Accelerometer data reduction:: A comparison of four reduction algorithms on select outcome variables
Accelerometer data reduction:: A comparison of four reduction algorithms on select outcome variables
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DOI：
10.1249/01.mss.0000185674.09066.8a
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发表时间：
2005-11-01
期刊：
影响因子：
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通讯作者：
Treuth, M
中科院分区：
文献类型：
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作者：
M창sse, LC;Fuemmeler, BF;Treuth, M
目的：加速度计被认为是评估自由身体活动的有效且客观的工具。尽管加速度计被广泛使用，但没有标准化的方法来处理和总结它们的数据，这限制了我们比较不同研究结果的能力。本文 a) 回顾了研究人员过去使用的决策规则，b) 比较了在公共数据集上使用不同决策规则的影响，c) 确定了加速计数据缩减时需要考虑的问题。方法：回顾了 2003 年和 2004 年发表的研究方法部分，以确定以前的研究人员使用哪些决策规则来确定佩戴时间、有效日期的最低佩戴要求、虚假数据、用于计算结果变量的天数，并提取中度至剧烈体力活动 (MVPA) 的次数。在本研究中，使用四种采用不同决策规则的数据缩减算法来分析同一数据集。结果：审查表明，在报告其决策规则的研究中，观察到了很大的差异。总体而言，分析表明使用不同的算法会影响几个重要的结果变量。最严格的算法显着缩短了佩戴时间，降低了每分钟和每天的活动次数，并减少了每天 MVPA 的分钟数。探索性敏感性分析表明，最严格的纳入标准会对样本大小和佩戴时间产生影响，进而影响许多结果变量。结论：这些发现表明，用于处理加速度计数据的决策规则对重要的结果变量具有重大影响。在制定指南之前，比较不同研究的结果仍然很困难。
Purpose: Accelerometers are recognized as a valid and objective tool to assess free-living physical activity. Despite the widespread use of accelerometers, there is no standardized way to process and summarize data from them, which limits our ability to compare results across studies. This paper a) reviews decision rules researchers have used in the past, b) compares the impact of using different decision rules on a common data set, and c) identifies issues to consider for accelerometer data reduction. Methods: The methods sections of studies published in 2003 and 2004 were reviewed to determine what decision rules previous researchers have used to identify wearing period, minimal wear requirement for a valid day, spurious data, number of days used to calculate the outcome variables, and extract bouts of moderate to vigorous physical activity (MVPA). For this study, four data reduction algorithms that employ different decision rules were used to analyze the same data set. Results: The review showed that among studies that reported their decision rules, much variability was observed. Overall, the analyses suggested that using different algorithms impacted several important outcome variables. The most stringent algorithm yielded significantly lower wearing time, the lowest activity counts per minute and counts per day, and fewer minutes of MVPA per day. An exploratory sensitivity analysis revealed that the most stringent inclusion criterion had an impact on sample size and wearing time, which in turn affected many outcome variables. Conclusions: These findings suggest that the decision rules employed to process accelerometer data have a significant impact on important outcome variables. Until guidelines are developed, it will remain difficult to compare findings across studies.