Promoting Physical Activity in Latinas via lnteractive Web-based Technology

摘要 在美国，拉美裔女性报告的不活动率高于非西班牙裔白人和男性 对应者，并且受相关健康状况(如癌症、高血压、心脏病)的影响不成比例 疾病、中风、糖尿病)。为了应对这场公共卫生危机，利用国家-- 在这种高危人群中增加体力活动(PA)需要最先进的技术、理论和方法 人口。最近，我们的团队进行了一项随机对照试验(N=205)，以测试A 在拉丁裔中进行文化适应、个性化定制的西班牙语互联网PA干预 (Pasos Hacia La Salud，R01CA159954)VS A Wellness Contact Control Internet Group。尽管结果是 有希望的是，大多数参与者仍然没有达到国家关于PA的指导方针。在正在进行的更新中 R01CA159954，我们将随机将300名拉丁裔女性分为1)定制的原创Pasos Hacia La Salud 基于互联网的PA干预(原始干预)或2)数据驱动的增强版PAOS Hacia La Salud PA干预(强化干预)。 拟议的补编寻求成为检查PA纵向模式的第一个试验 在拉丁裔中进行了一系列纵向研究的采用和维护。传统分析方法 将比较不同研究的结果，并在治疗控制结束时评估干预效果 作为基线。我们建议使用综合数据分析来合并跨研究和潜在类别的数据 用于确定PA采用和维护模式的模型。通过使用潜在类建模(LCM)，我们将 充分利用PA数据的纵向轮廓，以确定随时间变化的模式。LCM使用目标 数据(而不是先验假设)，将人群细分为不同的参与者组，这些参与者具有相似的 配置文件(在本例中为PA更改)。LCM不仅可以捕捉主体之间和主体内部的信息 异质性，但有助于确定种群内有意义的亚群。通过使用 这种创新的方法，我们不仅将揭示PA随时间的相互变化，而且还将阐明 这些模式与关键的心理社会、行为和人口统计预测因素之间的关联。

Abstract In the U.S., Latina women report higher rates of inactivity than their non-Hispanic White and male counterparts, and are disproportionately affected by related health conditions (e.g., cancer, hypertension, heart disease, stroke, diabetes). To address this public health crisis, evidence-based interventions that utilize state- of-the-art technology, theory and methods are needed to increase physical activity (PA) among this high-risk population. Recently, our team conducted a randomized controlled trial (N=205) to test the efficacy of a culturally adapted, individually tailored, Spanish-language Internet-based PA intervention among Latinas (Pasos Hacia La Salud, R01CA159954) vs. a Wellness Contact Control Internet Group. Although results were promising, the majority of participants still did not meet national guidelines for PA. In the ongoing renewal of R01CA159954, we will randomize 300 Latina women to either 1) the original Pasos Hacia La Salud tailored Internet-based PA intervention (Original Intervention) or 2) the data driven, enhanced version of the Pasos Hacia La Salud PA intervention (Enhanced Intervention). The proposed supplement seeks to become the first trial to examine longitudinal patterns of PA adoption and maintenance across a series of longitudinal studies among Latinas. Traditional analytic methods would compare findings across studies and would estimate intervention effects at end of treatment controlling for baseline. We propose to use Integrative Data Analysis to combine data across studies and Latent Class Models to identifies patterns of PA adoption and maintenance. By using Latent Class Modeling (LCM), we will make full use of the longitudinal profile of PA data to identify patterns of change over time. LCMs use objective data (rather than a priori hypotheses) to subdivide the population into distinct groups of participants with similar profiles (of PA changes in this case). LCM can not only capture between- and within-subject heterogeneity, but can aid in the identification of meaningful subgroups within the population. By using this innovative method, we will not only reveal the inter-variability of PA over time but also elucidate the associations between such patterns and key psychosocial, behavioral and demographic predictors.