Applying novel technologies and methods to inform the ontology of self-regulation

应用新技术和方法来告知自我监管本体

• 批准号: 9452329
• 负责人: Lisa A. Marsch
• 金额: $ 7.85万
• 依托单位: DARTMOUTH COLLEGE
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9452329
• 关键词: Adherence; Alcohol or Other Drugs use; Area; Atlases; Bayesian Modeling; Behavior; Behavior Therapy; Behavior assessment; Behavioral; Behavioral Mechanisms; Biological; Biological Assay; Catalogs; Cessation of life; Chronic Disease; Cognitive; Data; Data Set; Development; Diabetes Mellitus; Diet; Disease; Elderly; Emotional; Equation; Evaluation; Exercise; Factor Analysis; Food; Functional Magnetic Resonance Imaging; Funding; Goals; Health; Health behavior; Health behavior change; Healthcare; Image; Informal Social Control; Internet; Intervention; Laboratories; Literature; Measures; Mechanics; Medical; Medicine; Mental Health; Methods; Modeling; Monitor; Motivation; Obesity; Ontology; Outcome; Overweight; Pain management; Palate; Patient-Centered Care; Patient-Focused Outcomes; Pattern; Persons; Play; Population; Process; Regimen; Research; Resources; Rewards; Risk Behaviors; Role; Sampling; Science; Scientific Inquiry; Self Efficacy; Smoker; Smoking; Stimulus; Substance abuse problem; Technology; Testing; Time; Tobacco; United States National Institutes of Health; Validation; Work; base; behavior change; behavioral health; burden of illness; chronic pain; cost; cost effective; design; diet and exercise; effective intervention; emotion regulation; experience; health care service utilization; improved; medication compliance; mobile computing; neural circuit; new technology; novel; physical inactivity; population health; precision medicine; psychologic; public health relevance; ranpirnase; relating to nervous system; response; scale up; service utilization; technology validation; tool

 DESCRIPTION (provided by applicant): Health risk behavior, including poor diet, physical inactivity, tobacco and other substance use, causes as much as 40% of the illness, suffering, and early death related to chronic diseases. Non-adherence to medical regimens is an important exemplar of the challenges in changing health risk behavior -- and is common, costly (due to increased utilization of healthcare services), and associated with poor patient outcomes. Although an array of interventions have been shown to be effective in promoting health behavior change, much of this work has been siloed (focused on one disorder at a time). Additionally, interventions are typically intended to engage multiple mechanisms of behavior change, but the mechanisms by which they actually work are infrequently systematically examined. One promising domain of putative behavior change targets is that of self-regulation -- a person's ability to manage cognitive, motivational, and emotional resources to act in accordance with his/her long-term goals. The Center for Technology and Behavioral Health, a national NIH-funded P30 "Center of Excellence" uses science to inform the development, evaluation, and implementation of technology (web, mobile)-based self- regulation tools for behavior change targeting a wide array of populations and health behaviors. This work examines behavioral phenomena (and the mechanisms by which they work) ranging from substance abuse, mental health, chronic pain management, medication adherence, diet, exercise, diabetes, and smoking. Self- regulation tools offered on mobile platforms enable widespread reach and scalability of effective interventions. In this proposal, we plan to examine putative targets (processes) of behavior change within the self- regulation mechanism domain across contexts, populations, and assays - in 3 primary levels of analysis: (1) psychological (e.g., constructs such as self-efficacy; emotion regulation; response inhibition), (2) behavioral (e.g., tasks of reward responsiveness; temporal horizon), and (3) biological (structural and functional MRI of key neural circuitry). We will evaluate the extent to which we can engage and manipulate these putative targets both within and outside of laboratory settings (using a novel mobile self-regulation monitoring and intervention platform). We will conduct this work with two populations for which behavior plays a critical role in the course of medical regimen adherence, health, and health outcomes: (1) smokers and (2) obese/overweight persons. We will then examine cross-assay validity and cross-context and cross-sample reliability of assays to identify relations among self-regulatory targets. We will finally evaluate the degree to which engaging targets produces a desired change in medical regimen adherence (across 4 week self-regulation interventions) and health behavior among smokers and obese/overweight persons. This project will identify valid and replicable assays of mechanisms of self-regulation across populations to inform an ontology of self-regulation that can ultimately inform development of health behavior interventions of maximal efficacy and potency.

