Background

Step count monitors are frequently used in clinical research to measure walking activity. Systematically determining valid days and extracting informative measures of walking beyond total daily step count are among major analytical challenges.

Research Question

We introduce a novel data-driven anomaly detection algorithm to determine days representing typical walking activity (valid days) and examine the value of measures of walking fragmentation beyond total daily step count.

Methods

StepWatch data were collected on 230 adults with severe foot or ankle fractures. Average steps per minute (SC), average steps per active minute (SCA), active to sedentary transition probability (ASTP) and sedentary to active transition probability (SATP) were computed for each participant. The joint distribution of these measures was used to identify and eliminate invalid days through a multi-step process based on the support vector machine. The value of SCA, ASTP and SATP beyond SC were assessed by regressing Short Musculoskeletal Functional Assessment (SMFA), a measure of self-reported function, on these measures and quantifying the increase in the adjusted R-squared. In an unsupervised comparison, the total joint variability of SCA, ASTP and SATP was decomposed into the variability explained by SC and the unique variability of these three measures.

Results

Of the 4,448 days in the original data set, 39% were determined invalid. Individuals with higher average SC had higher SCA, lower ASTP and higher SATP. Measures of fragmentation (SCA, ASTP and SATP) explained 25% more of the variability in SMFA compared with SC alone. Approximately 41% of the variability in SCA, ASTP and SATP could not be explained by SC suggesting that these three measures provide unique information about walking patterns.

Significance

Applying SVM and quantifying fragmentation in walking bouts for step count data can help to more precisely assess activity in clinical studies employing this modality.

中文翻译：

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计步数据的方法：确定“有效”天数并量化步行次数的碎片。

背景

步数监控器在临床研究中经常用于测量步行活动。系统地确定有效天数并提取可超出每日总步数的信息量是主要的分析挑战。

研究问题

我们引入一种新颖的数据驱动异常检测算法来确定代表典型步行活动的天数（有效天数），并检查超出每日总步数的步行碎片测量值。

方法

收集了230例严重脚或踝部骨折的成年人的StepWatch数据。为每个参与者计算了平均每分钟步数（SC），每活动分钟的平均步数（SCA），活动到久坐的转变概率（ASTP）和久坐到活动的转变概率（SATP）。这些度量的联合分布用于通过基于支持向量机的多步骤过程来识别和消除无效天数。SCA，ASTP和SATP以外的SATP的价值通过对这些指标进行自我报告功能的量度的短肌骨骼功能评估（SMFA）进行回归，并量化调整后R平方的增加来评估。在无监督的比较中，SCA的总关节变异性

结果

在原始数据集中的4,448天中，有39％被确定为无效。平均SC较高的个体具有较高的SCA，较低的ASTP和较高的SATP。与单独使用SC相比，碎片测量（SCA，ASTP和SATP）解释了SMFA的变异性高出25％。SC无法解释SCA，ASTP和SATP中大约41％的变异性，这表明这三种测量方法提供了有关步行模式的独特信息。

意义

应用SVM并量化步行步数数据的碎片可帮助更精确地评​​估采用这种方式的临床研究活动。