Trial- vs. cycle-level detrending in the analysis of cyclical biomechanical data.

Biomechanical time series may contain low-frequency trends due to factors like electromechanical drift, attentional drift and fatigue. Existing detrending procedures are predominantly conducted at the trial level, removing trends that exist over finite, adjacent time windows, but this fails to consider what we term 'cycle-level trends': trends that occur in cyclical movements like gait and that vary across the movement cycle, for example: positive and negative drifts in early and late gait phases, respectively. The purposes of this study were to describe cycle-level detrending and to investigate the frequencies with which cycle-level trends (i) exist, and (ii) statistically affect results. Anterioposterior ground reaction forces (GRF) from the 41-subject, 8-speed, open treadmill walking dataset of Fukuchi (2018) were analyzed. Of a total of 552 analyzed trials, significant cycle-level trends were found approximately three times more frequently (21.1%) than significant trial-level trends (7.4%). In statistical comparisons of adjacent walking speeds (i.e., speed 1 vs. 2, 2 vs. 3, etc.) just 3.3% of trials exhibited cycle-level trends that changed the null hypothesis rejection decision. However 17.6% of trials exhibited cycle-level trends that qualitatively changed the stance phase regions identified as significant. Although these results are preliminary and derived from just one dataset, results suggest that cycle-level trends can contribute to analysis bias, and therefore that cycle-level trends should be considered and/or removed where possible. Software implementing the proposed cycle-level detrending is available at https://github.com/0todd0000/detrend1d.

Using Monte Carlo Simulation to Propagate Processing Parameter Uncertainty to the Statistical Analyses of Biomechanical Trajectories.

Biomechanical trajectories are often routed through a chain of processing steps prior to statistical analysis. As changes in processing parameter values can affect these trajectories, care is required when choosing data processing specifics. The purpose of this Research Note was to demonstrate a simple way to propagate data processing parameter uncertainty to statistical inferences regarding biomechanical trajectories. As an example application, the correlation between foot contact duration and vertical ground reaction force during constant-speed treadmill walking was considered. Uncertainty was modeled using plausible-range uniform distributions in three data processing steps, and Monte Carlo simulation was used to construct probabilistic representations of both individual vertical ground reaction force measurements and the ultimate statistical results. Whereas an initial, plausible set of parameter values yielded a significant correlation between contact duration and late-stance vertical ground reaction force, Monte Carlo simulations revealed strong sensitivity, with "significance" being reached in fewer than 40% of simulations, with relatively little net effect of parameter uncertainty magnitude. These results indicate that propagating processing parameter uncertainty to statistical results promotes a cautious, nuanced, and robust view of observed effects. By extension, Monte Carlo simulations may yield greater interpretive consistency across studies involving data processing uncertainties.

Modifications to head-trunk coordination dynamics during running and sidestepping.

The purpose of this study was to determine how the intrinsic head-trunk coordination dynamics that exist during forward running are modified during a dynamic sidestepping task. Fourteen athletes performed both forward running and sidestepping tasks. Head-trunk coordination and range of motion were assessed during the flight and stance phases in the transverse and sagittal planes. The sidestepping task resulted in greater in-phase head-trunk coordination during stance in the transverse plane (p < .001, ES = -1.71) and in reduced anti-phase coordination between head and trunk in the sagittal plane (p < .001, ES = 1.52). Statistical non-parametric mapping revealed that during sidestepping the sagittal plane coupling angle shifted away from anti-phase earlier during midstance. The sidestepping task resulted in greater transverse and sagittal plane head and trunk range of motion and greater vertical trunk centre of mass displacement. Sidestepping modified the intrinsic coordination dynamics that are present during forward running, with greater transverse plane head contributions and reductions in compensatory sagittal plane head motion, which may occur during the transition from weight acceptance to propulsion during the stance phase. These changes in the intrinsic coordination dynamics of the upper body during sidestepping tasks may impact visual perception and readiness compared to forward running during complex sports tasks.

Application of statistical parametric mapping for comparison of scapular kinematics and EMG.

