Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.
Large Scale Population Assessment of Physical Activity Using Wrist Worn Accelerometers: The UK Biobank Study.
复制标题
DOI：
10.1371/journal.pone.0169649
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Wareham NJ
中科院分区：
文献类型：
--
作者：
Doherty A;Jackson D;Hammerla N;Plötz T;Olivier P;Granat MH;White T;van Hees VT;Trenell MI;Owen CG;Preece SJ;Gillions R;Sheard S;Peakman T;Brage S;Wareham NJ
尚未在前瞻性队列中客观测量身体活动，这些队列的数量足够大，无法可靠地检测与多种健康结果的关联。技术进步使这成为可能。我们描述了用于收集和分析英国生物银行研究的10万多名参与者的加速度计测量的身体活动的方法，并报告了年龄，性别，日期，时间和季节的变化。通过电子邮件与参与者联系，让他们在七天内佩戴一个腕戴式加速度计。在校准、去除重力和传感器噪声以及识别磨损/非磨损事件后，从100 Hz原始三轴加速度数据中提取身体活动信息。我们报告了特定年龄和性别的佩戴时间合规性和加速度计测量的身体活动，总体和按小时，工作日-周末和季节。共收到103，712个数据集（44.8%的响应），中位佩戴时间为6.9天（IQR：6.5-7.0）。96，600名参与者（93.3%）提供了身体活动分析的有效数据。矢量幅度，整体身体活动的代理，每十年降低7.5%（2.35毫克）（科恩d = 0.9）。除了45- 54岁的人外，妇女的矢量幅度高于男子。一天中不同时间的矢量幅度存在重大差异（d = 0.66）。一周和周末之间的矢量幅度差异（男性d = 0.12，女性d = 0.09）和季节之间的差异（男性d = 0.27，女性d = 0.15）是小的。在大型研究中收集和分析客观的体力活动数据是可行的。总体身体活动的汇总测量值在老年参与者中较低，与年龄相关的活动差异在下午和晚上最为突出。这项工作为研究身体活动及其健康后果奠定了基础。我们的汇总变量是英国生物银行数据集的一部分，研究人员可以在未来的分析中将其用作暴露、混杂因素或结果变量。
Physical activity has not been objectively measured in prospective cohorts with sufficiently large numbers to reliably detect associations with multiple health outcomes. Technological advances now make this possible. We describe the methods used to collect and analyse accelerometer measured physical activity in over 100,000 participants of the UK Biobank study, and report variation by age, sex, day, time of day, and season. Participants were approached by email to wear a wrist-worn accelerometer for seven days that was posted to them. Physical activity information was extracted from 100Hz raw triaxial acceleration data after calibration, removal of gravity and sensor noise, and identification of wear / non-wear episodes. We report age- and sex-specific wear-time compliance and accelerometer measured physical activity, overall and by hour-of-day, week-weekend day and season. 103,712 datasets were received (44.8% response), with a median wear-time of 6.9 days (IQR:6.5–7.0). 96,600 participants (93.3%) provided valid data for physical activity analyses. Vector magnitude, a proxy for overall physical activity, was 7.5% (2.35mg) lower per decade of age (Cohen’s d = 0.9). Women had a higher vector magnitude than men, apart from those aged 45-54yrs. There were major differences in vector magnitude by time of day (d = 0.66). Vector magnitude differences between week and weekend days (d = 0.12 for men, d = 0.09 for women) and between seasons (d = 0.27 for men, d = 0.15 for women) were small. It is feasible to collect and analyse objective physical activity data in large studies. The summary measure of overall physical activity is lower in older participants and age-related differences in activity are most prominent in the afternoon and evening. This work lays the foundation for studies of physical activity and its health consequences. Our summary variables are part of the UK Biobank dataset and can be used by researchers as exposures, confounding factors or outcome variables in future analyses.