Daniel Hammarström

Validation of physiological tests in relation to competitive performances in elite male distance cross-country skiing

Abstract Carlsson, M, Carlsson, T, Hammarström, D, Tiivel, T, Malm, C, and Tonkonogi, M. Validation of physiological tests in relation to competitive performances in elite male distance cross-country skiing. J Strength Cond Res 26(6): 1496–1504, 2012—The purpose of the present study was to establish which physiological test parameters reflects the distance performances in the Swedish National Championships in cross-country skiing (SNC) and the International Ski Federations ranking points for distance performances (FISdist). The present study also aimed to create multiple regression models to describe skiing performance for the SNC distance races and International Ski Federations (FIS) ranking. Twelve male, Swedish, national elite, cross-country skiers (maximal oxygen consumption [V[Combining Dot Above]O2max] = 5.34 ± 0.34 L·min−1) volunteered to participate in the study. Their results in the 2008 SNC (15 km race [SNC15] and 30 km race [SNC30]) and FISdist points were used as performance data. On the week preceding the Championship, subjects completed a test battery consisting of 7 physiological tests: isokinetic knee extension peak torque (PT), vertical jumps (VJ), lactate threshold (LT), V[Combining Dot Above]O2max, and 3 double poling tests of different durations (DP20, DP60, and DP360). Correlations were established using Pearsons correlation analysis, and models to describe skiing performance were created using standard multiple linear regression analysis. Significant correlations were found between the performance parameters and test parameters derived from LT, V[Combining Dot Above]O2max, and DP60 tests. No correlations with any performance parameter were found for PT, VJ, DP20, and DP360 tests. For FISdist and SNC15, the models explain 81% and 78% of the variance in performance, respectively. No statistically valid regression model was found for SNC30. The results of this study imply that the physiological demands in male elite distance cross-country skiing performances are different in different events. To adequately evaluate a skiers performance ability in distance cross-country skiing, it is necessary to use test parameters and regression models that reflect the specific performance.

Scaling maximal oxygen uptake to predict performance in elite-standard men cross-country skiers

Abstract The purpose of this study was to: 1) establish the optimal body-mass exponent for maximal oxygen uptake (O2max) to indicate performance in elite-standard men cross-country skiers; and 2) evaluate the influence of course inclination on the body-mass exponent. Twelve elite-standard men skiers completed an incremental treadmill roller-skiing test to determine O2max and performance data came from the 2008 Swedish National Championship 15-km classic-technique race. Log-transformation of power-function models was used to predict skiing speeds. The optimal models were found to be: Race speed = 7.86 · O2max · m−0.48 and Section speed = 5.96 · O2max · m−(0.38 + 0.03 · α) · e−0.003 · Δ (where m is body mass, α is the section’s inclination and Δ is the altitude difference of the previous section), that explained 68% and 84% of the variance in skiing speed, respectively. A body-mass exponent of 0.48 (95% confidence interval: 0.19 to 0.77) best described O2max as an indicator of performance in elite-standard men skiers. The confidence interval did not support the use of either “1” (simple ratio-standard scaled) or “0” (absolute expression) as body-mass exponents for expressing O2max as an indicator of performance. Moreover, results suggest that course inclination increases the body-mass exponent for O2max.

Scaling of upper-body power output to predict time-trial roller skiing performance

Abstract The purpose of the present study was to establish the most appropriate allometric model to predict mean skiing speed during a double-poling roller skiing time-trial using scaling of upper-body power output. Forty-five Swedish junior cross-country skiers (27 men and 18 women) of national and international standard were examined. The skiers, who had a body mass (m) of 69.3 ± 8.0 kg (mean ± s), completed a 120-s double-poling test on a ski ergometer to determine their mean upper-body power output (W). Performance data were subsequently obtained from a 2-km time-trial, using the double-poling technique, to establish mean roller skiing speed. A proportional allometric model was used to predict skiing speed. The optimal model was found to be: Skiing speed = 1.057 · W 0.556 · m −0.315, which explained 58.8% of the variance in mean skiing speed (P < 0.001). The 95% confidence intervals for the scaling factors ranged from 0.391 to 0.721 for W and from −0.626 to −0.004 for m. The results in this study suggest that allometric scaling of upper-body power output is preferable for the prediction of performance of junior cross-country skiers rather than absolute expression or simple ratio-standard scaling of upper-body power output.

