Utility and feasibility of activity type to augment consumer wearable-based physical activity energy expenditure prediction equations using heartrate and movement in children

准确评估儿童(5-12岁)自由生活的身体活动能量消耗(PAEE)对于 了解儿童PAEE和健康结果之间复杂和相互依存的关系。这个 运动(例如，步数、计数、原始信号)和心率数据的组合提供了相当准确的 实验室条件下儿童PAEE的估计。然而，运动和心率的结合 (Move HR)可能不足以预测儿童自由生活的PAEE。对成年人的研究表明 包括活动类型(例如，参与者是步行、跑步、骑自行车等)提高了对免费- Living PAEE，相对于使用MOVE或MOVE HR。然而，这一证据是基于在 成年人。还没有关于儿童的研究调查了在PAEE预测方程中加入活动类型的影响 使用Move HR的。现有的消费者可穿戴设备(例如，Fitbit、Garmin)有望用于评估 儿童自由生活的PAEE。这些设备集成了内置的模式识别功能，可以自动 检测活动类型，并以用户友好的格式为最终用户提供这些指标；然而，未知 研究评估了消费者可穿戴设备自动检测儿童活动的能力。消费者 可穿戴设备还使用光体积描记器来捕捉心率，使用加速度计来捕捉运动。他们的独特之处 捕捉活动类型、人力资源和运动指标的能力可以显著提高对自由生活的估计 儿童的PAEE。目标1将评估包括通过直接观察捕获的活动类型对 使用来自消费者可穿戴设备(即Garmin Vivoactive 4S)的运动和HR数据的回归方程 和Fitbit Sense)。目标2将评估消费者可穿戴设备(即Garmin Vivoactive 4S和Fitbit)的能力 自动检测活动(例如，散步、跑步、骑自行车)。为了实现这些目标，这项研究将 充分利用验证设计(即半结构化体力活动协议和数据 管理/提取程序)，并利用从120名儿童(5-12岁)收集的数据 R01项目。该项目将使用分析技术，包括横截面时间序列(CSTS)、多变量 自适应回归样条法(MARS)、机器学习和等价性测试，以解决以下目标。 该项目的长期目标是在流行病学和干预方面推进对儿童PAEE的评估。 以儿童为基础的研究，这对于理解儿童PAEE之间的复杂关系至关重要 和健康结果。通过本项目的实施，将获得以下几点：深入了解 与儿童PAEE评估、设计和实施验证的专业知识相关的文献 评估儿童PAEE的研究，使用黄金标准评估儿童PAEE的动手培训 和活动类型，熟练掌握先进的分析技术，以及科学交流和 专业技能，包括同行评议的出版、科学演示、有指导的手稿评审、 以及一份博士后资助申请草稿。

Accurate assessment of children’s (5-12yrs) free-living physical activity energy expenditure (PAEE) is critical to understanding the complex and interdependent relationship between children’s PAEE and health outcomes. The combination of movement (e.g., steps, counts, raw signal) and heart rate data provides reasonably accurate estimates of children’s PAEE during lab conditions. However, the combination of movement and heart rate (MOVE+HR) may be inadequate for predicting children’s free-living PAEE. Studies in adults demonstrate including the type of activity (e.g., the participant is walking, running, cycling, etc.) improves estimates of free- living PAEE, relative to using movement or MOVE+HR. Yet, this evidence is based on studies conducted in adults. No studies of children have investigated the impact of adding activity type to PAEE prediction equations that use MOVE+HR. Existing consumer wearables (e.g., Fitbit, Garmin) are promising for the assessment of children’s free-living PAEE. These devices incorporate built-in pattern-recognition features that automatically detect activity type, and these metrics are provided in a user-friendly format for the end-user; however, no known studies have evaluated the ability of consumer wearables to autodetect activities in children. Consumer wearables also use photoplethysmography to capture HR and accelerometry to capture movement. Their unique ability to capture activity type, HR, and movement metrics could significantly improve estimates of free-living PAEE in children. Aim 1 will evaluate the impact of including activity type, captured via direct observation, to regression equations that use movement and HR data from consumer wearables (i.e., Garmin Vivoactive 4S and Fitbit Sense). Aim 2 will evaluate the ability of consumer wearables (i.e., Garmin Vivoactive 4S and Fitbit Sense) to automatically detect activities (i.e., walking, running, biking). To accomplish these aims, this study will capitalize on the validation design (i.e., semi-structured physical activity protocol and data management/extraction procedures) and draw on data collected from 120 children (5-12yrs) from an existing R01 project. This project will use analytical techniques, including cross-sectional time series (CSTS), multivariate adaptive regression spline (MARS), machine learning, and equivalence testing to address the following aims. The project’s long-term goal is to advance the assessment of children’s PAEE in epidemiologic- and intervention- based studies for children, which is critical to understanding the complex relationship between children’s PAEE and health outcomes. Through the execution of this project, the following will be gained: an in-depth knowledge of the literature related to assessment of PAEE in children, expertise designing and implementing validation studies assessing PAEE in children, hands-on training using gold standard measures to assess children’s PAEE and activity type, proficiency performing advanced analytical techniques, and scientific communication and grantsmanship skills, including peer-reviewed publication, scientific presentation, mentored manuscript review, and a drafted post-doctoral grant application.