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Self-selected running speeds do not alter plantar pressure distribution data in barefoot running
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Journal o f Biomechanics 2006, Vol. 39 (Suppl 1)
References Messier, S.P., et al. (1988). Etiologic factors associated with selected running injuries. Medicine and Science in Sports and Exercise 20: 501-505. Willems T, et al. (2006). A prospective study of gait related risk factors for exerciserelated lower leg pain, Gait & Posture 23: 91-98. 4172 Fr, 15:00-15:15 (P52) Plantar pressure distribution patterns after induced fatigue G. Schlee, T. Milani, A. Hein. Technische Universit~t Chemnitz, Institut ffJr Sportwissenschaft, Chemnitz, Germany Several authors have studied the changes on the plantar pressure distribution for different data collection conditions and subjects (Hennig and Milani, 2000). However, the effect of the induced fatigue on the plantar pressure distribution patterns is still unclear (Sterzing and Hennig, 1999). The objective of this study was to analyze the effect of induced fatigue on the plantar pressure distribution patterns in running at comfortable speed. 19 subjects participated in the study (10 men, 9 women). The data collection procedures were as follows: a) the subjects ran over a 15 long walkway at a speed of 3.5m/s (±3%) with the Pedar system attached to the foot (Novell GMBH), with a total of 10 foot steps acquired for analysis. All the subjects worn the same shoes; b) The subjects ran for 45 min over a treadmill, with an individual speed, that was previously determined by physiological test; c) After the treadmill run the subject repeated the same test procedures as in (a). Only data from the right foot were collected, with a total of five steps selected for further statistical analysis. Peak Plantar Pressure (PPP) and Relative Load (RL) were calculated and analyzed. In order to facilitate the data analysis, the surface of the foot was divided in 10 areas, according to Cavanagh and Ulbrecht (1992). Descriptive as well as inferential statistics (ANOVA one-way) were used to compare the data before and after induced fatigue. Significance level was set at p ~<0.05. The results of the ANOVA showed no significant difference for the analyzed variables on the different areas of the foot. Although some regions of the foot (like the hindfoot regions) experienced a non significant increase in the PPP and RL values, no significant changes on the plantar pressure distribution patterns can be found after induced fatigue. Plantar pressure measurements seem not to be able to identify changes in running style after fatigue. References Cavanagh P.R., Ulbrecht J.S. (1992). Clinical plantar pressure measurement in diabetes: rationale and methodology. Foot 2(4): 123-135. Hennig E.M., Milani T.L. (2000). Pressure distribution measurements for evaluation of running shoe properties. Sportver. Sportschad 14(3): 90-97. Sterzing T., Hennig E. (1999). Measurements of plantar pressures, rearfoot motion and tibial shock during running 10km on a 400m track. 4. Symposium of Footwear Biomechanics, Proceedings, Canmore, CA. 4738 Fr, 15:15-15:30 (P52) Self-selected running speeds do not alter plantar pressure distribution data in barefoot running C. Maiwald, S. Grau, I. Krauss, M. Mauch, T. Horstmann. University Hospital, Dept. of Sportsmedicine, Tuebingen, Germany Introduction: The aim of the present study was to investigate the influence of self-selected running speeds on intrasubjective plantar pressure data variability during barefoot running under spatially limited laboratory conditions. A new measurement setup for evaluating plantar pressures in runners was used and data reproducibility was assessed. Methods: 32 healthy subjects (16 male, 16 female) were included in the study. Plantar pressure measurements were conducted using an EMED-X pressure platform (Novel GmbH, Munich, Germany). Subjects had to perform two series of trials at both pre-specified and self-selected running speeds. Reproducibility was assessed by calculating both single and average trial intraclass correlation coefficients [1-4]. Results: Women tended to prefer slower running speeds when allowed to choose their pace, and they produced less variable data in their self-selected speed series when compared to the pre-specified running speeds. There was a pattern visible across all subject groups and running speeds that featured reduced data reproducibility in the heel and midfoot areas compared to the forefoot. Conclusion: Most effects of self-selected running speeds on plantar pressure data reproducibility in barefoot running can be considered negligible. However, differences in reproducibility within the seven foot areas were present and could be caused by measurement setup issues like aiming to step on the pressure platform or barefoot running as such. References Duhamel A., Bourriez J.L., Devos P. et al. (2004). Statistical tools for clinical gait analysis. Gait Posture 20:204-212. Shrout P.E., Fleiss J.L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 86/2: 420-428. Krebs D.E. (1986). Declare your ICC type. Phys Ther. 66/9: 1431.
