对体力活动水平变化较大的健康成人计算步数的开源算法进行14天自由生活评估。

Ivar Holm, Jonatan Fridolfsson, Mats Börjesson, Daniel Arvidsson
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引用次数: 0

摘要

背景:一个人的步数,传统上是用计步器来评估的,但越来越多地使用加速度计。ActiLife软件(AL)是将加速度计数据处理为步骤的最常用方法,但它不是开源的,这可能有助于理解测量误差。本研究的目的是比较GGIR包的开源算法部分和两种封闭算法AL normal (n)和低频extension (lfe)算法对Yamax计步器的步数评估,作为参考。对自由生活的健康成人进行了广泛的活动水平研究。结果:46名参与者按活动水平分为中低活动组和高活动组,同时佩戴加速度计和计步器14天。总共分析了614个完整的天数。Yamax和所有三种算法之间显示出显著的相关性,但除了ALn与Yamax外,所有比较都与配对t检验有显著差异。平均偏倚表明,ALn在中低运动组中略高估步数,在高运动组中略低估步数。平均误差百分比(MAPE)分别为17%和9%。在两组中,ALlfe都高估了大约6700步/天,MAPE在中低活动组为88%,在高活动组为43%。开源算法低估了与活动水平相关的系统误差。中低运动组MAPE为28%,高运动组为48%。结论:与Yamax计步器相比,该开源算法在中低活动量个体上的步数捕获效果较好,但在高活动量个体上的步数捕获效果不理想,在人群研究中应用前需要对其进行改进。没有低频扩展的AL算法测量的步数与自由生活中的Yamax相似,在有效的开源算法可用之前是一个有用的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Fourteen days free-living evaluation of an open-source algorithm for counting steps in healthy adults with a large variation in physical activity level.

Background: The number of steps by an individual, has traditionally been assessed with a pedometer, but increasingly with an accelerometer. The ActiLife software (AL) is the most common way to process accelerometer data to steps, but it is not open source which could aid understanding of measurement errors. The aim of this study was to compare assessment of steps from the open-source algorithm part of the GGIR package and two closed algorithms, AL normal (n) and low frequency extension (lfe) algorithms to Yamax pedometer, as reference. Free-living in healthy adults with a wide range of activity level was studied.

Results: A total 46 participants divided by activity level into a low-medium active group and a high active group, wore both an accelerometer and a pedometer for 14 days. In total 614 complete days were analyzed. A significant correlation between Yamax and all three algorithms was shown but all comparisons were significantly different with paired t-tests except for ALn vs Yamax. The mean bias shows that ALn slightly overestimated steps in the low-medium active group and slightly underestimated steps in high active group. The mean percentage error (MAPE) was 17% and 9% respectively. The ALlfe overestimated steps by approximately 6700/day in both groups and the MAPE was 88% in the low-medium active group and 43% in the high active group. The open-source algorithm underestimated steps with a systematic error related to activity level. The MAPE was 28% in the low-medium active group and 48% in the high active group.

Conclusion: The open-source algorithm captures steps fairly well in low-medium active individuals when comparing with Yamax pedometer, but did not show satisfactory results in more active individuals, indicating that it must be modified before implemented in population research. The AL algorithm without the low frequency extension measures similar number of steps as Yamax in free-living and is a useful alternative before a valid open-source algorithm is available.

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