Validity and Utility of Consumer-based Wearable Fitness Trackers to Monitor Free-Living Physical Activity Energy Expenditure and Sleep in Children 5-12 Years Old

基于消费者的可穿戴健身追踪器监测 5-12 岁儿童自由体力活动能量消耗和睡眠的有效性和实用性

• 批准号: 10596651
• 负责人: Robert G Weaver
• 金额: $ 63.48万
• 依托单位: UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-04-01 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10596651
• 关键词: 12 year old; Acceleration; Accelerometer; Adult; Age; Algorithms; Behavior; Biometry; Chest; Child; Clinical; Collection; Complex; Data; Devices; Elderly; Electrocardiogram; Energy Metabolism; Epidemiology; Equation; Gait abnormality; Goals; Health; Heart Rate; Hour; Indirect Calorimetry; Intervention; Intervention Studies; Laboratories; Learning; Link; Machine Learning; Measurement; Measures; Methods; Monitor; Movement; Nature; Outcome; Output; Pattern; Persons; Photoplethysmography; Physical activity; Physical assessment; Polysomnography; Population; Process; Protocols documentation; Research; Research Personnel; Research Priority; Series; Signal Transduction; Sleep; Sleep Disorders; Statistical Models; Techniques; Technology; Telemetry; Testing; Time; United States National Institutes of Health; Validation; Vision; Youth; actigraphy; application programming interface; battery life; design; design verification; epidemiology study; experience; fitbit; fitness; improved; innovation; motion sensor; neuromuscular; online repository; open source; sedentary; sleep behavior; validation studies; wearable device; wearable sensor technology

Accurate measurement of free-living physical activity (PA), energy expenditure (EE), and sleep of children (5-12yrs) is complex, with no single method free of limitations. Validation studies of PA, EE, and sleep have demonstrated that combining HR and accelerometry data (e.g., steps, counts, raw signal) provides the most accurate estimate of PA, EE, and sleep. Unfortunately, the simultaneous collection of HR and accelerometry over routine monitoring timeframes (e.g., 7 days) has been limited because historically measuring HR has relied on uncomfortable chest strap telemetry. Advancements in wearable technology have eliminated this issue by incorporating the noninvasive assessment of HR via photoplethysmography in widely-available consumer wearable devices (e.g., FitBits, Garmin) that also include accelerometry. Studies have shown that HR estimates from consumer wearables are comparable to those collected via ECG or chest strap telemetry. However, validation studies of consumer wearables have focused almost exclusively on proprietary activity output (e.g., steps) and have mostly been conducted on healthy adults, older adults, or clinical populations (e.g., people with neuromuscular or gait abnormalities). Consumer wearables hold promise for collecting PA, EE, and sleep data with children 5 to 12yrs, yet no studies have been conducted to establish their validity and utility/feasibility in this population. The objectives of the proposed project are to conduct a series of studies that include both laboratory and field-based protocols to evaluate the reliability, validity and utility/feasibility of consumer wearables for measuring children’s PA, EE, and sleep in free-living conditions. We will evaluate the different features of the devices (e.g., PA, HR) in the lab and in real-world conditions. In addition, we will evaluate the utility/feasibility of consumer wearables for multi-day wear compliance. We will accomplish the following aims. Aim 1. Develop and validate open- source equations to estimate PAEE and time spent physically active using the activity and HR data from consumer wearables compared to the PAEE output from the consumer wearables’ proprietary processing algorithms and a criterion measure of PAEE (i.e., indirect calorimetry). Aim 2. Develop and validate open-source equations to estimate total sleep time, sleep efficiency, and timing from consumer wearables using their activity and HR data compared to the sleep output from the consumer wearables’ proprietary processing algorithms and a criterion measure of sleep (i.e., PSG and actigraphy). Aim 3. Evaluate the validity of the PAEE and sleep equation estimates from the algorithms created in Aim 1 & 2 against a criterion (i.e., Actiheart) under free-living conditions. This project is significant because it will be among the first to establish the validity of consumer wearables for PAEE and sleep monitoring of children. This project is innovative as it will use advanced statistical modeling techniques, including machine learning, to systematically test the validity and utility/feasibility of consumer wearables for children. Our vision is to leverage the biometric data collected across consumer wearables to produce estimates of PAEE and sleep in children. This will allow practitioners and researchers alike to more accurately measure 24-hour movement behaviors in children.

准确测量自由活动的体育锻炼（PA），能量消耗（EE）和儿童睡眠（5-12岁）为 复杂，没有任何限制的方法。 PA，EE和睡眠的验证研究表明 组合HR和加速度计数据（例如，步骤，计数，原始信号）提供了PA，EE，，EE，最精确的估计值 和睡觉。不幸的是，在例行监视时间表上，人力资源和加速度计的简单集合（例如， 7天）之所以受到限制，是因为历史上的人力资源依赖不舒服的胸带遥测。 可穿戴技术的进步已通过纳入人力资源的无创评估来消除此问题 通过广泛的消费者可穿戴设备（例如Fitbits，Garmin）中的广泛的消费型图像学 加速度计。研究表明，消费者可穿戴设备的人力资源估计值与通过 ECG或胸带遥测。但是，消费者可穿戴设备的验证研究几乎完全关注 专有活性输出（例如，步骤），主要是针对健康的成年人，老年人或临床进行的 种群（例如，神经肌肉或步态异常的人）。消费者可穿戴设备有望收集 PA，EE和睡眠数据与5至12岁的儿童有关，但尚未进行研究以确定其有效性和 该人群的效用/可行性。拟议项目的目标是进行一系列研究，包括 用于评估消费者可穿戴设备的可靠性，有效性和可行性的实验室和基于现场的协议 用于在自由生活条件下测量儿童PA，EE和睡眠。我们将评估设备的不同功能 （例如，PA，HR）在实验室和实际条件下。此外，我们将评估消费者的效用/可行性 可穿戴适合多天的可穿戴设备。我们将完成以下目标。目标1。开发和验证开放 - 使用消费者的活动和人力资源数据来估算PAEE和时间上花费的时间的源方程式 与消费者可穿戴设备专有处理算法和A的可穿戴设备相比 PAEE的标准测量（即间接量热法）。目标2。开发和验证开源方程以估计 与消费者可穿戴设备的总睡眠时间，睡眠效率以及使用其活动和人力资源数据相比 消费者可穿戴设备专有处理算法和睡眠标准的睡眠输出（即 PSG和行动学）。 AIM 3。评估PAEE和睡眠方程的有效性，从创建的算法中评估 在自由生活条件下针对标准（即Actiheart）的AIM 1和2中。这个项目很重要，因为它将 在第一个确定消费者可穿戴设备的有效性和儿童睡眠监控的有效性。这个项目 具有创新性，因为它将使用包括机器学习在内的高级统计建模技术进行系统测试 儿童消费者可穿戴设备的有效性和效用/可行性。我们的愿景是利用收集的生物识别数据 跨消费者可穿戴设备，以产生儿童的估计和睡眠。这将允许从业者和 研究人员都可以更准确地衡量儿童的24小时运动行为。