Walking Forward is a community-based participatory research program in western South Dakota funded by the National Cancer Institute (NCI). The primary goal of this initiative is to address the high and ominously increasing cancer mortality rates among American Indians by facilitating access to innovative clinical trials, behavioral and genetic research and tailored patient navigation. The critical outcomes include: an unprecedented accrual rate of 25 percent in clinical trials, including cancer treatment and cancer control trials; a significant reduction in the number of missed treatment days among navigated American Indian cancer patients undergoing radiation therapy; and most importantly, establishment of trusting partnerships with the American Indian communities as reflected in enrollment in a genetic study involving the ataxia telangiectasia mutated gene. The results indicate that the Walking Forward approach presents an effective strategy to overcome the barriers to cancer care in this underserved community.
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Front Neurosci
December 2024
Department of Physical Therapy, Human Health Sciences, Graduate School of Medicine, Kyoto University, Kyoto, Japan.
Background: Understanding the muscle synergies shared between pedaling and walking is crucial for elucidating the mechanisms of human motor control and establishing highly individualized rehabilitation strategies. This study investigated how pedaling direction and speed influence the recruitment of walking-like muscle synergies.
Methods: Twelve healthy male participants pedaled at three speeds (60 RPM, 30 RPM, and 80 RPM) in two rotational directions (forward and backward).
Network
December 2024
Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, India.
Human Activity Recognition (HAR) systems are designed to continuously monitor human behaviour, mainly in the areas of entertainment and surveillance in intelligent home environments. In this manuscript, Human Activity Recognition utilizing optimized Attention Induced Multi head Convolutional Neural Network with Mobile Net V1 from Mobile Health Data (HAR-AMCNN-MNV1) is proposed. The input data is collected through MHEALTH and UCI HAR datasets.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Centre of Research, Education, Innovation and Intervention in Sport and Porto Biomechanics Laboratory, Faculty of Sport, University of Porto, 4200-450 Porto, Portugal.
Runners achieve forward locomotion through diverse techniques. However, understanding the behavior of the involved kinematical variables remains incomplete, particularly when running overground and along an intensity spectrum. We aimed to characterize the biomechanical and physiological adaptations while running at low, moderate, heavy and severe intensities.
View Article and Find Full Text PDFTraffic Inj Prev
December 2024
School of Vehicle and Mobility, State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China.
Objective: Understanding pedestrians' pre-crash avoidance kinematics is crucial for improving the identification of potential collision areas in interactions with highly automated vehicles (HAVs). Age significantly influences pedestrian avoidance velocity and the subsequent crash risks. However, current active safety systems in HAVs often overlook the influence of pedestrians' avoidance velocity and age on imminent accidents.
View Article and Find Full Text PDFGeriatr Gerontol Int
December 2024
Chengdu University of Traditional Chinese Medicine, Chengdu, China.
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