Oral Presentations Lahey M.A., Downey R.G., Saal F.E. (1983). Intraclass correlations: there's more than meets the eye. Psychol Bull. 93/3: 586-595.
6.9. Sport Analysis 7642 Mo, 08:15-08:45 (P6) Performance factors in ski jumping W. MLiller. Institute of Biophysics, Human Performance Research Center, KarI-Franzens and Medical University of Graz, Austria Ski jumping puts high demands on the athlete's ability to control posture and movement [1-3]. Performance is determined not only by motor abilities, but also by aerodynamic features and by low body weight [4]. Until 2004 many ski jumpers were underweight (e.g. 22% at OG 2002). Severe eating disorders were health problems of major concern. Meanwhile, fairness and health were improved by modified regulations which relate body weight (in terms of BMI) to ski length. Shorter skis (i.e. "smaller wings") compensate for the advantage of extremely low weight and thus it is not attractive to be underweight any more [4,5]. An improved measure for relative body weight (Mass Index M I = O.28m/s 2, s sitting height) has been suggested recently [5]. The analysis of top level ski jumping employs: Field studies, wind tunnel measurements, computational fluid dynamic (CFD), and computer simulation studies. During take-off the athlete accelerates perpendicular to the ramp due to the muscular forces exerted. Simultaneously, he produces an angular momentum for obtaining an advantageous angle of attack as soon as possible. During the flight the gravitational force Fg, the lift force Fi, and the drag force F d act upon the athlete: Fg = mg; Fi = lpv2ClA; Fd = IpV2CdA; with V 2 =e2 + j~2, .e=Vx ; J/= Vy. The flight path is described by the equations of motion: Cx = (-Fd COS q~- Fi sin q~)/m and Cy = (-Fd sin q~+ Fi cos q~)/m - g. The athlete can strongly influence aerodynamic forces - and thus performance - by changing his posture. This has to be considered for modelling approaches [1-4]. Aerodynamic characteristics associated with given postures can be measured in wind tunnels [2,4,6] or be determined by CFD. However, CFD results are often inaccurate because the Navier-Stokes equations which describe the dynamics of Newtonian fluids show major inherent mathematical difficulties. Complicated aerodynamics can already be found with simple objects [6] and aerodynamics of sports leads to basic problems of physics in all cases where turbulent flow occurs. Supported by IOC, FIS, Austrian Research Funds (P-15130, P-14388). References [1] W. MiJller, D. Platzer, B. Schm61zer. Nature 1995; 375: 455. [2] W. MiJller, D. Platzer, B. Schm61zer. J Biomechanics 1996; 29(8). [3] B. Schm61zer, W. M~iller. J Biomechanics 2005; 38: 1055-1065. [4] B. Schm61zer, W. M~iller. J Biomechanics 2002; 35: 1059-1069. [5] W. MiJller, et al. Int J Sports Med 2006; in press. [6] E. Reisenberger, W. Meile, G. Brenn, W. MiJller. Experiments in Fluids 2004; 37: 547-558. 4299 Mo, 08:45-09:00 (P6) Gait stability following c o n c u s s i o n L.-S. Chou, T.M. Parker, R. Catena, L.R. Osternig. Motion Analysis Laboratory, Department of Human Physiology, University of Oregon, Eugene, OR, USA Knowledge of functional impairment following a brain injury is critical to prevent re-injury. The purpose of this study was to examine the effect of concussion on gait stability while walking with divided attention. Fifteen subjects with concussions (CONC) and 15 uninjured controls (NORM) were observed while walking with undivided attention and while concurrently completing simple mental tasks. Testing began within 48 hours of injury and repeated at 5, 14, and 28 days post injury. NORMs were evaluated at similar intervals. Wholebody center of mass (COM) motion and center of pressure (COP) during gait were assessed using a motion analysis system and two force plates. Anterior-posterior and medial-lateral COM displacement (APROM, MLROM), peak forward velocity (ANTVEL), and maximum separation between COM and COP (APMAX) were used to examine dynamic stability. Three-way repeatedmeasures ANOVA with Tukey tests were completed to determine differences between group, task, and testing day. Several aspects of gait stability were compromised in the CONC group for up to four weeks after injury. Significant group by day interactions were found for MLROM (p<0.04) and APMAX (p < 0.00). Significant task by day interactions were found for APROM (p < 0.00) and ANTVEL (p < 0.00). Follow-up analyses revealed MLCOM was significantly greater for CONCs on days 2, 5, and 28. CONCs also had decreased APMAX at days 2, 14, and 28. APROM was significantly decreased for CONCs on days 2 and 5, and ANTVEL was significantly decreased on the dual-task for all 4 days. Findings of this study suggest that concussion may have long-term observable and measurable effects on the control of gait stability.
