Effects of increased anterior-posterior voluntary sway frequency on mechanical and perceived postural stability

Despite a substantial number of studies, the interaction between mechanical indicators of stability and perception of instability remains unclear. The purpose of this study was to determine the effect of sway frequency and verbal restraint on mechanical and perceived postural stability. Fourteen participants underwent a series of standing voluntary anterior-posterior swaying trials at three frequencies (20, 40, and 60 bpm) and two levels of restraint (non restraint and verbally restraint to swaying at the ankle). Repeated measures ANOVA tests revealed greater mechanical stability defined though the margin of stability, and greater horizontal ground reaction forces, while the centre of pressure excursions remained unchanged with increasing frequency. Furthermore, ground reaction forces were greater in the non-restraint condition. Moreover, a tendency toward greater perceived instability with increasing voluntary sway frequency was observed..Our results indicate that variations in sway frequency and verbal restraint resulted in noticeable alterations in mechanical indicators of stability, with no clear effect on perceived instability

Postural adjustments in catching: on the interplay between segment stabilization and equilibrium control

The purpose of this study was to investigate postural adjustments in one-handed ball catching. Specifically, the functional role of anticipatory postural adjustments (APA) during the initial arm raising and subsequent postural adjustments (SPA) for equilibrium control and ball-hand impact were scrutinized. Full-body kinematics and kinetics allowed an analysis of the mechanical consequences of raising up the arm and preparing for ball-hand impact. APA for catching were suggested to be for segment stabilization. SPA had a functional role for equilibrium control by an inverted pendulum mechanism but were also involved in preparing for the impact of the ball on the hand, which was illustrated by an increased postural response at the end of the movement. These results were compared with raising up the arm in a well-studied reaction-time task, for which an additional counter rotation equilibrium mechanism was observed. Together, our findings demonstrate that postural adjustments should be investigated in relation to their specific functional task constraints, rather than generalizing the functional role of these postural adjustments over different tasks

Statistical Parametric Mapping (SPM) for alpha-based statistical analyses of multi-muscle EMG time-series.

Multi-muscle EMG time-series are highly correlated and time dependent yet traditional statistical analysis of scalars from an EMG time-series fails to account for such dependencies. This paper promotes the use of SPM vector-field analysis for the generalised analysis of EMG time-series. We reanalysed a publicly available dataset of Young versus Adult EMG gait data to contrast scalar and SPM vector-field analysis. Independent scalar analyses of EMG data between 35% and 45% stance phase showed no statistical differences between the Young and Adult groups. SPM vector-field analysis did however identify statistical differences within this time period. As scalar analysis failed to consider the multi-muscle and time dependence of the EMG time-series it exhibited Type II error. SPM vector-field analysis on the other hand accounts for both dependencies whilst tightly controlling for Type I and Type II error making it highly applicable to EMG data analysis. Additionally SPM vector-field analysis is generalizable to linear and non-linear parametric and non-parametric statistical models, allowing its use under constraints that are common to electromyography and kinesiology

The probability of false positives in zero-dimensional analyses of one-dimensional kinematic, force and EMG trajectories

A false positive is the mistake of inferring an e↵ect when none exists, and although alpha controls the false positive (Type I error) rate in classical hypothesis testing, a given alpha value is accurate only if the underlying model of randomness appropriately reflects experimentally observed variance. Hypotheses pertaining to one-dimensional (1D) (e.g. time-varying) biomechanical trajectories are most often tested using a traditional zero-dimensional (0D) Gaussian model of randomness, but variance in these datasets variance is clearly 1D. The purpose of this study was to determine the likelihood that analyzing smooth 1D data with a 0D model of variance will produce false positives. We first used random field theory (RFT) to predict the probability of false positives in 0D analyses. We then validated RFT predictions via numerical simulations of smooth Gaussian 1D trajectories. Results showed that, across a range of public kinematic, force and EMG datasets, the median false positive rate was 0.382 and not the assumed alpha=0.05, even for a simple two-sample t test involving N=10 trajectories per group. The median false positive rates for experiments involving three-component vector trajectories was p=0.764. This rate increased to p=0.945 for two three-component vector trajectories, and to p=0.999 for six three-component vectors. This implies that experiments involving vector trajectories have a high probability of yielding 0D statistical significance when there is, in fact, no 1D effect. Either (a) explicit a priori identification of 0D metrics or (b) adoption of 1D methods can more tightly control alpha

