title: What Risk Factors Are Associated With Musculoskeletal Injury in US Army Rangers? A Prospective Prognostic Study.
abstract: BACKGROUND
  Musculoskeletal injury is the most common reason that soldiers are medically not ready to deploy. Understanding intrinsic risk factors that may place an elite soldier at risk of musculoskeletal injury may be beneficial in preventing musculoskeletal injury and maintaining operational military readiness. Findings from this population may also be useful as hypothesis-generating work for particular civilian settings such as law enforcement officers (SWAT teams), firefighters (smoke jumpers), or others in physically demanding professions.
  
  
  OBJECTIVE
  The purposes of this study were (1) to examine whether using baseline measures of self-report and physical performance can identify musculoskeletal injury risk; and (2) to determine whether a combination of predictors would enhance the accuracy for determining future musculoskeletal injury risk in US Army Rangers.
  
  
  METHODS
  Our study was a planned secondary analysis from a prospective cohort examining how baseline factors predict musculoskeletal injury. Baseline predictors associated with musculoskeletal injury were collected using surveys and physical performance measures. Survey data included demographic variables, injury history, and biopsychosocial questions. Physical performance measures included ankle dorsiflexion, Functional Movement Screen, lower and upper quarter Y-balance test, hop testing, pain provocation, and the Army Physical Fitness Test (consisting of a 2-mile run and 2 minutes of sit-ups and push-ups). A total of 320 Rangers were invited to enroll and 211 participated (66%). Occurrence of musculoskeletal injury was tracked for 1 year using monthly injury surveillance surveys, medical record reviews, and a query of the Department of Defense healthcare utilization database. Injury surveillance data were available on 100% of the subjects. Receiver operator characteristic curves and accuracy statistics were calculated to identify predictors of interest. A logistic regression equation was then calculated to find the most pertinent set of predictors. Of the 188 Rangers (age, 23.3 ± 3.7 years; body mass index, 26.0 ± 2.4 kg/m(2)) remaining in the cohort, 85 (45.2%) sustained a musculoskeletal injury of interest.
  
  
  RESULTS
  Smoking, prior surgery, recurrent prior musculoskeletal injury, limited-duty days in the prior year for musculoskeletal injury, asymmetrical ankle dorsiflexion, pain with Functional Movement Screen clearing tests, and decreased performance on the 2-mile run and 2-minute sit-up test were associated with increased injury risk. Presenting with one or fewer predictors resulted in a sensitivity of 0.90 (95% confidence interval [CI], 0.83-0.95), and having three or more predictors resulted in a specificity of 0.98 (95% CI, 0.93-0.99). The combined factors that contribute to the final multivariable logistic regression equation yielded an odds ratio of 4.3 (95% CI, 2.0-9.2), relative risk of 1.9 (95% CI, 1.4-2.6), and an area under the curve of 0.64.
  
  
  CONCLUSIONS
  Multiple factors (musculoskeletal injury history, smoking, pain provocation, movement tests, and lower scores on physical performance measures) were associated with individuals at risk for musculoskeletal injury. The summation of the number of risk factors produced a highly sensitive (one or less factor) and specific (three or more factors) model that could potentially be used to effectively identify and intervene in those persons with elevated risk for musculoskeletal injury. Future research should establish if screening and intervening can improve musculoskeletal health and if our findings among US Army Rangers translate to other occupations or athletes.
  
  
  METHODS
  Level II, prognostic study.
authors.affiliation: US Office of the Surgeon General, Defense Health Headquarters, 7700 Arlington Boulevard, Falls Church, VA, 22042, USA, Deydre.s.teyhen.mil@mail.mil.
authors.forename: Deydre S
authors.initials: DS
authors.lastname: Teyhen
authors.forename: Scott W
authors.initials: SW
authors.lastname: Shaffer
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Butler
authors.forename: Stephen L
authors.initials: SL
authors.lastname: Goffar
authors.forename: Kyle B
authors.initials: KB
authors.lastname: Kiesel
authors.forename: Daniel I
authors.initials: DI
authors.lastname: Rhon
authors.forename: Jared N
authors.initials: JN
authors.lastname: Williamson
authors.forename: Phillip J
authors.initials: PJ
authors.lastname: Plisky
mesh_terms: D000328:Adult; D019540:Area Under Curve; D006304:Health Status; D006305:Health Status Indicators; D006801:Humans; D016015:Logistic Models; D008297:Male; D008887:Military Medicine; D008889:Military Personnel; D015999:Multivariate Analysis; D009140:Musculoskeletal Diseases / Q000175:diagnosis / Q000209:etiology* / Q000503:physiopathology; D009784:Occupational Diseases / Q000175:diagnosis / Q000209:etiology* / Q000503:physiopathology; D016272:Occupational Health; D016017:Odds Ratio; D010808:Physical Examination; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors; D014481:United States; D055815:Young Adult