Physical Activity Measurement in Toddlers

项目摘要 缺乏运动是一个重大的健康问题，女性比男性受到的影响更大。缺乏运动倾向于 随着时间的推移，许多3或4岁的幼儿身体不活跃。因此，理解 需要从2岁或更小的年龄开始调查身体活动习惯是什么时候以及如何养成的。在……里面 幼儿(1岁或2岁)体力活动研究，然而，在以下方面存在主要的方法学差距 体力活动测量，特别是与加速度计数据处理相关的测量。这一差距限制了我们的能力 为了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截点方法得到了广泛的应用。然而，建议的切入点是 已经发现，蹒跚学步的孩子表现出的准确率很低(≤58%)。一种新的分析方法，机器学习，已经 在学龄前儿童和年龄较大的儿童中，被证明能够提供更准确的活动分类。我们的飞行员 研究还表明，机器学习方法在识别幼儿活动方面具有巨大的潜力。这个 这项拟议的研究的总体目标是更好地了解老年人身体活动行为的发展 儿童早期使用准确的体力活动测量工具。第一个目标是开发和验证 一种基于加速度计的幼儿活动识别机器学习算法。第二个目标是 按性别描述从12岁到36个月的体力活动水平。为了达到这些目标，我们 将从芝加哥的各种儿科诊所招募大约12个月大的124名儿童，并进行 在参与者12个月、18岁、24岁、30岁和36个月时进行五次评估(第一次到第五次)。我们会收集 五个自由生活环境(家庭、托儿所、室内游戏室、 数据将被分成训练集和测试集。 训练数据集将用于开发活动识别算法，测试数据集将 用于评估新开发的算法。我们还将在以下地点进行为期7天的加速度计评估 五浪中的每一浪。应用在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)和总体体力活动(分钟/天)。我们将使用增长曲线模型来 检查12到36岁之间的步行/跑步时间和总体体力活动时间的轨迹 几个月，包括作为预测因素的性行为。这项研究将有助于填补幼儿体检的方法学空白。 在幼儿体力活动中测量和拓展知识主体。

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.