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Cited by 52Knee injuries and reconstruction techniquesSports injuries and preventionLower Extremity Biomechanics and Pathologies

An Integrated Approach to Change the Outcome Part I: Neuromuscular Screening Methods to Identify High ACL Injury Risk Athletes

Open access
Myer, Gregory D., Ford, Kevin R., Brent, Jensen L., Hewett, Timothy E.·2012·Journal of Strength and Conditioning Research
DOI 10.1519/jsc.0b013e31825c2b8fPMC PMC4160042
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Abstract

An important step for treatment of a particular injury etiology is the appropriate application of a treatment targeted to the population at risk. An anterior cruciate ligament (ACL) injury risk algorithm has been defined that employs field-based techniques in lieu of laboratory-based motion analysis systems to identify athletes with high ACL injury risk landing strategies. The resultant field-based assessment techniques, in combination with the developed prediction algorithm, allow for low-cost identification of athletes who may be at increased risk of sustaining ACL injury. The combined simplicity and accuracy of the field-based tool facilitate its use to identify specific factors that may increase risk of injury in female athletes. The purpose of this report is to demonstrate novel algorithmic techniques to accurately capture and analyze measures of knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps to hamstrings ratio with video analysis software typically used by coaches, strength and conditioning specialists, and athletic trainers. The field-based measurements and software analyses were used in a prediction algorithm to identify those at potential risk of noncontact ACL injury that may directly benefit from neuromuscular training.

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  • External perceptual control engages automatic and implicit motor strategies that are more efficient than deliberate conscious control, leading to fast, robust learning with high retention and transfer.

    “This mode of control engages automatic and implicit motor strategies that operate more efficiently and effectively than deliberate, explicitly conscious control of movement28–37, which yields fast, robust learning, leading to high retention and transfer38–43.”

    From Injury Risk Factors Integrated Into Self-Guided Real-Time Biofeedback Improves High-Risk Biomechanics (2019) · cites this paper as [30]

    The Introduction and Practical Applications sections describe a field-based algorithm for identifying ACL injury risk and guiding targeted neuromuscular training. Searches for automatic or implicit motor control and for fast learning, retention, and transfer found no passage addressing those claims in our copy; page numbers were not available. (LLM, conf=0.76)

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Research works printed but not linked: 3

  1. [24]Myer GD Ford KR Khoury J Succop P Hewett TE Clinical correlates to laboratory measures for use in non-contact anterior cruciate ligament injury risk prediction algorithm Clin Biomech (Bristol, Avon) 25 693 699 2010 PMC2900517 20554101
  2. [26]Myer GD Ford KR Khoury J Succop P Hewett TE Development and validation of a clinic based prediction tool to identify high ACL injury risk female athletes Am J Sports Med In press PMC3407804 20595554
  3. [33]Padua DA Marshall SW Beutler AI Garrett WE Prospective cohort study of biomechanical risk factors of ACL injury: The JUMP-ACL study American Orthopaedic Society of Sports Medicine Annual Meeting Keystone, CO 2009 393 395

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Related

Authors
Myer, G. D., Ford, K. R., Brent, J. L., & Hewett, T. E.
Affiliations
Cincinnati Children's Hospital Medical Center (US)
Corresponding author
Myer, Gregory D.
ORCID
Venue
Journal of Strength and Conditioning Research
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2012-08
Volume
26
Issue
8
Pages
2265-2271
ISSN
1064-8011 (ISSN-L)
DOI
10.1519/jsc.0b013e31825c2b8f
PMC
PMC4160042
OpenAlex
W1994012926
Cited by
52
References
31
Citations by year
2026: 1 · 2025: 2 · 2024: 1 · 2023: 1 · 2022: 4 · 2021: 1 · 2020: 4 · 2019: 3 · 2018: 5 · 2017: 6 · 2016: 7 · 2015: 6 · 2014: 8 · 2013: 3
FWCI
5.54
Type
journal article
Language
en
Open access
Locations
  • The Journal of Strength and Conditioning Research (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health
  • National Institute of Arthritis and Musculoskeletal and Skin Diseases (R01 AR05563, R01AR049735, R01AR055563, R01AR056259)
Topics
  • Health Sciences › Medicine › Surgery › Knee injuries and reconstruction techniques
  • Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports injuries and prevention
  • Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
Keywords
anterior cruciate ligament injury, injury prediction, female athletes, landing technique, knee flexion range of motion, body mass, tibia length, video analysis, neuromuscular training, field-based assessment, non-contact ACL injury
MeSH terms
Anterior Cruciate Ligament Injuries, Algorithms, Basketball, Female, Humans, Knee, Soccer, Task Performance and Analysis, Range of Motion, Articular, Risk Assessment, Volleyball, Athletes

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