Scapular kinematics and EMG are frequently measured as a functional assessment of the shoulder. Previous studies have compared interval averaging for these time series data, but it is not clear whether this method exactly captures the dynamics of scapular kinematics and muscle activity. Statistical parametric mapping (SPM) can be used to compare time series data. The purpose of this study was to investigate whether there is a difference between the results of SPM and interval averaging (every 10° or 30°) in comparing scapular kinematics, EMG, and EMG ratio. Scapular kinematics and EMG of the upper trapezius (UT), middle trapezius (MT), and lower trapezius (LT) and serratus anterior (SA) were measured in 21 healthy males. Tasks included arm raising and lowering with or without load, and we compared scapular kinematics, EMG, and EMG ratio in the loaded and unloaded conditions. Results suggest disagreement between SPM and interval averaging. Characteristic results are that for scapular kinematics during lowering SPM showed a decrease in upward rotation in only the regions 113-65° and 42-30°, while interval averaging showed a decrease in all range. For EMG during lowering, SPM results were not significantly different in SA over 50-48 and 45-30°, while interval averaging suggested increased activity in all ranges. For EMG ratio during raising, SPM showed no significant difference, while interval averaging showed a decrease in UT/LT during the latter period. These results indicate that SPM provides better resolution regarding effect regions than interval averaging, and suggest that SPM may improve shoulder function assessment accuracy.

Simultaneously assessing amplitude and temporal effects in biomechanical trajectories using nonlinear registration and statistical nonparametric mapping.

Biomechanical trajectories generally embody amplitude and temporal effects, but these effects are often analyzed separately. Here we demonstrate how amplitude-phase separation techniques from the statistics literature can be used to simultaneously analyze both. The approach hinges on nonlinear registration, which temporally warps trajectories to minimize timing effects, and the resulting optimal time warps can be combined with the resulting amplitudes in a simultaneous test. We first analyzed two simulated datasets with controlled amplitude and temporal effects to demonstrate how amplitude-timing separation can avoid incorrect conclusions from common amplitude-only hypothesis testing. We then analyzed two experimental datasets, demonstrating how amplitude-phase separation can yield unique perspectives on the relative contributions of amplitude and timing effects embodied in biomechanical trajectories. Last, we show that the proposed approach can be sensitive to procedural and parameter specifics, so we recommend that these sensitivities should be explored and reported.

Intra-subject sample size effects in plantar pressure analyses.

BACKGROUND: Recent work using large datasets (>500 records per subject) has demonstrated seemingly high levels of step-to-step variation in peak plantar pressure within human individuals during walking. One intuitive consequence of this variation is that smaller sample sizes (e.g., 10 steps per subject) may be quantitatively and qualitatively inaccurate and fail to capture the variance in plantar pressure of individuals seen in larger data sets. However, this remains quantitatively unexplored reflecting a lack of detailed investigation of intra-subject sample size effects in plantar pressure analysis. METHODS: Here we explore the sensitivity of various plantar pressure metrics to intra-subject sample size (number of steps per subject) using a random subsampling analysis. We randomly and incrementally subsample large data sets (>500 steps per subject) to compare variability in three metric types at sample sizes of 5-400 records: (1) overall whole-record mean and maximum pressure; (2) single-pixel values from five locations across the foot; and (3) the sum of pixel-level variability (measured by mean square error, MSE) from the whole plantar surface. RESULTS: Our results indicate that the central tendency of whole-record mean and maximum pressure within and across subjects show only minor sensitivity to sample size >200 steps. However, <200 steps, and particularly <50 steps, the range of overall mean and maximum pressure values yielded by our subsampling analysis increased considerably resulting in potential qualitative error in analyses of pressure changes with speed within-subjects and in comparisons of relative pressure magnitudes across subjects at a given speed. Our analysis revealed considerable variability in the absolute and relative response of the single pixel centroids of five regions to random subsampling. As the number of steps analysed decreased, the absolute value ranges were highest in the areas of highest pressure (medial forefoot and hallux), while the largest relative changes were seen in areas of lower pressure (the midfoot). Our pixel-level measure of variability by MSE across the whole-foot was highly sensitive to our manipulation of sample size, such that the range in MSE was exponentially larger in smaller subsamples. Random subsampling showed that the range in pixel-level MSE only came within 5% of the overall sample size in subsamples of >400 steps. The range in pixel-level MSE at low subsamples (<50) was 25-75% higher than that of the full datasets of >500 pressure records per subject. Overall, therefore, we demonstrate a high probability that the very small sample sizes (n < 20 records), which are routinely used in human and animal studies, capture a relatively low proportion of variance evident in larger plantar pressure data set, and thus may not accurately reflect the true population mean.

