SMARTer weight loss management
更智能的减肥管理
基本信息
- 批准号:10567424
- 负责人:
- 金额:$ 69.04万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-07-01 至 2028-04-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAdultBehavior TherapyBehavioralBody Weight decreasedCaringChronic DiseaseClassificationClinicalCognitiveDiabetes MellitusDiabetes preventionDietDisinhibitionEatingExpenditureFutureHealth Care CostsHeterogeneityIndividualInterventionMaintenanceMediatingMediatorModelingNeighborhoodsObesityObesity EpidemicOverweightParticipantPatientsPoliciesPopulationPopulation ProgramsRandomizedRandomized, Controlled TrialsResearchResourcesRewardsTestingText MessagingTreatment CostWeightadaptive interventionadult obesityarmcomparison controlcostcost comparisoncost effectivecost effectivenesscravingdeprivationdesigndiabetes prevention programeconomic evaluationeffective interventionefficacy testingemotional eatingexecutive functionfood insecurityhead-to-head comparisonliteracymultiphase optimization strategynegative affectobese personobesity managementobesity treatmentoptimismpandemic diseasepatient responseprogramsrandomized, clinical trialsrelative effectivenessresearch clinical testingresponserestraintsmartphone applicationstandard carestandard of caretreatment as usualtreatment responderstreatment responsetrial comparingweight loss intervention
项目摘要
Project Summary
This application proposes to compare an optimized, resource-efficient, adaptive obesity treatment against a
gold-standard fixed treatment package and assessment only control. The obesity pandemic continues
unabated, presaging an onslaught of diabetes. Despite numerous initiatives, gold standard Diabetes
Prevention Program (DPP) intensive multicomponent behavioral treatment for overweight and obesity remains
too expensive, burdensome, and difficult to scale to suggest that it can be provided to the 2/3 of the population
that needs to lose weight. To address this challenge, we strive to optimize less burdensome treatment
approaches that can maximize weight loss in the population that has obesity with reduced resource
expenditure. In the SMART Weight Loss Management trial, we randomly assigned 400 adults with
overweight/obesity to a stepped care weight loss intervention in which first line treatment was either 1) a
smartphone app alone (App) or 2) the app plus coaching (App + C). Participants who did not attain adequate
weight loss (i.e., averaging >0.5 lb/week) were classified as nonresponders, and re-randomized to be stepped
up by a modest or vigorous addition of treatment components. Preliminary Results showed that: 1) More
patients achieved clinically meaningful 6 month weight loss with App + C than App; 2) App + C non-responders
who adhered to the vigorous step-up (text message and meal replacement) lost as much weight as responders
by 12-months. These compelling findings point to a need to test the efficacy of SMARTER stepped-care
intervention in a randomized controlled trial. The SMARTer trial is a three-arm, non-inferiority randomized
controlled trial that compares the optimized, adaptive SMARTer intervention against gold-standard DPP and
Control. The trial will address whether a scalable, stepped-care intervention can stand up to gold-standard
DPP by achieving comparable weight loss at less cost. If so, we will emerge with a scalable, effective
intervention that tailors to patient response using a stepped-care model. Alongside evaluation of clinical non-
inferiority, a comprehensive economic analysis will inform relative affordability and scalability. Hypotheses are
that: 1) SMARTer stepped-care will be non-inferior to gold standard DPP in its effect on 6 month weight loss;
and 2) The SMARTer intervention will be more cost-effective to implement. We will explore whether extending
the SMARTer intervention results in weight loss maintenance at 12 months compared to DPP and Control.
Lastly, we will explore mediators and moderators of SMARTer's effect on weight loss to inform future
intervention optimization. If successful, findings will support dissemination of a cost-effective obesity population
management strategy that facilitates treating obesity with the resources it needs – not more, and not less.
项目摘要
本申请提出将优化的、资源有效的、适应性肥胖治疗与优化的、资源有效的、适应性肥胖治疗进行比较。
金标准固定治疗包和评估仅控制。肥胖症的流行仍在继续
而且势头不减,预示着糖尿病的爆发尽管有许多倡议,黄金标准糖尿病
预防计划(DPP)针对超重和肥胖症的强化多组分行为治疗仍然存在
太昂贵,负担太重,而且很难扩大规模,以至于无法向2/3的人口提供
需要减肥的人为了应对这一挑战,我们努力优化负担较轻的治疗
方法,可以最大限度地减轻体重的人口有肥胖与减少资源
支出在SMART减肥管理试验中,我们随机分配了400名成年人,
将超重/肥胖患者分为阶梯式护理减肥干预组,其中一线治疗为:1)
单独的智能手机应用程序(App)或2)应用程序加辅导(App + C)。未达到适当水平的参与者
体重减轻(即,平均>0.5磅/周)被归类为无应答者,并重新随机化以进行逐步
通过适度或剧烈地添加处理组分而增加。Preliminary Results shows that:1)更多
与App相比,App + C组患者实现了具有临床意义的6个月体重减轻; 2)App + C无应答者
那些坚持大力加强(短信和代餐)的人减掉的体重和响应者一样多
12个月。这些令人信服的发现表明,需要测试SMARTER阶梯式护理的有效性
干预随机对照试验。SMARTer试验是一项三组、非劣效性、随机
对照试验,比较优化的自适应SMARTer干预与金标准DPP,
控制该试验将解决可扩展的阶梯式护理干预是否能够达到黄金标准
DPP通过以更低的成本实现可比的重量损失。如果是这样,我们将出现一个可扩展的,有效的
使用阶梯式护理模式根据患者反应进行定制的干预。除了评估临床非
但是,如果我们的产品质量低劣,那么一个全面的经济分析将为相对的可承受性和可扩展性提供信息。假设是
即:1)就6个月的减肥效果而言,SMARTer分步护理将不劣于金标准DPP;
(2)SMART干预措施的实施将更具成本效益。我们将探讨是否延长
与DPP和对照相比,SMARTer干预导致在12个月时维持体重减轻。
最后,我们将探讨SMARTer对减肥效果的中介者和调节者,以告知未来
干预优化如果成功,研究结果将支持成本效益肥胖人群的传播
管理战略,促进治疗肥胖的资源,它需要-不多,也不少。
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:
$ 69.04万 - 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:
$ 69.04万 - 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:
10033189 - 财政年份:2020
- 资助金额:
$ 69.04万 - 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:
$ 69.04万 - 项目类别:
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