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.

准确测量儿童(5-12岁)的自由生活体力活动(PA)、能量消耗(EE)和睡眠 复杂，没有一种方法不受限制。对PA、EE和SLEEP的验证研究表明 结合HR和加速度计数据(例如，步数、计数、原始信号)提供最准确的PA、EE、 然后睡觉。不幸的是，在常规监测时间范围内同时收集HR和加速度计(例如， 7天)是有限的，因为历史上测量心率依赖于不舒服的胸带遥测。 可穿戴技术的进步通过纳入人力资源的非侵入性评估消除了这个问题 通过在广泛可用的消费者可穿戴设备(例如FitBits、Garmin)中进行的光体积描记，这些设备还包括 加速度计。研究表明，消费者可穿戴设备的人力资源估算与通过 心电或胸带遥测。然而，消费者可穿戴设备的验证研究几乎完全集中在 专有活动输出(例如，步骤)，并且主要在健康成年人、老年人或临床上进行 人群(例如，有神经肌肉或步态异常的人)。消费者可穿戴设备有望回收 5-12岁儿童的PA、EE和睡眠数据，但尚未进行任何研究来确定其有效性和 在这一人群中的效用/可行性。拟议项目的目标是进行一系列研究，包括 评估消费者可穿戴设备的可靠性、有效性和实用性/可行性的实验室和现场方案 用于测量儿童在自由生活条件下的PA、EE和睡眠。我们将评估这些设备的不同功能 (例如，PA、HR)在实验室和实际条件下。此外，我们还将评估消费者的效用/可行性 可穿戴设备，符合多天佩戴要求。我们将实现以下目标。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估算PAEE和体力活动时间的源公式 可穿戴设备与消费者可穿戴设备专有处理算法和 PAEE的标准测量(即间接量热法)。目标2.开发并验证开放源码方程式以估计 消费者可穿戴设备的总睡眠时间、睡眠效率和计时，使用其活动和HR数据与 来自消费者可穿戴设备的专有处理算法的睡眠输出和睡眠的标准测量(即， PSG和动作记录法)。目的3.根据创建的算法评估PAEE和睡眠方程估计的有效性 在目标1和2中，在自由生活条件下反对一项标准(即，心动)。这个项目意义重大，因为它将是 最先确定消费者可穿戴设备在PAEE和儿童睡眠监测方面的有效性的公司之一。这个项目 具有创新性，因为它将使用先进的统计建模技术，包括机器学习，以系统地测试 儿童可穿戴消费品的有效性和实用性/可行性。我们的愿景是利用收集的生物识别数据 整个消费者可穿戴设备，以产生估计的PAEE和儿童睡眠。这将允许从业者和 研究人员都希望更准确地测量儿童的24小时运动行为。