Physical Activity Measurement in Toddlers

幼儿身体活动测量

• 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
• 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

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。缺乏身体活动往往会 随着时间的推移，许多 3 岁或 4 岁的幼儿缺乏身体活动。因此，了解 身体活动习惯何时以及如何养成需要从 2 岁或更小年龄开始进行调查。在 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。这个差距限制了我们的能力 准确估计幼儿的体力活动水平。为了处理加速度计数据，强度 基于加速度计的计数切点方法已得到广泛应用。然而，建议的切点 研究发现幼儿的准确率较低（≤58%）。一种新的分析方法——机器学习—— 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。我们的飞行员 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 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。第一个目标是开发和验证 一种基于加速度计的机器学习算法，用于幼儿活动识别。第二个目标是 描述 12 至 36 个月期间按性别划分的体力活动水平轨迹。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 对年龄为 12、18、24、30 和 36 个月的参与者进行五波评估（第 1 波至第 5 波）。我们将收集 五种自由生活环境（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。我们还将在以下位置进行为期 7 天的加速度计评估： 五波中的每一波。应用 AIM 1 中开发的算法，我们将估计每天花费在 步行/跑步（分钟/天）和总体身体活动（分钟/天）。我们将使用增长曲线模型 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体育方法学上的空白 活动测量并扩大幼儿体育活动的知识体系。