Promoting Physical Activity in Latinas via lnteractive Web-based Technology

摘要 在美国，拉丁裔妇女报告的不活动率高于非西班牙裔白色和男性 受相关健康状况的影响不成比例（例如，癌症，高血压，心脏 疾病、中风、糖尿病）。为了解决这一公共卫生危机，基于证据的干预措施，利用国家- 需要最先进的技术、理论和方法来增加这种高风险人群的身体活动（PA）。 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 在拉丁美洲人中进行文化适应、量身定制、基于互联网的西班牙语公共宣传干预 （Pasos Hacia La Salud，R01CA159954）vs. a Wellness Contact Control Internet Group.虽然结果是 尽管前景看好，但大多数参与者仍然没有达到PA的国家指导方针。在不断更新的 R 01 CA 159954，我们将随机300拉丁妇女要么1）原来的Pasos Hacia La Salud量身定制 基于互联网的PA干预（原始干预）或2）数据驱动，增强版的Pasos Hacia La Salud PA干预（加强干预）。 拟议的补充文件旨在成为第一个研究PA纵向模式的试验 在拉丁美洲人中进行的一系列纵向研究中，传统分析方法 将比较研究结果，并在治疗结束时估计干预效果， 基线。我们建议使用集成数据分析来联合收割机整合研究和潜在类别之间的数据 用于识别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数据的纵向分布，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），以将人口细分为具有类似特征的不同参与者群体 （在这种情况下，PA的变化）。LCM不仅可以捕获受试者之间和受试者内的 异质性，但可以帮助识别人群中有意义的亚组。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，而且还阐明了 这些模式与关键的社会心理、行为和人口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.