Physical activity phenotyping with activity bigrams, and their association with BMI
Physical activity phenotyping with activity bigrams, and their association with BMI
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DOI：
10.1093/ije/dyx093
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发表时间：
2017-12-01
影响因子：
7.7
通讯作者：
Gaunt, Tom R.
中科院分区：
文献类型：
--
作者：
Millard, Louise A. C.;Tilling, Kate;Gaunt, Tom R.
对身体活动的分析通常侧重于从加速度计记录中得出的少量汇总统计数据：每分钟的平均计数以及中等强度身体活动或久坐行为所花费的时间比例。我们展示了如何bigrams，从文本挖掘领域的一个概念，可以用来描述一个人的活动水平如何在（简短的）时间点的变化。例如，这些变量可以区分两个人在中等活动中花费相同的时间，其中一个人经常从一个时刻到下一个时刻保持中等活动，而另一个则没有。我们为每个参与者生成一个二元组频率的配置文件，并测试每个频率与身体质量指数（BMI）的关联，作为一个范例。我们发现二元组频率的变化和BMI之间的几个关联。例如，一个标准差的减少，在相邻分钟数久坐，然后适度的活动（或反之亦然），在中等然后剧烈活动中，相邻分钟数相应增加（反之亦然），与BMI降低2.36 kg/m2相关[95%置信区间（CI）：活动二元图是一种新颖的变量，它捕捉了一个人的活动从一个时刻到下一个时刻的变化。这些变量可以用来研究序列活动模式与其他特征的关联。
Analysis of physical activity usually focuses on a small number of summary statistics derived from accelerometer recordings: average counts per minute and the proportion of time spent in moderate-vigorous physical activity or in sedentary behaviour. We show how bigrams, a concept from the field of text mining, can be used to describe how a person's activity levels change across (brief) time points. These variables can, for instance, differentiate between two people spending the same time in moderate activity, where one person often stays in moderate activity from one moment to the next and the other does not.We use data on 4810 participants of the Avon Longitudinal Study of Parents and Children (ALSPAC). We generate a profile of bigram frequencies for each participant and test the association of each frequency with body mass index (BMI), as an exemplar.We found several associations between changes in bigram frequencies and BMI. For instance, a one standard deviation decrease in the number of adjacent minutes in sedentary then moderate activity (or vice versa), with a corresponding increase in the number of adjacent minutes in moderate then vigorous activity (or vice versa), was associated with a 2.36 kg/m(2) lower BMI [95% confidence interval (CI): -3.47, -1.26], after accounting for the time spent in sedentary, low, moderate and vigorous activity.Activity bigrams are novel variables that capture how a person's activity changes from one moment to the next. These variables can be used to investigate how sequential activity patterns associate with other traits.