The Preferred Movement Path Paradigm: Influence of Running Shoes on Joint Movement.

PURPOSE: (A) To quantify differences in lower extremity joint kinematics for groups of runners subjected to different running footwear conditions, and (B) to quantify differences in lower extremity joint kinematics on an individual basis for runners subjected to different running footwear conditions. METHODS: Three-dimensional ankle and knee joint kinematics were collected for 35 heel-toe runners when wearing three different running shoes and when running barefoot. Absolute mean differences in ankle and knee joint kinematics were computed between running shoe conditions. The percentage of individual runners who displayed differences below a 2°, 3°, and 5° threshold were also calculated. RESULTS: The results indicate that the mean kinematics of the ankle and knee joints were similar between running shoe conditions. Aside from ankle dorsiflexion and knee flexion, the percentage of runners maintaining their movement path between running shoes (i.e., less than 3°) was in the order of magnitude of about 80% to 100%. Many runners showed ankle and knee joint kinematics that differed between a conventional running shoe and barefoot by more than 3°, especially for ankle dorsiflexion and knee flexion. CONCLUSIONS: Many runners stay in the same movement path (the preferred movement path) when running in various different footwear conditions. The percentage of runners maintaining their preferred movement path depends on the magnitude of the change introduced by the footwear condition.

Comparison of centre of gravity and centre of pressure patterns in the golf swing.

Analysing the centre of pressure (COP) and centre of gravity (COG) could reveal stabilising strategies used by golfers throughout the golf swing. This study identified and compared golfers' COP and COG patterns throughout the golf swing in medial-lateral (ML) and anterior-posterior (AP) directions using principal component analysis (PCA) and examined their relationship to clubhead velocity. Three-dimensional marker trajectories were collected using Vicon motion analysis and force plate data from two Kistler force plates for 22 low-handicap golfers during drives. Golfers' COG and COP were expressed as a percentage distance between their feet. PCA was performed on COG and COP in ML and AP directions. Relationships between principal component (PC) scores were examined using Pearson correlation and regression analysis used to examine the relationship with clubhead velocity. ML COP movements varied in magnitude (PC1), rate of change and timing (PC2 and PC3). The COP and COG PC1 scores were strongly correlated in both directions (ML: r = 0.90, P < .05; AP: r = 0.81, P < .05). Clubhead velocity, explained by three PCs (74%), related to timing and rate of change in COPML near downswing (PC2 and PC3) and timing of COGML late backswing (PC2). The relationship between COPML and COGML PC1 scores identified extremes of COP and COG patterns in golfers and could indicate a golfer's dynamic balance. Golfers with earlier movement of COP to the front foot (PC2) and rate of change (PC3) patterns in ML COP, prior to the downswing, may be more likely to generate higher clubhead velocity.

Comparison of Two- and Three-Dimensional Methods for Analysis of Trunk Kinematic Variables in the Golf Swing.

Two-dimensional methods have been used to compute trunk kinematic variables (flexion/extension, lateral bend, axial rotation) and X-factor (difference in axial rotation between trunk and pelvis) during the golf swing. Recent X-factor studies advocated three-dimensional (3D) analysis due to the errors associated with two-dimensional (2D) methods, but this has not been investigated for all trunk kinematic variables. The purpose of this study was to compare trunk kinematic variables and X-factor calculated by 2D and 3D methods to examine how different approaches influenced their profiles during the swing. Trunk kinematic variables and X-factor were calculated for golfers from vectors projected onto the global laboratory planes and from 3D segment angles. Trunk kinematic variable profiles were similar in shape; however, there were statistically significant differences in trunk flexion (-6.5 ± 3.6°) at top of backswing and trunk right-side lateral bend (8.7 ± 2.9°) at impact. Differences between 2D and 3D X-factor (approximately 16°) could largely be explained by projection errors introduced to the 2D analysis through flexion and lateral bend of the trunk and pelvis segments. The results support the need to use a 3D method for kinematic data calculation to accurately analyze the golf swing.