A drop landing screening approach to monitor an individual using functional data analysis: An ACL injury case study

Stephens, Jessica; Chapman, Dale W.; Tate, Krystle; Warmenhoven, John

doi:10.1016/j.jsams.2019.03.010

Cited by 3 publications

(12 citation statements)

References 27 publications

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“…5,15 Testing properties using single time points to measure overall performance may be an inadequate approach when risk-profiling dynamic movements. 21 The present study showed continuous analysis provides an innovative alternative to traditional movement analysis approaches, identifying phase-specific mechanical loading differences between safe and high-risk landers. Incorporating powerful analytical methods such as fPCA and machine learning models into force-plate software may offer a unique opportunity to streamline the injury risk process.…”

Section: Perspectivementioning

confidence: 76%

“…Adding further complexity, feasible screening tools available to practitioners such as qualitative screening tools and quantitative traditional performance measures' ability to predict injury is questioned 5,15 . Testing properties using single time points to measure overall performance may be an inadequate approach when risk‐profiling dynamic movements 21 . The present study showed continuous analysis provides an innovative alternative to traditional movement analysis approaches, identifying phase‐specific mechanical loading differences between safe and high‐risk landers.…”

Section: Discussionmentioning

confidence: 87%

“…Whilst PCA has been previously used by Ueno and colleges to model internal knee loading in cadavers 25 . The work of Stephens, 21 extends upon this, applying fPCA in an athlete monitoring context and additional research is needed to identify movement profiles of healthy athletes.…”

Section: Introductionmentioning

confidence: 99%

“…Functional data analysis (FDA) has been used in recent landing and jumping research to identify variability from continuous kinetic and kinematic movement data. [20][21][22][23][24] Functional principal component analysis (fPCA) is one of the most common FDA techniques used to assess human movement. 21 Indeed, Stephens et al 21 showed that by analyzing the entire continuous forcetime data using fPCA, changes in an athlete's movement strategy post-ACL injury could be identified that were not evident when using discrete measures.…”

Section: Introductionmentioning

confidence: 99%

“…[20][21][22][23][24] Functional principal component analysis (fPCA) is one of the most common FDA techniques used to assess human movement. 21 Indeed, Stephens et al 21 showed that by analyzing the entire continuous forcetime data using fPCA, changes in an athlete's movement strategy post-ACL injury could be identified that were not evident when using discrete measures. Whilst PCA has been previously used by Ueno and colleges to model internal knee loading in cadavers.…”

Section: Introductionmentioning

confidence: 99%

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Curves and kinematics: Relationship between the force‐time curve and landing ability

Mifsud

Stephens

Warmenhoven

et al. 2022

Scandinavian Med Sci Sports

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Anterior cruciate ligament (ACL) injuries have a significant impact on athletic performance and long‐term quality of life. Force plates and qualitative screening tools are feasible and effective screening methods to identify abnormal movement quality associated with increased injury risk. Comparing qualitative assessments of landing ability with force‐time curves, may detect unique differences between safe and high‐risk athletic movement patterns. The aim of this study was to determine low‐ and high‐risk landing ability from qualitive landing assessments and to examine the resulting force‐time curves using functional principal component analysis (fPCA). Thirty‐one healthy academy athletes (10 males and 21 females) completed double‐ and single‐leg dominant and non‐dominant jump‐landing‐rebound tasks. All movements were filmed in multiple‐planes, and vertical ground reaction forces (vGRF) were simultaneously collected. The Landing Error Scoring System (LESS) and Single‐Leg Landing Error Scoring System (SL‐LESS) were used to score landing footage. From these scores, athletes were categorized into low‐risk and high‐risk groups for further analysis. fPCA was used to examine differences between landing quality groups force‐time curves. Compared to high‐risk landers, low‐risk landers demonstrated significantly longer contact times across all movements. Scores from fPC1 revealed safe and high‐risk landing techniques expose athletes to significantly different loading patterns during double‐ and single‐leg dominant movements. A significant positive relationship was observed between fPC1 and LESS scores, however this relationship was not observed in both single‐leg landing scores. Where possible incorporating curve analysis methods like fPCA into multi‐faceted screening approaches may help practitioners uncover unique insights into athletic loading strategies.

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Section: Perspectivementioning

confidence: 76%

Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Curves and kinematics: Relationship between the force‐time curve and landing ability

Mifsud

Stephens

Warmenhoven

et al. 2022

Scandinavian Med Sci Sports

Self Cite

View full text Add to dashboard Cite

show abstract

Over 30 years of using functional data analysis in human movement. What do we know, and is there more for sports biomechanics to learn?

Warmenhoven

2024

Sports Biomechanics

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Countermovement Jump and Squat Jump Force-Time Curve Analysis in Control and Fatigue Conditions

Hughes

Warmenhoven

Haff

et al. 2021

Journal of Strength and Conditioning Research

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Hughes, S, Warmenhoven, J, Haff, GG, Chapman, DW, and Nimphius, S. Countermovement jump and squat jump force-time curve analysis in control and fatigue conditions. J Strength Cond Res XX(X): 000-000, 2020-This study aimed to reanalyze previously published discrete force data from countermovement jumps (CMJs) and squat jumps (SJs) using statistical parametric mapping (SPM), a statistical method that enables analysis of data in its native, complete state. Statistical parametric mapping analysis of 1-dimensional (1D) force-time curves was compared with previous zero-dimensional (0D) analysis of peak force to assess sensitivity of 1D analysis. Thirty-two subjects completed CMJs and SJs at baseline, 15 minutes, 1, 24, and 48 hours following fatigue and control conditions in a pseudo random cross-over design. Absolute (CMJ ABS /SJ ABS) and time-normalized (CMJ NORM /SJ NORM) force-time data were analyzed using SPM 2-way repeated measures analysis of variance with significance accepted at a 5 0.05. The SPM indicated a magnitude of difference between force-time data with main effects for time (p , 0.001) and interaction (p , 0.001) observed in CMJ ABS , SJ ABS, and SJ NORM, whereas previously published 0D analysis reported no 2-way interaction in CMJ and SJ peak force. This exploratory research demonstrates the strength of SPM to identify changes between entire movement force-time curves. Continued development and use of SPM analysis techniques could present the opportunity for refined assessment of athlete fatigue and readiness with the analysis of complete force-time curves.

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A drop landing screening approach to monitor an individual using functional data analysis: An ACL injury case study

Cited by 3 publications

References 27 publications

Curves and kinematics: Relationship between the force‐time curve and landing ability

Curves and kinematics: Relationship between the force‐time curve and landing ability

Over 30 years of using functional data analysis in human movement. What do we know, and is there more for sports biomechanics to learn?

Countermovement Jump and Squat Jump Force-Time Curve Analysis in Control and Fatigue Conditions

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