Few studies have examined the effect of load transfer to the hands when calculating lumbar moments during lifting tasks. The purpose of this study was to investigate this relationship in order to develop a model that provides an accurate estimate o f measured load transfer force to the hands and is applicable to an industrial setting. The effect of gender, load lifted, lift speed, lift style and subject strength were examined as possible variables to improve the prediction of load transfer force.

Ten healthy men and eleven healthy women, with no past history of back pain volunteered to participate in the study. Kinematic data were collected using the OPTOTRACK™, a 3-D motion tracking system and a portable video camera. Load transfer to the hands was measured as the total load minus measured values from an AMTI™ force plate. Two methods of estimating load transfer to the hands, called the SLOPE and POINT methods, were calculated and independently input into a quasidynamic hands-down link segment model in order to calculate lumbar moments.

Results of the study indicated that the SLOPE method of estimating load transfer to the hands was superior to the POINT method and thus resulted in lumbar moment estimations closer to the lumbar moment values obtained when the measured force values were used in calculation. The ability of the SLOPE method to estimate load transfer to the hands was improved when information about load lifted, lift style, gender and strength were considered. Regression analysis revealed the following prediction equation for measured load transfer force (MLTF), y, derived with the independent variables slope cubed load transfer force, (SCLTF), gender (G), lift style (ST), load weight (W) and subject strength (SS):​

MLTF = -5.996 + 1.044(SCLTF) - 0.873(W) + 8.964(G) + 0.157(ST) - 0.066(SS) r² = 0.887, SEE = 18.40 N, p<0.001

These variables are simple to collect in an industrial setting, which makes this strategy for estimating load transfer forces both improved and practical. However, the improvement was less than expected. Slope load transfer force (SLTF) alone significantly predicted MLTF (r² = 0.867). Therefore, ergonomists can use the SLOPE method to predict load transfer forces since the predictive power gained with the above regression equation may be negligible considering other sources of error for data collection in industry.