Task demand and load carriage experience affect gait variability among military cadets

Army Combat Fitness Test Scores Moderate Cognitive Function Improvements After a Ruck March: A Hierarchical Linear Model Approach

ABSTRACT

Sax van der Weyden, M, Merrigan, JJ, Newman, K, Hahn, J, and Martin, J. Army Combat Fitness Test scores moderate cognitive function improvements after a ruck march: A hierarchical linear model approach. J Strength Cond Res 38(7): 1314-1320, 2024-Heavy load carriage is a common task for military personnel and can lead to undue physical and mental fatigue. The purpose of this study was to assess effects of load carriage on cognitive function (CF) and the role individual characteristics play in CF responses. Anthropometrics/body composition, demographics, and Army Combat Fitness Test (ACFT) scores were gathered from 47 Reserve Officers' Training Corps cadets. Cadets' CF was assessed through the concentration task grid (CTG) before (preruck), immediately after (postruck), and 3 weeks after (follow-up) a 16.09- to 19.31-km ruck march. Hierarchical linear modeling was used, with α < 0.05. The final model revealed growth, or improvement, in CF from preruck to postruck with growth decelerating, but still improved, at follow-up. The load carriage bout improved CF in most cadets, with average growth from 5.67- to 7.20-grid squares found. However, postruck CF did not return to the same level as a controlled follow-up (9.69). Sex (2.46, female), familiarity with the CTG (1.99), ACFT total scores (0.009), and relative ruck mass (-0.09) were all significant predictors of postruck CF. Army Combat Fitness Test scores (0.008) were also significant predictors of the slope between preruck and postruck, such that an ACFT score 59 points above average would result in a 1-grid square greater improvement in CF postruck. The significance of ACFT scores, lower relative ruck mass, and familiarization highlights the importance physical and mental preparation for desirable CF outcomes after occupational tasks.

Effect of Equipment on the Accuracy of Accelerometer-Based Human Activity Recognition in Extreme Environments

A little explored area of human activity recognition (HAR) is in people operating in relation to extreme environments, e.g., mountaineers. In these contexts, the ability to accurately identify activities, alongside other data streams, has the potential to prevent death and serious negative health events to the operators. This study aimed to address this user group and investigate factors associated with the placement, number, and combination of accelerometer sensors. Eight participants (age = 25.0 ± 7 years) wore 17 accelerometers simultaneously during lab-based simulated mountaineering activities, under a range of equipment and loading conditions. Initially, a selection of machine learning techniques was tested. Secondly, a comprehensive analysis of all possible combinations of the 17 accelerometers was performed to identify the optimum number of sensors, and their respective body locations. Finally, the impact of activity-specific equipment on the classifier accuracy was explored. The results demonstrated that the support vector machine (SVM) provided the most accurate classifications of the five machine learning algorithms tested. It was found that two sensors provided the optimum balance between complexity, performance, and user compliance. Sensors located on the hip and right tibia produced the most accurate classification of the simulated activities (96.29%). A significant effect associated with the use of mountaineering boots and a 12 kg rucksack was established.