整合基于报告的方法以加强对基于监测的研究的解释:来自健康项目的自由生活活动研究的结果

Nicholas R. Lamoureux, P. Hibbing, Charles Matthews, G. Welk
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引用次数: 0

摘要

基于加速度计的监测器通常用于评估身体活动行为,但缺乏上下文信息限制了数据的可解释性和价值。将基于报告的数据与基于监测的数据相结合,可以利用这两种方法的互补优势来对信息进行三角测量,并创建一个更完整的自由生活的物理行为图像。本调查利用从健康自由生活活动研究中收集的数据来测试用前一天回忆中24小时(ACT24)随时间完成的活动的上下文信息注释监测数据的可行性。评估包括来自134名成年人的数据,他们完成了24小时自由生活监测方案和24小时回顾性召回。分析的重点是ACT24和两种基于监测的方法(ActiGraph和SenseWear Armband)之间能量消耗估算的相对一致性。ACT24的每日能量消耗估计值与基于参考设备的估计值相当。ACT24和基于设备的方法之间的分钟级能量消耗一致性中等,与两种不同的基于监测器的方法之间的一致性相似。ACT24和基于设备的方法之间的这种分钟级协议证明了将自我报告与加速度计数据集成在一起的可行性和实用性,以提供更丰富的监测行为背景。这种类型的整合通过帮助数据解释和提供机会改进自由生活条件下的身体活动评估方法,为推进身体行为评估提供了希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integration of Report-Based Methods to Enhance the Interpretation of Monitor-Based Research: Results From the Free-Living Activity Study for Health Project
Accelerometry-based monitors are commonly utilized to evaluate physical activity behavior, but the lack of contextual information limits the interpretability and value of the data. Integration of report-based with monitor-based data allows the complementary strengths of the two approaches to be used to triangulate information and to create a more complete picture of free-living physical behavior. This investigation utilizes data collected from the Free-Living Activity Study for Health to test the feasibility of annotating monitor data with contextual information from the Activities Completed Over Time in 24-hr (ACT24) previous-day recall. The evaluation includes data from 134 adults who completed the 24-hr free-living monitoring protocol and retrospective 24-hr recall. Analyses focused on the relative agreement of energy expenditure estimates between ACT24 and two monitor-based methods (ActiGraph and SenseWear Armband). Daily energy expenditure estimates from ACT24 were equivalent to the reference device-based estimate. Minute-level agreement of energy expenditure between ACT24 and device-based methods was moderate and was similar to the agreement between two different monitor-based methods. This minute-level agreement between ACT24 and device-based methods demonstrates the feasibility and utility of integrating self-report with accelerometer data to provide richer context on the monitored behaviors. This type of integration offers promise for advancing the assessment of physical behavior by aiding in data interpretation and providing opportunities to improve physical activity assessment methods under free-living conditions.
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CiteScore
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