title: 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.
abstract: 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.
  
  
  
  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.
  
  
  
  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.
  
  
  
  NCT02776930.
authors.affiliation: Center for the Intrepid, Brooke Army Medical Center, JBSA, Fort Sam Houston, Texas, USA.|Doctoral Program in Physical Therapy, Baylor University, JBSA, Fort Sam Houston, Texas, USA.
authors.forename: Daniel I
authors.identifier: 0000-0002-4320-990X
authors.initials: DI
authors.lastname: Rhon
authors.affiliation: U.S. Army Health Clinic, Schofield Barracks, Hawaii, USA.
authors.forename: Deydre S
authors.initials: DS
authors.lastname: Teyhen
authors.affiliation: Doctoral Program in Physical Therapy, Baylor University, JBSA, Fort Sam Houston, Texas, USA.|Army Medical Department Center and School, Graduate School, JBSA, Fort Sam Houston, Texas, USA.
authors.forename: Scott W
authors.initials: SW
authors.lastname: Shaffer
authors.affiliation: University of the Incarnate Word, School of Physical Therapy, San Antonio, Texas, USA.
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Goffar
authors.affiliation: University of Evansville, School of Physical Therapy, Evansville, Indiana, USA.
authors.forename: Kyle
authors.initials: K
authors.lastname: Kiesel
authors.affiliation: University of Evansville, School of Physical Therapy, Evansville, Indiana, USA.
authors.forename: Phil P
authors.initials: PP
authors.lastname: Plisky
mesh_terms: D000328:Adult; D000465:Algorithms; D005260:Female; D006305:Health Status Indicators; D006801:Humans; D008297:Male; D008887:Military Medicine; D008889:Military Personnel; D009140:Musculoskeletal Diseases / Q000503:physiopathology / Q000534:rehabilitation*; D060051:Occupational Injuries / Q000503:physiopathology / Q000534:rehabilitation*; D010808:Physical Examination; D011237:Predictive Value of Tests; D015397:Program Evaluation; D011446:Prospective Studies; D062707:Return to Work / Q000706:statistics & numerical data; D018570:Risk Assessment