Validating Machine-Learned Classifiers of Sedentary Behavior and Physical Activit

描述（由申请人提供）：久坐行为（SB）是一种健康风险，独立于中度至剧烈的体力活动（MVPA）。流行病学数据揭示SB与许多癌症、心血管疾病以及代谢功能障碍的多种标志物之间的一致关联。减少SB被推荐为可行的新癌症预防策略。髋关节安装的加速度计是PA的基于场的标准测量，但不足以测量SB。错误分类最常发生在躯干位移有限、负载下或倾斜、振动表面上（例如，在汽车中），或者当它们太小而不能与非磨损时间区分开时。这是不可接受的，因为我们可能会错误地描述SB和PA的患病率。测量误差掩盖了行为与健康之间的真实关系，干预措施的效果可能无法被发现。[Our这项研究将改进实验室研究，实验室研究对在自然环境中（如驾驶或看电视）测试SB几乎没有帮助。SB的基于实验室的机器学习算法得到了人工起点和终点的帮助。基于自由生活环境研究的算法将更具有普遍性，适用于干预研究。在这项研究中，我们将使用加速度计，全球定位系统（GPS）和地理信息系统（GIS）数据，针对从SB到MVPA的连续行为改进和验证新的机器学习分类算法。本研究将重点关注最常被错误分类的行为：编码为轻度的中等强度活动，编码为久坐的轻度活动，以及编码为轻度或非佩戴时间的久坐活动。[We将比当前的自我报告和加速计估计提高30- 50%。我们将集中在四个主要的行为类别：躺，坐，站，和走动运动。将在2年内招募共计210名受试者（年龄6-10岁，n = 70; 16-55岁，n=70; 65-85岁，n= 70），并将在两个工作日和一个周末佩戴3个ActiGraph加速度计（两个髋关节，一个腕关节）、一个GPS设备和一个SenseCam（自动图像捕获设备）。[子样本将再重复该程序3天]。在这些天的每一天，参与者还将由训练有素的编码员直接观察6小时，编码员将使用为iPad开发的新型便携式行为评估系统记录自由生活行为。直接观测数据将为每隔一秒记录的注释数据文件提供行为的“地面实况”。对于未直接观察到的自由生活行为，将使用SenseCam图像。将采用机器学习内核方法。灵敏度、特异性、准确性和其他ROC图方法将用于比较从以下各项导出的分类器：（a）单轴与多轴加速度计数据;（B）运动“计数”与原始加速度数据;以及（c）髋关节与腕关节安装的加速度计。分析将确定增加全球定位系统和地理信息系统数据后灵敏度和特异性的提高情况。我们将评估对人群特定分类器的需求，并量化目前采用的临界点的测量误差。 公共卫生相关性：大多数美国人一天中的大部分时间都是坐着的，我们有新的科学证据表明，无论一个人做多少体力活动，这都会导致健康状况不佳。然而，我们并不十分准确地测量坐着的时间，当我们要求人们告诉我们他们做了多少时，他们的答案是不可靠的。我们的研究将使用小型传感器来客观地测量人们何时坐着或进行身体活动，我们将使用复杂的计算技术来总结这些运动模式。

DESCRIPTION (provided by applicant): Sedentary behavior (SB) is a health risk, independent of moderate-to-vigorous physical activity (MVPA). Epidemiologic data reveal consistent associations between SB and numerous cancers, cardiovascular disease, as well as multiple markers of metabolic dysfunction. Reducing SB is recommended as a viable new cancer prevention strategy. Hip mounted accelerometry is a field-based criterion measure of PA but is inadequate for measuring SB. Misclassification most often occurs when movements are performed with limited trunk displacement, under load or on an incline, on a vibrating surface (e.g., in a car), or when they are too small to be distinguished from non-wear time. This is unacceptable because we may then mischaracterize the prevalence of both SB and PA. Measurement error obscures true relationships between behavior and health, and the effects of interventions may go undetected. [Our study will improve upon laboratory studies that do little to test SB in natural environments such as driving or watching TV. Lab-based machine learning algorithms for SB are aided by the artificial start and end points. Algorithms based upon research in free living settings will be more generalizable and applicable to intervention research]. In this study we will refine and validate new machine-learned classification algorithms for a continuum of behaviors from SB to MVPA using accelerometer, Global Positioning System (GPS), and Geographic Information System (GIS) data. This study will focus on behaviors that are most frequently misclassified: moderate intensity activities that are coded as light, light activities that are coded as sedentary, and sedentary activities that are coded as light or non wear time. [We will improve upon current self-report and accelerometer estimates by 30-50%.] We will focus on four primary behavioral classes: lying, sitting, standing, and ambulatory locomotion. A total of 210 participants (ages 6-10 yrs, n = 70; 16-55 yrs, n=70; 65-85 yrs, n= 70) will be recruited over a 2-yr period and will wear 3 ActiGraph accelerometers (two hip, one wrist); a GPS device, and a SenseCam (an automatic image capture device) for two weekdays and 1 weekend day. [A subsample will repeat the procedures for a further 3 days]. For a 6 hr period on each of these days, participants will also be directly observed by trained coders who will record free living behaviors using a novel portable behavioral assessment system developed for the iPad. Direct observation data will provide 'ground-truths' of behavior for an annotated data file recorded at one-second intervals. For free-living behaviors not directly observed, SenseCam images will be used. Machine learning Kernel methods will be employed. Sensitivity, specificity, accuracy and other ROC graph methods will be used to compare classifiers derived from: (a) single axis vs. multi axis accelerometer data; (b) movement 'counts' vs. raw acceleration data; and (c) hip vs. wrist mounted accelerometers. Analyses will determine the improvement in sensitivity and specificity when GPS and GIS data are added. We will evaluate the need for population specific classifiers and quantify measurement error in cut-points that are currently employed. PUBLIC HEALTH RELEVANCE: The majority of the US population spends most of the day sitting and we have new scientific evidence that this can contribute to poor health regardless of how much physical activity a person does. However, we do not measure sitting time very accurately and when we ask people to tell us how much they do, their answers are unreliable. Our study will use small sensors to objectively measure when people sit or do physical activity, and we will use sophisticated computational techniques to summarize these movement patterns.