Addressing Rural Health Disparities by Optimizing "High Touch" Intervention Components in Digital Obesity Treatment

美国有超过1.3亿人超重和肥胖，农村社区的经验 肥胖症和相关慢性病的发病率明显较高。幸运的是，体重减轻了5-7%， 可以改善肥胖相关的并发症。虽然生活方式干预成功地产生体重 由于体重下降幅度如此之大，在农村地区，体重管理方案的覆盖面和可用性有限。 数字干预为向农村人口提供生活方式方案提供了一种有吸引力的替代方案。 然而，面对面的行为肥胖治疗计划比数字计划能更好地减肥， 这可能是因为面对面的程序通常包括人员密集的“高接触”治疗组件。 一些研究表明，通过电子邮件反馈， 或者加上在线小组会议，可以显着增加减肥。因此，为了减少肥胖- 鉴于农村人口经历的相关健康差距，现在是通过确定 产生最强权重的人类递送数字治疗组件的特定星座 损失结果。因此，本研究的目的是增加数字化对公共卫生的影响。 同时调查3种“高接触”干预对农村人群肥胖症的治疗效果 件.我们将使用MOST框架进行高效的2 x 2 x 2析因实验， 居住在非城市地区的参与者从美国各地在线招募。受试者（N=616; 22%的种族/少数民族; 40%的男性）将被随机分配至：（1）每周一次的促进同步组视频 （2）收到的自我监测反馈的类型（辅导员编写的还是事先编写的）;以及 (3)个人辅导电话（是与否）。根据实验结果，我们将确定一个优化的 每种成分（或成分组合）的贡献有意义（≥1.5 kg，6- 月），以增强减肥。我们将调查潜在的中介（例如，问责，社会支持， 自我调节、动机和问题解决），以及可能的中介（例如，性/性别， 种族/民族，年龄），以探索其对减肥结果的影响。我们还将检查治疗的实施情况 每个组成部分的成本，并对治疗后6个月的体重轨迹进行探索性分析 (i.e.,在12个月时），以阐明特定组分对体重控制的长期影响。最后， 这项研究将为确认最有前途的数字行为减肥干预奠定基础， 无地域边界的传播，以降低农村居民的肥胖率， 为最佳传播政策决策提供依据的证据。

Over 130 million individuals in the US have overweight and obesity, and rural communities experience significantly higher rates of obesity and related chronic diseases. Fortunately, weight losses of as little as 5-7% can ameliorate obesity-associated co-morbidities. Although lifestyle interventions successfully produce weight loss of this magnitude, the reach and availability of weight management programs is limited in rural areas. Digital interventions offer an attractive alternative for delivering lifestyle programs to rural populations. However, in-person behavioral obesity treatment programs achieve better weight losses than digital programs, likely because in-person programs typically include personnel-intensive “high touch” treatment components. Some studies indicate that having a human “behind the curtain” of a digital program, through emailed feedback or with the addition of online group sessions, can significantly increase weight loss. Thus, to reduce obesity- associated health disparities experienced by rural populations, it is time to move the field forward by identifying the specific constellation of human-delivered digital treatment components that produce the strongest weight loss outcomes. Therefore, the aims of this study are to increase the public health impact of digital obesity treatment for rural populations by simultaneously investigating 3 “high touch” intervention components. We will conduct a highly efficient 2 x 2 x 2 factorial experiment using the MOST framework with participants residing in non-urban areas recruited online from across the United States. Participants (N=616; 22% racial/ethnic minority; 40% male) will be randomized to: (1) weekly facilitated synchronous group video sessions (yes vs. no); (2) type of self-monitoring feedback received (counselor-crafted vs. pre-scripted); and (3) individual coaching calls (yes vs. no). Based on the results of the experiment, we will identify an optimized program in which each component (or combination of components) contributes meaningfully (≥1.5 kg at 6- months) to enhanced weight loss. We will investigate potential mediators (e.g., accountability, social support, self-regulation, motivation, and problem solving), as well as possible mediators (e.g., sex/gender, race/ethnicity, age), to explore their impact on weight loss outcomes. We will also examine treatment delivery costs for each component and conduct exploratory analyses of weight trajectories 6-months post-treatment (i.e., at 12 months) to elucidate the extended impact of the specific components on weight control. Ultimately, this research will set the stage for confirming the most promising digital behavioral weight loss intervention for dissemination without geographic borders to reduce obesity rates among rural residents and provide essential evidence to inform policy decisions on optimal dissemination.