Title: A mathematical modelling approach for analysing the risk of low back pain in college students using student desks

Authors: Priyadarshini Dasgupta; Lisa M. Kuhn; Kazim Sekeroglu; Aadhar Prasai

Addresses: Department of Industrial and Engineering Technology, Southeastern Louisiana University, Hammond, LA 70402, USA ' Department of Mathematics, Southeastern Louisiana University, Hammond, LA 70402, USA ' Department of Computer Science, Southeastern Louisiana University, Hammond, LA 70402, USA ' Department of Mathematics, Southeastern Louisiana University, Hammond, LA 70402, USA

Abstract: Low back pain (LBP) is frequently experienced by the occupational population who are necessitated to use chairs daily. At least one study has shown that disc degeneration can start as early as the teenage years. Moreover, college students spend excessive amounts of time sitting on non-ergonomic desks. The aim of our study was to determine if college students are entering the work force predisposed to LBP. An anonymous survey was conducted on 280 students and modelling was conducted using three approaches: machine learning, dynamical systems, and regression analysis. Supervised machine learning methods showed back pain can be predicted with high accuracy from the questionnaire. Mathematical predictive modelling was employed to determine the risk of chronic back pain as a result of using the student desks. Results from the models indicated college students are experiencing significantly more back pain than has been reported in studies on the general population.

Keywords: mathematical modelling; predictive analysis; disc degeneration; low back pain; LBP; machine learning.

DOI: 10.1504/IJHFE.2023.128577

International Journal of Human Factors and Ergonomics, 2023 Vol.10 No.1, pp.85 - 112

Received: 14 Apr 2022
Accepted: 09 Aug 2022
Published online: 26 Jan 2023 *

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