Rim-to-Rim wearables at the canyon for health (R2R WATCH): Correlation of clinical markers of stress with physiological COTS data

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Abstract

Commercial off-the-shelf (COTS) wearable devices can provide easily deployable physiologic measurement systems that generate large amounts of crucial health status data. This data, although similar to physiologic data recorded and used routinely in the health care environment, lacks validation in the non-clinical environment. To address this gap in knowledge and to translate clinical expertise to the field we examined healthy volunteers attempting a strenuous task of crossing the Grand Canyon from rim to rim (R2R) in a single day. Subjects completed a pre-crossing questionnaire with baseline biometric measurements and blood collection for analysis of a comprehensive metabolic panel. Enrolled subjects were then asked to wear COTS wearable fitness devices as they attempted the crossing. Subjects were asked to provide a post-crossing questionnaire, repeat biometric measurements and blood collections. We obtained 52 complete sets of pre- and post-hike blood samples. We identified multiple significant changes in metabolic measurements consistent with expected stresses endured. In addition to the subjective fatigue expectedly reported by subjects, subjects had signs of significant muscle breakdown, yet no subject required immediate medical attention upon completing the task. We linked these clinical markers of stress to the physiologic output from COTS wearable devices and are now able to translate the output measures of these devices to meaningful clinical outcomes. In addition, we have begun to establish new expected ranges for physiologic data during extreme stress that does not require immediate medical attention. This data is crucial to defining usage parameters for wearable devices in deployed field settings.

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Jelinkova, L., Pearce, E., Bossart, C., Garcia, R., & Femling, J. (2017). Rim-to-Rim wearables at the canyon for health (R2R WATCH): Correlation of clinical markers of stress with physiological COTS data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10284 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I, pp. 312–322). Springer Verlag. https://doi.org/10.1007/978-3-319-58628-1_25

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