Physical Activity Patterns via New Dimension-Informative Cluster Models.

描述（由申请人提供）：已知体力活动是各种健康结果的可改变风险因素，有效的试验可能对公共健康产生重大影响。身体活动是美国心脏协会（AHA）理想心血管健康指南的一个组成部分，该指南建议每周至少进行150分钟的中等强度活动，或75分钟的高强度活动。身体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，但由于时间和空间限制或伴随的医学合并症，遵循这些建议可能并不容易。在持续时间指南内，没有进一步的具体建议。很少有研究对体力活动变量进行了详细的定义，足以区分体力活动的特征或模式。认识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。本提案的目标是开发新的聚类分析方法，以适应问卷调查和加速度计产生的体力活动数据的特殊特征，将拟议的聚类分析应用于来自北方曼哈顿卒中研究（NOMAS）和纽约市内毒素、肥胖和哮喘先行研究（OEAHS）的体力活动数据，并通过所提出的方法分别验证所识别的模式作为心血管结果和肥胖的预测因子的效用。聚类分析将受试者划分为有意义的亚组，当亚组的数量和有关其组成的其他信息可能未知时。现有文献的聚类分析的身体活动数据的基础上的汇总措施，如消耗的热量或持续时间花费在固定数量的类别的活动。身体活动数据由活动的可变的而非固定的数量和类型组成，而且活动的数量是随机的和提供信息的。现有的基于模型的聚类分析在适应身体活动数据的复杂性方面具有局限性。我们提出了几个新的基于模型的聚类分析，将身体活动数据的特殊功能，现有的聚类分析不能容纳。拟议的模型将处理（i）结果的可变长度;（ii）结果维度具有信息性的情况;（iii）未经转换的严格积极结果;以及（iv）重复测量的身体活动数据。我们也将所提出的方法应用于加速度数据。我们将使用NOMAS问卷调查数据测试所识别的聚类或模式作为心血管结局的预测因子的效用，并使用OEAHS加速度计数据测试肥胖的预测因子。

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.