Concurrent Measurement of Global Positioning System and Event-Based Physical Activity Data: A Methodological Framework for Integration

Anna M. J. Iveson, M. Granat, B. Ellis, P. Dall
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引用次数: 1

Abstract

Objective: Global positioning system (GPS) data can add context to physical activity data and have previously been integrated with epoch-based physical activity data. The current study aimed to develop a framework for integrating GPS data and event-based physical activity data (suitable for assessing patterns of behavior). Methods: A convenience data set of concurrent GPS (AMOD) and physical activity (activPAL) data were collected from 69 adults. The GPS data were (semi)regularly sampled every 5 s. The physical activity data output was presented as walking events, which are continuous periods of walking with a time-stamped start time and duration (to nearest 0.1 s). The GPS outcome measures and the potential correspondence of their timing with walking events were identified and a framework was developed describing data integration for each combination of GPS outcome and walking event correspondence. Results: The GPS outcome measures were categorized as those deriving from a single GPS point (e.g., location) or from the difference between successive GPS points (e.g., distance), and could be categorical, scale, or rate outcomes. Walking events were categorized as having zero (13% of walking events, 3% of walking duration), or one or more (52% of walking events, 75% of walking duration) GPS points occurring during the event. Additionally, some walking events did not have GPS points suitably close to allow calculation of outcome measures (31% of walking events, 22% of walking duration). The framework required different integration approaches for each GPS outcome type, and walking events containing zero or more than one GPS points.
全球定位系统和基于事件的体育活动数据的并发测量:一个集成的方法框架
目的:全球定位系统(GPS)数据可以为体育活动数据添加上下文,并且以前已经与基于时代的体育活动数据集成。目前的研究旨在开发一个框架,用于整合GPS数据和基于事件的身体活动数据(适用于评估行为模式)。方法:收集69例成人的GPS (AMOD)和身体活动(activPAL)数据。GPS数据每5秒定期(半)采样一次。身体活动数据输出以步行事件的形式呈现,步行事件是连续的步行时段,并带有时间戳的开始时间和持续时间(最接近0.1秒)。我们确定了GPS结果测量及其时间与步行事件的潜在对应关系,并开发了一个框架来描述GPS结果和步行事件对应关系的每种组合的数据集成。结果:GPS结果测量被分类为来自单个GPS点(例如,位置)或来自连续GPS点(例如,距离)之间的差异,并且可以是分类、尺度或速率结果。步行事件被归类为在事件中没有(13%的步行事件,3%的步行时间)或一个或多个(52%的步行事件,75%的步行时间)GPS点。此外,一些步行事件没有合适的GPS点来计算结果(31%的步行事件,22%的步行持续时间)。该框架要求针对每种GPS结果类型和包含零个或多个GPS点的行走事件采用不同的集成方法。
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CiteScore
2.90
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