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17 Subclassification of recreational runners with a running-related injury based on running kinematics measured with two-dimensional video analysis
  1. Bart Dingenen1,
  2. Filip Staes2,
  3. Romy Vanelderen1,
  4. Linde Ceyssens1,
  5. Peter Malliaras3,5,
  6. Christian Barton4,5,6,
  7. Kevin Deschamps7
  1. 1Rehabilitation Research Centre, Biomedical Research Institute, Faculty of Medicine and Life Sciences, UHasselt, Agoralaan A, Belgium
  2. 2KU Leuven Musculoskeletal Rehabilitation Research Group, Department of Rehabilitation Sciences, Faculty of Kinesiology and Rehabilitation Sciences, Belgium
  3. 3Department of Physiotherapy, School of Primary and Allied Health Care, Faculty of Medicine, Nursing and Health Science, Monash University, Australia
  4. 4La Trobe Sport and Exercise Medicine Research Centre, School of Allied Health, La Trobe University, Australia
  5. 5Complete Sports Care, Australia
  6. 6Department of Surgery, St Vincent’s Hospital, University of Melbourne, Australia
  7. 7KU Leuven, Department of Rehabilitation Sciences, Faculty of Kinesiology and Rehabilitation Sciences, Campus Bruges, Belgium


Introduction The aim of this study was to explore whether homogeneous subgroups could be classified within the running kinematics of a group of recreational runners with a running-related injury (RRI).

Materials and methods Fifty-three recreational runners (15 males, 38 females) with an RRI ran on a treadmill at preferred speed. Digital videos were recorded in the frontal and sagittal plane with two iPads. Outcome measures included foot and tibia inclination at initial contact, and hip adduction and knee flexion during midstance. All angles were manually drawn using Kinovea and an average of seven consecutive steps was calculated for each angle. The four outcome measures were clustered using K-means cluster analysis (n=2–10). Silhouette coefficients were used to detect optimal clustering.

Results The cluster analysis led to the classification of two distinct subgroups (mean silhouette coefficient=0.53). Cluster 1 (n=39) was characterized by higher foot inclination and tibia inclination at initial contact, higher knee flexion during midstance, and lower hip adduction during midstance compared to cluster 2 (n=14). Fifteen out of 17 (88%) shin injuries were classified in cluster 1. Other injuries were more divided over both clusters. The ratio males/females was higher in cluster 1 (44%) versus cluster 2 (27%).

Conclusion This is the first study to classify subgroup profiles of running kinematics in recreational runners with an RRI based on two-dimensional video analysis. Two distinct subgroups were identified. This subclassification can help clinicians in their clinical reasoning process when evaluating kinematics of runners with an RRI and developing targeted gait-retraining strategies.

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