Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UNITED KINGDOM.
NIHR Leicester Biomedical Research Centre, Leicester, UNITED KINGDOM.
Med Sci Sports Exerc. 2019 Nov;51(11):2410-2422. doi: 10.1249/MSS.0000000000002047.
The physical activity profile can be described from accelerometer data using two population-independent metrics: average acceleration (ACC, volume) and intensity gradient (IG, intensity). This article aims 1) to demonstrate how these metrics can be used to investigate the relative contributions of volume and intensity of physical activity for a range of health markers across data sets and 2) to illustrate the future potential of the metrics for generation of age and sex-specific percentile norms.
Secondary data analyses were conducted on five diverse data sets using wrist-worn accelerometers (ActiGraph/GENEActiv/Axivity): children (n = 145), adolescent girls (n = 1669), office workers (n = 114), premenopausal (n = 1218) and postmenopausal (n = 1316) women, and adults with type 2 diabetes (n = 475). Open-source software (GGIR) was used to generate ACC and IG. Health markers were (a) zBMI (children), (b) %fat (adolescent girls and adults), (c) bone health (pre- and postmenopausal women), and (d) physical function (adults with type 2 diabetes).
Multiple regression analyses showed that IG, but not ACC, was independently associated with zBMI/%fat in children and adolescents. In adults, associations were stronger and the effects of ACC and IG were additive. For bone health and physical function, interactions showed associations were strongest if IG was high, largely irrespective of ACC. Exemplar illustrative percentile "norms" showed the expected age-related decline in physical activity, with greater drops in IG across age than ACC.
The ACC and the IG accelerometer metrics facilitate the investigation of whether volume and intensity of physical activity have independent, additive, or interactive effects on health markers. In future studies, the adoption of data-driven metrics would facilitate the generation of age- and sex-specific norms that would be beneficial to researchers.
可以使用两个与人群无关的指标从加速度计数据中描述身体活动概况：平均加速度（ACC，量）和强度梯度（IG，强度）。本文旨在 1）展示如何使用这些指标来研究身体活动量和强度对一系列健康指标的相对贡献，跨越多个数据集，以及 2）说明这些指标在生成年龄和性别特定百分位数规范方面的未来潜力。
使用佩戴在手腕上的加速度计（ActiGraph/GENEActiv/Axivity）对五个不同数据集进行二次数据分析：儿童（n=145）、青春期女孩（n=1669）、上班族（n=114）、绝经前（n=1218）和绝经后（n=1316）妇女，以及 2 型糖尿病成年患者（n=475）。使用开源软件（GGIR）生成 ACC 和 IG。健康指标为：（a）zBMI（儿童），（b）%体脂（青春期女孩和成年），（c）骨骼健康（绝经前和绝经后妇女），和（d）身体功能（2 型糖尿病成年患者）。
多元回归分析表明，IG 而不是 ACC 与儿童和青少年的 zBMI/%体脂独立相关。在成年人中，相关性更强，且 ACC 和 IG 的影响具有加性。对于骨骼健康和身体功能，如果 IG 较高，则交互作用表明相关性最强，而与 ACC 无关。有代表性的说明性百分位数“规范”表明身体活动量随年龄的预期下降，而 IG 随年龄的下降幅度大于 ACC。
ACC 和 IG 加速度计指标有助于研究身体活动的量和强度是否对健康指标具有独立、累加或交互作用。在未来的研究中，采用数据驱动的指标将有助于生成年龄和性别特定的规范，这对研究人员将是有益的。
