Physical Activity Measurement in Toddlers

项目摘要 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 随着时间的推移，许多3岁或4岁的幼儿缺乏体力活动。因此了解 何时以及如何养成体育活动习惯需要从2岁或更小的年龄开始调查。在 幼儿（1岁或2岁）身体活动研究，然而，一个主要的方法差距存在， 身体活动测量，特别是与加速度计数据处理有关的测量。这个差距限制了我们的能力 来准确估计幼儿的身体活动水平。为了处理加速度计数据，强度- 基于加速度计的计数截点法得到了广泛的应用。然而，建议的分割点 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很大的潜力。的 这项拟议研究的首要目标是更好地了解身体活动行为的发展， 使用精确的身体活动测量工具的早期儿童。第一个目标是开发和验证 一种基于加速计的机器学习算法，用于幼儿活动识别。第二个目标是 按性别描述12至36个月的身体活动水平的轨迹。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， 在参与者年龄12、18、24、30和36个月时进行五波评估（第1至5波）。我们将收集 在五个自由生活环境（家庭，儿童保育班，室内游戏室， 室外游乐场，和汽车骑）在波1至4。数据将被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天的加速计评估， 五个波浪中的每一个。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）和整体身体活动（分钟/天）。我们将使用增长曲线模型， 检查12至36岁之间的步行/跑步时间和整体身体活动时间的轨迹 月，包括性作为预测因素。本研究将有助于填补幼儿体格检查的方法学空白 活动测量和扩大知识体系在幼儿体育活动。

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.