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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.

Publication ,  Journal Article
Rhon, DI; Teyhen, DS; Shaffer, SW; Goffar, SL; Kiesel, K; Plisky, PP
Published in: Inj Prev
February 2018

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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Published In

Inj Prev

DOI

EISSN

1475-5785

Publication Date

February 2018

Volume

24

Issue

1

Start / End Page

81 / 88

Location

England

Related Subject Headings

  • Risk Assessment
  • Return to Work
  • Public Health
  • Prospective Studies
  • Program Evaluation
  • Predictive Value of Tests
  • Physical Examination
  • Occupational Injuries
  • Musculoskeletal Diseases
  • Military Personnel
 

Citation

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Rhon, D. I., Teyhen, D. S., Shaffer, S. W., Goffar, S. L., Kiesel, K., & Plisky, P. P. (2018). 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. Inj Prev, 24(1), 81–88. https://doi.org/10.1136/injuryprev-2016-042234
Rhon, Daniel I., Deydre S. Teyhen, Scott W. Shaffer, Stephen L. Goffar, Kyle Kiesel, and Phil P. Plisky. “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.Inj Prev 24, no. 1 (February 2018): 81–88. https://doi.org/10.1136/injuryprev-2016-042234.

Published In

Inj Prev

DOI

EISSN

1475-5785

Publication Date

February 2018

Volume

24

Issue

1

Start / End Page

81 / 88

Location

England

Related Subject Headings

  • Risk Assessment
  • Return to Work
  • Public Health
  • Prospective Studies
  • Program Evaluation
  • Predictive Value of Tests
  • Physical Examination
  • Occupational Injuries
  • Musculoskeletal Diseases
  • Military Personnel