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消费者活动监测器的有效性及一种使用智能手机数据测量不同活动类型下步数的算法。

Validity of Consumer Activity Monitors and an Algorithm Using Smartphone Data for Measuring Steps during Different Activity Types.

机构信息

Department of Sport Science and Sport, Friedrich-Alexander University Erlangen-Nürnberg, 91058 Erlangen, Germany.

Department of Sport Science, Division of Health and Physical Activity, Otto-von-Guericke University, 39104 Magdeburg, Germany.

出版信息

Int J Environ Res Public Health. 2020 Dec 12;17(24):9314. doi: 10.3390/ijerph17249314.

Abstract

: Consumer activity monitors and smartphones have gained relevance for the assessment and promotion of physical activity. The aim of this study was to determine the concurrent validity of various consumer activity monitor models and smartphone models for measuring steps. : Participants completed three activity protocols: (1) overground walking with three different speeds (comfortable, slow, fast), (2) activities of daily living (ADLs) focusing on arm movements, and (3) intermittent walking. Participants wore 11 activity monitors (wrist: 8; hip: 2; ankle: 1) and four smartphones (hip: 3; calf: 1). Observed steps served as the criterion measure. The mean average percentage error (MAPE) was calculated for each device and protocol. : Eighteen healthy adults participated in the study (age: 28.8 ± 4.9 years). MAPEs ranged from 0.3-38.2% during overground walking, 48.2-861.2% during ADLs, and 11.2-47.3% during intermittent walking. Wrist-worn activity monitors tended to misclassify arm movements as steps. Smartphone data collected at the hip, analyzed with a separate algorithm, performed either equally or even superiorly to the research-grade ActiGraph. : This study highlights the potential of smartphones for physical activity measurement. Measurement inaccuracies during intermittent walking and arm movements should be considered when interpreting study results and choosing activity monitors for evaluation purposes.

摘要

消费者活动监测器和智能手机在评估和促进身体活动方面变得越来越重要。本研究的目的是确定各种消费者活动监测器模型和智能手机模型在测量步数方面的同时效度。

参与者完成了三个活动方案

(1)在三种不同速度(舒适、缓慢、快速)下的地面行走,(2)专注于手臂运动的日常生活活动(ADL),以及(3)间歇性行走。参与者佩戴了 11 个活动监测器(手腕:8 个;臀部:2 个;脚踝:1 个)和 4 部智能手机(臀部:3 个;小腿:1 个)。观察到的步数作为标准测量值。为每个设备和方案计算了平均绝对百分比误差(MAPE)。

18 名健康成年人参与了这项研究(年龄:28.8±4.9 岁)。在地面行走过程中,MAPE 范围为 0.3-38.2%;在 ADL 过程中,MAPE 范围为 48.2-861.2%;在间歇性行走过程中,MAPE 范围为 11.2-47.3%。腕部佩戴的活动监测器倾向于将手臂运动错误分类为步数。臀部佩戴的智能手机数据,使用单独的算法进行分析,表现与研究级别的 ActiGraph 相当或甚至更好。

本研究强调了智能手机在身体活动测量方面的潜力。在解释研究结果和选择评估用的活动监测器时,应考虑间歇性行走和手臂运动期间的测量不准确问题。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a64b/7764011/88ab67e126fb/ijerph-17-09314-g0A1.jpg

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