Center for Health and Performance, Department of Food and Nutrition, and Sport Science, Faculty of Education, University of Gothenburg, Gothenburg, Sweden.
Department of Physical Activity and Health, Swedish School of Sport and Health Sciences, Stockholm, Sweden.
Scand J Med Sci Sports. 2024 Jan;34(1):e14541. doi: 10.1111/sms.14541. Epub 2023 Nov 20.
There is a lack of a methodological standard to process accelerometer data to measures of physical activity, which impairs data quality and comparability. This study investigated the effect of different combinations of settings of multiple processing components, on the measure of physical activity and the association with measures of cardiometabolic health in an unselected population of middle-aged individuals.
Free-living hip accelerometer data, aerobic fitness, body mass index, HDL:total cholesterol ratio, blood glucose, and systolic blood pressure were achieved from 4391 participants 50-64 years old included in The Swedish CArdioPulmonary bioImage Study (SCAPIS) baseline measurement (cross-sectional). Lab data were also included for calibration of accelerometers to provide comparable measure of physical activity intensity and time spent in different intensity categories, as well as to enhance understanding. The accelerometer data processing components were hardware recalibration, frequency filtering, number of accelerometer axes, epoch length, wear time criterium, time composition (min/24 h vs. % of wear time). Partial least regression and ordinary least regression were used for the association analyses.
The setting of frequency filter had the strongest effect on the physical activity intensity measure and time distribution in different intensity categories followed by epoch length and number of accelerometer axes. Wear time criterium and recalibration of accelerometer data were less important. The setting of frequency filter and epoch length also showed consistent important effect on the associations with the different measures of cardiometabolic health, while the effect of recalibration, number of accelerometer axes, wear time criterium and expression of time composition was less consistent and less important. There was a large range in explained variance of the measures of cardiometabolic health depending on the combination of processing settings, for example, 12.1%-20.8% for aerobic fitness and 5.8%-14.0% for body mass index.
There was a large variation in the physical activity intensity measure and the association with different measures of cardiometabolic health depending on the combination of settings of accelerometer data processing components. The results provide a fundament for a standard to process hip accelerometer data to assess the physical activity in middle-aged populations.
目前缺乏一种将加速度计数据转化为体力活动测量的方法学标准，这会降低数据质量和可比性。本研究旨在探讨在未经选择的中年人群中，不同处理组件设置组合对体力活动测量的影响，以及与心脏代谢健康测量的关联。
从参加瑞典心肺生物影像研究（SCAPIS）基线测量（横断面）的 4391 名 50-64 岁的参与者中获得自由生活的髋部加速度计数据、有氧健身、体重指数、高密度脂蛋白：总胆固醇比值、血糖和收缩压。实验室数据也包括在内，用于对加速度计进行校准，以提供可比较的体力活动强度和不同强度类别的时间分配测量，同时增强对其的理解。加速度计数据处理组件包括硬件重新校准、频率滤波、加速度计轴数、时间片段长度、佩戴时间标准、时间组成（每 24 小时分钟数/百分比与佩戴时间）。部分最小二乘回归和普通最小二乘回归用于关联分析。
频率滤波的设置对体力活动强度测量和不同强度类别的时间分配影响最大，其次是时间片段长度和加速度计轴数。佩戴时间标准和加速度计数据的重新校准影响较小。频率滤波和时间片段长度的设置也对与不同心脏代谢健康测量的关联有一致且重要的影响，而重新校准、加速度计轴数、佩戴时间标准和时间组成的表达则影响不太一致且不太重要。不同处理设置组合对心脏代谢健康测量的解释方差有很大差异，例如，有氧健身的解释方差为 12.1%-20.8%，体重指数的解释方差为 5.8%-14.0%。
加速度计数据处理组件设置的组合会对体力活动强度测量和与不同心脏代谢健康测量的关联产生很大的影响。研究结果为处理髋部加速度计数据以评估中年人群体力活动提供了一个标准基础。
