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一项试点研究:在1型糖尿病患者和非1型糖尿病患者的一系列动态运动强度下验证选定的研究级和基于消费者的可穿戴设备——一种新方法。

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.

作者信息

Yavelberg Loren, Zaharieva Dessi, Cinar Ali, Riddell Michael C, Jamnik Veronica

机构信息

1 Department of Kinesiology and Health Science, Faculty of Health, Physical Activity and Chronic Disease Unit, York University, Toronto, ON, Canada.

2 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA.

出版信息

J Diabetes Sci Technol. 2018 May;12(3):569-576. doi: 10.1177/1932296817750401. Epub 2018 Jan 10.

Abstract

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, P < .001, 0.64 ± 1.65 kilocalories· minute, P < .001, respectively). The Metria underestimates EE during vigorous-to-maximal PA intensity (V-MI) (-1.78 ± 2.77 kilocalories · minute, 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 and HR reliably differentiates PA intensities.

摘要

背景

可穿戴技术日益普及,因此有必要评估其区分身体活动(PA)强度的准确性。这些设备在为慢性病的一级预防和二级管理定制PA干预措施方面可能发挥不可或缺的作用。例如,在1型糖尿病(T1D)患者中,PA根据强度、模式(即有氧、无氧、混合)和持续时间对血糖浓度有很大影响。血糖反应的这种变异性凸显了在人工胰腺系统等新兴领域应用可靠的可穿戴技术的重要性。

方法

参与者在配备多个研究级(Fitmate、Metria、Bioharness)和消费级(佳明、Fitbit)可穿戴设备的情况下,完成了三次40分钟的动态非稳态运动。根据设备的最大灵敏度(如逐次呼吸、逐搏或分钟时间戳)提取数据,并平均为每分钟的数据。将感兴趣的变量,即心率(HR)、呼吸频率和能量消耗(EE),与经过验证的标准测量值进行比较。

结果

与通过实验室间接量热法标准得出的EE相比,Metria活动贴片在轻度至中度PA强度(L-MI)和中度至剧烈PA强度(M-VI)期间高估了EE(均值±标准差)(分别为0.28±1.62千卡·分钟,P<.001;0.64±1.65千卡·分钟,P<.001)。Metria在剧烈至最大PA强度(V-MI)期间低估了EE(-1.78±2.77千卡·分钟,P<.001)。同样,与Polar心率监测器相比,Bioharness在L-MI(-1±8次/分钟,P<.001)和M-VI(5±11次/分钟,P<.001)时分别低估了HR。在持续的L-MI活动期间,与Fitmate相比,佳明设备的EE存在显著差异(P<.001)。

结论

总体而言,我们的研究表明,在广泛的动态运动强度范围内,当前的研究级可穿戴技术在HR和EE方面的误差约为10%。对于新兴的研究级仪器,这种准确度在临床和实际应用中对于基于研究或消费者使用而言都是可以接受的。总之,使用千卡·分钟EE和HR的研究级可穿戴技术能够可靠地区分PA强度。

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本文引用的文献

1
Variable Accuracy of Wearable Heart Rate Monitors during Aerobic Exercise.
Med Sci Sports Exerc. 2017 Aug;49(8):1697-1703. doi: 10.1249/MSS.0000000000001284.
2
Handling Exercise During Closed Loop Control.
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4
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Diabetes Technol Ther. 2017 Jun;19(6):331-339. doi: 10.1089/dia.2016.0399. Epub 2017 May 1.
5
Piloting a Remission Strategy in Type 2 Diabetes: Results of a Randomized Controlled Trial.
J Clin Endocrinol Metab. 2017 May 1;102(5):1596-1605. doi: 10.1210/jc.2016-3373.
6
Assessment of laboratory and daily energy expenditure estimates from consumer multi-sensor physical activity monitors.
PLoS One. 2017 Feb 24;12(2):e0171720. doi: 10.1371/journal.pone.0171720. eCollection 2017.
7
Exercise management in type 1 diabetes: a consensus statement.
Lancet Diabetes Endocrinol. 2017 May;5(5):377-390. doi: 10.1016/S2213-8587(17)30014-1. Epub 2017 Jan 24.
8
Comparison of wrist-worn and hip-worn activity monitors under free living conditions.
J Med Eng Technol. 2017 Apr;41(3):200-207. doi: 10.1080/03091902.2016.1271046. Epub 2017 Jan 12.
9
Classification of Physical Activity: Information to Artificial Pancreas Control Systems in Real Time.
J Diabetes Sci Technol. 2015 Oct 6;9(6):1200-7. doi: 10.1177/1932296815609369.
10
Exercise and the Development of the Artificial Pancreas: One of the More Difficult Series of Hurdles.
J Diabetes Sci Technol. 2015 Oct 1;9(6):1217-26. doi: 10.1177/1932296815609370.

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