Swartz A M, Strath S J, Bassett D R, O'Brien W L, King G A, Ainsworth B E
Department of Exercise Science and Sport Management, University of Tennessee, Knoxville 37996, USA.
Med Sci Sports Exerc. 2000 Sep;32(9 Suppl):S450-6. doi: 10.1097/00005768-200009001-00003.
This study was designed to establish prediction models that relate hip and wrist accelerometer data to energy expenditure (EE) in field and laboratory settings. We also sought to determine whether the addition of a wrist accelerometer would significantly improve the prediction of EE (METs), compared with a model that used a hip accelerometer alone.
Seventy participants completed one to six activities within the categories of yardwork, housework, family care, occupation, recreation, and conditioning, for a total of 5 to 12 participants tested per activity. EE was measured using the Cosmed K4b2 portable metabolic system. Simultaneously, two Computer Science and Applications, Inc. (CSA) accelerometers (model 7164), one worn on the wrist and one worn on the hip, recorded body movement. Correlations between EE measured by the Cosmed and the counts recorded by the CSA accelerometers were calculated, and regression equations were developed to predict EE from the CSA data.
The wrist, hip, and combined hip and wrist regression equations accounted for 3.3%, 31.7%, and 34.3% of the variation in EE, respectively. The addition of the wrist accelerometer data to the hip accelerometer data to form a bivariate regression equation, although statistically significant (P = 0.002), resulted in only a minor improvement in prediction of EE. Cut points for 3 METs (574 hip counts), 6 METs (4945 hip counts), and 9 METs (9317 hip counts) were also established.
The small amount of additional accuracy gained from the wrist accelerometer is offset by the extra time required to analyze the data and the cost of the accelerometer.
本研究旨在建立预测模型，将髋部和腕部加速度计数据与现场和实验室环境中的能量消耗（EE）相关联。我们还试图确定，与仅使用髋部加速度计的模型相比，添加腕部加速度计是否会显著改善EE（代谢当量）的预测。
70名参与者完成了庭院工作、家务、家庭护理、职业、娱乐和健身等类别中的一至六项活动，每项活动测试5至12名参与者。使用Cosmed K4b2便携式代谢系统测量EE。同时，两个计算机科学与应用公司（CSA）加速度计（型号7164），一个戴在手腕上，一个戴在髋部，记录身体运动。计算Cosmed测量的EE与CSA加速度计记录的计数之间的相关性，并建立回归方程以根据CSA数据预测EE。
腕部、髋部以及髋部和腕部联合的回归方程分别解释了EE变化的3.3%、31.7%和34.3%。将腕部加速度计数据添加到髋部加速度计数据中以形成双变量回归方程，尽管具有统计学意义（P = 0.002），但在EE预测方面仅带来了微小的改善。还确定了3代谢当量（574次髋部计数）、6代谢当量（4945次髋部计数）和9代谢当量（9317次髋部计数）的切点。
腕部加速度计获得的少量额外准确性被分析数据所需的额外时间和加速度计的成本所抵消。
