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

Effects of anteriorly-loaded treadmill walking on dynamic gait stability in young adults

BACKGROUND

Anteriorly-loaded walking is common in many occupations and may increase fall risk. Dynamic gait stability, defined by the Feasible Stability Region (FSR) theory, quantifies the kinematic relationship between the body's center of mass (COM) and base of support (BOS). FSR-based dynamic gait stability has been used to evaluate the fall risk.

RESEARCH QUESTION

How does front load carriage affect dynamic gait stability, step length, and trunk angle among young adults during treadmill walking?

METHODS

In this between-subject design study, 30 healthy young adults were evenly randomized into three load groups (0%, 10%, or 20% of body weight). Participants carried their assigned load while walking on a treadmill at a speed of 1.2 m/s. Body kinematics were collected during treadmill walking. Dynamic gait stability (the primary variable) was calculated for two gait events: touchdown and liftoff. Step length and trunk angle were measured as secondary variables. One-way analysis of variance was conducted to detect any group-related differences for all variables. Post-hoc analysis with Bonferroni correction was performed when main group differences were found.

RESULTS

No significant differences but medium to large effect sizes were found between groups for dynamic gait stability at touchdown (p = 0.194, η² = 0.114) and liftoff (p = 0.122, η² = 0.139). Trunk angle significantly increased (indicating backward lean) with the front load at touchdown (p < 0.001, η² = 0.648) and liftoff (p < 0.001, η² = 0.543). No significant between-group difference was found related to the step length (p = 0.344, η² = 0.076).

SIGNIFICANCE

Carrying a front load during walking significantly alters the trunk orientation and may change the COM-BOS kinematic relationship and, therefore, fall risk. The findings could inform the design of future studies focusing on the impact of anterior load carriage on fall risk during different locomotion.

The energetic, kinematic and kinetic responses to load carried on the back, on the head and in a doublepack

The determinants of energy saving phenomena reported for load carried on the head, back and in a doublepack remain unclear. This study compared the energetic, kinematic and kinetic responses to head (H), back (B) and doublepack (DP) loading. Fifteen volunteers walked on an instrumented treadmill at 3 km^.h^-1 with 0, 3, 12 and 20 kg in each loading method. Whole body motion, ground reaction forces (GRF) and metabolic cost were measured. H was less economical than B (p = 0.014) and DP (p = 0.010). H was also associated with increased step length (p = 0.045), decreased cadence (p = 0.001), greater trunk (p < 0.001) and hip (p < 0.001) extension and greater minimum vertical GRF (p = 0.001) than B and DP. In conclusion, no energy saving was found for head- or back-loading but economy may be improved with methods that cause smaller perturbations from unloaded walking. Practitioner summary: Energy saving phenomena have been reported for load carried on the head, back and in a doublepack, yet the determinants are unclear. This study shows that smaller perturbations from unloaded to loaded walking are associated with improved economy for certain load carriage conditions, such as the doublepack.

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

In endurance running, where fluid and nutritional support is not always readily available, the carriage of water and nutrition is essential. To compare the economy and physiological demands of different carriage systems, 12 recreational runners (mean age 22.8 ± 2.2 years, body mass index 24.5 ± 1.8 kg m⁻², VO₂max 50.4 ± 5.3 ml kg⁻¹ min⁻¹), completed four running tests, each of 60-min duration at individual running speeds (mean running speed 9.5 ± 1.1 km h⁻¹) on a motorized treadmill, after an initial exercise test. Either no load was carried (control) or loads of 1.0 kg, in a handheld water bottle, waist belt, or backpack. Economy was assessed by means of energy cost (CR), oxygen cost (O₂ cost), heart rate (HR), and rate of perceived exertion (RPE). CR [F(2,20) = 37.74, p < 0.01, η_p² = 0.79], O₂ cost [F(2,20) = 37.98, p < 0.01, η_p² = 0.79], HR [F(2,18) = 165.62, p < 0.01, η_p² = 0.95], and RPE [F(2,18) = 165.62, p < 0.01, η_p² = 0.95] increased over time, but no significant differences were found between the systems. Carrying a handheld water bottle, waist belt, or backpack, weighing 1.0 kg, during a 60-min run exhibited similar physiological changes. Runners’ choice may be guided by personal preference in the absence of differences in economy (CR, O₂ cost, HR, and RPE).

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

Lifting and carrying heavy objects is a major aspect of physically intensive jobs. Wearable sensors have previously been used to classify different ways of picking up an object, but have seen only limited use for automatic classification of load position and weight while a person is walking and carrying an object. In this proof-of-concept study, we thus used wearable inertial and electromyographic sensors for offline classification of different load positions (frontal vs. unilateral vs. bilateral side loads) and weights during gait. Ten participants performed 19 different carrying trials each while wearing the sensors, and data from these trials were used to train and evaluate classification algorithms based on supervised machine learning. The algorithms differentiated between frontal and other loads (side/none) with an accuracy of 100%, between frontal vs. unilateral side load vs. bilateral side load with an accuracy of 96.1%, and between different load asymmetry levels with accuracies of 75-79%. While the study is limited by a lack of electromyographic sensors on the arms and a limited number of load positions/weights, it shows that wearable sensors can differentiate between different load positions and weights during gait with high accuracy. In the future, such approaches could be used to control assistive devices or for long-term worker monitoring in physically demanding occupations.

A comparison of economy between two different backpack designs for runners

We compared two backpack designs (back/front or back only) in twelve recreational runners (age 22.0 ± 1.7years). An initial incremental exercise test (VO₂max 52.2 ± 4.7 ml kg^-1.min^-1) was conducted, followed by four tests of 20 min duration (running speed 9.8 ± 1.1 km/h) with loads carried of 0, 1 kg, 3 kg, and 6 kg with the two backpack designs in a randomized order. Economy was assessed by energy cost of running (CR), oxygen cost (O₂ cost), heart rate (HR) and rate of perceived exertion (RPE). Repeated measure ANOVA revealed a non-significant main effect for CR, O₂ cost, HR, RPE between systems. Post-hoc comparison of significant time × position interaction showed for CR, F(3,33) = 5.34, p < .01, η_p² = 0.33, and O₂ cost, F(3,33) = 5.15, p < .01, η_p² = 0.32, that carrying weight in the back/front were significantly lower after 20 min (CR: p = .02 and O₂ cost: p = .03). These results suggest, that for longer runs the equal distribution of weight is advantageous.

Inter-individual variability in load carriage economy and comparisons between different load conditions

Equivocal findings exist for the economy associated with load carried close to the body's centre of mass. Individual variation could explain some of the equivocal findings. This research aimed to examine the extent of individual variation in loaded walking economy. Eighteen females carried load on the back, head and split between the front and back. Individual variation in relative load carriage economy (ELI) was primarily assessed using standard deviation, coefficients of variation (CV) and intraclass correlation coefficients (ICC). There was large inter-individual variation in ELI values with highest mean CV's of 16%, 12% and 10% for head-, back- and combined front and back-loading. Mean ELI values were not significantly different between methods. The large amount of individual variation found here suggests future load carriage research should account for individual variation, particularly when considering sample size and when making inferences on the economy associated with different types of load carriage using group mean data.