基于节奏的步行代谢方程的建立。
Development of a Cadence-based Metabolic Equation for Walking.
机构信息
Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC.
Department of Kinesiology, The University of Alabama, Tuscaloosa, AL.
出版信息
Med Sci Sports Exerc. 2021 Jan;53(1):165-173. doi: 10.1249/MSS.0000000000002430.
PURPOSE
This study aimed to develop cadence-based metabolic equations (CME) for predicting the intensity of level walking and evaluate these CME against the widely adopted American College of Sports Medicine (ACSM) Metabolic Equation, which predicts walking intensity from speed and grade.
METHODS
Two hundred and thirty-five adults (21-84 yr of age) completed 5-min level treadmill walking bouts between 0.22 and 2.24 m·s, increasing by 0.22 m·s for each bout. Cadence (in steps per minute) was derived by dividing directly observed steps by bout duration. Intensity (oxygen uptake; in milliliters per kilogram per minute) was measured using indirect calorimetry. A simple CME was developed by fitting a least-squares regression to the cadence-intensity relationship, and a full CME was developed through best subsets regression with candidate predictors of age, sex, height, leg length, body mass, body mass index (BMI), and percent body fat. Predictive accuracy of each CME and the ACSM metabolic equation was evaluated at normal (0.89-1.56 m·s) and all (0.22-2.24 m·s) walking speeds through k-fold cross-validation and converted to METs (1 MET = 3.5 mL·kg·min).
RESULTS
On average, the simple CME predicted intensity within 1.8 mL·kg·min (0.5 METs) at normal walking speeds and with negligible (<0.01 METs) bias. Including age, leg length, and BMI in the full CME marginally improved predictive accuracy (≤0.36 mL·kg·min [≤0.1 METs]), but may account for larger (up to 2.5 mL·kg·min [0.72 MET]) deviations in the cadence-intensity relationships of outliers in age, stature, and/or BMI. Both CME demonstrated 23%-35% greater accuracy and 2.2-2.8 mL·kg·min (0.6-0.8 METs) lower bias than the ACSM metabolic equation's speed-based predictions.
CONCLUSIONS
Although the ACSM metabolic equation incorporates a grade component and is convenient for treadmill-based applications, the CME developed herein enables accurate quantification of walking intensity using a metric that is accessible during overground walking, as is common in free-living contexts.
目的
本研究旨在开发基于步频的代谢方程（CME）以预测平地行走的强度，并评估这些 CME 与广泛应用的美国运动医学学院（ACSM）代谢方程的效果，后者通过速度和坡度预测行走强度。
方法
235 名成年人（21-84 岁）在 0.22 至 2.24 m·s 之间进行 5 分钟的平地跑步机行走，每个阶段增加 0.22 m·s。步频（每分钟步数）通过将观察到的步数除以回合持续时间直接得出。使用间接测热法测量强度（耗氧量；毫升/千克/分钟）。通过对步频-强度关系进行最小二乘回归拟合，开发了一个简单的 CME，并通过候选预测因子（年龄、性别、身高、腿长、体重、体重指数（BMI）和体脂百分比）的最佳子集回归开发了一个完整的 CME。通过 k 折交叉验证评估每个 CME 和 ACSM 代谢方程在正常（0.89-1.56 m·s）和所有（0.22-2.24 m·s）行走速度下的预测准确性，并转换为 METs（1 MET = 3.5 mL·kg·min）。
结果
平均而言，简单的 CME 在正常行走速度下预测强度的误差在 1.8 毫升/千克/分钟（0.5 METs）左右，且偏差可忽略不计（<0.01 METs）。在完整的 CME 中包含年龄、腿长和 BMI，可略微提高预测准确性（≤0.36 毫升/千克/分钟[≤0.1 METs]），但可能会导致年龄、身高和/或 BMI 异常值的步频-强度关系出现更大的偏差（高达 2.5 毫升/千克/分钟[0.72 METs]）。两种 CME 的准确性均提高了 23%-35%，比 ACSM 代谢方程基于速度的预测值的偏差低 2.2-2.8 毫升/千克/分钟（0.6-0.8 METs）。
结论
尽管 ACSM 代谢方程包含坡度成分，并且便于在跑步机上应用，但本文开发的 CME 可以使用在地面行走时可获得的指标准确量化行走强度，这在自由生活环境中很常见。
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