Two-dimensional analyses of sprint kinetics are commonly undertaken but often ignore the metatarsal-phalangeal (MTP) joint and model the foot as a single segment. The aim of this study was to quantify the role of the MTP joint in the early acceleration phase of a sprint and to investigate the effect of ignoring the MTP joint on the calculated joint kinetics at the other stance leg joints. High-speed video and force platform data were collected from four to five trials for each of three international athletes. Resultant joint moments, powers, and net work at the stance leg joints during the first stance phase after block clearance were calculated using three different foot models. Considerable MTP joint range of motion (>30°) and a peak net MTP plantar flexor moment of magnitude similar to the knee joint were observed, thus highlighting the need to include this joint for a more complete picture of the lower limb energetics during early acceleration. Inclusion of the MTP joint had minimal effect on the calculated joint moments, but some of the calculated joint power and work values were significantly (P < .05) and meaningfully affected, particularly at the ankle. The choice of foot model is therefore an important consideration when investigating specific aspects of sprinting technique.
Neil E. Bezodis, Aki I.T. Salo and Grant Trewartha
Ian N. Bezodis, David G. Kerwin, Stephen-Mark Cooper and Aki I.T. Salo
Purpose: To understand how training periodization influences sprint performance and key step characteristics over an extended training period in an elite sprint training group. Methods: Four sprinters were studied during 5 mo of training. Step velocities, step lengths, and step frequencies were measured from video of the maximum velocity phase of training sprints. Bootstrapped mean values were calculated for each athlete for each session, and 139 within-athlete, between-sessions comparisons were made with a repeated-measures analysis of variance. Results: As training progressed, a link in the changes in velocity and step frequency was maintained. There were 71 between-sessions comparisons with a change in step velocity yielding at least a large effect size (>1.2), of which 73% had a correspondingly large change in step frequency in the same direction. Within-athlete mean session step length remained relatively constant throughout. Reductions in step velocity and frequency occurred during training phases of high-volume lifting and running, with subsequent increases in step velocity and frequency happening during phases of low-volume lifting and high-intensity sprint work. Conclusions: The importance of step frequency over step length to the changes in performance within a training year was clearly evident for the sprinters studied. Understanding the magnitudes and timings of these changes in relation to the training program is important for coaches and athletes. The underpinning neuromuscular mechanisms require further investigation but are likely explained by an increase in force-producing capability followed by an increase in the ability to produce that force rapidly.
Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Danny Holdcroft and Aki I.T. Salo
Purpose: Athletes’ force–power characteristics influence sled velocity during the skeleton start, which is a crucial determinant of performance. This study characterized force–power profile changes across an 18-month period and investigated the associations between these changes and start performance. Methods: Seven elite- and 5 talent-squad skeleton athletes’ (representing 80% of registered athletes in the country) force–power profiles and dry-land push-track performances were assessed at multiple time points over two 6-month training periods and one 5-month competition season. Force–power profiles were evaluated using an incremental leg-press test (Keiser A420), and 15-m sled velocity was recorded using photocells. Results: Across the initial maximum strength development phases, increases in maximum force (F max) and decreases in maximum velocity (V max) were typically observed. These changes were greater for talent (23.6% and −12.5%, respectively) compared with elite (6.1% and −7.6%, respectively) athletes. Conversely, decreases in F max (elite −6.7% and talent −10.3%) and increases in V max (elite 8.1% and talent 7.7%) were observed across the winter period, regardless of whether athletes were competing (elite) or accumulating sliding experience (talent). When the training emphasis shifted toward higher-velocity, sprint-based exercises in the second training season, force–power profiles seemed to become more velocity oriented (higher V max and more negative force–velocity gradient), which was associated with greater improvements in sled velocity (r = .42 and −.45, respectively). Conclusions: These unique findings demonstrate the scope to influence force–power-generating capabilities in well-trained skeleton athletes across different training phases. To enhance start performance, it seems important to place particular emphasis on increasing maximum muscle-contraction velocity.
Steffi L. Colyer, Keith A. Stokes, James L.J. Bilzon, Marco Cardinale and Aki I.T. Salo
An extensive battery of physical tests is typically employed to evaluate athletic status and/or development, often resulting in a multitude of output variables. The authors aimed to identify independent physical predictors of elite skeleton start performance to overcome the general problem of practitioners employing multiple tests with little knowledge of their predictive utility.
Multiple 2-d testing sessions were undertaken by 13 high-level skeleton athletes across a 24-wk training season and consisted of flexibility, dry-land push-track, sprint, countermovement-jump, and leg-press tests. To reduce the large number of output variables to independent factors, principal-component analysis (PCA) was conducted. The variable most strongly correlated to each component was entered into a stepwise multiple-regression analysis, and K-fold validation assessed model stability.
PCA revealed 3 components underlying the physical variables: sprint ability, lower-limb power, and strength–power characteristics. Three variables that represented these components (unresisted 15-m sprint time, 0-kg jump height, and leg-press force at peak power, respectively) significantly contributed (P < .01) to the prediction (R 2 = .86, 1.52% standard error of estimate) of start performance (15-m sled velocity). Finally, the K-fold validation revealed the model to be stable (predicted vs actual R 2 = .77; 1.97% standard error of estimate).
Only 3 physical-test scores were needed to obtain a valid and stable prediction of skeleton start ability. This method of isolating independent physical variables underlying performance could improve the validity and efficiency of athlete monitoring, potentially benefitting sport scientists, coaches, and athletes alike.