Landmark-free, parametric hypothesis tests regarding two-dimensional contour shapes using coherent point drift registration and statistical parametric mapping.

This paper proposes a computational framework for automated, landmark-free hypothesis testing of 2D contour shapes (i.e., shape outlines), and implements one realization of that framework. The proposed framework consists of point set registration, point correspondence determination, and parametric full-shape hypothesis testing. The results are calculated quickly (<2 s), yield morphologically rich detail in an easy-to-understand visualization, and are complimented by parametrically (or nonparametrically) calculated probability values. These probability values represent the likelihood that, in the absence of a true shape effect, smooth, random Gaussian shape changes would yield an effect as large as the observed one. This proposed framework nevertheless possesses a number of limitations, including sensitivity to algorithm parameters. As a number of algorithms and algorithm parameters could be substituted at each stage in the proposed data processing chain, sensitivity analysis would be necessary for robust statistical conclusions. In this paper, the proposed technique is applied to nine public datasets using a two-sample design, and an ANCOVA design is then applied to a synthetic dataset to demonstrate how the proposed method generalizes to the family of classical hypothesis tests. Extension to the analysis of 3D shapes is discussed.

Sample size estimation for biomechanical waveforms: Current practice, recommendations and a comparison to discrete power analysis.

Testing a prediction is fundamental to scientific experiments. Where biomechanical experiments involve analysis of 1-Dimensional (waveform) data, sample size estimation should consider both 1D variance and hypothesised 1D effects. This study exemplifies 1D sample size estimation using typical biomechanical signals and contrasts this with 0D (discrete) power analysis. For context, biomechanics papers from 2018 and 2019 were reviewed to characterise current practice. Sample size estimation occurred in approximately 4% of 653 papers and reporting practice was mixed. To estimate sample sizes, common biomechanical signals were sourced from the literature and 1D effects were generated artificially using the open-source power1d software. Smooth Gaussian noise was added to the modelled 1D effect to numerically estimate the sample size required. Sample sizes estimated using 1D power procedures varied according to the characteristics of the dataset, requiring only small-to-moderate sample sizes of approximately 5-40 to achieve target powers of 0.8 for reported 1D effects, but were always larger than 0D sample sizes (from N + 1 to >N + 20). The importance of a priori sample size estimation is highlighted and recommendations are provided to improve the consistency of reporting. This study should enable researchers to construct 1D biomechanical effects to address adequately powered, hypothesis-driven, predictive research questions.

Timing of gait events affects whole trajectory analyses: A statistical parametric mapping sensitivity analysis of lower limb biomechanics.

Time continuous analyses, such as statistical parametric mapping (SPM), have been increasingly used in biomechanics research to determine differences between populations, interventions and methodologies. Currently, it is not known how sensitive time-continuous analyses are to timing variability that occur in gait data. We evaluated this sensitivity by examining the frequency of significant SPM outcomes between two walking speeds when lower limb kinematics and kinetics were segmented and aligned based on 40 repeatable gait events. These events, defined in the supplementary material, include a commonly used event like foot contact and other events that have been previously demonstrated to be repeatable. Repeatable gait events were determined from joint and segment kinematics, joint kinetics as well as ground reaction forces. We examined the frequency of statistical outcomes for a single subject with different numbers of strides analyzed and for a cohort of 10 subjects. Our findings demonstrate that gait interventions, such as changes in walking speed, can induce temporal shifts that affect time-continuous outcomes for both cohort- and subject-level analyses. As both timing and magnitude are important in gait data, researchers are encouraged to perform additional analyses to understand how both of these variables affect time-continuous analysis outcomes. Finally, we demonstrate that multiple SPM tests can be performed to determine if statistical outcomes are due to temporal shifting or differences in magnitude. It is important to understand how both timing and magnitude of biomechanical data influences time continuous analyses as these analyses inform injury prevention, device development and basic understanding of biomechanics.

Muscle size-scaled shear elastic modulus: A muscle force index independent of maximal voluntary contraction, assessed during elbow extension.

