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Original Research

Development of an Ergonomic Tool to Predict Carpal Tunnel Syndrome Risk Based on Estimated Carpal Tunnel Pressure

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Pages 32-42 | Received 07 Aug 2017, Accepted 15 Mar 2018, Published online: 27 Mar 2018
 

Abstract

OCCUPATIONAL APPLICATIONS An ergonomic tool that predicts Carpal Tunnel Syndrome (CTS) risk based on predicted carpal tunnel pressure (CTP) was developed and a preliminary evaluation was made in a large manufacturing environment. Elevated CTP has been associated with CTS. However, CTP is invasive to measure in vivo, and is thus not practical in an applied setting. This project represents the development of an ergonomic tool based on predicted CTP that could be an asset to ergonomists both in job evaluation and (re)design to reduce occupational CTS risk in workers with otherwise normal wrists.

TECHNICAL ABSTRACT Background: CTS remains an important issue in the workplace. Increased carpal tunnel pressure (CTP) may lead to the aggravation or development of CTS. A CTP of 30 mmHg has been used as a threshold limit value for CTS risk. Deviation from a neutral wrist, neutral forearm, and relaxed fingers results in an increase in CTP. Fingertip loading has also been shown to increase CTP independently of posture. Purpose: To develop an ergonomic tool to predict carpal tunnel syndrome (CTS) risk based on the predicted carpal tunnel pressure (CTP) in healthy wrists. Method: A tool was developed to predict CTS-risk based on CTP determined from the literature. The tool was evaluated by comparing the output of the tool (CTS risk) to the incidence of CTS in a large manufacturing environment. Results: The model predicted a mean (S), time-weighted CTP of 21.3 (0.4) mmHg (range 18.5–27.8 mmHg). Evaluative results were promising, as CTS risk was slightly higher in jobs with a historical incidence of CTS. Conclusion: While the tool predicted CTS risk based on CTP, too few CTS claims existed to develop a strong correlation. Further refinement and investigation are needed to include combined postures and mechanical compression, and to further validate the tool.

Additional information

Funding

This work was supported by AUTO21 Network of Centres of Excellence (ID: AE403-AME), Natural Sciences and Engineering Research Council of Canada (Discovery Grant #217382-09).

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