Integration of physiological and accelerometer data to improve physical activity assessment
Integration of physiological and accelerometer data to improve physical activity assessment
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DOI：
10.1249/01.mss.0000185650.68232.3f
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发表时间：
2005-11-01
期刊：
影响因子：
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通讯作者：
Ekelund, U
中科院分区：
文献类型：
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作者：
Strath, SJ;Brage, S;Ekelund, U
目的：准确测量体力活动（PA）是确定活动与健康之间剂量反应关系的先决条件。 HR 和加速度计 (ACC) 的结合有望提高 PA 评估的准确性，但目前尚不清楚目前提出的建模技术如何比较，以及不同水平的 HR 个体校准 (IC) 在多大程度上影响监测准确性。方法：本研究共招募了 10 名男性和女性（25.8 +/- 3.4 岁、1.70 +/- 0.1 m、71.7 +/- 11.8 kg、24.4 +/- 5.0 kg(.)m(-2)），其中可获取手臂曲柄和跑步机活动期间 HR 与 PA 能量消耗 (PAEE) 的 IC。参与者完成 6 小时的自由生活活动，在此期间收集 PAEE（通过间接量热法获得）、HR、髋关节 ACC、手臂 ACC 和腿部 ACC。然后，PAEE 通过结合 HR 和 ACC 的两种不同方法（臂腿 HR+M 和分支模型）进行建模，均采用 HR 的 IC 和组级校准 (GC)，并且还仅根据髋部 ACC 估计进行建模。将 PAEE 的估计值与 PAEE 的标准值进行比较。结果：使用 IC 时，臂腿 HR+M 和分支模型对 PAEE 的综合估计相似（分别为 R-2 = 0.81、SEE = 0.55 MET 和 R-2 = 0.75、SEE = 0.61 MET）。当使用GC时，所有PAEE的估计都有较大的误差，但分支模型的性能比手臂-腿HR+M模型受到的影响要小（分别为R2 = 0.75，SEE = 0.67 METs和R-2 = 0.67，SEE = 0.88 METs）。两种组合建模技术都比单次测量髋关节 ACC 估计更精确（R-2 = 0.41，SEE = 0.96 MET）。结论：HR与ACC的结合提高了PAEE估算的准确性，可应用于大规模流行病学研究。
Purpose: Accurate measurement of physical activity (PA) is a prerequisite to determine dose-response relationships between activity and health. The combination of HR and accelerometers (ACC) holds promise for improving the accuracy of PA assessment, but it is unclear how currently proposed modeling techniques compare and to what extent different levels of individual calibration (IC) of HR influence monitoring accuracy. Methods: A total of 10 men and women (25.8 +/- 3.4 yr, 1.70 +/- 0.1 m, 71.7 +/- 11.8 kg, 24.4 +/- 5.0 kg(.)m(-2)) were recruited for this study, in which IC of HR to PA energy expenditure (PAEE) during both arm crank and treadmill activity were available. Participants completed 6 h of free-living activity, during which PAEE (obtained with indirect calorimetry), HR, hip ACC, arm ACC, and leg ACC were collected. PAEE was then modeled from two different methods of combining HR and ACC (arm-leg HR+M and branched model), both with IC and group-level calibration (GC) of HR, and also from hip ACC estimates alone. Estimates of PAEE were compared with criterion values for PAEE. Results: Combined estimates of PAEE from the arm-leg HR+M and the branched model were similar when IC was used (R-2 = 0.81, SEE = 0.55 METs and R-2 = 0.75, SEE = 0.61 METs, respectively). When using GC, all estimates of PAEE had larger error, but the performance of the branched model suffered less than the arm-leg HR+M model (R2 = 0.75, SEE = 0.67 METs and R-2 = 0.67, SEE = 0.88 METs, respectively). Both combination modeling techniques were more precise than single-measure hip ACC estimates (R-2 = 0.41, SEE = 0.96 METs). Conclusion: The combination of HR and ACC improves the accuracy of PAEE estimates and could be applied in large-scale epidemiological studies.