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P24 Descriptive predictors of movement based on bayesian probabilities in subjects with chronic ankle instability
  1. JT Hopkins1,
  2. SJ Son1,
  3. GL Page2,
  4. S Reese2,
  5. MK Seeley1
  1. 1Human Performance Research Centre, Brigham Young University, USA
  2. 2Department of Statistics, Brigham Young University, USA


Study Design Descriptive laboratory study.

Objectives Use descriptive subject data to predict specific movement strategies in subjects with chronic ankle instability.

Background Subjects with chronic ankle instability are identified with a set of standard, accepted criteria, however, several movement strategies have been identified within this patient population using Bayesian clustering of representative movement functions (curves). The next step is to use a Bayesian probability approach to predict movements based on available subject descriptive data.

Methods and Measures 100 subjects (22±2 years) with a history of ankle sprains (4.4±3.2), who scored below 90% (83±9) on the FAAM ADL, below 75% (62±13) on the FAAM Sport, reported at least 2 ‘yes’ responses (4±1) on the MAII, and had no sprain in the previous 3 months; completed a series of max vertical/side jumps. Lower extremity kinematic (250 Hz), kinetic (2500 Hz), and EMG (2000 Hz) data were collected. Bayesian statistical modelling was used to create clusters from representative functions, and descriptive subject data were built into the model to determine which variables, or combination of variables, predicted specific movement patterns or clusters.

Results Several variables predicted specific movement patterns with high probability.

Conclusion A probability based approach to describing relationships between descriptive subject data and specific movement patterns could provide valuable information to clinicians who might look to identify specific movement alterations in patients with ankle instability.

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