A Pilot Study Validating Select Research-Grade and Consumer-Based Wearables Throughout a Range of Dynamic Exercise Intensities in Persons With and Without Type 1 Diabetes: A Novel Approach
| Autor: | Michael C. Riddell, Veronica K. Jamnik, Loren Yavelberg, Dessi P. Zaharieva, Ali Cinar |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Adult
Male medicine.medical_specialty Endocrinology Diabetes and Metabolism Biomedical Engineering Physical activity Wearable computer Special Section: Combining Diabetes Data from Wearable Devices Pilot Projects 030209 endocrinology & metabolism Bioengineering Artificial pancreas Wearable Electronic Devices Young Adult 03 medical and health sciences 0302 clinical medicine Physical medicine and rehabilitation Heart Rate Primary prevention Accelerometry Internal Medicine medicine Humans Exercise Wearable technology Type 1 diabetes business.industry Calorimetry Indirect 030229 sport sciences medicine.disease Diabetes Mellitus Type 1 Female Energy Metabolism business Anaerobic exercise |
| Zdroj: | Journal of Diabetes Science and Technology. 12:569-576 |
| ISSN: | 1932-2968 |
| DOI: | 10.1177/1932296817750401 |
| Popis: | Background: The increasing popularity of wearable technology necessitates the evaluation of their accuracy to differentiate physical activity (PA) intensities. These devices may play an integral role in customizing PA interventions for primary prevention and secondary management of chronic diseases. For example, in persons with type 1 diabetes (T1D), PA greatly affects glucose concentrations depending on the intensity, mode (ie, aerobic, anaerobic, mixed), and duration. This variability in glucose responses underscores the importance of implementing dependable wearable technology in emerging avenues such as artificial pancreas systems. Methods: Participants completed three 40-minute, dynamic non-steady-state exercise sessions, while outfitted with multiple research (Fitmate, Metria, Bioharness) and consumer (Garmin, Fitbit) grade wearables. The data were extracted according to the devices’ maximum sensitivity (eg, breath by breath, beat to beat, or minute time stamps) and averaged into minute-by-minute data. The variables of interest, heart rate (HR), breathing frequency, and energy expenditure (EE), were compared to validated criterion measures. Results: Compared to deriving EE by laboratory indirect calorimetry standard, the Metria activity patch overestimates EE during light-to-moderate PA intensities (L-MI) and moderate-to-vigorous PA intensities (M-VI) (mean ± SD) (0.28 ± 1.62 kilocalories· minute-1, P < .001, 0.64 ± 1.65 kilocalories· minute-1, P < .001, respectively). The Metria underestimates EE during vigorous-to-maximal PA intensity (V-MI) (–1.78 ± 2.77 kilocalories · minute-1, P < .001). Similarly, compared to Polar HR monitor, the Bioharness underestimates HR at L-MI (–1 ± 8 bpm, P < .001) and M-VI (5 ± 11 bpm, P < .001), respectively. A significant difference in EE was observed for the Garmin device, compared to the Fitmate ( P < .001) during continuous L-MI activity. Conclusions: Overall, our study demonstrates that current research-grade wearable technologies operate within a ~10% error for both HR and EE during a wide range of dynamic exercise intensities. This level of accuracy for emerging research-grade instruments is considered both clinically and practically acceptable for research-based or consumer use. In conclusion, research-grade wearable technology that uses EE kilocalories · minute-1 and HR reliably differentiates PA intensities. |
| Databáze: | OpenAIRE |
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