Heart Rate and Movement Integration to Improve Physical Activity Assessment

心率和运动整合以改善体力活动评估

• 批准号: 7882466
• 负责人: SCOTT J STRATH
• 金额: $ 34.71万
• 依托单位: UNIVERSITY OF WISCONSIN MILWAUKEE
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-15 至 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7882466
• 关键词: Activities of Daily Living; Address; Adult; American; American Heart Association; Behavior; Behavioral; Body fat; Calibration; Cardiovascular Diseases; Centers for Disease Control and Prevention (U.S.); Characteristics; Cholesterol; Chronic Disease; Complex; Computer software; Data; Development; Devices; Disease; Dose; Effectiveness; Energy Metabolism; Environment; Equation; Frequencies; Health; Heart Rate; Household; Indirect Calorimetry; Individual; Intervention; Label; Laboratories; Life; Life Style; Link; Literature; Malignant Neoplasms; Measurement; Measures; Mental Depression; Methodology; Methods; Modeling; Monitor; Motion; Movement; Names; Nature; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Pattern; Physical Fitness; Physical activity; Physiological; Plague; Population; Public Health; Qualifying; Recommendation; Relative (related person); Reporting; Research; Research Personnel; Risk Factors; Series; Simulate; Sports Medicine; Stimulus; Surgeon; Techniques; Time; Transportation; Update; Validation; Validity and Reliability; Variant; Walking; Water; base; college; improved; innovation; instrument; monitoring device; mortality; psychologic; public health relevance; response; sedentary; sensor; therapy design; tool

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.

描述（由申请人提供）：在开发和使用基于心率和加速度的运动传感器以预测体育锻炼（PA）水平方面取得了进展。但是，这两种方法在隔离时都具有固有的弱点，并且没有提供足够的准确性来满足研究需求。因此，通过将这两种客观评估方法整合到单个单元设备中来取得进步。迄今为止，单个单元设备仍然受到弱点的困扰，这与缺乏使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繁琐。该提案的目的解决了基于心率和基于加速度计的PA评估方法中的这些弱点。具体目的是：1）开发和验证条件模型以整合生理和运动数据以改善体育活动强度（PAI）的估计； 2）评估不同的动态场校准活动，以个性化校准标准，以估算PAI用于有条件建模的方法； 3）将集成传感器方法与单个心率估计值（柔性心率方法）和单个加速度计估计值（回归裁切点和回归方程方法）进行比较，以评估在不同的短期持续时间模拟的日常生活方式活动中，以评估PA和体育活动相关的能量支出（PAEE）； 4）将集成传感器方法与单个心率估计（弹性心率方法）和单个加速度计估计（回归切点和回归方程方法）进行比较，以评估PAEE和总每日能量消耗（TDEE）（TDEE）在延长的自由生活期间。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单个动态实验室校准来解决上述目标，并开发相对于间接热量法的个性化心率和加速度计校准。基于这些结果，将对实验室数据进行建模，以探索条件整合模型以预测PAI水平。然后，将进行现场动态校准，以集成到条件模型中，以探索现场校准的使用，而不是需要实验室校准以个性化心率数据。然后，将利用所得模型和最精确的现场校准标准来预测收集的心率和加速度计数据，并将其与模拟生活方式活动期间的间接热量表PAEE度量进行比较。与双重标记的水技术相比，这种综合方法将在自由生活活动期间得到进一步验证。预测PAEE的结果有条件的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转移PA评估领域，从而从综合心率和加速度计数据中获得有效可靠的PA估计。这将使研究人员能够采用这项技术来确定由于活动干预而导致的行为改变，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：对体育活动的准确评估（PA）对于确定基于行为的PA干预的有效性以及我们对PA与健康之间剂量反应关系的理解至关重要。该提案的总体目的是广泛验证新的单位单位集成心率加速度计，并开发有效的条件模型和动态场校准技术并增强使用的可行性和实用性。