Pennington Biomedical Research Center, Baton Rouge, LA 70808, USA.
Appl Physiol Nutr Metab. 2012 Dec;37(6):1091-100. doi: 10.1139/h2012-097. Epub 2012 Sep 11.
The purpose of this study was to evaluate the potential for using accelerometer-determined ambulatory activity indicators (steps per day and cadence) to predict total energy expenditure (TEE) and physical activity energy expenditure (PAEE) derived from doubly labeled water (DLW). Twenty men and 34 women (20-36 years of age) provided complete anthropometric, accelerometer, resting metabolic rate (RMR), and DLW data. TEE and PAEE were determined for the same week that accelerometers were worn during waking hours. Accelerometer data included mean steps per day, peak 30-min cadence (average steps per minute for the highest 30 min of the day), and time spent in each incremental cadence band: 0 (nonmovement), 1-19 (incidental movement), 20-39 (sporadic movement), 40-59 (purposeful steps), 60-79 (slow walking), 80-99 (medium walking), 100-119 (brisk walking), and 120+ steps·min(-1) (indicative of all faster ambulatory activities). Regression analyses were employed to develop sex-specific equations for predicting TEE and PAEE. The final model predicting TEE included body weight, steps per day, and time in incremental cadence bands and explained 79% (men) and 65% (women) of the variability. The final model predicting PAEE included peak 30-min cadence, steps per day, and time in cadence bands and explained 76% (men) and 46% (women) of the variability. Time in cadence bands alone explained 39%-73% of the variability in TEE and 30%-63% of the variability in PAEE. Prediction models were stronger for men than for women.
这项研究的目的是评估使用加速度计确定的日常活动指标(每天的步数和步频)来预测总能量消耗(TEE)和双标记水(DLW)得出的身体活动能量消耗(PAEE)的可能性。20 名男性和 34 名女性(20-36 岁)提供了完整的人体测量学、加速度计、静息代谢率(RMR)和 DLW 数据。TEE 和 PAEE 是在佩戴加速度计的同一周内的清醒时间内确定的。加速度计数据包括平均每天的步数、最高 30 分钟的步频(一天中最高 30 分钟的平均步数/分钟)以及每个递增步频带中花费的时间:0(非运动)、1-19(偶然运动)、20-39(零星运动)、40-59(有目的的步伐)、60-79(缓慢行走)、80-99(中等行走)、100-119(快走)和 120+步·分钟(-1)(表示所有更快的日常活动)。回归分析用于为预测 TEE 和 PAEE 制定性别特异性方程。预测 TEE 的最终模型包括体重、每天的步数和递增步频带中的时间,解释了 79%(男性)和 65%(女性)的变异性。预测 PAEE 的最终模型包括最高 30 分钟的步频、每天的步数和步频带中的时间,解释了 76%(男性)和 46%(女性)的变异性。仅步频带中的时间就解释了 TEE 变异性的 39%-73%和 PAEE 变异性的 30%-63%。预测模型在男性中的表现优于女性。