Military medicine
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To support soldier readiness and mitigate the mental health consequences of deployments, Army regulation mandates soldiers to receive Deployment Cycle Resilience Training (DCRT) throughout their deployment cycle. A recent evaluation revealed several issues with the existing version that threatened the relevancy and usefulness of the training. The present article details the systematic approach taken by the Research Transition Office at the Walter Reed Army Institute of Research to revise the DCRT curriculum and presents the revision updates that are now included in DCRT version 3. ⋯ The revisions outlined in this article enhance the training quality and potential effectiveness of DCRT, which can positively influence soldier and family readiness and mission success. Furthermore, the deliberate and iterative curriculum revision process can serve as a guide to other curriculum development projects, specifically within the military context. Implementation considerations and potential limitations are provided, and future directions are discussed to include the ongoing evaluation.
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Traumatic brain injury (TBI) is highly prevalent among active duty service members (ADSMs) and imposes a significant health burden, particularly on mental health (e.g., post-traumatic stress disorder [PTSD] and depressive symptoms). Little is known about how TBI setting characteristics impact PTSD and depressive symptom expression in service members undergoing interdisciplinary TBI care. ⋯ There was a differential impact of TBI settings, particularly between TBI sustained before military service and that from combat deployment among ADSMs enrolled in outpatient TBI programs. This may be indicative of differences in the characteristics of these environments (e.g., injury severity) or the impact of such an event during recovery from current TBIs. The large percentage of ADSMs who present with clinically-elevated mental health symptoms after treatment may suggest the need for additional resources to address mental health needs before, during, and after treatment in TBI programs.
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This study quantified parameters related to muscle morphology using a group of upright seated female and male volunteers with a head-supported mass. ⋯ The cross-sectional area, angulation, and centroid radii data for flexor and extensor muscles of the cervical spine serve as a dataset that may be used to better define morphologies in computational models and obtain segmental motions and loads under external mechanical forces. These data can be used in computational models for injury prevention, mitigation, and readiness.
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Multicenter Study Observational Study
Relation of Aerobic Activity to Cognition and Well-being in Chronic Mild Traumatic Brain Injury: A LIMBIC-CENC Study.
Because chronic difficulties with cognition and well-being are common after mild traumatic brain injury (mTBI) and aerobic physical activity and exercise (PAE) is a potential treatment and mitigation strategy, we sought to determine their relationship in a large sample with remote mTBI. ⋯ An association between the aerobic activity level and the preselected primary cognitive performance outcome was not demonstrated using this study sample and methods. However, higher aerobic activity levels were associated with better subjective well-being. This supports a clinical recommendation for regular aerobic exercise among persons with chronic or remote mTBI. Future longitudinal analyses of the exercise-cognition relationship in chronic mTBI populations are recommended.
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Remote military operations require rapid response times for effective relief and critical care. Yet, the military theater is under austere conditions, so communication links are unreliable and subject to physical and virtual attacks and degradation at unpredictable times. Immediate medical care at these austere locations requires semi-autonomous teleoperated systems, which enable the completion of medical procedures even under interrupted networks while isolating the medics from the dangers of the battlefield. However, to achieve autonomy for complex surgical and critical care procedures, robots require extensive programming or massive libraries of surgical skill demonstrations to learn effective policies using machine learning algorithms. Although such datasets are achievable for simple tasks, providing a large number of demonstrations for surgical maneuvers is not practical. This article presents a method for learning from demonstration, combining knowledge from demonstrations to eliminate reward shaping in reinforcement learning (RL). In addition to reducing the data required for training, the self-supervised nature of RL, in conjunction with expert knowledge-driven rewards, produces more generalizable policies tolerant to dynamic environment changes. A multimodal representation for interaction enables learning complex contact-rich surgical maneuvers. The effectiveness of the approach is shown using the cricothyroidotomy task, as it is a standard procedure seen in critical care to open the airway. In addition, we also provide a method for segmenting the teleoperator's demonstration into subtasks and classifying the subtasks using sequence modeling. ⋯ Results indicate that the proposed interaction features for the segmentation and classification of surgical tasks improve classification accuracy. The proposed method for learning surgemes from demonstrations exceeds popular methods for skill learning. The effectiveness of the proposed approach demonstrates the potential for future remote telemedicine on battlefields.