McDermott, Clare M., McCarren, Andrew ORCID: 0000-0002-7297-0984, Moran, Kieran ORCID: 0000-0003-2015-8967 and Moyna, Niall ORCID: 0000-0003-1061-8528 (2017) Concurrent validity of Fitbit Charge HR and Microsoft Band 2 to measure heart rate. In: Faculty of Sport and Exercise Medicine Conference , RCSI, 15-16 Sept 2017, Dublin, Ireland.
Abstract
Purpose: Wrist-worn monitors are developed to unobtrusively measure heart rate (HR) at rest and during exercise. assessed the concurrent validity and reliability of the Microsoft Band 2 (Microsoft-Band2) and Fitbit Charge HR (Fitbit) to measure HR at rest and during exercise.
Methods: Healthy men (n=12) and women (n=12) (mean (± SD); age 24.3 ± 3.1 yr) were tested on two occasions separated by at least 7 d. The same protocol was used during each visit and consisted of 3-min conditions in the following order - supine, sitting, 6 km.h-1 walk, 10 km.h-1 run, and 12.km.h-1 run. HR was continuously measured using a Holter monitor, Microsoft-Band2, and Fitbit, and averaged across each 3-min condition. A Bland Altman analysis was conducted to calculate the intervals of agreement (95%). A 2 tailed t-test at α = 0.05 was also used to compare the mean differences in measurements with the Holter for both devices and an F-test (α = 0.05) was used to compare the measurement dispersion characteristics of both devices.
Results: The intervals of agreement for the Fitbit had comparable dispersion characteristics with the Microsoft-Band2 with the exception of the supine condition (p = 0.004). The difference between Fitbit and Holter are significantly further from zero than the difference between Microsoft-Band2 and Holter for sitting (p = 0.004) and 6 km.h-1-walk (p = 0.001).
Conclusion: Microsoft-Band2 is more accurate than Fitbit at seated rest and during low intensity exercise, walking, and is comparable to Fitbit at 10km.h-1 run.
Metadata
Item Type: | Conference or Workshop Item (Poster) |
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Event Type: | Conference |
Refereed: | No |
Subjects: | Medical Sciences > Sports sciences |
DCU Faculties and Centres: | DCU Faculties and Schools > Faculty of Science and Health > School of Health and Human Performance Research Initiatives and Centres > INSIGHT Centre for Data Analytics DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing |
Use License: | This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License |
Funders: | European Union's Horizon 2020 Framework Programme for Research and Innovation Action under Grant Agreement no.643491 |
ID Code: | 22007 |
Deposited On: | 15 Sep 2017 15:25 by Giulia Migliorato . Last Modified 26 Jun 2019 10:38 |
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