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Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting

Liew, Bernard XW and Rugamer, David and Stocker, Almond and De Nunzio, Alessandro Marco (2019) 'Classifying neck pain status using scalar and functional biomechanical variables – development of a method using functional data boosting.' Gait and Posture. ISSN 0966-6362

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Abstract

Background Individuals with neck pain have different movement and muscular activation (collectively termed as biomechanical variables) patterns compared to healthy individuals. Incorporating biomechanical variables as covariates into prognostic models is challenging due to the high dimensionality of the data. Research question What is the classification performance of neck pain status of a statistical model which uses both scalar and functional biomechanical covariates? Methods Motion capture with electromyography assessment on the sternocleidomastoid, splenius cervicis, erector spinae, was performed on 21 healthy and 26 individuals with neck pain during walking over three gait conditions (rectilinear, curvilinear clockwise (CW) and counterclockwise (CCW)). After removing highly collinear variables, 94 covariates across the three conditions were used to classify neck pain status using functional data boosting (FDboost). Results Two functional covariates trunk lateral flexion angle during CCW gait, and trunk flexion angle during CW gait; and a scalar covariate, hip jerk index during CCW gait were selected. The model achieved an estimated AUC of 80.8%. For hip jerk index, an increase in hip jerk index by one unit increased the log odds of being in the neck pain group by 0.37. A 1° increase in trunk lateral flexion angle throughout gait alone reduced the probability of being in the neck pain group from 0.5 to 0.15. A 1° increase in trunk flexion angle throughout gait alone increased the probability of being in the neck pain group from 0.5 to 0.9. Significance Interpreting the physiological significance of the extracted covariates, with other biomechanical variables, suggests that individuals with neck pain performed curvilinear walking using a stiffer strategy, compared to controls; and this increased the risk of being in the neck pain group. FDboost can produce clinically interpretable models with complex high dimensional data and could be used in future prognostic modelling studies in neck pain research.

Item Type: Article
Uncontrolled Keywords: Walking, Biomechanics, Neck pain, Machine learning, Functional regression
Divisions: Faculty of Science and Health > Sport, Rehabilitation and Exercise Sciences, School of
Depositing User: Elements
Date Deposited: 13 Dec 2019 10:49
Last Modified: 13 Dec 2020 02:00
URI: http://repository.essex.ac.uk/id/eprint/26230

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