PT  - JOURNAL ARTICLE
AU  - Graham, Ryan B
AU  - Mir-Orefice, A
AU  - Mavor, M P
AU  - Bode, V G
AU  - Doyle, T L A
AU  - Kelly, K R
AU  - Silverman, A K
AU  - Sessoms, P H
TI  - Novel approaches to evaluate characteristics that affect military load carriage
AID  - 10.1136/military-2024-002899
DP  - 2025 May 21
TA  - BMJ Military Health
PG  - military-2024-002899
4099  - http://militaryhealth.bmj.com/content/early/2025/05/21/military-2024-002899.short
4100  - http://militaryhealth.bmj.com/content/early/2025/05/21/military-2024-002899.full
AB  - Carrying heavy body-borne loads, an essential component of a service member’s duties, is a significant injury risk factor. Physiological and biomechanical data can help illuminate the relationship between load carriage and injuries for service members. This review highlights characteristics that affect load carriage performance and summarises novel approaches to evaluate associated biomechanical changes. Personal characteristics, such as physical fitness and body composition, are good predictors of injury risk and load carriage ability. Effective training programmes can improve load carriage ability by altering fitness and body composition; however, careful planning is needed to integrate training with regular duties to prevent overtraining and, consequently, reduce injury risk in service members. Recent research supports the need for sex-specific training programmes since men and women achieve different training outcomes from similar stimuli. To further minimise injury risk, it is necessary to consider the effects of equipment characteristics (eg, load distribution, form and comfort) on physiological and biomechanical responses. Moreover, novel approaches to evaluate the effects of the various characteristics on load carriage performance are summarised in this review. Markerless motion capture and inertial measurement units have recently been used to evaluate kinematic changes while wearing various combat ensembles. Musculoskeletal modelling can complement kinematic analyses by evaluating internal joint mechanics during dynamic movements. By using frameworks that can leverage modelling approaches in real-time, service members can receive data-driven biofeedback on their load carriage performance and understand the loading experienced by their tissues to ultimately help mitigate their injury risks.