Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal.
Research Center in Physical Activity, Health and Leisure (CIAFEL), Faculty of Sport, University of Porto, Porto, Portugal.
Gait Posture. 2020 Feb;76:104-109. doi: 10.1016/j.gaitpost.2019.11.008. Epub 2019 Nov 10.
Almost all accelerometer calibration studies were developed for non-obese people, which hampers an accurate prediction of energy expenditure (EE) and induces a misclassification of sedentary activity (SA) and physical activity intensities (PAI) in class II-III obese people.
The purpose of this study was to develop regression equations to predict EE and cut-points to classify SA and PAI in severe obese people based on several metrics obtained from hip and back accelerometer placement data.
43 class II-III obese participants performed a protocol that included sitting and standing positions and walking at several speeds. During the protocol participants wore an accelerometer at hip and back, and respiratory gas exchange was measured by indirect calorimetry. Accelerometer metrics analyzed were: activity counts, mean amplitude deviation and euclidean norm minus one. EE was predicted through linear mixed models while cut-points to classify SA and PAI were obtained applying receiver operating characteristic curves. Leave-one-out cross-validation data was used to calculate Bland-Altman plots, prediction accuracy, Kappa statistic and percent agreement.
All prediction models presented a quadratic equation that had as predictors body mass and one of the accelerometer metrics. Predicted EE indicated a good agreement and a root mean square error below 1.02 kcal min. Global classification agreement from developed cut-points was categorized as almost perfect with a percent agreement above 84 %. Prediction accuracy and classification agreement were similar among accelerometer metrics in each position and between them in hip and back placement.
Hip and back accelerometer data collected in severe obese people allow to accurately estimate EE and to correctly classify SA and PAI. These results enable future studies to adopt appropriate regression equations and cut-points developed for class II-III obese people rather than those established for non-obese people.
几乎所有的加速度计校准研究都是针对非肥胖人群进行的,这阻碍了对能量消耗 (EE) 的准确预测,并导致 II-III 类肥胖人群中久坐活动 (SA) 和身体活动强度 (PAI) 的分类错误。
本研究的目的是开发回归方程,以预测严重肥胖人群的 EE,并根据髋部和背部加速度计放置数据获得的几个指标来确定 SA 和 PAI 的分类切点。
43 名 II-III 类肥胖参与者进行了一项包括坐姿和站立位以及以不同速度行走的方案。在该方案中,参与者在髋部和背部佩戴加速度计,并通过间接测热法测量呼吸气体交换。分析的加速度计指标包括:活动计数、平均幅度偏差和欧几里得范数减一。通过线性混合模型预测 EE,通过接收者操作特征曲线获得分类 SA 和 PAI 的切点。使用留一法交叉验证数据计算 Bland-Altman 图、预测准确性、Kappa 统计和百分比一致性。
所有预测模型均采用二次方程,其预测因子为体重和加速度计指标之一。预测 EE 显示出良好的一致性,均方根误差低于 1.02 kcal min。开发的切点的总体分类一致性归类为几乎完美,一致性百分比高于 84%。在每个位置和髋部与背部之间的位置,加速度计指标之间的预测准确性和分类一致性相似。
严重肥胖人群的髋部和背部加速度计数据可准确估计 EE,并正确分类 SA 和 PAI。这些结果使未来的研究能够采用针对 II-III 类肥胖人群而不是非肥胖人群开发的适当回归方程和切点。