No abstract

A common approach in biomechanical analysis of running technique is to average data from several gait cycles to compute a 'representative mean.' However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to 'global' means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5m·s-1, 3.5m·s-1 and 4.5m·s-1). 'Ground truth' values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5-30) consecutive gait cycles. We also compared representative means calculated from n (range = 5-15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the 'ground truth' mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of 'error' were observed between the 2.5m·s-1 and 3.5m·s-1 speeds at comparable gait cycle numbers - where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e. 5-10). At the 4.5m·s-1 speed, maximum errors typically exceeded 2-4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e. 25-30) was required to achieve low errors (i.e. 1-2 degrees) at the 4.5m·s-1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e. < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5m·s-1 and 3.5m·s-1 speeds, and consistent but larger (i.e. up to 2-4 degrees) at the 4.5m·s-1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential 'error.' Analysing a small sample (i.e. 5-10 cycles) will typically result in minimal 'error' (i.e. << 2 degrees), particularly at lower speeds (i.e. 2.5m·s-1 and 3.5m·s-1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential 'error' when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential 'error' introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.

Background Variation in tibia geometry is a risk factor for tibial stress fractures. Geometric variability in bones is often quantified using statistical shape modelling. Statistical shape models (SSM) offer a method to assess three-dimensional variation of structures and identify the source of variation. Although SSM have been used widely to assess long bones, there is limited open-source datasets of this kind. Overall, the creation of SSM can be an expensive process, that requires advanced skills. A publicly available tibia shape model would be beneficial as it enables researchers to improve skills. Further, it could benefit health, sport and medicine with the potential to assess geometries suitable for medical equipment, and aid in clinical diagnosis. This study aimed to: (i) quantify tibial geometry using a SSM; and (ii) provide the SSM and associated code as an open-source dataset. Methods Lower limb computed tomography (CT) scans from the right tibia-fibula of 30 cadavers (male n = 20, female n = 10) were obtained from the New Mexico Decedent Image Database. Tibias were segmented and reconstructed into both cortical and trabecular sections. Fibulas were segmented as a singular surface. The segmented bones were used to develop three SSM of the: (i) tibia; (ii) tibia-fibula; and (iii) cortical-trabecular. Principal component analysis was applied to obtain the three SSM, with the principal components that explained 95% of geometric variation retained. Results Overall size was the main source of variation in all three models accounting for 90.31%, 84.24% and 85.06%. Other sources of geometric variation in the tibia surface models included overall and midshaft thickness; prominence and size of the condyle plateau, tibial tuberosity, and anterior crest; and axial torsion of the tibial shaft. Further variations in the tibia-fibula model included midshaft thickness of the fibula; fibula head position relative to the tibia; tibia and fibula anterior-posterior curvature; fibula posterior curvature; tibia plateau rotation; and interosseous width. The main sources of variation in the cortical-trabecular model other than general size included variation in the medulla cavity diameter; cortical thickness; anterior-posterior shaft curvature; and the volume of trabecular bone in the proximal and distal ends of the bone. Conclusion Variations that could increase the risk of tibial stress injury were observed, these included general tibial thickness, midshaft thickness, tibial length and medulla cavity diameter (indicative of cortical thickness). Further research is needed to better understand the effect of these tibial-fibula shape characteristics on tibial stress and injury risk. This SSM, the associated code, and three use examples for the SSM have been provided in an open-source dataset. The developed tibial surface models and statistical shape model will be made available for use at: https://simtk.org/projects/ssm_tibia.

A common approach in the biomechanical analysis of running technique is to average data from several gait cycles to compute a ‘representative mean.’ However, the impact of the quantity and selection of gait cycles on biomechanical measures is not well understood. We examined the effects of gait cycle selection on kinematic data by: (i) comparing representative means calculated from varying numbers of gait cycles to ‘global’ means from the entire capture period; and (ii) comparing representative means from varying numbers of gait cycles sampled from different parts of the capture period. We used a public dataset (n = 28) of lower limb kinematics captured during a 30-second period of treadmill running at three speeds (2.5 m s−1, 3.5 m s−1 and 4.5 m s−1). ‘Ground truth’ values were determined by averaging data across all collected strides and compared to representative means calculated from random samples (1,000 samples) of n (range = 5–30) consecutive gait cycles. We also compared representative means calculated from n (range = 5–15) consecutive gait cycles randomly sampled (1,000 samples) from within the same data capture period. The mean, variance and range of the absolute error of the representative mean compared to the ‘ground truth’ mean progressively reduced across all speeds as the number of gait cycles used increased. Similar magnitudes of ‘error’ were observed between the 2.5 m s−1 and 3.5 m s−1 speeds at comparable gait cycle numbers —where the maximum errors were < 1.5 degrees even with a small number of gait cycles (i.e., 5–10). At the 4.5 m s−1 speed, maximum errors typically exceeded 2–4 degrees when a lower number of gait cycles were used. Subsequently, a higher number of gait cycles (i.e., 25–30) was required to achieve low errors (i.e., 1–2 degrees) at the 4.5 m s−1 speed. The mean, variance and range of absolute error of representative means calculated from different parts of the capture period was consistent irrespective of the number of gait cycles used. The error between representative means was low (i.e., < 1.5 degrees) and consistent across the different number of gait cycles at the 2.5 m s−1 and 3.5 m s−1 speeds, and consistent but larger (i.e., up to 2–4 degrees) at the 4.5 m s−1 speed. Our findings suggest that selecting as many gait cycles as possible from a treadmill running bout will minimise potential ‘error.’ Analysing a small sample (i.e., 5–10 cycles) will typically result in minimal ‘error’ (i.e., < 2 degrees), particularly at lower speeds (i.e., 2.5 m s−1 and 3.5 m s−1). Researchers and clinicians should consider the balance between practicalities of collecting and analysing a smaller number of gait cycles against the potential ‘error’ when determining their methodological approach. Irrespective of the number of gait cycles used, we recommend that the potential ‘error’ introduced by the choice of gait cycle number be considered when interpreting the magnitude of effects in treadmill-based running studies.