Physical Activity Measurement in Toddlers

幼儿身体活动测量

基本信息

批准号：
10545040
负责人：
Soyang Kwon
金额：
$ 38.78万
依托单位：
LURIE CHILDREN'S HOSPITAL OF CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-01-01 至 2026-12-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10545040
关键词：
2 year old 3 year old 5 year old Accelerometer Address Adolescence Adult Affect Age Age Months Algorithms Behavior Chicago Child Child Care Child Rearing Childhood Classification Clinic Dancing Data Data Set Development Developmental Process Ensure Epidemiology Ethnic Origin Female Goals Growth Guidelines Habits Health Health Benefit Hip region structure Home Investigation Knowledge Learning Light Machine Learning Measurement Mediation Methodology Minority Modeling Motor Motor Skills National Heart, Lung, and Blood Institute Nursery Schools Parents Participant Performance Physical activity Pilot Projects Population Posture Premature Birth Process Public Health Race Recommendation Reporting Research Activity Running Sampling School-Age Population Sex Differences Source Code Sum Surveys Testing Time Toddler Training United States Walking aged analytical tool boys computerized data processing early childhood girls interest machine learning algorithm male physical inactivity recruit response sedentary sex social influence tool

项目摘要

Project Summary Physical inactivity is a significant health problem, affecting females more than males. Physical inactivity tends to track over time and many young children aged 3 or 4 years are physically inactive. Therefore, understanding when and how physical activity habits develop requires investigation starting at age 2 years or younger. In toddler (age 1 or 2 years) physical activity research, however, a major methodological gap exists regarding physical activity measurement, particularly related to accelerometer data processing. This gap limits our ability to accurately estimate physical activity levels among toddlers. To process accelerometer data, an intensity- based accelerometer count cut-point approach has been widely used. However, the cut-points suggested for toddlers have been found to present low accuracy (≤58%). A new analytic approach, machine learning, has been shown to provide more accurate activity classification among preschoolers and older children. Our pilot study also suggests that the machine learning approach has great potential for toddler activity recognition. The overarching goal of this proposed study is to better understand the development of physical activity behavior in early childhood using an accurate physical activity measurement tool. The first aim is to develop and validate an accelerometer-based machine learning algorithm for toddler activity recognition. The second aim is to describe the trajectory of physical activity levels from age 12 to 36 months by sex. To achieve these aims, we will recruit 124 children at approximate age 12 months from various pediatric clinics in Chicago and conduct five waves of assessments at participant age 12, 18, 24, 30, and 36 months (waves 1 to 5). We will collect accelerometer and video data (ground truth) in five free-living settings (home, childcare class, indoor playroom, outdoor playground, and car-ride) in waves 1 to 4. The data will be split into a training set and a testing set. The training dataset will be used to develop an activity recognition algorithm and the testing dataset will be used to evaluate the newly developed algorithm. We will also conduct 7-day accelerometer assessments at each of the five waves. Applying the algorithm developed in AIM 1, we will estimate daily time spent in walking/running (minutes/day) and overall physical activity (minutes/day). We will use growth curve models to examine the trajectories of walking/running time and overall physical activity time over age between 12 and 36 months, including sex as a predictor. This study will help to fill the methodological gap in toddler physical activity measurement and expand the body of knowledge in early childhood physical activity.

项目概要缺乏身体活动是一个严重的健康问题，对女性的影响大于男性。随着时间的推移，许多 3 岁或 4 岁的幼儿身体不活跃，因此需要理解。何时以及如何养成体育活动习惯需要从 2 岁或以下开始进行调查。然而，幼儿（1 岁或 2 岁）体育活动研究在方法论方面存在重大差距身体活动测量，特别是与加速度计数据处理相关的这种差距限制了我们的能力。准确估计幼儿的身体活动水平处理加速度计数据，强度 - 基于加速度计计数的切点方法已被广泛使用，但是，建议的切点为。研究发现，幼儿的准确度较低（≤58%）。我们的试点已被证明可以为学龄前儿童和年龄较大的儿童提供更准确的活动分类。研究还表明，机器学习方法在幼儿活动识别方面具有巨大潜力。这项研究的总体目标是更好地了解身体活动行为的发展幼儿期使用准确的身体活动测量工具的首要目标是开发和验证。用于幼儿活动识别的基于加速度计的机器学习算法。描述 12 至 36 个月期间按性别划分的体力活动水平的轨迹为了实现这些目标，我们。将从芝加哥的各个儿科诊所招募 124 名大约 12 个月大的儿童，并进行我们将收集参与者年龄为 12、18、24、30 和 36 个月的五波评估（第 1 波至第 5 波）。五个自由生活环境（家庭、儿童保育班、室内游戏室、第 1 至 4 波中的户外游乐场和汽车骑行）。数据将分为训练集和测试集。训练数据集将用于开发活动识别算法，测试数据集将用于用于评估新开发的算法，我们还将进行为期 7 天的加速度计评估。应用 AIM 1 中开发的算法，我们将估算每天花费的时间。步行/跑步（分钟/天）和总体身体活动（分钟/天）我们将使用生长曲线模型来计算。检查 12 岁至 36 岁之间步行/跑步时间和总体身体活动时间的轨迹月，包括将性别作为预测因素，这项研究将有助于填补幼儿身体的方法学空白。活动测量并扩大幼儿体育活动的知识体系。