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

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˙O2peak) 61.3 ± 5.8 mL·kg·min) 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 × 16 min in weeks 1-4, 4 × 8 min in weeks 5-8, and 4 × 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 mmol·L blood lactate in 4 × 16-, 4 × 8-, and 4 × 4-min sessions, respectively (P < 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˙O2peak, and power at 4 mmol·L lactate. RESULTS All groups improved 5%-10% in mean power during a 40-min all-out trial, peak power output, and V˙O2peak postintervention (P < 0.05), but no adaptation differences emerged among the three training groups (P > 0.05). Further, an individual response analysis indicated similar likelihood of large, moderate, or nonresponses, respectively, in response to each training group (P > 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.

Effects of High-Intensity Training on Physiological and Hormonal Adaptions in Well-Trained Cyclists

Purpose Investigate development of specific performance adaptions and hormonal responses every fourth week during a 12-wk high-intensity training (HIT) period in groups with different interval-training prescriptions. Methods Sixty-three well-trained cyclists performing a 12-wk intervention consisting of two to three HIT sessions per week in addition to ad libitum low-intensity training. Groups were matched for total training load, but increasing HIT (INC) group (n = 23) performed interval-sessions as 4 × 16 min in weeks 1–4, 4 × 8 min in weeks 5–8, and 4 × 4 min in weeks 9–12. Decreasing HIT (DEC) group (n = 20) performed interval sessions in the opposite order as INC, and mixed HIT (MIX) group (n = 20) performed all interval-sessions in a mixed distribution during 12 wk. Cycling-tests and measures of resting blood hormones were conducted pre, weeks 4, 8, and 12. Results INC and MIX achieved >70% of total change in workload eliciting 4 mmol·L−1 [la−] (Power4mM) and V˙O2peak during weeks 1–4, versus only 34%–38% in DEC. INC induced larger improvement versus DEC during weeks 1–4 in Power4mM (effect size, 0.7) and V˙O2peak (effect size, 0.8). All groups increased similarly in peak power output during weeks 1–4 (64%–89% of total change). All groups’ pooled, total and free testosterone and free testosterone/cortisol ratio decreased by 22% ± 15%, 13% ± 23%, and 14% ± 31% (all P < 0.05), and insulin-like growth factor-1 increased by 10% ± 14% (P < 0.05) during weeks 1–4. Conclusions Most of progression in Power4mM, V˙O2peak and peak power output was achieved during weeks 1–4 in INC and MIX, and accompanied by changes in resting blood hormones consistent with increased but compensable stress load. In these well-trained subjects, accumulating 2–3 h·wk−1 performing 4 × 16 min work bouts at best effort induces greater adaptions in Power4mM and V˙O2peak than accumulating ~1 h·wk−1 performing best effort intervals as 4 × 4 min.

Optimal V. O2max-to-mass ratio for predicting 15 km performance among elite male cross-country skiers

The aim of this study was 1) to validate the 0.5 body-mass exponent for maximal. oxygen uptake (V.O2max) as the optimal predictor of performance in a 15 km classical-technique skiing competition among elite male cross-country skiers and 2) to evaluate the influence of distance covered on the body-mass exponent for V.O2max among elite male skiers. Twenty-four elite male skiers (age: 21.4±3.3 years [mean ± standard deviation]) completed an incremental treadmill roller-skiing test to determine their V.O2max. Performance data were collected from a 15 km classical-technique cross-country skiing competition performed on a 5 km course. Power-function modeling (ie, an allometric scaling approach) was used to establish the optimal body-mass exponent for V.O2max to predict the skiing performance. The optimal power-function models were found to be racespeed=8.83⋅(V˙O2maxm−0.53)0.66 and lapspeed=5.89⋅(V˙O2maxm−(0.49+0.0181lap))0.43e0.010age, which explained 69% and 81% of the variance in skiing speed, respectively. All the variables contributed to the models. Based on the validation results, it may be recommended that V.O2max divided by the square root of body mass (mL · min−1 · kg−0.5) should be used when elite male skiers’ performance capability in 15 km classical-technique races is evaluated. Moreover, the body-mass exponent for V.O2max was demonstrated to be influenced by the distance covered, indicating that heavier skiers have a more pronounced positive pacing profile (ie, race speed gradually decreasing throughout the race) compared to that of lighter skiers.