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Developing predictive models for return to work using the Military Power, Performance and Prevention (MP3) musculoskeletal injury risk algorithm: a study protocol for an injury risk assessment programme
  1. Daniel I Rhon1,2,
  2. Deydre S Teyhen3,
  3. Scott W Shaffer2,4,
  4. Stephen L Goffar5,
  5. Kyle Kiesel6,
  6. Phil P Plisky6
  1. 1Center for the Intrepid, Brooke Army Medical Center, JBSA, Fort Sam Houston, Texas, USA
  2. 2Doctoral Program in Physical Therapy, Baylor University, JBSA, Fort Sam Houston, Texas, USA
  3. 3U.S. Army Health Clinic, Schofield Barracks, Hawaii, USA
  4. 4Army Medical Department Center and School, Graduate School, JBSA, Fort Sam Houston, Texas, USA
  5. 5University of the Incarnate Word, School of Physical Therapy, San Antonio, Texas, USA
  6. 6University of Evansville, School of Physical Therapy, Evansville, Indiana, USA
  1. Correspondence to Dr Daniel Rhon, 1315 Roger Brooke Drive, San Antonio, TX 78250, USA;{at}


Background Musculoskeletal injuries are a primary source of disability in the US Military, and low back pain and lower extremity injuries account for over 44% of limited work days annually. History of prior musculoskeletal injury increases the risk for future injury. This study aims to determine the risk of injury after returning to work from a previous injury. The objective is to identify criteria that can help predict likelihood for future injury or re-injury.

Methods There will be 480 active duty soldiers recruited from across four medical centres. These will be patients who have sustained a musculoskeletal injury in the lower extremity or lumbar/thoracic spine, and have now been cleared to return back to work without any limitations. Subjects will undergo a battery of physical performance tests and fill out sociodemographic surveys. They will be followed for a year to identify any musculoskeletal injuries that occur. Prediction algorithms will be derived using regression analysis from performance and sociodemographic variables found to be significantly different between injured and non-injured subjects.

Discussion Due to the high rates of injuries, injury prevention and prediction initiatives are growing. This is the first study looking at predicting re-injury rates after an initial musculoskeletal injury. In addition, multivariate prediction models appear to have move value than models based on only one variable. This approach aims to validate a multivariate model used in healthy non-injured individuals to help improve variables that best predict the ability to return to work with lower risk of injury, after a recent musculoskeletal injury.

Trial registration number NCT02776930.

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  • Contributors We are thankful for the contribution of MAJ Scott Carow, MAJ Darren Hearn and Drs Matt Hartsorne, Danielle Langness, Rachel Mayhew, Laurel Proulx and Katie Dry in helping with training, establishing training procedures and assistance with setting up the data collection process.

  • Funding This research was funded by the Department of Defence Military Operational Medicine Research Program under programme number (W81XWH-13-MOMJPC5-IPPEHA).

  • Disclaimer This research was supported by the Department of Defence Military Operational Medicine Research Program under programme number (W81XWH-13-MOMJPC5-IPPEHA). The view(s) expressed herein are those of the author(s) and do not reflect the official policy or position of Brooke Army Medical Center, the US Army Medical Department, the US Army Office of the Surgeon General, the Department of the Army, Department of Defence or the US Government.

  • Competing interests None declared.

  • Ethics approval US Army Western Region IRB.

  • Provenance and peer review Not commissioned; internally peer reviewed.

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