Kuster Roman P, Grooten Wilhelmus J A, Blom Victoria, Baumgartner Daniel, Hagströmer Maria, Ekblom Örjan
Division of Physiotherapy, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 141 83 Stockholm, Sweden.
IMES Institute of Mechanical Systems, School of Engineering, ZHAW Zurich University of Applied Sciences, 8401 Winterthur, Switzerland.
Int J Environ Res Public Health. 2021 May 27;18(11):5782. doi: 10.3390/ijerph18115782.
Sedentary behaviour is an emergent public health topic, but there is still no method to simultaneously measure both components of sedentary behaviour-posture and energy expenditure-with one sensor. This study investigated the accuracy and precision of measuring sedentary time when combining the proprietary processing of a posture sensor (activPAL) with a new energy expenditure algorithm and the proprietary processing of a movement sensor (ActiGraph) with a published posture algorithm. One hundred office workers wore both sensors for an average of 7 days. The activPAL algorithm development used 38 and the subsequent independent method comparison 62 participants. The single sensor sedentary estimates were compared with Bland-Atman statistics to the Posture and Physical Activity Index, a combined measurement with both sensors. All single-sensor methods overestimated sedentary time. However, adding the algorithms reduced the overestimation from 129 to 21 (activPAL) and from 84 to 7 min a day (ActiGraph), with far narrower 95% limits of agreements. Thus, combining the proprietary data with the algorithms is an easy way to increase the accuracy and precision of the single sensor sedentary estimates and leads to sedentary estimates that are more precise at the individual level than those of the proprietary processing are at the group level.
久坐行为是一个新兴的公共卫生话题，但目前仍没有一种方法能够通过一个传感器同时测量久坐行为的两个组成部分——姿势和能量消耗。本研究调查了将姿势传感器（activPAL）的专有处理与一种新的能量消耗算法相结合，以及将运动传感器（ActiGraph）的专有处理与一种已发表的姿势算法相结合时，测量久坐时间的准确性和精确性。一百名办公室工作人员同时佩戴这两种传感器，平均佩戴7天。activPAL算法开发使用了38名参与者，后续独立方法比较使用了62名参与者。将单传感器久坐时间估计值与布兰德-奥特曼统计法进行比较，以与姿势和身体活动指数（一种结合了两种传感器的测量方法）进行对比。所有单传感器方法均高估了久坐时间。然而，添加这些算法后，高估幅度从每天129分钟降至21分钟（activPAL），从每天84分钟降至7分钟（ActiGraph），95%一致性界限也窄得多。因此，将专有数据与算法相结合是提高单传感器久坐时间估计准确性和精确性的一种简便方法，并且在个体层面上得出的久坐时间估计比专有处理在群体层面上得出的结果更为精确。
