Dept of Kinesiology, University of Massachusetts, Amherst, MA, USA.
J Phys Act Health. 2010 Jul;7(4):508-16. doi: 10.1123/jpah.7.4.508.
To compare intensity misclassification and activity MET values using measured RMR (measMET) compared with 3.5 ml x kg(-1) x min(-1) (standMET) and corrected METs [corrMET = mean standMET x (3.5 / Harris-Benedict RMR)] in subgroups.
RMR was measured for 252 subjects following a 4-hr fast and before completion of 11 activities. VO2 was measured during activity using indirect calorimetry (n = 2555 activities). Subjects were classified by BMI category (normal-weight or overweight/obese), sex, age (decade 20, 30, 40, or 50 y), and fitness quintiles (low to high). Activities were classified into low, moderate, and vigorous intensity categories.
The (mean +/- SD) measMET was 6.1 +/- 2.64 METs. StandMET [mean (95% CI)] was (0.51(0.42, 0.59) METs) less than measMET. CorrMET was not statistically different from measMET (-0.02 (-0.11, 0.06) METs). 12.2% of the activities were misclassified using standMETs compared with an 8.6% misclassification rate for METs based on predicted RMR (P < .0001). StandMET differences and misclassification rates were highest for low fit, overweight, and older individuals while there were no differences when corrMETs were used.
Using 3.5 ml x kg(-1) x min(-1) to calculate activity METs causes higher misclassification of activities and inaccurate point estimates of METs than a corrected baseline which considers individual height, weight, and age. These errors disproportionally impact subgroups of the population with the lowest activity levels.
比较使用实测静息代谢率（measMET）与 3.5ml/kg/min（standMET）和校正代谢当量[corrMET = 平均 standMET x (3.5/Harris-Benedict RMR)]测量值在亚组中的强度分类错误和活动 MET 值。
252 名受试者在禁食 4 小时后，在完成 11 项活动前测量 RMR。使用间接测热法（n = 2555 项活动）测量活动期间的 VO2。受试者按 BMI 类别（正常体重或超重/肥胖）、性别、年龄（20、30、40 或 50 岁）和体能五分位数（低至高）分类。活动分为低强度、中等强度和高强度类别。
（mean +/- SD）measMET 为 6.1 +/- 2.64 METs。standMET [mean（95%CI）]比 measMET 低（0.51（0.42，0.59）METs）。corrMET 与 measMET 无统计学差异（-0.02（-0.11，0.06）METs）。使用 standMETs 分类时，有 12.2%的活动被错误分类，而基于预测 RMR 的 METs 分类错误率为 8.6%（P<.0001）。standMET 的差异和错误分类率在低体能、超重和年龄较大的个体中最高，而使用 corrMETs 时则没有差异。
使用 3.5ml/kg/min 计算活动 METs 会导致活动的更高错误分类和 METs 的不准确点估计，而不是考虑个体身高、体重和年龄的校正基线。这些错误不成比例地影响活动水平最低的人群亚组。
