Human upper performance limits in the 100-m sprint remain the subject of much debate. The aim of this commentary is to highlight the vulnerabilities of prognoses from historical trends by shedding light on the mechanical and physiological limitations associated with human sprint performance. Several conditions work against the athlete with increasing sprint velocity; air resistance and braking impulse in each stride increase while ground-contact time typically decreases with increasing running velocity. Moreover, muscle-force production declines with increasing speed of contraction. Individual stature (leg length) strongly limits stride length such that conditioning of senior sprinters with optimized technique mainly must be targeted to enhance stride frequency. More muscle mass means more power and thereby greater ground-reaction forces in sprinting. However, as the athlete gets heavier, the energy cost of accelerating that mass also increases. This probably explains why body-mass index among world-class sprinters shows low variability and averages 23.7 ± 1.5 and 20.4 ± 1.4 for male and female sprinters, respectively. Performance development of world-class athletes indicates that ~8% improvement from the age of 18 represents the current maximum trainability of sprint performance. However, drug abuse is a huge confounding factor associated with such analyses, and available evidence suggests that we are already very close to “the citius end” of 100-m sprint performance.
Search Results
You are looking at 1 - 10 of 17 items for
- Author: Espen Tønnessen x
- Refine by Access: All Content x
Thomas Haugen, Espen Tønnessen, and Stephen Seiler
Thomas Haugen, Espen Tønnessen, and Stephen Seiler
Purpose:
A review of published studies monitoring sprint performance reveals considerable variation in start distance behind the initial timing gate. The aim of the current study was to generate correction factors across varying flying-start distances used in sprint testing with photocells.
Methods:
Forty-four well-trained junior soccer players (age 18.2 ± 1.0 y, height 175 ± 8 cm, body mass 68.4 ± 8.9 kg) performed sprint testing on an indoor sprint track. They were allocated to 3 groups based on sprintperformance level. Times for 10- and 200-m sprint with foot placement ranging from 0.5 to 15 m back from the initial timing gate were recorded twice for each athlete.
Results:
Correction-factor equation coefficients were generated for each of the 3 analyzed groups derived from the phase-decay equation y = (y 0 − PL) × exp(−k × x) + PL, where y = time difference (0.5-m flying start as reference), x = flying-start distance, y 0 is the y value when time is zero, PL (plateau) is the y value at infinite times, and k is the rate constant, expressed in reciprocal of the x-axis time units; if x is in seconds, then k is expressed in inverse seconds. R 2 was ≥.998 across all athlete groups and sprint distances, demonstrating excellent goodness of fit. Within-group time differences were significant (P < .05) across all flying-start distance checkpoints for all groups. Between-groups time-saving differences up to 0.04 s were observed between the fastest and the slowest groups (P < .05).
Conclusions:
Small changes in flying-start distances can cause time differences larger than the typical gains made from specific training, or even the difference between the fastest and slowest elite team-sport athletes. The presented correction factors should facilitate more meaningful comparisons of published sprint-performance results.
Øystein Sylta, Espen Tønnessen, and Stephen Seiler
Purpose:
The authors directly compared 3 frequently used methods of heart-rate-based training-intensity-distribution (TID) quantification in a large sample of training sessions performed by elite endurance athletes.
Methods:
Twenty-nine elite cross-country skiers (16 male, 13 female; 25 ± 4 y; 70 ± 11 kg; 76 ± 7 mL · min−1 · kg−1 VO2max) conducted 570 training sessions during a ~14-d altitude-training camp. Three analysis methods were used: time in zone (TIZ), session goal (SG), and a hybrid session-goal/time-in-zone (SG/TIZ) approach. The proportion of training in zone 1, zone 2, and zone 3 was quantified using total training time or frequency of sessions, and simple conversion factors across different methods were calculated.
Results:
Comparing the TIZ and SG/TIZ methods, 96.1% and 95.5%, respectively, of total training time was spent in zone 1 (P < .001), with 2.9%/3.6% and 1.1%/0.8% in zones 2/3 (P < .001). Using SG, this corresponded to 86.6% zone 1 and 11.1%/2.4% zone 2/3 sessions. Estimated conversion factors from TIZ or SG/TIZ to SG and vice versa were 0.9/1.1, respectively, in the low-intensity training range (zone 1) and 3.0/0.33 in the high-intensity training range (zones 2 and 3).
Conclusions:
This study provides a direct comparison and practical conversion factors across studies employing different methods of TID quantification associated with the most common heart-rate-based analysis methods.
Øystein Sylta, Espen Tønnessen, and Stephen Seiler
Purpose:
The purpose of this study was to validate the accuracy of self-reported (SR) training duration and intensity distribution in elite endurance athletes.
