Does the amount of lower extremity movement variability differ between injured and uninjured populations? A systematic review

Baida, Samuel R.; Gore, Shane; Franklyn-Miller, Andrew; Moran, Kieran

doi:10.1111/sms.13036

Cited by 57 publications

(38 citation statements)

References 59 publications

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“…The observation of a predominantly high level of weight-shifting freedom in the study group was consistent with the earlier hypothesis that variability in the human motor system is the pervasive "healthy norm" [26]. The adaptability and flexibility of a dynamic system are expressed as a multiplicity of modes of action [2], consequently, weight-shifting freedom appears to be a desired feature for gait efficiency. The purpose of the foot is to perform propulsion while maintaining balance; thus, the foot requires freedom of motion to adapt to various terrains.…”

Section: Discussionsupporting

confidence: 88%

Constraints on variation of weight-shifting by foot during walking in adolescents with valgus alignment of hallux

Latour¹,

Arlet²,

Latour³

et al. 2020

Acta Bioeng Biomech

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Purpose: Etiology of hallux valgus (HV) remains unclear and effective treatments and prophylaxis for this condition are lacking and conclusions of researches concerning HV are inconsistent. Recognition of the function-structure interrelation in foot at the early stage of valgus alignment of first metatarsophalangeus joint (1stMTPJ) would help explain the etiology of HV. Methods: The frequency of weight-shifting patterns during walking and the body mass index (BMI) were assessed relative to goniometric measurements of the 1stMTPJ angle in adolescents. Weight-shifting patterns were identified with a plantar pressure analysis, performed with the Gaitline-Maxline Distance method. Results: As the 1stMTPJ angle widened, the frequency distributions of identified weight-shifting patterns became increasingly lopsided, due to the favoring or disfavoring of one pattern at the expense of the other. The two sexes showed opposite trends in the weight-shifting patterns that were favored/disfavored, relative to participants with a 0º 1stMTPJ angle. The clear predominance of a central-shift pattern, at the expense of a medial-shift, occurred with the largest (20º) 1stMTPJ angle among girls. Additionally, the BMI distributions associated with 1stMTPJ angle characteristics showed opposite trends between the sexes. Conclusions: Valgus alignment of 1stMTPJ among adolescents were associated with limitations in weight-shifting freedom of the foot during walking, that could be involved in maintaining balance. An 1stMTPJ angle of 20º, where distinct limitations were observed, might serve as a criterion of HV pathology.

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Section: Discussionsupporting

confidence: 88%

Constraints on variation of weight-shifting by foot during walking in adolescents with valgus alignment of hallux

Latour¹,

Arlet²,

Latour³

et al. 2020

Acta Bioeng Biomech

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“…Further, the manner in which the data driven framework approached the engineering feature was based on no input of expert knowledge (e.g. complexity [59], stiffness [60], variability [61]) or any advanced feature engineering techniques (principal component scores, non negative matrix factorisation or interactions between features). No additional engineering feature was used to keep the interpretable and meaningful for rehabilitation, while additional more complex features are likely to increase the reported classification accuracies.…”

Section: Discussionmentioning

confidence: 99%

Objective classification and scoring of movement deficiencies in patients with anterior cruciate ligament reconstruction

et al. 2019

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Motion analysis systems are widely employed to identify movement deficiencies—e.g. patterns that potentially increase the risk of injury or inhibit performance. However, findings across studies are often conflicting in respect to what a movement deficiency is or the magnitude of association to a specific injury. This study tests the information content within movement data using a data driven framework that was taught to classify movement data into the classes: NORM, ACL OP and ACL NO OP , without the input of expert knowledge. The NORM class was presented by 62 subjects (124 NORM limbs), while 156 subjects with ACL reconstruction represented the ACL OP and ACL NO OP class (156 limbs each class). Movement data from jumping, hopping and change of direction exercises were examined, using a variety of machine learning techniques. A stratified shuffle split cross-validation was used to obtain a measure of expected accuracy for each step within the analysis. Classification accuracies (from best performing classifiers) ranged from 52 to 81%, using up to 5 features. The exercise with the highest classification accuracy was the double leg drop jump (DLDJ; 81%), the highest classification accuracy when considering only the NORM class was observed in the single leg hop (81%), while the DLDJ demonstrated the highest classification accuracy when considering only for the ACL OP and ACL NO OP class (84%). These classification accuracies demonstrate that biomechanical data contains valuable information and that it is possible to differentiate normal from rehabilitating movement patterns. Further, findings highlight that a few features contain most of the information, that it is important to seek to understand what a classification model has learned, that symmetry measures are important, that exercises capture different qualities and that not all subjects within a normative cohort utilise ‘true’ normative movement patterns (only 27 to 71%).