The reliability of data produced by isokinetic dynamometry (IKD) of knee extension and flexion movement

To investigate the feasibility of using isolated knee performance to measure the f-v profile of human muscle in vivo, we were using isokinetic dynamometry (IKD) technique that span the entire f-v profile of skeletal muscle. With institutional ethics approval, eleven healthy males (mean ± SD: age, 24.9 ± 3.1 years; body mass, 80.2 ± 10.1 kg; height, 176.2 ± 7.1 cm) completed this study. The participants were physically active and were familiarized with the techniques and protocol prior to completing 4 experimental sessions (1 introductory session and 3 repeated experiment sessions). The sessions was used a protocol of knee extensor function on IKD at isometric mode and angular velocities of 60˚.s-1, 120˚.s-1, 240˚.s-1 and 450˚.s-1. IKD data were not significantly different across the 3 repeated trials, demonstrating there was no evidence of systematic bias. The average coefficient of variation (CV) of IKD was 9.3 ± 3.0% ranging from 7.0 to 15.7%. Therefore the isolated knee performance to measure the f-v profile of human muscle in vivo using the IKD was feasible.Keywords: f-v profile, isokinetic dynamometry, skeletal muscl

Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power.

One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA

Asymmetry after Hamstring Injury in English Premier League: Issue Resolved, Or Perhaps Not?

Hamstring injuries constitute one of the most concerning injuries in English Premier League football, due to its high primary incidence but also its recurrence. Functional methods assessing hamstring function during high-risk performance tasks such as sprinting are vital to identify potential risk factors. The purpose of this study was to assess horizontal force deficits during maximum sprint running on a non-motorized treadmill in football players with previous history of hamstring strains as a pre-season risk-assessment in a club setting. 17 male football players from one Premier League Club were divided into 2 groups, experimental (n= 6, age = 24.5 ± 2.3 years) and control (n= 11, age = 21.3 ± 1.2 years), according to history of previous hamstring injury. Participants performed a protocol including a 10 seconds maximum sprint on a non-motorized treadmill. Force deficits during acceleration phase and steady state phases of the sprint were assessed between limbs and between groups. The main outcome measures were horizontal and vertical peak forces during the acceleration phase or steady state. There were no significant differences in peak forces between previously injured and non-injured limbs, or between groups, challenging the ideas around functional force deficits in sprint running as a diagnostic measure of hamstring re-injury risk

Can segmental model reductions quantify whole-body balance accurately during dynamic activities?

When investigating whole-body balance in dynamic tasks, adequately tracking the whole-body centre of mass (CoM) or derivatives such as the extrapolated centre of mass (XCoM) can be crucial but add considerable measurement efforts. The aim of this study was to investigate whether reduced kinematic models can still provide adequate CoM and XCoM representations during dynamic sporting tasks. Seventeen healthy recreationally active subjects (14 males and 3 females; age, 24.9±3.2years; height, 177.3±6.9cm; body mass 72.6±7.0kg) participated in this study. Participants completed three dynamic movements, jumping, kicking, and overarm throwing. Marker-based kinematic data were collected with 10 optoelectronic cameras at 250Hz (Oqus Qualisys, Gothenburg, Sweden). The differences between (X)CoM from a full-body model (gold standard) and (X)CoM representations based on six selected model reductions were evaluated using a Bland-Altman approach. A threshold difference was set at ±2cm to help the reader interpret which model can still provide an acceptable (X)CoM representation. Antero-posterior and medio-lateral displacement profiles of the CoM representation based on lower limbs, trunk and upper limbs showed strong agreement, slightly reduced for lower limbs and trunk only. Representations based on lower limbs only showed less strong agreement, particularly for XCoM in kicking. Overall, our results provide justification of the use of certain model reductions for specific needs, saving measurement effort whilst limiting the error of tracking (X)CoM trajectories in the context of whole-body balance investigation

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) may be over-or-underestimated by approximately 0.01 (for the currently simulated scenarios), but never exceed α by definition. 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