Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.
Center for m2Health, Palo Alto University, Palo Alto, California.
Int J Eat Disord. 2019 Nov;52(11):1224-1228. doi: 10.1002/eat.23169. Epub 2019 Sep 10.
In recent years, online screens have been commonly used to identify individuals who may have eating disorders (EDs), many of whom may be interested in treatment. We describe a new empirical approach that takes advantage of current evidence on empirically supported, effective treatments, while at the same time, uses modern statistical frameworks and experimental designs, data-driven science, and user-centered design methods to study ways to expand the reach of programs, enhance our understanding of what works for whom, and improve outcomes, overall and in subpopulations. The research would focus on individuals with EDs identified through screening and would use continuously monitored data, and interactions of interventions/approaches to optimize reach, uptake, engagement, and outcome. Outcome would be assessed at the population, rather than individual level. The idea worth researching is to determine if an optimization outcome model produces significantly higher rates of clinical improvement at a population level than do current approaches, in which traditional interventions are only offered to the few people who are interested in and able to access them.
近年来,在线屏幕已被广泛用于识别可能患有饮食失调症(ED)的个体,其中许多人可能对治疗感兴趣。我们描述了一种新的实证方法,该方法利用了关于经过实证支持的有效治疗的现有证据,同时利用现代统计框架和实验设计、数据驱动的科学和以用户为中心的设计方法来研究扩大计划范围的方法、提高我们对针对谁有效的理解,以及总体上和在亚人群中改善结果。该研究将侧重于通过筛查确定的 ED 个体,并将使用连续监测的数据以及干预/方法的相互作用来优化范围、采用率、参与度和结果。结果将在人群层面而不是个体层面进行评估。值得研究的想法是确定优化结果模型是否会比当前方法在人群层面上产生更高的临床改善率,在当前方法中,传统干预仅提供给少数有兴趣且能够获得这些干预的人。