A predictive model to estimate knee-abduction moment: Implications for development of a clinically applicable patellofemoral pain screening tool in female athletes

Gregory D. Myer, Kevin R. Ford, Kim D.Barber Foss, Mitchell J. Rauh, Mark V. Paterno, Timothy E. Hewett

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Context: Prospective measures of high external kneeabduction moment (KAM) during landing identify female athletes at increased risk of patellofemoral pain (PFP). A clinically applicable screening protocol is needed. Objective: To identify biomechanical laboratory measures that would accurately quantify KAM loads during landing that predict increased risk of PFP in female athletes and clinical correlates to laboratory-based measures of increased KAM status for use in a clinical PFP injury-risk prediction algorithm. We hypothesized that we could identify clinical correlates that combine to accurately determine increased KAM associated with an increased risk of developing PFP. Design: Descriptive laboratory study. Setting: Biomechanical laboratory. Patients or Other Participants: Adolescent female basketball and soccer players (n = 698) from a single-county public school district. Main Outcome Measure(s): We conducted tests of anthropometrics, maturation, laxity, flexibility, strength, and landing biomechanics before each competitive season. Pearson correlation and linear and logistic regression modeling were used to examine high KAM (>15.4 Nm) compared with normal KAM as a surrogate for PFP injury risk. Results: The multivariable logistic regression model that used the variables peak knee-abduction angle, center-of-mass height, and hip rotational moment excursion predicted KAM associated with PFP risk (>15.4 NM of KAM) with 92% sensitivity and 74% specificity and a C statistic of 0.93. The multivariate linear regression model that included the same predictors accounted for 70% of the variance in KAM. We identified clinical correlates to laboratory measures that combined to predict high KAM with 92% sensitivity and 47% specificity. The clinical prediction algorithm, including knee-valgus motion (odds ratio [OR]=1.46, 95% confidence interval [CI]=1.31, 1.63), center-ofmass height (OR = 1.21, 95% CI = 1.15, 1.26), and hamstrings strength/body fat percentage (OR = 1.80, 95% CI = 1.02, 3.16) predicted high KAM with a C statistic of 0.80. Conclusions: Clinical correlates to laboratory-measured biomechanics associated with an increased risk of PFP yielded a highly sensitive model to predict increased KAM status. This screening algorithm consisting of a standard camcorder, physician scale for mass, and handheld dynamometer may be used to identify athletes at increased risk of PFP.

Original languageEnglish (US)
Pages (from-to)389-398
Number of pages10
JournalJournal of athletic training
Issue number3
StatePublished - 2014


  • Assessment tools
  • High-risk biomechanics
  • Knee injury prevention
  • Patellofemoral risk factors
  • Targeted neuromuscular training

ASJC Scopus subject areas

  • Orthopedics and Sports Medicine
  • Physical Therapy, Sports Therapy and Rehabilitation


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