Heart Rate and Movement Integration to Improve Physical Activity Assessment

描述(由申请人提供)：在开发和使用基于心率和加速度计的运动传感器来预测体力活动(PA)水平方面取得了进展。然而，这两种方法在单独使用时都有固有的缺陷，不能提供足够的精度来满足研究需要。为此，通过将这两种客观评估方法集成到单一单元设备中而取得了进步。到目前为止，单一单元设备仍然受到弱点的困扰，与缺乏利用数据的简化建模策略有关，需要复杂的实验室校准，以及设备佩戴起来很笨拙。这项建议的目的是解决基于心率和加速度计的综合PA评估方法中的这些弱点。具体目标是：1)开发和验证条件模型以整合生理和运动数据以改进体力活动强度(PAI)的估计；2)评估不同的动态场校准活动以个体化校准标准以估计条件建模方法中使用的PAI；3)比较综合传感器方法与单一心率估计(FLEX心率法)和单一加速计估计(回归切点和回归方程式方法)在实验室和野外环境中进行的不同短期模拟日常生活活动中的PA和体力活动相关能量消耗(PAEE)；4)比较综合传感器法、单心率估计法(FLEX心率法)和单加速度计估计法(回归切点和回归方程式法)在延长自由生活期间的PAEE和总每日能量消耗(TDEE)方面的差异。我们合格的研究团队将首先使用集成传感器方法进行个人动态实验室校准，并开发与间接量热法相关的个性化心率和加速度计校准，从而实现上述目标。根据这些结果，实验室数据将被模型化，以探索预测PAI水平的条件综合模型。然后将进行现场动态校准，以便整合到条件模型中，以探索使用现场校准而不是实验室校准来个体化心率数据的必要性。最终的模型和最精确的现场校准标准将被用来从收集的心率和加速计数据中预测PAEE，并与模拟生活方式活动中的间接热量测量PAEE进行比较。这一综合方法将在自由生活活动期间得到进一步验证，与双标记水技术相比，PAEE和TDEE估计。由此产生的预测PAEE的条件建模方法将被实施到流行的商业软件包中，并提供给活动研究人员。拟议的一系列研究的结果将通过提供创新的方法来从综合心率和加速计数据中获得对PA的有效和可靠的估计，从而推动PA评估领域的发展。这将使研究人员能够利用这项技术来确定由于活动干预而导致的行为变化，并进一步了解PA与健康之间的剂量-反应关系。

DESCRIPTION (provided by applicant): Progress has been made in developing and using both heart rate and accerometer based motion sensors to predict physical activity (PA) level. However, both of these methods have inherent weaknesses when used in isolation, and do not provide sufficient accuracy to satisfy the research need. For this reason, advancement has been made by integrating these two objective assessment methodologies into single unit devices. To date, single unit devices remain plagued by weaknesses, pertaining to the lack of simplified modeling strategies to utilize the data, the need for complicated laboratory calibrations, and devices being cumbersome to wear. The aims of this proposal address these weaknesses in integrative heart rate and accelerometer- based PA assessment methodologies. The specific aims are: 1) To develop and validate conditional models to integrate physiological and movement data to improves estimates of physical activity intensity (PAI); 2) To evaluate different dynamic field calibration activities to individualize calibration standards to estimate PAI for use in conditional modeling approaches; 3) To compare the integrative sensor approach to single heart rate estimates (FLEX heart rate approach) and single accelerometer estimates (regression cut-point and regression equation approach) for assessing PA and physical activity related energy expenditure (PAEE) during different short duration simulated everyday lifestyle activities conducted in a laboratory and field setting; and 4) To compare the integrative sensor approach to single heart rate estimates (FLEX heart rate approach) and single accelerometer estimates (regression cut-point and regression equation approach) for assessing PAEE and total daily energy expenditure (TDEE) during an extended period of free-living. Our qualified research team will address the above aims by first carrying out individual dynamic laboratory calibrations using the integrative sensor approach, and developing individualized heart rate and accelerometer calibrations relative to indirect calorimetry. Based upon these results the laboratory data will be modeled to explore conditional integrative models to predict PAI levels. Field dynamic calibrations will then be conducted for integration into the conditional models to explore the use of field calibrations rather than the need for laboratory calibration to individualize heart rate data. The resulting model and most precise field calibration standard will then be utilized to predict PAEE from collected heart rate and accelerometer data and compared to indirect calorimetry PAEE measures during simulated lifestyle activities. This integrative approach will be further validated during a period of free-living activity, with PAEE and TDEE estimates compared to the doubly labeled water technique. The resulting conditional modeling approach to predict PAEE will be implemented into popular commercial software packages and made available to activity researchers. The results of the proposed series of studies will move the field of PA assessment forward by providing innovative approaches to obtain valid and reliable estimates of PA from integrative heart rate and accelerometer data. This will enable researchers to employ this technique to determine behavioral change due to an activity intervention, and further our understanding of the dose-response relationship between PA and health.