A predictive logistic regression equation for neck pain in helicopter aircrew

Area of Science:

• Aviation Medicine
• Occupational Health
• Biomechanics

Background:

• Neck strain is a common issue in helicopter aircrew.
• Previous studies lacked a comprehensive approach combining questionnaire and physiological data.
• Cervical spine musculature in aircrew requires further investigation.

Purpose of the Study:

• To identify predictors of neck pain in helicopter aircrew.
• To develop a simple assessment tool for neck strain.
• To combine questionnaire data with physiological measurements for multivariate analysis.

Main Methods:

• Collected questionnaire data on neck pain, flight history, and physical fitness from 40 aircrew members.
• Performed isometric testing of cervical spine muscles (flexion, extension, lateral flexion) measuring maximal voluntary contraction (MVC) and time-to-fatigue (TTF).
• Utilized electromyography (EMG), near-infrared spectroscopy (NIRS), and ratings of perceived exertion (RPE) during testing; analyzed data using logistic regression.

Main Results:

• A logistic regression model accurately predicted neck pain in a subset of aircrew (N=19).
• The predictive model utilized two easily measurable variables: longest night vision goggle (NVG) mission duration (hours) and aircrew height (meters).
• Cross-validation with the remaining subjects (N=21) confirmed the accuracy of the developed regression equation.

Conclusions:

• A simple regression equation can predict neck strain in helicopter aircrew.
• The formula combines longest NVG mission duration and aircrew height.
• This assessment tool is practical for global operational units, requiring no specialized equipment or training.