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.

项目摘要 身体上的不活动是一个重大的健康问题，对女性的影响比男性更大。身体上的不活动趋势 随着时间的流逝，许多3到4岁的年幼儿童身体不活跃。因此，理解 何时以及如何发展体育锻炼需要从2岁或以下的年龄开始进行调查。在 但是，幼儿（1岁或2岁）的体育活动研究，存在有关 体育活动测量，尤其与加速度计数据处理有关。这个差距限制了我们的能力 处理加速度计数据，强度 - 基于基于的加速度计计数切点方法已被广泛使用。但是，建议 发现幼儿的精度较低（≤58％）。一种新的分析方法，机器学习， 我们被证明可以在学龄前儿童和年龄较大的孩子中提供更准确的活动分类。我们的飞行员 研究还表明，机器学习方法具有幼儿活动识别的巨大潜力。 这项拟议的研究的总体目标是更好地了解体育活动行为的发展 使用准确的体育活动测量工具的幼儿期。第一个目的是开发和验证 基于加速度计的机器学习算法，用于蹒跚学步的活动识别。第二个目标是 描述通过性别从12至36个月开始的体育活动水平的轨迹。为了实现这些目标，我们 将在芝加哥的各个儿科诊所招募124名儿童124岁的儿童 参与年龄12、18、24、30和36个月的评估浪潮（波1至5）。我们将收集 加速度计和视频数据（地面真相）五个自由生活环境（家庭，托儿所，室内游戏室， 波浪1至4中的室外游乐场和骑车）。数据将分为训练集和测试集。 培训数据集将用于开发活动识别算法，测试数据集将是 用于评估新开发的算法。我们还将在 五波中的每一个。应用AIM 1中开发的算法，我们将估计每天花费的时间 步行/跑步（分钟/天）和整体体育锻炼（分钟/天）。我们将使用增长曲线模型 检查步行/跑步时间和整体体育锻炼时间超过12至36的轨迹 几个月，包括性作为预测因素。这项研究将有助于填补蹒跚学步的物理的方法论差距 活动测量并扩大儿童早期体育锻炼的知识体系。