Deydre S Teyhen1, Scott W Shaffer2, Stephen L Goffar3, Kyle Kiesel4, Robert J Butler5, Daniel I Rhon6, Phillip J Plisky4. 1. Walter Reed Army Institute of Research (WRAIR), Silver Spring, Maryland. 2. Department of Physical Therapy, Texas State University, Round Rock, Texas. 3. School of Physical Therapy, University of the Incarnate Word, San Antonio, Texas. 4. Department of Physical Therapy, University of Evansville, Evansville, Indiana. 5. St Louis Cardinals, St Louis, Missouri. 6. Doctoral Physical Therapy Programs (DPT and DSc), Baylor University, JBSA Fort Sam Houston, Texas.
Abstract
BACKGROUND: Musculoskeletal injuries are a primary source of disability. Understanding how risk factors predict injury is necessary to individualize and enhance injury reduction programs. HYPOTHESIS: Because of the multifactorial nature of musculoskeletal injuries, multiple risk factors will provide a useful method of categorizing warrior athletes based on injury risk. STUDY DESIGN: Prospective observational cohort study. LEVEL OF EVIDENCE: Level 2. METHODS: Baseline data were collected on 922 US Army soldiers/warrior athletes (mean age, 24.7 ± 5.2 years; mean body mass index, 26.8 ± 3.4 kg/m2) using surveys and physical measures. Injury occurrence and health care utilization were collected for 1 year. Variables were compared in healthy versus injured participants using independent t tests or chi-square analysis. Significantly different factors between each group were entered into a logistic regression equation. Receiver operating characteristic curve and accuracy statistics were calculated for regression variables. RESULTS: Of the 922 warrior athletes, 38.8% suffered a time-loss injury (TLI). Overall, 35 variables had a significant relationship with TLIs. The logistic regression equation, consisting of 11 variables of interest, was significant (adjusted R2 = 0.21; odds ratio, 5.7 [95% CI, 4.1-7.9]; relative risk, 2.5 [95% CI, 2.1-2.9]; area under the curve, 0.73). Individuals with 2 variables had a sensitivity of 0.89, those with 7 or more variables had a specificity of 0.94. CONCLUSION: The sum of individual risk factors (prior injury, prior work restrictions, lower perceived recovery from injury, asymmetrical ankle dorsiflexion, decreased or asymmetrical performance on the Lower and Upper Quarter Y-Balance test, pain with movement, slower 2-mile run times, age, and sex) produced a highly sensitive and specific multivariate model for TLI in military servicemembers. CLINICAL RELEVANCE: A better understanding of characteristics associated with future injury risk can provide a foundation for prevention programs designed to reduce medical costs and time lost.
BACKGROUND: Musculoskeletal injuries are a primary source of disability. Understanding how risk factors predict injury is necessary to individualize and enhance injury reduction programs. HYPOTHESIS: Because of the multifactorial nature of musculoskeletal injuries, multiple risk factors will provide a useful method of categorizing warrior athletes based on injury risk. STUDY DESIGN: Prospective observational cohort study. LEVEL OF EVIDENCE: Level 2. METHODS: Baseline data were collected on 922 US Army soldiers/warrior athletes (mean age, 24.7 ± 5.2 years; mean body mass index, 26.8 ± 3.4 kg/m2) using surveys and physical measures. Injury occurrence and health care utilization were collected for 1 year. Variables were compared in healthy versus injured participants using independent t tests or chi-square analysis. Significantly different factors between each group were entered into a logistic regression equation. Receiver operating characteristic curve and accuracy statistics were calculated for regression variables. RESULTS: Of the 922 warrior athletes, 38.8% suffered a time-loss injury (TLI). Overall, 35 variables had a significant relationship with TLIs. The logistic regression equation, consisting of 11 variables of interest, was significant (adjusted R2 = 0.21; odds ratio, 5.7 [95% CI, 4.1-7.9]; relative risk, 2.5 [95% CI, 2.1-2.9]; area under the curve, 0.73). Individuals with 2 variables had a sensitivity of 0.89, those with 7 or more variables had a specificity of 0.94. CONCLUSION: The sum of individual risk factors (prior injury, prior work restrictions, lower perceived recovery from injury, asymmetrical ankle dorsiflexion, decreased or asymmetrical performance on the Lower and Upper Quarter Y-Balance test, pain with movement, slower 2-mile run times, age, and sex) produced a highly sensitive and specific multivariate model for TLI in military servicemembers. CLINICAL RELEVANCE: A better understanding of characteristics associated with future injury risk can provide a foundation for prevention programs designed to reduce medical costs and time lost.
Entities:
Keywords:
injury prediction; intrinsic risk factors; movement tests; musculoskeletal injury
Authors: Scott W Shaffer; Deydre S Teyhen; Chelsea L Lorenson; Rick L Warren; Christina M Koreerat; Crystal A Straseske; John D Childs Journal: Mil Med Date: 2013-11 Impact factor: 1.437
Authors: Deydre S Teyhen; Scott W Shaffer; Robert J Butler; Stephen L Goffar; Kyle B Kiesel; Daniel I Rhon; Robert E Boyles; Daniel J McMillian; Jared N Williamson; Phillip J Plisky Journal: Mil Med Date: 2016-10 Impact factor: 1.437
Authors: Joseph A Alemany; Timothy T Bushman; Tyson Grier; Morgan K Anderson; Michelle Canham-Chervak; William J North; Bruce H Jones Journal: J Sci Med Sport Date: 2017-08-23 Impact factor: 4.319
Authors: Deydre S Teyhen; Daniel I Rhon; Robert J Butler; Scott W Shaffer; Stephen L Goffar; Danny J McMillian; Robert E Boyles; Kyle B Kiesel; Phillip J Plisky Journal: J Athl Train Date: 2016-10-03 Impact factor: 2.860
Authors: Ben R Hando; W Casan Scott; Jacob F Bryant; Juste N Tchandja; Ryan M Scott; Siddharrtha S Angadi Journal: Orthop J Sports Med Date: 2021-10-29
Authors: Daniel I Rhon; Deydre S Teyhen; Kyle Kiesel; Scott W Shaffer; Stephen L Goffar; Tina A Greenlee; Phillip J Plisky Journal: Arthrosc Sports Med Rehabil Date: 2022-01-28