Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure
Branched equation modeling of simultaneous accelerometry and heart rate monitoring improves estimate of directly measured physical activity energy expenditure
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DOI：
10.1152/japplphysiol.00703.2003
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发表时间：
2004-01-01
影响因子：
3.3
通讯作者：
Wareham, NJ
中科院分区：
文献类型：
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作者：
Brage, S;Brage, N;Wareham, NJ
心率监测与运动配准相结合可以提高运动能量消耗(PAEE)的测量精度。以前的尝试要么使用回归方法，后者没有充分利用同步数据，要么没有定量地使用运动数据。该研究的目的是通过使用心率和加速度计的个体校准(IC)或相应的均值组校准(GC)方程来评估PAEE的分支模型估计的精度。在12名男性(20.6-25.2 kg/m(2))中，分别建立了跑步机步行和跑步的体力活动强度(PAI)的IC和GC方程[计算机科学与应用(CSA)]。在22h全身量热法中每分钟记录HR和CSA，并在HR与CSA(1-P1-4)的四种不同权重(P1-4)下换算为PAI：如果是CSA和Gt；x，则使用P1权重如果HR和Gt；y，否则使用P-2。类似地，如果CSA小于或等于x，则如果HR>；z，我们使用P-3，否则使用P-4。计算12.5h非睡眠期的PAEE作为PAI的时间积分。先验假设P-1=1，P-2=P-3=0.5，P-4=0，x=5次/分钟，y=步行/跑步转换心率，z=屈曲心率。这些参数也是在临时性后进行了估计。先验模型的平均+/-SD估计误差在IC和GC分别为-4.4+/-29和3.5+/-20%。相应的后模型误差分别为-1.5+/-13和0.1+/-9.8%。所有分支模型的误差(P小于或等于0.035)均低于CSA(小于或等于-45%)和HR(大于或等于39%)及其非分支组合(大于或等于25.7%)的单项估计值。总而言之，通过分支建模将HR和CSA结合在一起可以改善PAEE的估计。IC在这种建模技术中可能不那么重要。
The combination of heart rate (HR) monitoring and movement registration may improve measurement precision of physical activity energy expenditure (PAEE). Previous attempts have used either regression methods, which do not take full advantage of synchronized data, or have not used movement data quantitatively. The objective of the study was to assess the precision of branched model estimates of PAEE by utilizing either individual calibration (IC) of HR and accelerometry or corresponding mean group calibration (GC) equations. In 12 men (20.6 - 25.2 kg/m(2)), IC and GC equations for physical activity intensity (PAI) were derived during treadmill walking and running for both HR ( Polar) and hip-acceleration [ Computer Science and Applications (CSA)]. HR and CSA were recorded minute by minute during 22 h of whole body calorimetry and converted into PAI in four different weightings (P1-4) of the HR vs. the CSA (1- P1-4) relationships: if CSA > x, we used the P1 weighting if HR > y, otherwise P-2. Similarly, if CSA less than or equal to x, we used P-3 if HR > z, otherwise P-4. PAEE was calculated for a 12.5-h nonsleeping period as the time integral of PAI. A priori, we assumed P-1 = 1, P-2 = P-3 = 0.5, P-4 = 0, x = 5 counts/min, y = walking/ running transition HR, and z = flex HR. These parameters were also estimated post hoc. Means +/- SD estimation errors of a priori models were - 4.4 +/- 29 and 3.5 +/- 20% for IC and GC, respectively. Corresponding post hoc model errors were - 1.5 +/- 13 and 0.1 +/- 9.8%, respectively. All branched models had lower errors ( P less than or equal to 0.035) than single-measure estimates of CSA ( less than or equal to - 45%) and HR ( greater than or equal to 39%), as well as their nonbranched combination ( greater than or equal to 25.7%). In conclusion, combining HR and CSA by branched modeling improves estimates of PAEE. IC may be less crucial with this modeling technique.