous research showed that gross efficiency (GE) declines during exercise and therefore influences the expenditure of anaerobic and aerobic resources.
To calculate the anaerobic work produced during cycling time trials of different length, with and without a GE correction.
Anaerobic work was calculated in 18 trained competitive cyclists during 4 time trials (500, 1000, 2000, and 4000-m). Two additional time trials (1000 and 4000 m) that were stopped at 50% of the corresponding “full” time trial were performed to study the rate of the decline in GE.
Correcting for a declining GE during time-trial exercise resulted in a significant (P < .001) increase in anaerobically attributable work of 30%, with a 95% confidence interval of [25%, 36%]. A significant interaction effect between calculation method (constant GE, declining GE) and distance (500, 1000, 2000, 4000 m) was found (P < .001). Further analysis revealed that the constant-GE calculation method was different from the declining method for all distances and that anaerobic work calculated assuming a constant GE did not result in equal values for anaerobic work calculated over different time-trial distances (P < .001). However, correcting for a declining GE resulted in a constant value for anaerobically attributable work (P = .18).
Anaerobic work calculated during short time trials (<4000 m) with a correction for a declining GE is increased by 30% [25%, 36%] and may represent anaerobic energy contributions during high-intensity exercise better than calculating anaerobic work assuming a constant GE.
Dionne A. Noordhof, Roy C.M. Mulder, Katherine R. Malterer, Carl Foster, and Jos J. de Koning
To evaluate whether gross efficiency (GE), determined during submaximal cycling, is lower after time trials and if the magnitude of the decrease differs in relation to race distance. Secondary purposes were to study the rate of the decline in GE and whether changes in muscle-fiber recruitment could explain the decline.
Cyclists completed 9 GE tests consisting of submaximal exercise performed before and after time trials of different length (500 m, 1000 m, 2000 m, 4000 m, 15,000 m, and 40,000 m). In addition, subjects performed time trials as if they were a 1000-m, 4000-m, or 40,000-m time trial during which they were stopped at 50% of the final time of the preceding “full” time trial. Power output, gas exchange, and EMG were measured continuously throughout the GE tests.
A significant interaction effect between distance and time was found for GE (P = .001). GE was significantly lower immediately after the time trials than before (P < .05), and the decline in GE differed between distances (P < .001). GE seemed to decline linearly during the relatively short trials, while it declined more hyperbolically during the 40,000-m. A significant effect of time (P = .04) on mean EMG amplitude was found. However, post hoc comparisons showed no significant differences in mean EMG amplitude between the different time points (before and after the time trials).
GE decreases during time-trial exercise. Unfortunately, the cause of the decrease remains uncertain. Future modeling studies should consider using a declining instead of a constant GE. In sport situations, the declining GE has to be taken into account when selecting a pacing strategy.
Dionne A. Noordhof, Carl Foster, Marco J.M. Hoozemans, and Jos J. de Koning
Speed skating posture, or technique, is characterized by the push-off angle or effectiveness (e), determined as the angle between the push-off leg and the ice; the preextension knee angle (θ 0); and the trunk angle (θ 1). Together with muscle-power output and environmental conditions, skating posture, or technique, determines velocity (v).
To gain insight into technical variables that are important to skate efficiently and perform well, e, θ 0, θ 1, and skating v were determined every lap during a 5000-m World Cup. Second, the authors evaluated if changes (Δ) in e, θ 0, and θ 1 are associated with Δv.
One camera filmed the skaters from a frontal view, from which e was determined. Another camera filmed the skaters from a sagittal view, from which θ 0 and θ 1 were determined. Radio-frequency identification tags around the ankles of the skaters measured v.
During the race, e progressively increased and v progressively decreased, while θ 0 and θ 1 showed a less consistent pattern of change. Generalized estimating equations showed that Δe is significantly associated with Δv over the midsection of the race (β = −0.10, P < .001) and that Δθ 0 and Δθ 1 are not significantly associated with Δv.
The decrease in skating v over the race is not due to increases in power losses to air friction, as knee and trunk angle were not significantly associated with changes in velocity. The decrease in velocity can be partly ascribed to the decrease in effectiveness, which reflects a decrease in power production associated with fatigue.
Dionne A. Noordhof, Roy C.M. Mulder, Jos J. de Koning, and Will G. Hopkins
Analysis of sport performance can provide effects of environmental and other venue-specific factors in addition to estimates of within-athlete variability between competitions, which determines smallest worthwhile effects.
To analyze elite long-track speed-skating events.
Log-transformed performance times were analyzed with a mixed linear model that estimated percentage mean effects for altitude, barometric pressure, type of rink, and competition importance. In addition, coefficients of variation representing residual venue-related differences and within-athlete variability between races within clusters spanning ~8 d were determined. Effects and variability were assessed with magnitude-based inference.
A 1000-m increase in altitude resulted in very large mean performance improvements of 2.8% in juniors and 2.1% in seniors. An increase in barometric pressure of 100 hPa resulted in a moderate reduction in performance of 1.1% for juniors but an unclear effect for seniors. Only juniors competed at open rinks, resulting in a very large reduction in performance of 3.4%. Juniors and seniors showed small performance improvements (0.4% and 0.3%) at the more important competitions. After accounting for these effects, residual venue-related variability was still moderate to large. The within-athlete within-cluster race-to-race variability was 0.3–1.3%, with a small difference in variability between male (0.8%) and female juniors (1.0%) and no difference between male and female seniors (both 0.6%).
The variability in performance times of skaters is similar to that of athletes in other sports in which air or water resistance limits speed. A performance enhancement of 0.1–0.4% by top-10 athletes is necessary to increase medal-winning chances by 10%.