Can a Psychologic Profile Predict Successful Return to Full Duty After a Musculoskeletal Injury?

Background: Psychologic variables have been shown to have a strong relationship with recovery from injury and return to work or sports. The extent to which psychologic variables predict successful return to work in military settings is unknown.

Questions/purposes: In a population of active duty soldiers, (1) can a psychologic profile determine the risk of injury after return to full duty? (2) Do psychologic profiles differ between soldiers sustaining injuries in the spine (thoracic or lumbar) and those with injuries to the lower extremities?

Methods: Psychologic variables were assessed in soldiers returning to full, unrestricted duty after a recent musculoskeletal injury. Most of these were noncombat injuries from work-related physical activity. Between February 2016 and September 2017, 480 service members who were cleared to return to duty after musculoskeletal injuries (excluding those with high-velocity collisions, pregnancy, or amputation) were enrolled in a study that tracked subsequent injuries over the following year. Of those, we considered individuals with complete 12-month follow-up data as potentially eligible for analysis. Based on that, approximately 2% (8 of 480) were excluded because they did not complete baseline surveys, approximately 2% (11 of 480) were separated from the military during the follow-up period and had incomplete injury data, 1% (3 of 480) were excluded for not serving in the Army branch of the military, and approximately 2% (8 of 480) were excluded because they were not cleared to return to full duty. This resulted in 450 soldiers analyzed. Individuals were 86% (385 of 450) men; 74% (331 of 450) had lower extremity injuries and 26% (119 of 450) had spinal injuries, including soft tissue aches and pains (for example, strains and sprains), fractures, and disc herniations. Time-loss injury within 1 year was the primary outcome. While creating and validating a new prediction model using only psychological variables, 19 variables were assessed for nonlinearity, further factor selection was performed through elastic net, and models were internally validated through 2000 bootstrap iterations. Performance was deciphered through calibration, discrimination (area under the curve [AUC]), R 2 , and calibration in the large. Calibration assesses predicted versus actual risk by plotting the x and y intersection of these values; the more similar predicted risk values are to actual ones, the closer the slope of the line formed by the intersection points of all subjects is to equaling "1" (optimal calibration). Likewise, perfect discrimination (predicted injured versus actual injured) presents as an AUC of 1. Perfect calibration in the large would equal 0 because it represents the average predicted risk versus the actual outcome rate. Sensitivity analyses stratified groups by prior injury region (thoracic or lumbar spine and lower extremity) as well as the severity of injury by days of limited duty (moderate [7-27 days] and severe [28 + days]).

Results: A model comprising primarily psychologic variables including depression, anxiety, kinesiophobia, fear avoidance beliefs, and mood did not adequately determine the risk of subsequent injury. The derived logistic prediction model had 18 variables: R 2 = 0.03, calibration = 0.63 (95% confidence interval [CI] 0.30 to 0.97), AUC = 0.62 (95% CI 0.52 to 0.72), and calibration in the large = -0.17. Baseline psychologic profiles between body regions differed only for depression severity (mean difference 1 [95% CI 0 to 1]; p = 0.04), with greater mean scores for spine injuries than for lower extremity injuries. Performance was poor for those with prior spine injuries compared with those with lower extremity injuries (AUC 0.50 [95% CI 0.42 to 0.58] and 0.63 [95% CI 0.57 to 0.69], respectively) and moderate versus severe injury during the 1-year follow-up (AUC 0.61 [95% CI 0.51 to 0.71] versus 0.64 [95% CI 0.64 to 0.74], respectively).

Conclusion: The psychologically based model poorly predicted subsequent injury. This study does not minimize the value of assessing the psychologic profiles of injured athletes, but rather suggests that models looking to identify injury risk should consider a multifactorial approach that also includes other nonpsychologic factors such as injury history. Future studies should refine the most important psychologic constructs that can add the most value and precision to multifactorial models aimed at identifying the risk of injury.

Level Of Evidence: Level III, prognostic study.