Utilizing Data Clustering for Hypotheses Discovery in Multimodal Exercise and Health Interventions with Limited Sample Size

Mckay Russell, Guanrong Cai, Steven Merino, Clemente Rodriguez, Zachary Scholefield, J. Moore, George Salem, Chengwei Lei
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Abstract

Multimodal exercise (MME) interventions are beneficial for physical fitness, psychosocial health, cognition, or combinations of these aspects of health and wellness in healthy and clinical populations. However, MME intervention studies are laborious to conduct and difficult to assess due to the number of constructs needed to be assessed. The current study is a secondary analysis of the data obtained from the Golf for Healthy Aging (GHA) exercise intervention study. The goal of this work was to develop an analytical framework, using mathematical abstraction and modified K-means clustering, to assess the interrelations of GHA outcome variables, in order to discover novel, testable hypotheses regarding intervention effects for future studies.
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利用数据聚类在有限样本量的多模式运动和健康干预中发现假设
在健康和临床人群中,多模式运动(MME)干预措施有利于身体健康、社会心理健康、认知或这些健康和健康方面的组合。然而,由于需要评估的构念数量众多,MME干预研究进行起来很费力,而且难以评估。目前的研究是对高尔夫促进健康老龄化(GHA)运动干预研究数据的二次分析。这项工作的目标是开发一个分析框架,使用数学抽象和改进的k均值聚类,来评估GHA结果变量的相互关系,以便为未来的研究发现新的,可测试的干预效果假设。
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