 描述(申请人提供)：健康风险行为，包括饮食不良、缺乏运动、吸烟和其他物质使用，导致与慢性病有关的疾病、痛苦和过早死亡的比例高达40%。不遵守医疗方案是改变健康风险行为挑战的重要例证--而且是常见的、代价高昂的(由于医疗服务利用率增加)，并与糟糕的患者结局相关。尽管一系列干预措施已被证明在促进健康行为改变方面是有效的，但这项工作的大部分都是孤立的(一次只关注一种疾病)。此外，干预措施通常旨在利用行为改变的多种机制，但很少有人系统地检查它们实际发挥作用的机制。假设的行为改变目标的一个有希望的领域是自我调节--一个人管理认知、动机和情感资源的能力，以根据他/她的长期目标采取行动。科技与行为健康中心是由美国国立卫生研究院资助的全国P30“卓越中心”，它利用科学为基于技术(网络、移动)的自我调节工具的开发、评估和实施提供信息，以针对广泛的人群和健康行为改变行为。这项工作考察了行为现象(以及它们的工作机制)，包括药物滥用、心理健康、慢性疼痛管理、服药依从性、饮食、锻炼、糖尿病和吸烟。移动平台上提供的自律工具使有效干预的广泛覆盖面和可扩充性得以实现。在这项建议中，我们计划在自我调节机制领域内跨背景、人群和分析检查行为变化的假定目标(过程)-在三个主要分析水平：(1)心理(例如，自我效能、情绪调节、反应抑制等概念)，(2)行为(例如，奖赏反应任务；时间视域)，以及(3)生物(关键神经回路的结构和功能磁共振)。我们将评估我们可以在实验室内外(使用一个新的移动自我调节监测和干预平台)参与和操纵这些假定目标的程度。我们将在以下两个人群中开展这项工作：(1)吸烟者和(2)肥胖/超重者，这两个人群的行为在遵守医疗方案的过程、健康和健康结果中起着关键作用。然后，我们将检查分析的交叉效度以及跨背景和跨样本的可靠性，以确定自我调节目标之间的关系。我们将最终评估参与目标在多大程度上对吸烟者和肥胖/超重者的医疗方案依从性(持续4周的自我调节干预)和健康行为产生预期的变化。该项目将确定有效的和可复制的人群自我调节机制的分析，以提供自我调节的本体论，最终能够为最大效力和效力的健康行为干预措施的发展提供信息。

项目成果

Psychometric and Machine Learning Approaches to Reduce the Length of Scales.

心理测量和机器学习方法可以减少量表的长度。

• DOI: 10.1080/00273171.2020.1781585
• 发表时间: 2021-11
• 期刊: Multivariate behavioral research
• 影响因子: 3.8
• 作者: Gonzalez O

The cognitive and perceptual correlates of ideological attitudes: a data-driven approach.

• DOI: 10.1098/rstb.2020.0424
• 发表时间: 2021-04-12
• 期刊: Philosophical transactions of the Royal Society of London. Series B, Biological sciences
• 作者: Zmigrod L;Eisenberg IW;Bissett PG;Robbins TW;Poldrack RA
• 通讯作者: Poldrack RA

Relating psychiatric symptoms and self-regulation during the COVID-19 crisis.

• DOI: 10.1038/s41398-022-02030-9
• 发表时间: 2022-07-11
• 期刊: TRANSLATIONAL PSYCHIATRY
• 影响因子: 6.8
• 作者: Vaghi, Matilde M.;Hagen, McKenzie P.;Jones, Henry M.;Mumford, Jeanette A.;Bissett, Patrick G.;Poldrack, Russell A.
• 通讯作者: Poldrack, Russell A.