Methods:
Twenty-four elite cross-country skiers (25 ± 4 y, 67.9 ± 9.88 kg, 75.9 ± 6.50 mL · min−1 · kg−1) SR all training sessions during an ~14-d altitude-training camp. Heart rate (HR) and some blood lactate measurements were collected during 466 training sessions. SR training was compared with recorded training duration from HR monitors, and SR intensity distribution was compared with expert analysis (EA) of all session data.
Results:
SR training was nearly perfectly correlated with recorded training duration (r = .99), but SR training was 1.7% lower than recorded training duration (P < .001). SR training duration was also nearly perfectly correlated (r = .95) with recorded training duration >55% HRmax, but SR training was 11.4% higher than recorded training duration >55% HRmax (P < .001) due to SR inclusion of time <55% HRmax. No significant differences were observed in intensity distribution in zones 1–2 between SR and EA comparisons, but small discrepancies were found in zones 3–4 (P < .001).
Conclusions:
This study provides evidence that elite endurance athletes report their training data accurately, although some small differences were observed due to lack of a SR “gold standard.” Daily SR training is a valid method of quantifying training duration and intensity distribution in elite endurance athletes. However, additional common reporting guidelines would further enhance accuracy.
Guro Strøm Solli, Espen Tønnessen, and Øyvind Sandbakk
Purpose: To investigate the factors associated with underperformance and the subsequent changes in training characteristics and supportive actions when returning to the world’s best cross-country skier. Methods: The participant is the most decorated winter Olympian, with 8 Olympic gold medals, 18 World Championship titles, and 114 World Cup victories. Training data were categorized by training form (endurance, strength, and speed); intensity (low, moderate, and high); and mode (running, cycling, and skiing/roller skiing). In addition, test data were retrospectively analyzed, and interviews were performed with the participant and her support team. Results: After the competitive season, the participant had 8 weeks without systematic training and an evaluation process aiming to detect the factors contributing to underperformance. Here physiological, technical, and psychological challenges were detected. As a consequence, the participant included less high-intensity training (1.2 vs 2.1 sessions/wk, P = .011); more moderate-intensity training (0.9 vs 0.4 sessions/wk, P = .016); and more low-intensity training (6.9 vs 5.9 sessions/wk, P = .036) during the general preparation phase but with similar total endurance training load as previous season. In addition, more strength training (1.6 vs 1.1 h/wk, P = .036) and new ski-specific strength exercises were included. Finally, the athlete’s autonomy when planning and adjusting training was increased, nontraining stressors were reduced, more frequent testing was included, systematic mental training was initiated, her nutritional strategy was adjusted, and her asthma treatment was optimized. Conclusions: Overall, the current case study could be used as a framework for the holistic approach to treating an overtraining condition and for generation of new hypothesis in this exiting area.
Thomas A. Haugen, Espen Tønnessen, and Stephen Seiler
Purpose:
The purpose of this investigation was to compare sprint and countermovement-jump (CMJ) performance among female competitive soccer players as a function of performance level, field position, and age. In addition, the authors wanted to quantify the evolution of these physical characteristics among elite players over a 15-y period.
Methods:
194 female elite players (22± 4.1 y, 63 ± 5.6 kg), including an Olympic winning squad, tested 40-m sprint with electronic timing and CMJ on a force platform at the Norwegian Olympic training center from 1995 to 2010.
Results:
Moderate to large velocity differences across performance levels and positions were observed. National-team players were 2% faster than 1st-division players (P = .027, d = 0.5) and 5% faster than 2nd-division players (P < .001, d = 1.3) over 0–20 m. National-team players jumped 8–9% higher than 1st-division players (P = .001, d = 0.6) and junior elite players (P = .023, d = 0.5). Forwards were 3–4% faster than midfielders (P < .001, d = 0.8) and goalkeepers (P = .003, d = 0.9) over 0–20 m. No differences in velocity or CMJ height were observed among the age categories. Players from 2006–2010 were 2% faster (P < .05, d = 0.6) than players from 1995–1999 over 20 m, whereas no differences in 20- to 40-m velocity or CMJ performance were observed.
Conclusions:
This study provides effect-magnitude estimates for the influence of performance level, age, and player position on sprint and CMJ performance in female soccer players. While 20- to 40-m velocity and CMJ performance have remained stable over the time, there has been a moderate but positive development in 0- to 20-m velocity among elite performers.
Thomas A. Haugen, Espen Tønnessen, and Stephen Seiler
Purpose:
To compare sprint and countermovement-jump (CMJ) performance among competitive soccer players as a function of performance level, field position, and age. In addition, the authors wanted to quantify the evolution of these physical characteristics among professional players over a 15-y period.
