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.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Rhon, DI; Teyhen, DS; Shaffer, SW; Goffar, SL; Kiesel, K; Plisky, PP

Published Date

  • February 2018

Published In

Volume / Issue

  • 24 / 1

Start / End Page

  • 81 - 88

PubMed ID

  • 27884941

Pubmed Central ID

  • 27884941

Electronic International Standard Serial Number (EISSN)

  • 1475-5785

Digital Object Identifier (DOI)

  • 10.1136/injuryprev-2016-042234

Language

  • eng

Conference Location

  • England