Prediction of Maximum Static Grip Strength in a Standing Posture and with Preferred Grip Span in a Chinese Sample

Abstract

OCCUPATIONAL APPLICATIONSWe developed a model for predicting maximum hand grip strength. This model was derived from data on the maximum hand grip strength of a sample of Chinese adults in a standing posture using their preferred grip span. The model’s validity was supported by the high correlation found between the participants’ actual and predicted strengths, a low standard error of the estimate value, and a low mean absolute percentage error. The model provides practitioners with a simple and practical method for obtaining reference grip strengths for tasks that involve gripping in standing work postures, and can aid in designing hand tools that enable users to grip with their preferred grip span, both of which can help prevent hand injuries caused by a mismatch between workers’ grip capabilities and task demands.TECHNICAL ABSTRACT Background: Hand grip strength varies between populations. To design tasks and hand tools for a particular population, it is necessary to have normative data on that population’s maximum hand grip strength. Previous studies have developed models for predicting hand grip strength on the basis of demographic and anthropometric characteristics. However, few of these models considered working postures (e.g., standing) or individual preferences in grip span. Purpose: To develop and validate a model for predicting maximum static grip strength that considers an individual’s posture and preferred grip span. Method: Maximum hand grip strength was obtained from 200 Chinese adults, in a neutral standing position and with vertically oriented straight arms. Data from 180 of these adults were used to develop a prediction model that considered age, gender, and anthropometric characteristics, using stepwise multiple regression analysis. Data from the remaining 20 participants were used to validate the model, based on the correlation between actual and predicted strength, the standard error of the estimate (SEE), and the mean absolute percentage error (MAPE). We also compared our new prediction model with two published models. Results: Age, gender, and hand length were each significant predictors of hand grip strength. There was a high correlation between the actual and model-predicted strengths (r = 0.82; p < 0.001); the SEE was 55.9 N and the MAPE was 13.7%. Conclusion: The high correlation and low SEE and MAPE values support validity of our new model for predicting maximum hand grip strength. This model provides a convenient and practical method for predicting grip strength, for tasks that require users to grip something while standing with an adjustable grip span.