Shear elastic modulus (G) can differ among individuals due to muscle size and other factors, even for constant muscle force. Inter-individual comparisons of G usually require normalization by maximal voluntary contraction (MVC), but MVC procedures may not be appropriate for certain clinical populations including those presenting with pain or other compromised functionality. This study aimed to test whether muscle size-scaled G, which does not require MVC testing, would yield stronger correlation with absolute torque than unscaled G. Twelve-healthy males performed isometric elbow extension across a range of torque magnitudes (from 5 Nm until 60% MVC). G of the triceps brachii was measured using shear wave elastography during each trial. Cross-sectional area (CSA) and muscle thickness (MT) of the triceps brachii were measured at rest. Scaled G was calculated as a product of G and CSA or MT ("G-CSA" and "G-MT", respectively). Within-individual linear regressions were conducted between absolute torque and the three force indicator variables. The regression slopes' coefficient of variation (CV) was calculated for each indicator across individuals. Between-individual correlation coefficients were calculated, after pooling all data across individuals into a single regression analysis for each indicator. Linear regression found that inter-individual slope variation increased in the following order: G-CSA, G-MT, and unscaled G (CV = 0.15, 0.18, and 0.29, respectively). Pooled-individual correlation coefficients were significantly higher in G-CSA and G-MT than in unscaled G (r = 0.948, 0.924, and r = 0.783, respectively). These results suggest that muscle size-scaled G may be more appropriate than unscaled G when comparing shear moduli across individuals.

Integrated jerk as an indicator of affinity for artificial agent kinematics: laptop and virtual reality experiments involving index finger motion during two-digit grasping.

Uncanny valley research has shown that human likeness is an important consideration when designing artificial agents. It has separately been shown that artificial agents exhibiting human-like kinematics can elicit positive perceptual responses. However the kinematic characteristics underlying that perception have not been elucidated. This paper proposes kinematic jerk amplitude as a candidate metric for kinematic human likeness, and aims to determine whether a perceptual optimum exists over a range of jerk values. We created minimum-jerk two-digit grasp kinematics in a prosthetic hand model, then added different amplitudes of temporally smooth noise to yield a variety of animations involving different total jerk levels, ranging from maximally smooth to highly jerky. Subjects indicated their perceptual affinity for these animations by simultaneously viewing two different animations side-by-side, first using a laptop, then separately within a virtual reality (VR) environment. Results suggest that (a) subjects generally preferred smoother kinematics, (b) subjects exhibited a small preference for rougher-than minimum jerk kinematics in the laptop experiment, and that (c) the preference for rougher-than minimum-jerk kinematics was amplified in the VR experiment. These results suggest that non-maximally smooth kinematics may be perceptually optimal in robots and other artificial agents.

Using directional statistics to test hypotheses regarding rigid body attitude: Comparison to univariate and multivariate Cardan angle tests.

Rigid body attitude and single-joint kinematics are typically expressed using three Cardan angles which represent rotations in anatomical planes. It was recently shown in the Biomechanics literature that Cardan angles inaccurately estimate true mean attitude due to an important mathematical inadequacy: attitude under-representation; at least four quantities are needed to unambiguously specify attitude. Directional statistics, which is the multivariate generalization of (univariate) circular statistics, solves this problem using four-dimensional unit vectors and the mathematics of hyperspherical geometry. The purpose of this study was to compare the results of directional analysis to the results of uni- and multi-variate Cardan analysis for representative joint kinematic data during gait. We analyzed hip, knee and pelvis data from three open datasets and report exemplary results for knee kinematics in v-cut vs. side shuffle tasks. We also conducted Monte Carlo simulations, using synthetic data with precisely controlled true angular effects, to systematically compare directional and Cardan analyses. Results show that directional analysis yielded considerably smaller p values (p<0.03) than Cardan analysis (p>0.055) for the exemplary dataset. Simulation results confirmed that directional analysis is considerably more powerful (i.e., much more able to detect true angular effects) than both uni- and multi-variate Cardan analysis. These results suggest that directional statistics should be used to analyse attitude, including 3D joint kinematics, to avoid false negatives.

A comparison of random-field-theory and false-discovery-rate inference results in the analysis of registered one-dimensional biomechanical datasets.

