Physical Activity Patterns via New Dimension-Informative Cluster Models.

描述(由申请人提供)：众所周知，体力活动是各种健康结果的可改变的风险因素，有效的试验可能对公众健康产生重大影响。体力活动是美国心脏协会(AHA)理想心血管健康指南的一部分，该指南建议每周至少进行150分钟的中等强度运动，或75分钟的剧烈运动。体力活动计划是心血管疾病一级和二级预防策略的关键组成部分，但由于时间和空间的限制，或伴随的医疗合并症，遵循这些建议可能并不容易。在持续时间指导方针内，没有进一步的具体建议。很少有研究对体力活动变量进行足够详细的定义，以区分不同的体力活动轮廓或模式。认识到现有的体力活动模式和体力活动的变化模式有助于设计有效的试验。这项建议的目标是开发新的聚类分析方法，以适应来自问卷和加速测量的体力活动数据的特殊特征，将拟议的聚类分析应用于曼哈顿北部中风研究(NOMAS)和纽约市Head Start(OEAHS)的内毒素、肥胖和哮喘研究的体力活动数据，并通过拟议的方法验证识别的模式分别作为心血管结果和肥胖症预测因子的有效性。当子组的数量和有关其组成的其他信息可能未知时，聚类分析将受试者划分为有意义的子组。关于体力活动数据的聚类分析的现有文献是基于诸如消耗的卡路里或花在固定数量的活动类别上的持续时间等汇总衡量标准。体力活动数据由可变的、非固定的活动数量和类型组成，而且活动的数量具有随机性和信息性。现有最先进的基于模型的聚类分析在适应体力活动数据的复杂性方面存在局限性。我们提出了几种新的基于模型的聚类分析，融合了体力活动数据的特殊特征，这是现有的聚类分析无法适应的。建议的模型将处理(I)可变长度的结果；(Ii)当结果的维度具有信息性时的情况；(Iii)没有转换的严格的积极结果；以及(Iv)重复测量的体力活动数据。我们还将把所提出的方法应用于加速度计数据。我们将使用NOMAS问卷数据测试识别的集群或模式作为心血管结果预测因子的有效性，并使用OEAHS加速度计数据测试肥胖预测因子的有效性。

DESCRIPTION (provided by applicant): Physical activity is known to be a modifiable risk factor for various health outcomes and an effective trial could have significant effect on public health. Physical activity is a component of the American Heart Association (AHA) guidelines for ideal cardiovascular health, which advise at least 150 minutes per week of moderate intensity, or 75 minutes of vigorous intensity activity. A physical activity program is a critical component o primary and secondary prevention strategies for cardiovascular disease, and yet it may not be easy to follow these recommendations due to time and space constraints, or concomitant medical comorbities. Within the time duration guidelines, no further specific recommendations are available. Few studies defined physical activity variable detail enough to distinguish differen profiles or patterns of physical activity. Recognizing existing patterns of physical activity and patterns of changes in physical activity can help to design an effective trial. Goals of this proposal are to develop new cluster analysis methods to accommodate special features of physical activity data arising from questionnaire and accelerometry, apply the proposed cluster analysis to physical activity data from the Northern Manhattan Stroke Study (NOMAS) and the Endotoxin, Obesity, and Asthma in NYC Head Start (OEAHS) study, and validate utility of the identified patterns via proposed methods as predictors of cardiovascular outcome and obesity, respectively. Cluster analysis partitions subjects into meaningful subgroups, when the number of subgroups and other information about their composition may be unknown. Existing literature on cluster analysis of physical activity data are based on summary measures such as calorie consumed or duration spent on fixed number of categories of activities. Physical activity data are composed of variable, not fixed, number and type of activities and furthermore the number of activities is random and informative. State-of-the-art existing model-based cluster analysis has limitations to accommodate complexity of physical activity data. We propose several new model-based cluster analyses incorporating special features of physical activity data that existing cluster analysis cannot accommodate. The proposed model will handle (i) variable length of outcomes; (ii) the case when the dimension of outcome is informative; (iii) strictly positive outcomes without transformation; and (iv) repeatedly measured physical activity data. We will also apply the proposed method to accelerometry data. We will test utility of the identified clusters or patterns as predictors of cardiovascular outcomes using NOMAS questionnaire data, and predictors of obesity using OEAHS accelerometry data.