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Sensors 2017, 17(1), 211; doi:10.3390/s17010211

Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I

1
Laboratory of Welfare Technologies—Telehealth & Telerehabilitation, SMI, Department of Health Science and Technology, Aalborg University, Aalborg 9100, Denmark
2
Medical Informatics Group, Department of Health Science and Technology, Aalborg University, Aalborg 9100, Denmark
3
Department of Cardiology, Copenhagen University Hospital Bispebjerg, Copenhagen NV 2400, Denmark
*
Author to whom correspondence should be addressed.
Academic Editors: Octavian Adrian Postolache, Alex Casson and Subhas Mukhopadhyay
Received: 2 November 2016 / Revised: 16 January 2017 / Accepted: 17 January 2017 / Published: 22 January 2017
(This article belongs to the Special Issue Sensing Technology for Healthcare System)
View Full-Text   |   Download PDF [1944 KB, uploaded 22 January 2017]   |  

Abstract

Commercial self-monitoring devices are becoming increasingly popular, and over the last decade, the use of self-monitoring technology has spread widely in both consumer and medical markets. The purpose of this study was to evaluate five commercially available self-monitoring devices for further testing in clinical applications. Four activity trackers and one sleep tracker were evaluated based on step count validity and heart rate validity. Methods: The study enrolled 22 healthy volunteers in a walking test. Volunteers walked a 100 m track at 2 km/h and 3.5 km/h. Steps were measured by four activity trackers and compared to gyroscope readings. Two trackers were also tested on nine subjects by comparing pulse readings to Holter monitoring. Results: The lowest average systematic error in the walking tests was −0.2%, recorded on the Garmin Vivofit 2 at 3.5 km/h; the highest error was the Fitbit Charge HR at 2 km/h with an error margin of 26.8%. Comparisons of pulse measurements from the Fitbit Charge HR revealed a margin error of −3.42% ± 7.99% compared to the electrocardiogram. The Beddit sleep tracker measured a systematic error of −3.27% ± 4.60%. Conclusion: The measured results revealed the current functionality and limitations of the five self-tracking devices, and point towards a need for future research in this area. View Full-Text
Keywords: activity tracker; pulse; physical activity; gait; slow walking; step detection; heart rate activity tracker; pulse; physical activity; gait; slow walking; step detection; heart rate
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Leth, S.; Hansen, J.; Nielsen, O.W.; Dinesen, B. Evaluation of Commercial Self-Monitoring Devices for Clinical Purposes: Results from the Future Patient Trial, Phase I. Sensors 2017, 17, 211.

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