Jos J. de Koning
Jos J. de Koning
Jos J. de Koning
The quality of performance during international competitions such as the Olympic Games and various world championships is often judged by the number of world records attained. The simple fact that world records continue to improve is evidence that sports performance is progressing. Does this also mean that athletes are improving? Is the continual progression of world-record performances evidence that contemporary athletes are superior to the athletes who performed in the past? Technological developments may obscure insight into the athletic enhancement made by athletes over the years. This commentary tries to separate technological and athletic enhancement in the progression of world records by the use of a power balance model.
Jos J. de Koning
Jos J. de Koning
Jos J. de Koning and Dionne A. Noordhof
Teun van Erp, Carl Foster and Jos J. de Koning
Purpose: The relationship between various training-load (TL) measures in professional cycling is not well explored. This study investigated the relationship between mechanical energy spent (in kilojoules), session rating of perceived exertion (sRPE), Lucia training impulse (LuTRIMP), and training stress score (TSS) in training, races, and time trials (TT). Methods: For 4 consecutive years, field data were collected from 21 professional cyclists and categorized as being collected in training, racing, or TTs. Kilojoules (kJ) spent, sRPE, LuTRIMP, and TSS were calculated, and the correlations between the various TLs were made. Results: 11,655 sessions were collected, from which 7596 sessions had heart-rate data and 5445 sessions had an RPE score available. The r between the various TLs during training was almost perfect. The r between the various TLs during racing was almost perfect or very large. The r between the various TLs during TTs was almost perfect or very large. For all relationships between TSS and 1 of the other measurements of TL (kJ spent, sRPE, and LuTRIMP), a significant different slope was found. Conclusion: kJ spent, sRPE, LuTRIMP, and TSS all have a large or almost perfect relationship with each other during training, racing, and TTs, but during racing, both sRPE and LuTRIMP have a weaker relationship with kJ spent and TSS. Furthermore, the significant different slope of TSS vs the other measurements of TL during training and racing has the effect that TSS collected in training and road races differs by 120%, whereas the other measurements of TL (kJ spent, sRPE, and LuTRIMP) differ by only 73%, 67%, and 68%, respectively.
Jasper Reenalda, Maurice T.F. Maas and Jos J. de Koning
To examine the influence of induced changes in the morphology of the leg by adding mass on the optimal step length (OSL) in experienced runners to get more insight into parameters that influence preferred step length (PSL) and OSL.
Thirteen experienced male runners (mean age 26.9 ± 6.1 y, height 183.7 ± 7.1 cm, mass 71.8 ± 5.9 kg) ran on a treadmill in 3 different conditions: unloaded (UL), loaded with 2 kg mass at the ankles (MA), and loaded with 2 kg mass at the hips (MH) at 7 different step lengths (SLs). SL deviations were expressed as deviations in relative leg length (%LL) from the individual PSL: 0%LL, ±5%LL, ±10%LL, and ±15%LL. Trials lasted 8 min, and 8 min of rest was given between trials. Oxygen uptake (V̇O2) was expressed as a fraction of V̇O2 at PSL + 0%LL in the unloaded condition (%V̇O2). The %SL with the lowest value of %V̇O2 was considered the OSL for this group of participants.
OSL at the UL condition was 6% shorter than PSL. The MA condition resulted in a 7%LL larger OSL than at UL and MH (P < .05).
The mass distribution of the leg is a determinant of the OSL. As a consequence of the added mass to the ankles, OSL was 7%LL longer. Morphological characteristics of the leg might therefore play an important role in determining the runner’s individual optimal SL.
Carl Foster, Jos J. de Koning and Christian Thiel
The official world records (WR) for the 1-mile run for men (3:43.13) and for women (4:12.58) have improved 12.2% and 32.3%, respectively, since the first WR recognized by the International Association of Athletics Federations. Previous observations have suggested that the pacing pattern for successive laps is characteristically faster-slower-slowest-faster. However, modeling studies have suggested that uneven energy-output distribution, particularly a high velocity at the end of the race, is essentially wasted kinetic energy that could have been used to finish sooner. Here the authors report that further analysis of the pacing pattern in 32 men’s WR races is characterized by a progressive reduction in the within-lap variation of pace, suggesting that improving the WR in the 1-mile run is as much about how energetic resources are managed as about the capacity of the athletes performing the race. In the women’s WR races, the pattern of lap times has changed little, probably secondary to a lack of depth in the women’s fields. Contemporary WR performances have been achieved a coefficient of variation of lap times on the order of 1.5–3.0%. Reasonable projection suggests that the WR is overdue for improving and may require lap times with a coefficient of variation of ~1%.
Jac Orie, Nico Hofman, Jos J. de Koning and Carl Foster
During the last decade discussion about training-intensity distribution has been an important issue in sports science. Training-intensity distribution has not been adequately investigated in speed skating, a unique activity requiring both high power and high endurance.
To quantify the training-intensity distribution and training hours of successful Olympic speed skaters over 10 Olympiads.
Olympic-medal-winning trainers/coaches and speed skaters were interviewed and their training programs were analyzed. Each program was qualified and quantified: workout type (specific and nonspecific) and training zones (zone 1 ≤2 mMol/L lactate, zone 2 2–4 mMol/L lactate, zone 3 lactate >4 mMol/L). Net training times were calculated.
The relation between total training hours and time (successive Olympiads) was not progressive (r = .51, P > .5). A strong positive linear relation (r = .96, P < .01) was found between training distribution in zone 1 and time. Zones 2 and 3 both showed a strong negative linear relation to time (r = –.94, P < .01; r = –.97, P < .01). No significant relation was found between speed skating hours and time (r = –.11, P > .05). This was also the case for inline skating and time (r = –.86, P > .05).
These data indicate that in speed skating there was a shift toward polarized training over the last 38 y. This shift seems to be the most important factor in the development of Olympic speed skaters. Surprisingly there was no relation found between training hours, skating hours, and time.