BACKGROUND: The inflation of falsely rejected hypotheses associated with multiple hypothesis testing is seen as a threat to the knowledge base in the scientific literature. One of the most recently developed statistical constructs to deal with this problem is the false discovery rate (FDR), which aims to control the proportion of the falsely rejected null hypotheses among those that are rejected. FDR has been applied to a variety of problems, especially for the analysis of 3-D brain images in the field of Neuroimaging, where the predominant form of statistical inference involves the more conventional control of false positives, through Gaussian random field theory (RFT). In this study we considered FDR and RFT as alternative methods for handling multiple testing in the analysis of 1-D continuum data. The field of biomechanics has recently adopted RFT, but to our knowledge FDR has not previously been used to analyze 1-D biomechanical data, nor has there been a consideration of how FDR vs. RFT can affect biomechanical interpretations. METHODS: We reanalyzed a variety of publicly available experimental datasets to understand the characteristics which contribute to the convergence and divergence of RFT and FDR results. We also ran a variety of numerical simulations involving smooth, random Gaussian 1-D data, with and without true signal, to provide complementary explanations for the experimental results. RESULTS: Our results suggest that RFT and FDR thresholds (the critical test statistic value used to judge statistical significance) were qualitatively identical for many experimental datasets, but were highly dissimilar for others, involving non-trivial changes in data interpretation. Simulation results clarified that RFT and FDR thresholds converge as the true signal weakens and diverge when the signal is broad in terms of the proportion of the continuum size it occupies. Results also showed that, while sample size affected the relation between RFT and FDR results for small sample sizes (<15), this relation was stable for larger sample sizes, wherein only the nature of the true signal was important. DISCUSSION: RFT and FDR thresholds are both computationally efficient because both are parametric, but only FDR has the ability to adapt to the signal features of particular datasets, wherein the threshold lowers with signal strength for a gain in sensitivity. Additional advantages and limitations of these two techniques as discussed further. This article is accompanied by freely available software for implementing FDR analyses involving 1-D data and scripts to replicate our results.

On the validity of statistical parametric mapping for nonuniformly and heterogeneously smooth one-dimensional biomechanical data.

Nonuniform (non-constant) temporal smoothness can arise in biomechanical processes like impacts, and heterogeneous smoothness (unequal smoothness across observations) can arise in mechanically diverse comparisons such as padded vs. unpadded impacts, where padded dynamics are generally smoother than unpadded dynamics. It has been reported that statistical parametric mapping's (SPM's) probability values can be invalid for such cases. The purpose of this paper was to clarify the scope of validity for SPM analysis of nonuniformly and heterogeneously smooth one-dimensional (1D) data. We simulated a variety of nonuniformly and heterogeneously smooth Gaussian 1D data over a range of smoothness values, and computed Type I error rates across 10,000 simulation iterations for each smoothness type. Results showed that, in all cases, SPM accurately controlled error at the prescribed α=0.05. Moreover, the distribution of false positives was uniform across time, implying that all regions are equally likely to produce false positives, irrespective of local roughness. We nevertheless show that cluster-level inferences (i.e., p values specific to local regions of significance), while never exceeding alpha (by definition), may be over-or-underestimated by approximately 0.01 for the currently simulated scenarios. We conclude that SPM's null hypothesis rejection decisions are valid for both nonuniform and heterogeneous 1D data, but that clusters' p values may be marginally too small/large in rough/smooth regions, respectively. Since cluster-level p values never exceed α, these p value errors are negligible for hypothesis testing purposes. Nevertheless, inter-cluster p value comparisons should be avoided. Implications for statistical power and general results interpretation are discussed.

Foot pressure distribution in White Rhinoceroses (Ceratotherium simum) during walking.

White rhinoceroses (Ceratotherium simum) are odd-toed ungulates that belong to the group Perissodactyla. Being second only to elephants in terms of large body mass amongst extant tetrapods, rhinoceroses make fascinating subjects for the study of how large land animals support and move themselves. Rhinoceroses often are kept in captivity for protection from ivory poachers and for educational/touristic purposes, yet a detrimental side effect of captivity can be foot disease (i.e., enthesopathies and osteoarthritis around the phalanges). Foot diseases in large mammals are multifactorial, but locomotor biomechanics (e.g., pressures routinely experienced by the feet) surely can be a contributing factor. However, due to a lack of in vivo experimental data on rhinoceros foot pressures, our knowledge of locomotor performance and its links to foot disease is limited. The overall aim of this study was to characterize peak pressures and center of pressure trajectories in white rhinoceroses during walking. We asked two major questions. First, are peak locomotor pressures the lowest around the fat pad and its lobes (as in the case of elephants)? Second, are peak locomotor pressures concentrated around the areas with the highest reported incidence of pathologies? Our results show a reduction of pressures around the fat pad and its lobes, which is potentially due to the material properties of the fat pad or a tendency to avoid or limit "heel" contact at impact. We also found an even and gradual concentration of foot pressures across all digits, which may be a by-product of the more horizontal foot roll-off during the stance phase. While our exploratory, descriptive sample precluded hypothesis testing, our study provides important new data on rhinoceros locomotion for future studies to build on, and thus impetus for improved implementation in the care of captive/managed rhinoceroses.

