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身体活动（PA）水平方面已经取得了进展。然而，这两种方法在单独使用时都有固有的缺点，并且不能提供足够的精度来满足研究需要。因此，通过将这两种客观评估方法集成到单个单元设备中已经取得了进展。迄今为止，单单元设备仍然受到弱点的困扰，这些弱点涉及缺乏利用数据的简化建模策略，需要复杂的实验室校准，以及设备穿戴繁琐。本提案的目的是解决综合心率和基于加速计的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，以改善对身体活动强度（派）的估计; 2）评估不同的动态场校准活动，以个性化校准标准，以估计用于条件建模方法的派; 3）将综合传感器方法与单个心率估计进行比较（FLEX心率方法）和单个加速度计估计值（回归临界点和回归方程法），用于评估PA和体力活动相关能量消耗（PAEE）在实验室和现场环境中进行的不同短期模拟日常生活方式活动期间;以及4）将综合传感器方法与单个心率估计进行比较（FLEX心率方法）和单一加速计估计值（回归临界点和回归方程方法），用于评估延长的自由生活期间的PAEE和每日总能量消耗（TDEE）。我们合格的研究团队将通过首先使用集成传感器方法进行个体动态实验室校准，并开发相对于间接量热法的个体心率和加速度计校准来实现上述目标。基于这些结果，将对实验室数据进行建模，以探索预测派水平的条件整合模型。然后将进行现场动态校准，以整合到条件模型中，以探索现场校准的使用，而不是需要实验室校准来个性化心率数据。然后，将利用所得模型和最精确的现场校准标准从收集的心率和加速度计数据预测PAEE，并将其与模拟生活方式活动期间的间接量热法PAEE测量进行比较。这种综合方法将进一步验证在一段时间的自由生活活动，PAEE和TDEE估计相比，双标记水技术。由此产生的条件建模方法来预测PAEE将实施到流行的商业软件包，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方法来从综合心率和加速度计数据中获得有效和可靠的PA估计值，从而推动PA评估领域的发展。这将使研究人员能够采用这种技术来确定由于活动干预引起的行为变化，并进一步了解PA与健康之间的剂量反应关系。公共卫生相关性：身体活动（PA）的准确评估是必不可少的，以确定基于行为的PA干预措施的有效性，并进一步了解PA与健康之间的剂量-反应关系。本提案的总体目标是广泛验证一种新的单一单元综合心率加速度计装置，并开发有效的条件模型和动态现场校准技术，并提高使用的可行性和实用性。

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. PUBLIC HEALTH RELEVANCE: The accurate assessment of physical activity (PA) is essential to determine the effectiveness of behaviorally based PA interventions, and further our understanding of the dose-response relationship between PA and health. The overall aim of this proposal is to extensively validate a new single unit integrative heart rate accelerometry device, and to develop valid conditional models and dynamic field calibration techniques and to enhance the feasibility and practicality of use.