A comparison of economy and sagittal plane trunk movements among back-, back/front- and head-loading

Abstract

It has been suggested that freedom of movement in the trunk could influence load carriage economy. This study aimed to compare the economy and sagittal plane trunk movements associated with three load carriage methods that constrain posture differently. Eighteen females walked at 3 kmh with loads of 0, 3, 6, 9, 12, 15 and 20 kg carried on the back, back/front and head. Load carriage economy was assessed using the Extra Load Index (ELI). Change in sagittal plane trunk forward lean and trunk angle excursion from unloaded to loaded walking were assessed. Results show no difference in economy between methods (p = .483), despite differences in the change in trunk forward lean (p = .001) and trunk angle excursion (p = .021) from unloaded to loaded walking. We conclude that economy is not different among the three methods of load carriage, despite significant differences in sagittal plane trunk movements. Practitioner summary: This article shows, based on mean data, that there is no difference in economy among back, back/front and head-loading, despite differences in trunk movement. It is possible a combination of factors align to influence individual economy, rather than a single set of factors, applicable to all individuals for each method.

The Optimal Weight Carriage System for Runners: Comparison Between Handheld Water Bottles, Waist Belts, and Backpacks

“…In loaded running, it has been shown that a front/back backpack design is more economic, especially after prolonged running (Scheer et al, 2020b). Postural sway and trunk movement from backpack usage may also negatively affect energy expenditure (Folland et al, 2017;Hudson et al, 2018).…”

“…Those studies generally examine loads that are heavier than those encountered in loaded running, as well as slower locomotion speeds, thus making the transfer of results to runners challenging or impossible (Knapik et al, 1996;Simpson et al, 2017). Studies on these populations showed that load position may influence energy expenditure (Lloyd and Cooke, 2000;Hudson et al, 2018), suggesting that load close to the center of mass may be the most effective way of carrying load, especially when walking (Knapik et al, 1996;Abe et al, 2004;Hudson et al, 2018).…”

“…Energy expenditure can also be increased by biomechanical alterations such as postural sway and trunk movement produced by using backpacks (Folland et al, 2017;Hudson et al, 2018). Evenly distributing the weight in front/back backpack designs can be more economic during walking (Lloyd and Cooke, 2000) and running; however, this may only become apparent after prolonged running (Scheer et al, 2020b).…”

Load Position and Weight Classification during Carrying Gait Using Wearable Inertial and Electromyographic Sensors

“…Furthermore, the study only included a few different weights, which were not tailored to individual participants’ abilities; classification into, e.g., loads that are easy vs. difficult to lift for a specific participant may have resulted in very different results. The study also only included one frontal box position as opposed to, e.g., holding a box low or high in front of the body, and did not include any back load, commonly seen in other studies of carrying behavior [ 23 , 26 , 27 ]. All participants also walked at a self-selected walking speed, which may not have adequately captured differences between slow and fast gait.…”

“…Conversely, carrying behavior has been studied with wearable sensors in significantly less detail. While camera-based studies have shown significant differences between different load locations and weights [ 22 , 23 , 24 , 25 , 26 , 27 , 28 ], to the best of our knowledge only four studies have attempted to automatically classify different load carriage types with wearable sensors, which would be the prerequisite for control of assistive devices. Benocci et al [ 29 ] used four IMUs and a k-nearest-neighbor classifier to differentiate among six backpack positions (all with the same weight): none, on both shoulders, on only left shoulder, on only right shoulder, in right hand, and in left hand; they achieved an overall classification accuracy of 96.7%.…”