Bayesian inverse kinematics vs. least-squares inverse kinematics in estimates of planar postures and rotations in the absence of soft tissue artifact.

A variety of inverse kinematics (IK) algorithms exist for estimating postures and displacements from a set of noisy marker positions, typically aiming to minimize IK errors by distributing errors amongst all markers in a least-squares (LS) sense. This paper describes how Bayesian inference can contrastingly be used to maximize the probability that a given stochastic kinematic model would produce the observed marker positions. We developed Bayesian IK for two planar IK applications: (1) kinematic chain posture estimates using an explicit forward kinematics model, and (2) rigid body rotation estimates using implicit kinematic modeling through marker displacements. We then tested and compared Bayesian IK results to LS results in Monte Carlo simulations in which random marker error was introduced using Gaussian noise amplitudes ranging uniformly between 0.2 mm and 2.0 mm. Results showed that Bayesian IK was more accurate than LS-IK in over 92% of simulations, with the exception of one center-of-rotation coordinate planar rotation, for which Bayesian IK was more accurate in only 68% of simulations. Moreover, while LS errors increased with marker noise, Bayesian errors were comparatively unaffected by noise amplitude. Nevertheless, whereas the LS solutions required average computational durations of less than 0.5 s, average Bayesian IK durations ranged from 11.6 s for planar rotation to over 2000 s for kinematic chain postures. These results suggest that Bayesian IK can yield order-of-magnitude IK improvements for simple planar IK, but also that its computational demands may make it impractical for some applications.

Smoothing can systematically bias small samples of one-dimensional biomechanical continua.

The quality with which smoothing algorithms perform is often assessed in simulation by starting with a known 1D datum, adding noise, smoothing the noisy data, then quantifying the difference between the smoothed data and known datum, often using mean-square error (MSE). While effectively summarizing overall difference, MSE fails to capture localized, one-sided errors. This paper describes how smoothing noisy 1D data using a variety of algorithms can introduce systematic bias, and quantifies this bias using the false positive rate (FPR): the probability that a smoothing algorithm will yield a dataset whose 1D mean differs significantly from its true 1D datum. A simulation study was conducted involving six 1D datum continua, and four smoothing algorithms whose parameters were systematically manipulated along with sample size and noise amplitude. Approximately ten million simulation iterations were evaluated. FPRs were calculated at α=0.05, based on the calculated smoothness of the resulting datasets. Results showed that FPRs were much higher than the expected value of α, and in many cases approached 100%. FPRs were highest with aggressive smoothing parameters, large sample sizes and small noise amplitudes, irrespective of both smoothing algorithm and the 1D datum. These results suggest that smoothing 1D biomechanical data can introduce statistical bias with relatively high probability. The implications are experiment-specific because the biomechanical meaning of 1D changes can vary vastly between datasets. Smoothing-induced bias should be a cause for general concern when small 1D changes have non-trivial biomechanical consequences.

The role of pelvis-thorax coupling in controlling within-golf club swing speed.

Pelvis-thorax coordination has been recognised to be associated with swing speed. Increasing angular separation between the pelvis and thorax has been thought to initiate the stretch shortening cycle and lead to increased clubhead speed. The purpose of this study was to determine whether pelvis-thorax coupling played a significant role in regulating clubhead speed, in a group of low-handicap golfers (mean handicap = 4.1). Sixteen participants played shots to target distances determined based on their typical 5- and 6-iron shot distances. Half the difference between median 5- and 6-iron distance for each participant was used to create three swing effort conditions: "minus", "norm", and "plus". Ten shots were played under each swing effort condition using both the 5-iron and 6-iron, resulting in six shot categories and 60 shots per participant. No significant differences were found for X-factor for club or swing effort. X-factor stretch showed significant differences for club and swing effort. Continuous relative phase (CRP) results mainly showed evidence of the stretch shortening cycle in the downswing and that it was more pronounced late in the downswing as swing effort increased. Substantial inter-individual CRP variability demonstrated the need for individual analyses when investigating coordination in the golf swing.