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With life there is motion. Activity biomarkers signal important health and performance outcomes

Journal

JOURNAL OF SCIENCE AND MEDICINE IN SPORT
Volume 26, Issue -, Pages S3-S8

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.jsams.2023.01.009

Keywords

Accelerometry; Energy expenditure; Pattern recognition; Musculoskeletal injury; Wearable monitoring technologies; Physiological models

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Measures of human motion provide valuable information about health and physiological status. Examples of motion-based biomarkers include patterns of movement, quantified physical activity, and characteristic gaits. Practical measurement technologies and evolving physiological models and algorithms have made it possible to assess these biomarkers, with access to motion data and contextual information. Quantifying physical activity now goes beyond step counts, with accurate estimates of energy expenditure useful for weight management and health outcomes. Specific gaits can predict injury risk, and mood status can be inferred from certain types of human movement. Wearable systems can aid in the early identification of movement disorders and improve the management of chronic diseases. Emerging technologies, such as GPS and accelerometers, enable associations between health outcomes and performance, leading to better predictive models and algorithms.
Measures of human motion provide a rich source of health and physiological status information. This paper provides examples of motion-based biomarkers in the form of patterns of movement, quantified physical activity, and characteristic gaits that can now be assessed with practical measurement technologies and rapidly evolving physiological models and algorithms, with research advances fed by the increasing access to motion data and associated contextual information. Quantification of physical activity has progressed from step counts to good estimates of energy expenditure, useful to weight management and to activity-based health outcomes. Activity types and intensity durations are important to health outcomes and can be accurately classified even from carried smart phone data. Specific gaits may predict injury risk, including some re-trainable injurious running or modifiable load carriage gaits. Mood status is reflected in specific types of human movement, with slumped posture and shuffling gait signaling depression. Increased variability in body sway combined with contextual information may signify heat strain, physical fatigue associated with heavy load carriage, or specific neuropsychological conditions. Movement disorders might be identified earlier and chronic diseases such as Parkinson's can be better medically managed with automatically quantified information from wearable systems. Increased path tortuosity suggests head injury and dementia. Rapidly emerging wear-and-forget systems involving global positioning system and inertial navigation, triaxial accelerometry, smart shoes, and functional fiber-based clothing are making it easier to make important health and performance outcome associations, and further refine predictive models and algorithms that will improve quality of life, protect health, and enhance performance. Published by Elsevier Ltd on behalf of Sports Medicine Australia. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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