Evaluating the EVO treatment optimized for resource constraints: Elements Vital to treat Obesity
评估针对资源限制优化的 EVO 治疗:治疗肥胖的重要要素
基本信息
- 批准号:10033189
- 负责人:
- 金额:$ 55.05万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2020
- 资助国家:美国
- 起止时间:2020-07-15 至 2025-06-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAdherenceAgeAmericanBehavior TherapyBehavioralBody Weight ChangesBody Weight decreasedCaloriesChurchClinicalClinical TrialsCommunitiesDataDevelopmentDietElementsEmploymentExcisionFatty acid glycerol estersFutureGoalsGoldGuidelinesHeadHealth Care CostsHealthcareHigh PrevalenceIndividualInformal Social ControlInterventionKilogramMaintenanceMediator of activation proteinMonitorMotivationObesityParticipantPatientsPersonsPhysical activityPoliciesPopulationPrevalenceProcessPublic HealthQuality of lifeRandomized Controlled TrialsReadingReportingResearchResearch DesignResearch PersonnelResourcesSelf EfficacySocioeconomic StatusSpecific qualifier valueTestingText MessagingTreatment CostWagesWorkWorkplaceactive methodbasecostcost effectivenesscost estimatecost-effectiveness evaluationdesigndiabetes prevention programeconomic evaluationeffectiveness evaluationhead-to-head comparisonimprovedintervention costmobile applicationmortalitymultiphase optimization strategyobesity treatmentpreservationprimary outcomeprogramspsychoeducationrandomized trialsexsmartphone Applicationstandard carestandard of caresuccesstreatment effecttreatment optimizationtreatment programtrial designweight loss interventionweight loss program
项目摘要
PROJECT SUMMARY
High prevalence of obesity contributes to stagnant mortality rates, increased health care spending, decreased
employment, and lower wages. Current gold standard treatments are costly and burdensome, limiting
accessibility to the majority of the public and having little effect on this public health crisis. Many attempts have
been made to improve the delivery of the successful Diabetes Prevention Program (DPP), shown to produce
an average of 6.5kg weight loss over 6 months. However, research to date has lacked a systematic
optimization of the DPP treatment package, resulting in packages that have essential parts missing, continue
to be burdensome for the patient, or do not produce enough weight loss. The proposed work represents the
next step in an efficient, systematic process to identify active treatment components with the goal of
assembling a treatment package that can produce sufficient weight loss at a reduced cost and burden. Our
prior trial, Opt-IN, was a highly efficient, fully powered full factorial randomized trial designed to test main
effects and interactions of additional treatment components (e.g., text messages, meal replacements), when
added to a minimal intervention of self-monitoring, psychoeducation, diet and physical activity goals, and
biweekly coaching calls. The results supported that significant weight loss could be attained from a minimal
level of intervention with no additional components. One notable finding demonstrated that 24 coaching
sessions was no better than receiving 12 sessions, significantly reducing potential cost of an intervention.
While the Opt-IN trial was designed to test contribution of components, a full head-to-head randomized
controlled trial will need to be conducted between the newly optimized intervention (EVO: Elements Vital to
treat Obesity) and the current gold standard DPP. Furthermore, full economic analyses that use concurrent
clinical trial data from weight loss treatments is lacking, and as such, we intend to do so to inform future
dissemination, implementation, and/or further optimization of treatments. Aim 1 is to test whether weight loss
from baseline to 6 months achieved by EVO is non-inferior to DPP. Aim 2 will focus on conducting a full
economic evaluation consistent with current guidelines. Finally, exploratory aims will investigate weight loss
maintenance at 12 months as well as change in self-regulation, moderators (i.e., Age, Sex, Socioeconomic
status) and mediators (i.e., self-monitoring adherence, self-efficacy) of treatment effects.
项目总结
高肥胖率导致死亡率停滞不前,医疗保健支出增加,
就业,以及更低的工资。目前的黄金标准治疗既昂贵又繁重,限制了
向大多数公众开放,对这场公共卫生危机几乎没有影响。很多次尝试都
为改进成功的糖尿病预防计划(DPP)的实施而做出的努力,显示出产生了
6个月内平均减重6.5公斤。然而,到目前为止,研究还缺乏系统的
对DPP治疗方案进行优化,导致缺少必要部件的方案继续
给病人带来负担,或者没有产生足够的减肥效果。拟议的工作代表了
下一步是有效的、系统的过程,以确定活性治疗成分,目标是
组装一种治疗方案,可以在降低成本和负担的情况下产生足够的减肥效果。我们的
之前的试验,Opt-In,是一项高效、完全有效的全因素随机试验,旨在测试Main
附加治疗组件(例如,短信、代餐)的影响和相互作用
再加上自我监控、心理教育、饮食和体力活动目标的最低限度干预,以及
每两周打一次教练电话。结果表明,从最低限度的减重可以达到显著的减肥效果。
不含其他组件的干预级别。一项值得注意的发现表明,24名教练
会议并不比接受12次会议更好,显著降低了干预的潜在成本。
虽然Opt-In试验旨在测试组件的贡献,但完全正面随机
将需要在新优化的干预措施(EVO:至关重要的要素)之间进行对照试验
治疗肥胖症)和目前的金本位民进党。此外,使用并发的完整经济分析
减肥治疗的临床试验数据缺乏,因此,我们打算这样做,以告知未来
传播、实施和/或进一步优化治疗。目标1是测试体重是否减轻
从基线到6个月,EVO达到的效果不亚于DPP。目标2将专注于进行全面的
符合现行指导方针的经济评价。最后,探索性目标将调查减肥情况。
12个月的维持以及自我调节、调节因素(即年龄、性别、社会经济)的变化
状态)和治疗效果的中介因素(即自我监测依从性、自我效能)。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Angela Fidler Pfammatter其他文献
A machine-learned model for predicting weight loss success using weight change features early in treatment
一个使用治疗早期体重变化特征来预测减肥成功的机器学习模型
- DOI:
10.1038/s41746-024-01299-y - 发表时间:
2024-11-29 - 期刊:
- 影响因子:15.100
- 作者:
Farzad Shahabi;Samuel L. Battalio;Angela Fidler Pfammatter;Donald Hedeker;Bonnie Spring;Nabil Alshurafa - 通讯作者:
Nabil Alshurafa
Angela Fidler Pfammatter的其他文献
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{{ truncateString('Angela Fidler Pfammatter', 18)}}的其他基金
Evaluating the EVO treatment optimized for resource constraints: Elements Vital to treat Obesity
评估针对资源限制优化的 EVO 治疗:治疗肥胖的重要要素
- 批准号:
10437676 - 财政年份:2020
- 资助金额:
$ 55.05万 - 项目类别:
Evaluating the EVO treatment optimized for resource constraints: Elements Vital to treat Obesity
评估针对资源限制优化的 EVO 治疗:治疗肥胖的重要要素
- 批准号:
10745145 - 财政年份:2020
- 资助金额:
$ 55.05万 - 项目类别:
Evaluating the EVO treatment optimized for resource constraints: Elements Vital to treat Obesity
评估针对资源限制优化的 EVO 治疗:治疗肥胖的重要要素
- 批准号:
10216252 - 财政年份:2020
- 资助金额:
$ 55.05万 - 项目类别:
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