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“…Recent theories have demonstrated that motor variability not only leverages equifinality, making the system more adaptable and stable to perturbation (i.e., overcoming varying terrain or recovering from a slip/trip) ( Cusumano and Dingwell, 2013 ; Dingwell et al, 2017 ), it also has other cost function benefits ( Gates and Dingwell, 2008 ). Specifically, by capitalizing on a larger workspace (greater relative variability) of movement patterns to perform steady-state (constant locomotion velocity) behaviors, energy can be dispersed through more supportive, anatomical structures, whereas limited motor variability (lower relative variability) may lead to site-specific mechanical overloading (cumulative mechanical stress) that can result in musculoskeletal injury (MSI) ( Baida et al, 2018 ; Nordin and Dufek, 2019 ). Likewise, motor variability can distribute positive mechanical workloads across a greater number of muscle fibers improving metabolic efficiency by reducing the fatigue of a specific subset of muscle fibers ( Gates and Dingwell, 2008 ).…”

Section: Introductionmentioning

confidence: 99%

Load Magnitude and Locomotion Pattern Alter Locomotor System Function in Healthy Young Adult Women

Krajewski

Dever

Johnson

et al. 2020

Front. Bioeng. Biotechnol.

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Introduction: During cyclical steady state ambulation, such as walking, variability in stride intervals can indicate the state of the system. In order to define locomotor system function, observed variability in motor patterns, stride regulation and gait complexity must be assessed in the presence of a perturbation. Common perturbations, especially for military populations, are load carriage and an imposed locomotion pattern known as forced marching (FM). We examined the interactive effects of load magnitude and locomotion pattern on motor variability, stride regulation and gait complexity during bipedal ambulation in recruit-aged females. Methods: Eleven healthy physically active females (18-30 years) completed 1-min trials of running and FM at three load conditions: no additional weight/bodyweight (BW), an additional 25% of BW (BW + 25%), and an additional 45% of BW (BW + 45%). A goal equivalent manifold (GEM) approach was used to assess motor variability yielding relative variability (RV; ratio of "good" to "bad" variability) and detrended fluctuation analysis (DFA) to determine gait complexity on stride length (SL) and stride time (ST) parameters. DFA was also used on GEM outcomes to calculate stride regulation. Results: There was a main effect of load (p = 0.01) on RV; as load increased, RV decreased. There was a main effect of locomotion (p = 0.01), with FM exhibiting greater RV than running. Strides were regulated more tightly and corrected quicker at BW + 45% compared (p < 0.05) to BW. Stride regulation was greater for FM compared to running. There was a main effect of load for gait complexity (p = 0.002); as load increased gait complexity decreased, likewise FM had less (p = 0.02) gait complexity than running. Discussion: This study is the first to employ a GEM approach and a complexity analysis to gait tasks under load carriage. Reduction in "good" variability as load increases potentially exposes anatomical structures to repetitive site-specific loading.

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Does the amount of lower extremity movement variability differ between injured and uninjured populations? A systematic review

Cited by 57 publications

References 59 publications

Constraints on variation of weight-shifting by foot during walking in adolescents with valgus alignment of hallux

Constraints on variation of weight-shifting by foot during walking in adolescents with valgus alignment of hallux

Objective classification and scoring of movement deficiencies in patients with anterior cruciate ligament reconstruction

Load Magnitude and Locomotion Pattern Alter Locomotor System Function in Healthy Young Adult Women

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