A Predictive Logistic Regression Equation for Neck Pain in Helicopter Aircrew

Introduction: While many studies have investigated neck strain in helicopter aircrew, no one study has used a comprehensive approach involving multivariate analysis of questionnaire data in combination with physiological results related to the musculature of the cervical spine. Methods: There were 40 aircrew members who provided questionnaire results detailing lifetime prevalence of neck pain, flight history, physical fitness results, and physiological variables. Isometric testing data for flexion (Fix), extension (Ext), and right (RFlx) and left (LFlx) lateral flexion of the cervical spine that included maximal voluntary contraction (MVC) force and submaximal exercise at 70% MCV until time-to-fatigue (TTF) was also collected. Muscles responsible for the work performed were monitored with electromyography (EMG) and near-infrared spectroscopy (NIRS) and the associated ratings of perceived exertion (RPE) were collected simultaneously. Results were compiled and analyzed by logistic regression to identify the variables that were predictive of neck pain. Results: While many variables were included in the logistic regression, the final regression equation required two, easy to measure variables. The longest single night vision goggle (NVG) mission (NVGmax; h) combined with the height of the aircrew member in meters (m) provided an accurate logistic regression equation for approximately one-half of our sample (N = 19). Cross-validation of the remaining subjects (N = 21) confirmed this accuracy. Conclusion: Our regression equation is simple and can be used by global operational units to provide a cursory assessment without the need for acquiring specialized equipment or training.