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Assessing bouts of activity using modeled clinically validated physical activity on commodity hardware

机译:使用在商品硬件上建模的经过临床验证的身体活动来评估活动周期

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Human activity can be measured through identification of bouts of activity. The Freedson cut point method used by ActiGraph has become one very common and well accepted standard for estimating times of continuous moderate to vigorous physical activity (MVPA). However, such methods do not directly apply to other data sources such as the Fitbit Flex, a wrist worn wireless pedometer. In previous research by the authors, a model was presented to improve the estimates of physical activity (PA) level in the Fitbit devices. This paper considers the estimates of activity bouts, building on the modeled PA level from the Fitbit Flex as compared to the results from the ActiGraph GT3X. The purpose of this paper is to compare the “gold standard” ActiGraph to modeled Fitbit Freedson methods and to establish normative values of expected errors in bout detection between the two devices and methods, both of which are proxy methods aimed at measuring actual physical activity levels. Here we compare bout identification using three measures, the ActiGraph Freedson method, Fitbit Intensity Score, and the modeled Fitbit Freedson using three different outcomes. First, we compare a baseline of per subject per day number and duration of bouts from an ActiGraph GT3X to the results found from using the same methods on the Intensity Score reported by Fitbit and the modeled Fitbit Freedson method. Next, we compare the difference in duration of bouts identified in each data source matched according to similar start and end times. Finally, we compare the bouts found from the three methods to bouts identified in a self report diary.
机译:人类活动可以通过确定一系列活动来测量。 ActiGraph所使用的Freedson切点法已成为一种非常普遍且广为接受的标准,用于估算持续的中等至剧烈的身体活动(MVPA)的时间。但是,此类方法不能直接应用于其他数据源,例如Fitbit Flex(腕带式无线计步器)。在作者先前的研究中,提出了一个模型来改善Fitbit设备中的体力活动(PA)水平的估计。本文基于与FitiGraph GT3X的结果相比较的Fitbit Flex建模的PA水平,来考虑活动爆发的估计。本文的目的是将“黄金标准” ActiGraph与模拟的Fitbit Freedson方法进行比较,并在两种设备和方法之间建立关于回合检测的预期误差的规范值,这两种方法都是旨在测量实际身体活动水平的代理方法。 。在这里,我们比较使用三种测量方法进行的回合识别,即ActiGraph Freedson方法,Fitbit强度得分和使用三种不同结果的建模Fitbit Freedson。首先,我们将每天从ActiGraph GT3X进行的发作次数和持续时间的基线与使用Fitbit报告的强度得分和建模的Fitbit Freedson方法使用相同方法得出的结果进行比较。接下来,我们比较根据相似的开始时间和结束时间在每个数据源中识别出的回合持续时间的差异。最后,我们将三种方法中的结果与自我报告日记中确定的结果进行比较。

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