Descriptive analysis of the kinematics of the neck in its three directions of movement, RA, FL and FE
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Abstract
Introduction: numerous studies use numerical variables of cervical movement to estimate the severity of pathologies, the correlation with disability levels is limited, rarely exceeding 0.4, and even lower in cases of non-specific neck pain. Objectives: this study proposes the use of Functional Data Analysis (FDA), specifically scalar-over-function regression, to predict the Cervical Disability Index (NDI) in individuals with non-specific neck pain, using whole cervical movement as a predictor. Methodology: a cross-sectional study was conducted with a sample of individuals with non-specific neck pain. Cervical kinematics were recorded in the planes of axial rotation (AR), flexion-extension (EF) and lateral flexion (FL) using inertial/electromagnetic sensors. Unlike the traditional peak-based approach, Functional Data Analysis (FDA) was employed to treat entire motion curves as units of information. Scalar-over-function regression models were implemented, using both position and angular velocity curves to predict the Cervical Disability Index (NDI) score. Results: the implementation of several functional regression models doubled the multiple correlation coefficient obtained with scalar predictors. Conclusions: the most effective model, which uses angular velocity curves, achieved a multiple correlation coefficient of 0.64. In addition to their greater predictive power, functional models allow for a deeper interpretation of the relationship between kinematic curves and NDI, identifying which segments of the curves most influence NDI variations. General area of study: Biomechanics. Specific area of study: Musculoskeletal model. Type of item: original.
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