Methods:
939 athletes (22.1 ± 4.3 y), including national-team players, tested 40-m sprint with electronic timing and CMJ on a force platform at the Norwegian Olympic Training Center between 1995 and 2010.
Results:
National-team and 1st-division players were faster (P < .05) than 2nd-division (1.0–1.4%), 3rd- to 5th-division (3.0–3.8%), junior national-team (1.7–2.2%), and junior players (2.8–3.7%). Forwards were faster than defenders (1.4%), midfielders (2.5%), and goalkeepers (3.2%) over 0–20 m (P < .001). Midfielders jumped ~2.0 cm lower than the other playing positions (P < .05). Sprinting velocity peaked in the age range 20–28 y and declined significantly thereafter (P < .05). Players from 2006–2010 had 1–2% faster 0–20 m and peak velocity than players from the 1995–1999 and 2000–2005 epochs, whereas no differences in CMJ performance were observed.
Conclusions:
This study provides effect-magnitude estimates for the influence of performance level, position, and age on sprint and CMJ performance in soccer. While CMJ performance has remained stable over the time, there has been a small but positive development in sprinting velocity among professional players.
Espen Tønnessen, Thomas A. Haugen, Erlend Hem, Svein Leirstein, and Stephen Seiler
Purpose:
To generate updated Olympic-medal benchmarks for V̇O2max in winter endurance disciplines, examine possible differences in V̇O2max between medalists and nonmedalists, and calculate gender difference in V̇O2max based on a homogeneous subset of world-leading endurance athletes.
Methods:
The authors identified 111 athletes who participated in winter Olympic Games/World Championships in the period 1990 to 2013. All identified athletes tested V̇O2max at the Norwegian Olympic Training Center within ±1 y of their championship performance. Testing procedures were consistent throughout the entire period.
Results:
For medal-winning athletes, the following relative V̇O2max values (mean:95% confidence intervals) for men/women were observed (mL · min–1 · kg–1): 84:87-81/72:77-68 for cross-country distance skiing, 78:81-75/68:73-64 for cross-country sprint skiing, 81:84-78/67:73-61 for biathlon, and 77:80-75 for Nordic combined (men only). Similar benchmarks for absolute V̇O2max (L/min) in male/female athletes are 6.4:6.1-6.7/4.3:4.1-4.5 for cross-country distance skiers, 6.3:5.8-6.8/4.0:3.7-4.3 for cross-country sprint skiers, 6.2:5.7-6.4/4.0:3.7-4.3 for biathletes, and 5.3:5.0-5.5 for Nordic combined (men only). The difference in relative V̇O2max between medalists and nonmedalists was large for Nordic combined, moderate for cross-country distance and biathlon, and small/trivial for the other disciplines. Corresponding differences in absolute V̇O2max were small/trivial for all disciplines. Male cross-country medalists achieve 15% higher relative V̇O2max than corresponding women.
Conclusions:
This study provides updated benchmark V̇O2max values for Olympic-medal-level performance in winter endurance disciplines and can serve as a guideline of the requirements for future elite athletes.
Thomas Haugen, Espen Tønnessen, Silvana Bucher Sandbakk, and Øyvind Sandbakk
Julia Kathrin Baumgart, Espen Tønnessen, Morten Eklund, and Øyvind Sandbakk
Purpose: To describe the training volume, intensity distribution, and use of swimming styles during a Paralympic cycle in a multiple swimming champion with paraplegia. Methods: The female Paralympic swimmer was 23–26 years of age and had a body mass of 60 to 62 kg and a body height of 174 cm. She has a spinal cord injury at the Th6 level, competed in the S5/SB4 Para swimming classes, and uses a wheelchair for mobility. Training time, as well as distance in the different intensity zones and swimming styles, was registered with the “workouts for swim coaches” software throughout a full Paralympic cycle. Results: The Para swimmer performed a total of 388, 524, 471, and 656 annual hours of swimming, corresponding to 1126, 1504, 1463, and 1993 km, in the 2012–13, 2013–14, 2014–15, and 2015–16 seasons, respectively. In addition, she performed 1 to 3 weekly dry-land strength sessions and 4 to 6 weekly dry-land basic skill sessions. She conducted 91% to 94% of the swimming distance in each macrocycle at low intensity, 2% to 4% at moderate intensity, and 3% to 6% at high intensity. She performed 78% to 84% of the swimming distance in each macrocycle in the freestyle swimming technique and the remaining 16% to 22% in the backstroke, breaststroke, and butterfly techniques. Conclusion: This case study exemplifies how a female Paralympic swimmer with paraplegia progressed her training in the seasons leading up to the Paralympic Games, reaching an annual training distance of 2000 km, which is similar to that of able-bodied swimmers.
