Modeling heart rate of individual and team manual handling with one hand using generalized additive mixed models

TL;DR

This study models heart rate changes during individual and team manual handling tasks using a statistical model to better understand physiological responses.

Contribution

The study introduces a Generalized Additive Mixed Model to predict heart rate during manual handling with high validity.

Findings

Heart rate increased significantly with higher workload and taller partner in team handling.

Nonlinear associations of resting heart rate, body weight, and body height variables were significant predictors of heart rate.

The model explained 89% of heart rate variation, outperforming linear-only models.

Abstract

Despite the fact that team manual handling is common in different working environments, the previous studies in this regard, particularly those with a physiological approach are quite limited. The present study is an attempt to model the heart rate (HR) of individual and team manual handling with one hand.

Twenty-five young men (aged 21.24±1.42 year) volunteered for this study. The experiments included individual and two-person handling of the load with three different weights with and without height difference. The participants’ HR was registered at the end of the route by a chest-strap pulse monitor and a polar watch according to the manufacturer's recommendation. A multivariate Generalized Additive Mixed Model (MGAMM) was used for modeling heart rate based on explanatory variables of workload, carry method, HRrest, body weight, height, knee height, shoulder height, elbow height, and hand height. The significance level of the tests was considered as <0.05.

Based on the MGAMM, the average HR (bpm) of participants increased as the workload increased (P<0.001). Handling the load with a taller person increased the HR compared to shorter partner (P<0.001). Moreover, the nonlinear associations of the resting HR (P<0.001), body weight (P<0.001), height (P<0.001), and the height of elbow, hand and knee (P<0.001) were statistically significant. The adjusted R2 of the model was 0.89 indicating that about 90 percent of the variations observed in HR could be explained by the variables in the model. This was greater than the model considering only linear effects (R2 =0.60).

The model obtained in this study can predict the heart rate of individual and team one-handed handling with high validity. The MGAMM can be used in modeling heart rate in manual handling.

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