Biomechanical Studies of Elite Female Distance Runners*

Williams, Keith R.; Cavanagh, P. R.; Ziff, Jodi L.

doi:10.1055/s-2008-1025715

Cited by 87 publications

(54 citation statements)

References 9 publications

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“…Likewise, we found small correlation between contact time and running economy in men and a large correlation in women; furthermore, there was no correlation between flight time and running economy in men, but very large correlation in women. Previous studies completed have found that longer contact times and shorter flight times were associated with poorer economy (54), which our female results support, whereas others have found the opposite relationship (43), and others no relationship (13,55), which our male results support…”

Section: Discussionsupporting

confidence: 78%

“…The results of previous studies have identified a number of biomechanical variables that relate to running economy, including stride length that is freely chosen (11,13,14,38,42,54), low vertical oscillation of body center of mass (13,54), and low peak ground reaction forces (54,55). In this study, we considered the basic biomechanical characteristics most often reported in the literature.…”

Section: Discussionmentioning

confidence: 99%

“…The most striking and ubiquitous finding regarding stride length and running is that a freely chosen stride length is most economical (12,24,38,42,55). Experimentally induced deviations from this freely chosen stride length have invariably evoked increased oxygen cost (14,24).…”

Section: Discussionmentioning

confidence: 99%

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Lower-Body Determinants of Running Economy in Male and Female Distance Runners

Barnes

McGuigan

Kilding

2014

Journal of Strength and Conditioning Research

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Barnes, KR, Mcguigan, MR, and Kilding, AE. Lower-body determinants of running economy in male and female distance runners. J Strength Cond Res XX(X): 000-000, 2013-A variety of training approaches have been shown to improve running economy in well-trained athletes. However, there is a paucity of data exploring lower-body determinants that may affect running economy and account for differences that may exist between genders. Sixty-three male and female distance runners were assessed in the laboratory for a range of metabolic, biomechanical, and neuromuscular measures potentially related to running economy (ml$kg 21 $min 21 ) at a range of running speeds. At all common test velocities, women were more economical than men (effect size [ES] = 0.40); however, when compared in terms of relative intensity, men had better running economy (ES = 2.41). Leg stiffness (r = 20.80) and moment arm length (r = 0.90) were large-extremely largely correlated with running economy and each other (r = 20.82). Correlations between running economy and kinetic measures (peak force, peak power, and time to peak force) for both genders were unclear. The relationship in stride rate (r = 20.27 to 20.31) was in the opposite direction to that of stride length (r = 0.32-0.49), and the relationship in contact time (r = 20.21 to 20.54) was opposite of that of flight time (r = 0.06-0.74). Although both leg stiffness and moment arm length are highly related to running economy, it seems that no single lower-body measure can completely explain differences in running economy between individuals or genders. Running economy is therefore likely determined from the sum of influences from multiple lower-body attributes.

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Section: Discussionsupporting

confidence: 78%

Section: Discussionmentioning

confidence: 99%

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Lower-Body Determinants of Running Economy in Male and Female Distance Runners

Barnes

McGuigan

Kilding

2014

Journal of Strength and Conditioning Research

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“…1,2 Gait asymmetry can be the primary cause of an injury and it can be responsible for an injury. 1,3,4 Previously, gait asymmetries ranging from 3% for knee angle at touchdown to 54% for hip-angle velocity were reported in female runners, 5,6 suggesting that a single threshold level of normal to problematic gait asymmetry does not exist and that there are wide variations in gait mechanics and asymmetries. 1,18 Also, gait asymmetry may not be evident at the beginning of a race but may emerge with fatigue.…”

mentioning

confidence: 99%

Gait Asymmetry During 400- to 1000-m High-Intensity Track Running in Relation to Injury History