Journal o f Biomechanics 2006, Vol. 39 (Suppl 1)
References Messier, S.P., et al. (1988). Etiologic factors associated with selected running injuries. Medicine and Science in Sports and Exercise 20: 501-505. Willems T, et al. (2006). A prospective study of gait related risk factors for exerciserelated lower leg pain, Gait & Posture 23: 91-98. 4172 Fr, 15:00-15:15 (P52) Plantar pressure distribution patterns after induced fatigue G. Schlee, T. Milani, A. Hein. Technische Universit~t Chemnitz, Institut ffJr Sportwissenschaft, Chemnitz, Germany Several authors have studied the changes on the plantar pressure distribution for different data collection conditions and subjects (Hennig and Milani, 2000). However, the effect of the induced fatigue on the plantar pressure distribution patterns is still unclear (Sterzing and Hennig, 1999). The objective of this study was to analyze the effect of induced fatigue on the plantar pressure distribution patterns in running at comfortable speed. 19 subjects participated in the study (10 men, 9 women). The data collection procedures were as follows: a) the subjects ran over a 15 long walkway at a speed of 3.5m/s (±3%) with the Pedar system attached to the foot (Novell GMBH), with a total of 10 foot steps acquired for analysis. All the subjects worn the same shoes; b) The subjects ran for 45 min over a treadmill, with an individual speed, that was previously determined by physiological test; c) After the treadmill run the subject repeated the same test procedures as in (a). Only data from the right foot were collected, with a total of five steps selected for further statistical analysis. Peak Plantar Pressure (PPP) and Relative Load (RL) were calculated and analyzed. In order to facilitate the data analysis, the surface of the foot was divided in 10 areas, according to Cavanagh and Ulbrecht (1992). Descriptive as well as inferential statistics (ANOVA one-way) were used to compare the data before and after induced fatigue. Significance level was set at p ~<0.05. The results of the ANOVA showed no significant difference for the analyzed variables on the different areas of the foot. Although some regions of the foot (like the hindfoot regions) experienced a non significant increase in the PPP and RL values, no significant changes on the plantar pressure distribution patterns can be found after induced fatigue. Plantar pressure measurements seem not to be able to identify changes in running style after fatigue. References Cavanagh P.R., Ulbrecht J.S. (1992). Clinical plantar pressure measurement in diabetes: rationale and methodology. Foot 2(4): 123-135. Hennig E.M., Milani T.L. (2000). Pressure distribution measurements for evaluation of running shoe properties. Sportver. Sportschad 14(3): 90-97. Sterzing T., Hennig E. (1999). Measurements of plantar pressures, rearfoot motion and tibial shock during running 10km on a 400m track. 4. Symposium of Footwear Biomechanics, Proceedings, Canmore, CA. 4738 Fr, 15:15-15:30 (P52) Self-selected running speeds do not alter plantar pressure distribution data in barefoot running C. Maiwald, S. Grau, I. Krauss, M. Mauch, T. Horstmann. University Hospital, Dept. of Sportsmedicine, Tuebingen, Germany Introduction: The aim of the present study was to investigate the influence of self-selected running speeds on intrasubjective plantar pressure data variability during barefoot running under spatially limited laboratory conditions. A new measurement setup for evaluating plantar pressures in runners was used and data reproducibility was assessed. Methods: 32 healthy subjects (16 male, 16 female) were included in the study. Plantar pressure measurements were conducted using an EMED-X pressure platform (Novel GmbH, Munich, Germany). Subjects had to perform two series of trials at both pre-specified and self-selected running speeds. Reproducibility was assessed by calculating both single and average trial intraclass correlation coefficients [1-4]. Results: Women tended to prefer slower running speeds when allowed to choose their pace, and they produced less variable data in their self-selected speed series when compared to the pre-specified running speeds. There was a pattern visible across all subject groups and running speeds that featured reduced data reproducibility in the heel and midfoot areas compared to the forefoot. Conclusion: Most effects of self-selected running speeds on plantar pressure data reproducibility in barefoot running can be considered negligible. However, differences in reproducibility within the seven foot areas were present and could be caused by measurement setup issues like aiming to step on the pressure platform or barefoot running as such. References Duhamel A., Bourriez J.L., Devos P. et al. (2004). Statistical tools for clinical gait analysis. Gait Posture 20:204-212. Shrout P.E., Fleiss J.L. (1979). Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 86/2: 420-428. Krebs D.E. (1986). Declare your ICC type. Phys Ther. 66/9: 1431.
Oral Presentations Lahey M.A., Downey R.G., Saal F.E. (1983). Intraclass correlations: there's more than meets the eye. Psychol Bull. 93/3: 586-595.
