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，与中等到剧烈的体力活动(MVPA)无关。流行病学数据显示，SB与许多癌症、心血管疾病以及代谢功能障碍的多个标记物之间存在一致的关联。减少SB被推荐为一种可行的新癌症预防策略。髋部安装的加速度计是一种基于现场的PA标准测量方法，但不足以测量SB。错误分类最常发生在以下情况下：在躯干位移有限的情况下、在负载下或在斜坡上、在振动表面上(例如，在汽车中)，或者当它们太小而不能与不磨损的时间区分时。这是不可接受的，因为我们可能会错误地描述SB和PA的流行率。测量误差掩盖了行为和健康之间的真实关系，干预措施的效果可能没有被发现。[我们的研究将在实验室研究的基础上有所改进，这些研究几乎没有在自然环境中测试某人，比如开车或看电视。针对SB的基于实验室的机器学习算法由人工起点和终点辅助。基于自由生活环境下的研究的算法将更具普遍性，并适用于干预研究]。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，对从SB到MVPA的一系列行为改进和验证新的机器学习分类算法。这项研究将重点关注最常被错误分类的行为：编码为轻的中等强度活动，编码为久坐的轻活动，以及编码为轻或不穿戴时间的久坐活动。[我们将比目前的自我报告和加速度计估计提高30%-50%。]我们将专注于四个主要的行为类别：躺着、坐着、站着和移动。总共210名参与者(6-10岁，n=70；16-55岁，n=70；65-85岁，n=70)将在两个工作日和一个周末两个工作日和一个周末佩戴3个Actigraph加速度计(两个臀部，一个手腕)、一个GPS设备和一个SenseCam(自动图像捕获设备)。[一次抽样将在接下来的3天内重复上述程序]。在每一天的6小时内，参与者还将由训练有素的程序员直接观察，他们将使用为iPad开发的新型便携式行为评估系统记录自由生活行为。直接观测数据将为以一秒为间隔记录的带注释的数据文件提供行为的“地面真相”。对于没有直接观察到的自由生活行为，将使用SenseCam图像。将采用机器学习核方法。灵敏度、特异度、准确度和其他ROC图表方法将用于比较来自：(A)单轴和多轴加速度计数据；(B)运动计数和原始加速度数据；以及(C)髋部和腕部安装的加速度计的分类器。分析将确定加入GPS和地理信息系统数据后灵敏度和特异度的提高。我们将评估对特定于人口的分类器的需求，并量化目前使用的切入点的测量误差。 与公共健康相关：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，我们没有非常准确地衡量坐着的时间，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.