Gilgen-Ammann¹,

Taube²,

Wyss³

2017

International Journal of Sports Physiology and Performance

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Purpose: To quantify gait asymmetry in well-trained runners with and without previous injuries during interval training sessions incorporating different distances. Methods: Twelve well-trained runners participated in 8 high-intensity interval-training sessions on a synthetic track over a 4-wk period. The training consisted of 10 × 400, 8 × 600, 7 × 800, and 6 × 1000-m running. Using an inertial measurement unit, the ground-contact time (GCT) of every step was recorded. To determine gait asymmetry, the GCTs between the left and right foot were compared. Results: Overall, gait asymmetry was 3.3% ± 1.4%, and over the course of a training session, the gait asymmetry did not change (F 1,33 = 1.673, P = .205). The gait asymmetry of the athletes with a previous history of injury was significantly greater than that of the athletes without a previous injury. However, this injuryrelated enlarged asymmetry was detectable only at short (400 m), but not at longer, distances (600-1000 m). Conclusion: The gait asymmetry of well-trained athletes differed, depending on their history of injury and the running distance. To detect gait asymmetries, high-intensity runs over relatively short distances are recommended.Keywords: imbalance, field condition, fatigue, inertial measurement unit, high performance Athletes and coaches assume that gait asymmetry affects athletic performance, as 1 of the lower limbs is exposed to more stress than the other. 1,2 Gait asymmetry can be the primary cause of an injury and it can be responsible for an injury. 1,3,4 Previously, gait asymmetries ranging from 3% for knee angle at touchdown to 54% for hip-angle velocity were reported in female runners, 5,6 suggesting that a single threshold level of normal to problematic gait asymmetry does not exist and that there are wide variations in gait mechanics and asymmetries. 1,18 Also, gait asymmetry may not be evident at the beginning of a race but may emerge with fatigue. Regarding injury history, some earlier studies of athletes with and without a previous injury found no differences in gait asymmetry. 1,7,8 However, all the subjects in the aforementioned studies were either novice runners, 7 the assessments took place in a laboratory setting (on a treadmill) at submaximal speeds only, 1,7-9 and only 1 trial 10 or a few gait cycles 9 were analyzed. Consequently, it is unclear whether these results are congruent with real practice during training or competition on realistic floor conditions. For instance, a previous study provided evidence that gait asymmetry is reduced on a treadmill. 11 Hence, to better understand the occurrence of gait asymmetry, data from well-trained runners over entire runs over various distances, at high speeds, and at high intensities need to be obtained in field measurements.The purpose of this study therefore was threefold: to quantify gait asymmetry in healthy well-trained runners during high-intensity track runs of different lengths and speeds (400-1000 m), to evaluate the effect of an injury in the previous 24 months on gait as...

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“…Shoulder width seems to be a predictor for swimming performance, which is necessary to be in the first group getting out of the water. In contrast, pelvis width should be smaller, which was already shown for distance runners (Anderson, 1996;Williams, Cavanagh, & Ziff, 1987), and is therefore plausible in connection with the importance of the run part in elite Olympic distance triathlon. The ANN model used the five anthropometric parameters, body weight, BMI, lean body mass, and absolute as well as relative body fat, which were identified through dominance paired comparison as most important for overall race time in elite Olympic distance triathlon.…”

Section: Performance Predictionmentioning

confidence: 52%

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

Hoffmann

Moeller

Seidel

et al. 2017

International Journal of Computer Science in Sport

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Two different computational approaches were used to predict Olympic distance triathlon race time of German male elite triathletes. Anthropometric measurements and two treadmill running tests to collect physiological variables were repeatedly conducted on eleven male elite triathletes between 2008 and 2012. After race time normalization, exploratory factor analysis (EFA), as a mathematical preselection method, followed by multiple linear regression (MLR) and dominance paired comparison (DPC), as a preselection method considering professional expertise, followed by nonlinear artificial neural network (ANN) were conducted to predict overall race time. Both computational approaches yielded two prediction models. MLR provided R² = 0.41 in case of anthropometric variables (predictive: pelvis width and shoulder width) and R² = 0.67 in case of physiological variables (predictive: maximum respiratory rate, running pace at 3-mmol·L -1 blood lactate and maximum blood lactate). ANNs using the five most important variables after DPC yielded R² = 0.43 in case of anthropometric variables and R² = 0.86 in case of physiological variables. The advantage of ANNs over MLRs was the possibility to take non-linear relationships into account. Overall, race time of male elite triathletes could be well predicted without interfering with individual training programs and season calendars.

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Biomechanical Studies of Elite Female Distance Runners*

Cited by 87 publications

References 9 publications

Lower-Body Determinants of Running Economy in Male and Female Distance Runners

Lower-Body Determinants of Running Economy in Male and Female Distance Runners

Gait Asymmetry During 400- to 1000-m High-Intensity Track Running in Relation to Injury History

Predicting Elite Triathlon Performance: A Comparison of Multiple Regressions and Artificial Neural Networks

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