Control Systems Engineering to Address the Problem of Weight Loss Maintenance: A System Identification Experiment to Model Behavioral & Psychosocial Factors Measured by Ecological Momentary Assessment
解决减肥维持问题的控制系统工程:行为建模的系统识别实验
基本信息
- 批准号:10749979
- 负责人:
- 金额:$ 66.67万
- 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:2023-09-15 至 2028-06-30
- 项目状态:未结题
- 来源:
- 关键词:AddressAdultAffectAlgorithmsAreaBehaviorBehavior TherapyBehavioralBehavioral ModelBiological ModelsBody WeightBody Weight decreasedCardiovascular systemCellular PhoneComplexDataDesire for foodDiseaseEatingEcological momentary assessmentEducational process of instructingEnergy IntakeEngineeringFutureGenerationsIndividualInformal Social ControlInterventionMaintenanceMalignant NeoplasmsMapsMathematicsMeasuresModelingMonitorOperative Surgical ProceduresParticipantPatientsPatternPharmacologic SubstancePhasePhysiologicalPsychosocial FactorPsychosocial InfluencesRegistriesResearchResearch MethodologyRewardsRiskRisk FactorsRisk ReductionSamplingSeveritiesSeverity of illnessStressful EventSystemTechniquesTechnologyTestingTheoretical modelTimeTranslatingWeightWeight maintenance regimenWorkadaptive interventionautomated interventionbarrier to carebehavior influenceclinically significantcomorbiditycostdesigndynamic systemeffective interventionexperienceexperimental studyindividual patientinnovationmobile computingmultidisciplinaryobesity treatmentpersonalized interventionphysical inactivitypreventpsychosocialrelapse preventionsocial health determinantstheoriesvigilanceweight maintenance
项目摘要
PROJECT SUMMARY/ABSTRACT
The most major and critical barrier to the treatment of obesity and comorbid conditions is weight loss
maintenance. A range of established treatments reliably produce clinically significant initial weight losses of 3-
30% of body weight, which substantially reduce risk and severity of disease, even when the weight loss is
modest. However, weight loss maintenance is uniformly poor, with most patients regaining at least some
weight and behaviorally treated patients returning to baseline weight within 5 years, thereby renewing risk for
weight-related illness. While regain is common, it is difficult to predict when or why an individual will begin to
regain lost weight. We therefore propose an experiment that will enable a future just-in-time adaptive
intervention (JITAI) that passively monitors triggers for lapse, identifies which triggers are most likely to
contribute to lapse for each patient, accurately predicts when a patient is entering a period of heightened risk
for lapse, determines the type(s) of intervention(s) that are likely to prevent the lapse, administers intervention
for as long as needed to reestablish healthy behavioral patterns for weight maintenance, and then returns to
passive monitoring. This automated intervention is nearly within reach via a combination of mobile
technologies, analytics, and behavioral intervention techniques that our team has already established,
including: (a) an Ecological Momentary Assessment platform to measure daily weight and related behavioral
and psychosocial influences; (b) an analytic framework, Control Systems Engineering, capable of modeling
complex patterns of behavioral and psychosocial influences on weight, and determining how best to intervene
on this “system” to facilitate weight loss maintenance; and (c) a toolbox of empirically validated behavioral
intervention strategies known to be effective for addressing common causes of weight regain. This proposal
aims to support our multidisciplinary team in combining our areas of expertise and prior work to enable a
“system identification (ID) experiment” with N=120 participants who have recently lost ≥3% of initial body
weight in a 6-month behavioral obesity treatment (N=180 will undergo behavioral obesity treatment to produce
this sample), who will be studied over a 12-month maintenance period. The data will be used to validate and
refine a theoretical model of weight loss maintenance. During the study, 4 interventions from the behavioral
toolbox will be administered randomly, thus providing necessary data on: (a) how lapse triggers are related to
each other and weight, and (b) which interventions are effective for addressing which triggers, for whom, and
under what circumstances. The end result of this project will be a control systems algorithm that can predict
when, for whom, and how to intervene to prevent weight regain, and a mobile platform that can be used to
deliver JITAI in the next phase of research. This highly innovative approach to weight loss maintenance could
ultimately prevent regain in large numbers of individuals at low cost using smartphone technology that is
already ubiquitous.
项目总结/文摘
项目成果
期刊论文数量(0)
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科研奖励数量(0)
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Eric Hekler其他文献
Eric Hekler的其他文献
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{{ truncateString('Eric Hekler', 18)}}的其他基金
Advanced data analytics training for behavioral and social sciences research
针对行为和社会科学研究的高级数据分析培训
- 批准号:
10402911 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10668422 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10759023 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Advanced data analytics training for behavioral and social sciences research
针对行为和社会科学研究的高级数据分析培训
- 批准号:
10160959 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Advanced data analytics training for behavioral and social sciences research
针对行为和社会科学研究的高级数据分析培训
- 批准号:
10649605 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10599617 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10456317 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10826070 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10367716 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
Optimizing Individualized and Adaptive mHealth Interventions via Control Systems Engineering Methods
通过控制系统工程方法优化个性化和适应性移动医疗干预措施
- 批准号:
10216204 - 财政年份:2020
- 资助金额:
$ 66.67万 - 项目类别:
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