The effectiveness of scaling procedures for comparing ground reaction forces

Comparison of ground reaction forces as running speed increases between male and female runners

Introduction: The biomechanics associated with human running are affected by gender and speed. Knowledge regarding ground reaction force (GRF) at various running speeds is pivotal for the prevention of injuries related to running. This study aimed to investigate the gait pattern differences between males and females while running at different speeds, and to verify the relationship between GRFs and running speed among both males and females. Methods: GRF data were collected from forty-eight participants (thirty male runners and eighteen female runners) while running on an overground runway at seven discrete speeds: 10, 11, 12, 13, 14, 15 and 16 km/h. Results: The ANOVA results showed that running speed had a significant effect (p < 0.05) on GRFs, propulsive and vertical forces increased with increasing speed. An independent t-test also showed significant differences (p < 0.05) in vertical and anterior-posterior GRFs at all running speeds, specifically, female runners demonstrated higher propulsive and vertical forces than males during the late stance phase of running. Pearson correlation and stepwise multiple linear regression showed significant correlations between running speed and the GRF variables. Discussion: These findings suggest that female runners require more effort to keep the same speed as male runners. This study may provide valuable insights into the underlying biomechanical factors of the movement patterns at GRFs during running.

A comparison of five methods to normalize joint moments during running

BACKGROUND

Net joint moments (NJM) are typically normalized for a (combination of) physical body characteristics such as mass, height, and limb length using ratio scaling to account for differences in body characteristics between individuals. Four assumptions must be met when normalizing NJM data this way to ensure valid conclusions. First, the relationship between the non-normalized NJM and participant characteristic should be linear. Second, the regression line between NJM and the characteristic(s) used should pass through the origin. Third, scaling should not significantly perturb the statistical distribution of the data. Fourth, normalizing a NJM should eliminate its correlation with the characteristic(s) normalized for.

RESEARCH QUESTION

This study assessed these assumptions using data collected among 59 individuals running at 10 km h-1.

METHODS

Standard inverse dynamics analyses were conducted, and ratios were computed between the sagittal-plane hip, knee and ankle NJM's and the participant's mass, height, leg length, mass × height, and mass × leg length.

RESULTS

The most important finding of this study was that none of the scaling variables fulfilled all assumptions across all joints. However, scaling by mass, mass*height and mass*leg length satisfied the assumptions for the knee joint moment and log-transformed hip joint moment, suggesting these methods generally performed best.

SIGNIFICANCE

Our findings suggests that scaling by mass, mass*height and mass*leg length may be considered to normalize joint moments during running. Nevertheless, we urge researchers to check the statistical assumptions to ensure valid conclusions. We provide supplementary code to check the statistical assumptions, and discuss consequences of inappropriate scaling.

Allometric Scaling Hip Joint Moments Optimally Reduces Anthropometric Differences in Males and Females

Running biomechanics are scaled to reduce the effects of anthropometric differences between participants. Ratio scaling has limitations, and allometric scaling has not been applied to hip joint moments. The aim was to compare raw, ratio and allometrically scaled hip joint moments. Sagittal and frontal plane moments of 84 males and 47 females were calculated while running at 4.0 m/s. Raw data were ratio scaled by body mass (BM), height (HT), leg length (LL) and BM multiplied by HT (BM*HT) and LL (BM*LL). Log-linear (for BM, HT and LL individually) or log-multilinear regression (BM*HT and BM*LL) exponents were calculated. Correlations and r² values assessed the effectiveness of each scaling method. Eighty-five per cent of raw moments were positively correlated to the anthropometrics with r² values of 10-19%. In ratio scaling, 26-43% were significantly correlated to the moments and a majority were negative, indicating overcorrections. The most effective scaling procedure was the allometric BM*HT, as the mean shared variance between the hip moment and anthropometrics was 0.1-0.2% across all sexes and moments and none had significant correlations. Allometric scaling of hip joint moments during running are advised if the goal is to remove the underlying effects of anthropometrics across male and female participants.

Evaluation of spinal force normalization techniques

Division normalization is commonly used in biomechanics studies to remove the effect of anthropometric differences (e.g., body weight) on kinetic variables, facilitating comparison across a population. In spine biomechanics, spinal forces are commonly divided by the body weight or the intervertebral load during a standing posture. However, it has been suggested that offset and power curve normalization are more appropriate than division normalization for normalizing kinetic variables such as ground reaction forces during walking and running. The present study investigated, for the first time, the effectiveness of four techniques for normalizing spinal forces to remove the effect of body weight. Spinal forces at all lumbar levels were estimated using a detailed OpenSim musculoskeletal model of the spine for 11 scaled models (50-100 kg) and during 13 trunk flexion tasks. Pearson correlations of raw and normalized forces against body weight were used to assess the effectiveness of each normalization technique. Body weight and standing division normalization could only successfully normalize L₄L₅ spinal forces in three tasks, and L₅S₁ loads in five and three tasks, respectively; however, offset and power curve normalization techniques were successful across all lumbar spine levels and all tasks. Offset normalization successfully removed the effect of body weight and maintained the influence of flexion angle on spinal forces. Thus, we recommend offset normalization to account for anthropometric differences in studies of spinal forces.

Including jump height when normalizing single hop impact kinetics can change the directionality of findings

BACKGROUND

Assessing landing kinetics during hop testing could improve return to sport decisions following anterior cruciate ligament reconstruction (ACL) reconstruction. However, different methods for normalizing kinetic outcomes could influence the interpretation of landing kinetics and therefore the clinical recommendations.

METHODS

Twenty-one females who had returned to sport following primary unilateral ACL reconstructed completed two single hops for maximum distance on each limb. Hop distance, hop height, peak impact force, and impulse were computed for each hop, and peak impact force and impulse magnitudes were assessed when 1) non-normalized 2) normalized by bodyweight, and 3) normalized by peak potential energy during the hop.

FINDINGS

Along with hop distance and height, peak impact force and impulse were found to be lower on the surgical limb relative to the non-surgical limb for both non-normalized data and when normalized to bodyweight only (p < 0.001, d > 0.95). However, peak impact force and impulse were found to be higher on the surgical limb relative to the non-surgical limb when normalizing outcomes to peak potential energy (p < 0.001, d > 1.03).

INTERPRETATION

Different normalization methods result in different interpretations of single hop kinetics. ACL reconstruction patients have shorter hop distances, lower hop heights, lower force magnitudes, and worse energy absorption when hopping on their surgical limb, relative to their non-surgical limb. We believe that normalizing landing kinetics using bodyweight and using peak potential energy provide different information, and as such, we suggest that future research use both methods based on the research question.