6.9. Sport Analysis 7642 Mo, 08:15-08:45 (P6) Performance factors in ski jumping W. MLiller. Institute of Biophysics, Human Performance Research Center, KarI-Franzens and Medical University of Graz, Austria Ski jumping puts high demands on the athlete's ability to control posture and movement [1-3]. Performance is determined not only by motor abilities, but also by aerodynamic features and by low body weight [4]. Until 2004 many ski jumpers were underweight (e.g. 22% at OG 2002). Severe eating disorders were health problems of major concern. Meanwhile, fairness and health were improved by modified regulations which relate body weight (in terms of BMI) to ski length. Shorter skis (i.e. "smaller wings") compensate for the advantage of extremely low weight and thus it is not attractive to be underweight any more [4,5]. An improved measure for relative body weight (Mass Index M I = O.28m/s 2, s sitting height) has been suggested recently [5]. The analysis of top level ski jumping employs: Field studies, wind tunnel measurements, computational fluid dynamic (CFD), and computer simulation studies. During take-off the athlete accelerates perpendicular to the ramp due to the muscular forces exerted. Simultaneously, he produces an angular momentum for obtaining an advantageous angle of attack as soon as possible. During the flight the gravitational force Fg, the lift force Fi, and the drag force F d act upon the athlete: Fg = mg; Fi = lpv2ClA; Fd = IpV2CdA; with V 2 =e2 + j~2, .e=Vx ; J/= Vy. The flight path is described by the equations of motion: Cx = (-Fd COS q~- Fi sin q~)/m and Cy = (-Fd sin q~+ Fi cos q~)/m - g. The athlete can strongly influence aerodynamic forces - and thus performance - by changing his posture. This has to be considered for modelling approaches [1-4]. Aerodynamic characteristics associated with given postures can be measured in wind tunnels [2,4,6] or be determined by CFD. However, CFD results are often inaccurate because the Navier-Stokes equations which describe the dynamics of Newtonian fluids show major inherent mathematical difficulties. Complicated aerodynamics can already be found with simple objects [6] and aerodynamics of sports leads to basic problems of physics in all cases where turbulent flow occurs. Supported by IOC, FIS, Austrian Research Funds (P-15130, P-14388). References [1] W. MiJller, D. Platzer, B. Schm61zer. Nature 1995; 375: 455. [2] W. MiJller, D. Platzer, B. Schm61zer. J Biomechanics 1996; 29(8). [3] B. Schm61zer, W. M~iller. J Biomechanics 2005; 38: 1055-1065. [4] B. Schm61zer, W. M~iller. J Biomechanics 2002; 35: 1059-1069. [5] W. MiJller, et al. Int J Sports Med 2006; in press. [6] E. Reisenberger, W. Meile, G. Brenn, W. MiJller. Experiments in Fluids 2004; 37: 547-558. 4299 Mo, 08:45-09:00 (P6) Gait stability following c o n c u s s i o n L.-S. Chou, T.M. Parker, R. Catena, L.R. Osternig. Motion Analysis Laboratory, Department of Human Physiology, University of Oregon, Eugene, OR, USA Knowledge of functional impairment following a brain injury is critical to prevent re-injury. The purpose of this study was to examine the effect of concussion on gait stability while walking with divided attention. Fifteen subjects with concussions (CONC) and 15 uninjured controls (NORM) were observed while walking with undivided attention and while concurrently completing simple mental tasks. Testing began within 48 hours of injury and repeated at 5, 14, and 28 days post injury. NORMs were evaluated at similar intervals. Wholebody center of mass (COM) motion and center of pressure (COP) during gait were assessed using a motion analysis system and two force plates. Anterior-posterior and medial-lateral COM displacement (APROM, MLROM), peak forward velocity (ANTVEL), and maximum separation between COM and COP (APMAX) were used to examine dynamic stability. Three-way repeatedmeasures ANOVA with Tukey tests were completed to determine differences between group, task, and testing day. Several aspects of gait stability were compromised in the CONC group for up to four weeks after injury. Significant group by day interactions were found for MLROM (p<0.04) and APMAX (p < 0.00). Significant task by day interactions were found for APROM (p < 0.00) and ANTVEL (p < 0.00). Follow-up analyses revealed MLCOM was significantly greater for CONCs on days 2, 5, and 28. CONCs also had decreased APMAX at days 2, 14, and 28. APROM was significantly decreased for CONCs on days 2 and 5, and ANTVEL was significantly decreased on the dual-task for all 4 days. Findings of this study suggest that concussion may have long-term observable and measurable effects on the control of gait stability.