Physical Activity Patterns via New Dimension-Informative Cluster Models.

通过新维度信息集群模型的身体活动模式。

• 批准号: 8657101
• 负责人: Ken Cheung
• 金额: $ 34.96万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-17 至 2016-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8657101
• 关键词: Agreement; American Heart Association; Asthma; Calories; Cardiovascular Diseases; Cardiovascular system; Categories; Cluster Analysis; Data; Databases; Dimensions; Elderly; Endotoxins; Frequencies; Goals; Guidelines; Head Start Program; Health; Length; Link; Literature; Measures; Medical; Methods; Modeling; Nature; Obesity; Outcome; Pattern; Physical activity; Prevention strategy; Primary Prevention; Procedures; Public Health; Questionnaires; Recommendation; Reporting; Risk; Risk Factors; Secondary Prevention; Stroke; Subgroup; Testing; Time; Validation; Work; base; cardiovascular risk factor; design; heuristics; method development; model development; modifiable risk; obesity risk; programs; statistics

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.

描述（由申请人提供）：已知体育活动是各种健康结果的可修改风险因素，有效试验可能会对公共卫生产生重大影响。体育锻炼是美国心脏协会（AHA）理想心血管健康指南的组成部分，该指南建议每周至少150分钟的中等强度或75分钟的剧烈强度活动。体育锻炼计划是心血管疾病的主要和次要预防策略的关键组成部分，但是由于时间和空间限制或随之而来的医疗合并，遵循这些建议可能并不容易。在时间持续时间指南内，没有其他具体建议可用。很少有研究定义了足够的身体活动可变细节，以区分不同的体育锻炼概况或模式。识别现有的体育活动模式和体育锻炼变化的模式可以帮助设计有效的试验。该提案的目标是开发新的聚类分析方法，以适应问卷和加速度计量学引起的体育活动数据的特殊特征，将拟议的集群分析应用于曼哈顿北部曼哈顿中风研究（NOMAS）的体育活动数据（NOMAS）以及内毒素，肥胖症，肥胖症和妇女的心脏均在NYC Head Start（OEAEAS）中的识别方法（OEAHS），并验证了识别方法，并验证了识别的识别方法。结果和肥胖。当亚组的数量和有关其组成的其他信息可能未知时，群集分析对象分为有意义的亚组。有关体育活动数据集群分析的现有文献是基于摘要措施，例如消耗卡路里或在固定类别的活动上花费的持续时间。体育活动数据由变量而不是固定，活动数量和类型组成，此外，活动的数量是随机且信息丰富的。最新的基于模型的集群分析具有适应体育活动数据复杂性的局限性。我们提出了几个新的基于模型的集群分析，其中包含了现有集群分析无法适应的体育活动数据的特殊功能。所提出的模型将处理（i）变量的结果长度； （ii）结果的尺寸是有益的； （iii）严格的积极结果而没有转化； （iv）反复测量了体育活动数据。我们还将将提出的方法应用于加速度计。我们将使用NOMAS问卷数据测试已确定的簇或模式作为心血管结果的预测指标，并使用OEAHS加速度计数据进行肥胖的预测指标。