Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA.
J Appl Physiol (1985). 2010 Feb;108(2):314-27. doi: 10.1152/japplphysiol.00374.2009. Epub 2009 Dec 3.
Movement sensing using accelerometers is commonly used for the measurement of physical activity (PA) and estimating energy expenditure (EE) under free-living conditions. The major limitation of this approach is lack of accuracy and precision in estimating EE, especially in low-intensity activities. Thus the objective of this study was to investigate benefits of a distributed lag spline (DLS) modeling approach for the prediction of total daily EE (TEE) and EE in sedentary (1.0-1.5 metabolic equivalents; MET), light (1.5-3.0 MET), and moderate/vigorous (> or = 3.0 MET) intensity activities in 10- to 17-year-old youth (n = 76). We also explored feasibility of the DLS modeling approach to predict physical activity EE (PAEE) and METs. Movement was measured by Actigraph accelerometers placed on the hip, wrist, and ankle. With whole-room indirect calorimeter as the reference standard, prediction models (Hip, Wrist, Ankle, Hip+Wrist, Hip+Wrist+Ankle) for TEE, PAEE, and MET were developed and validated using the fivefold cross-validation method. The TEE predictions by these DLS models were not significantly different from the room calorimeter measurements (all P > 0.05). The Hip+Wrist+Ankle predicted TEE better than other models and reduced prediction errors in moderate/vigorous PA for TEE, MET, and PAEE (all P < 0.001). The Hip+Wrist reduced prediction errors for the PAEE and MET at sedentary PA (P = 0.020 and 0.021) compared with the Hip. Models that included Wrist correctly classified time spent at light PA better than other models. The means and standard deviations of the prediction errors for the Hip+Wrist+Ankle and Hip were 0.4 +/- 144.0 and 1.5 +/- 164.7 kcal for the TEE, 0.0 +/- 84.2 and 1.3 +/- 104.7 kcal for the PAEE, and -1.1 +/- 97.6 and -0.1 +/- 108.6 MET min for the MET models. We conclude that the DLS approach for accelerometer data improves detailed EE prediction in youth.
使用加速度计进行运动感应通常用于测量身体活动（PA）并估算自由生活条件下的能量消耗（EE）。这种方法的主要局限性是在估算 EE 方面缺乏准确性和精密度，尤其是在低强度活动中。因此，本研究的目的是研究分布式滞后样条（DLS）建模方法在预测 10 至 17 岁青少年的总日常 EE（TEE）和久坐（1.0-1.5 代谢当量；MET）、轻（1.5-3.0 MET）和中/剧烈（≥3.0 MET）强度活动中的 EE 的益处（n = 76）。我们还探讨了 DLS 建模方法预测体力活动 EE（PAEE）和 METs 的可行性。运动通过放置在臀部、手腕和脚踝上的 Actigraph 加速度计进行测量。以整个房间间接热量计作为参考标准，使用五重交叉验证法开发和验证用于 TEE、PAEE 和 MET 的预测模型（臀部、手腕、脚踝、臀部+手腕、臀部+手腕+脚踝）。这些 DLS 模型预测的 TEE 与房间热量计测量值没有显著差异（所有 P > 0.05）。臀部+手腕+脚踝预测 TEE 优于其他模型，并降低了 TEE、MET 和 PAEE 中中/剧烈 PA 的预测误差（所有 P < 0.001）。与臀部相比，臀部+手腕降低了久坐 PA 时 PAEE 和 MET 的预测误差（P = 0.020 和 0.021）。包含手腕的模型正确分类了轻 PA 的时间，优于其他模型。臀部+手腕+脚踝和臀部的预测误差平均值和标准差分别为 TEE 的 0.4 +/- 144.0 和 1.5 +/- 164.7 kcal、PAEE 的 0.0 +/- 84.2 和 1.3 +/- 104.7 kcal 以及 MET 模型的-1.1 +/- 97.6 和-0.1 +/- 108.6 MET min。我们得出结论，加速度计数据的 DLS 方法可提高青少年对详细 EE 的预测。
