课题基金 / 基金详情

Roybal Center for Personalized Trials: Physical Activity Promotion to Foster Healthy Aging

Roybal Center for Personalized Trials: Physical Activity Promotion to Foster Healthy Aging
皇家个性化试验中心:促进体育活动促进健康老龄化
批准号:
10463635
负责人:
Karina W. Davidson
金额:
$42.94万
依托单位国家:
美国
项目类别:
财政年份:
2020
资助国家:
美国
项目状态:
已结题
起止时间:
2020-08-15 至 2024-05-31

项目摘要

项目成果

Karina W. Davidson的其他基金

相似基金

相关文献

中文摘要
翻译
罗伊巴尔个人化试验中心的目标:促进身体活动以促进养育 成功老龄化是为了促进老年人体力活动的干预开发研究 因此启动了几个独立的研究项目。我们将完成以下活动: 1.全面、创造性地中试I-V阶段行为改变干预措施,以 增加锻炼,减少久坐行为 2.建立和维护跨学科的成功老龄化行为改变网络 协助快速确定和完善最具创新性的飞行员体力活动研究 3.通过测试行为改变理论、技术和方法,为行为改变科学做出贡献 促进老年人体力活动的机制 4.向任职人数不足的调查人员提供试点项目和未来的研究支持计划 5.建立一套以证据为基础、可传播的个性化行为改变干预措施 改善老年人生活质量和数量的行动机制措施。 Pilot#1的目标是有效地识别不同的行为改变技术(即构建块), 是改善老年人低强度体力活动(步行)的首选和有效的方法, 社区居住的成年人将纳入后期多行为改变技术干预。 Pilot#2的目的是在第二个样本中正式测试习惯养成理论,以改善走路 老年人,否则健康的,社区居住的成年人,通过使用其行为改变技术和 行动的假想机制--认知自动性。如果检测到,在实现行走时的异质性 习惯将为后期的习惯干预研究提供信息。 我们将推进有关偏好和行为改变有效性的翻译知识 促进老年人体力活动的技术。我们将有一个由国际调查人员组成的网络 在他们的网络中传播行为改变干预发展活动,以及他们的 研究计划。我们将进行18项试点研究。我们将提供有用的工具来创建 行为改变干预,并确定个性化方法或N-of-1方法对阶段I是否有用 其他体力活动干预措施的发展。我们将提供一个有用的存储库 行为改变的技巧和作用机制,使我们对身体产生很大的影响 活动干预与其他老年人口的发展,以及其他体力活动 干预措施。如果我们创造了一套实用的、以科学为基础、以预防为导向的 在现实世界中成功实施的行为干预,以改善健康和 老年人的活力。
英文摘要
The goal of this Roybal Center for Personalized Trials: Physical Activity Promotion to Foster Successful Aging is to generate intervention development research for promoting physical activity in older adults, and so launch several independent programs of research. We will accomplish these activities: 1. Comprehensively and creatively pilot-test a number of stage I-V behavior change interventions to increase exercise and decrease sedentary behavior 2. Develop and maintain an interdisciplinary Network on Behavior Change in Successful Aging for aiding in the rapid identification and refinement of the most innovative pilot physical activity studies 3. Contribute to the science of behavior change by testing behavior change theories, techniques, and mechanisms for promoting physical activity in older adults 4. Provide pilot project and future program of research support to under-represented investigators 5. Build an evidence-based, disseminable suite of personalized behavior change interventions and measures of mechanisms of action for improving the quality and quantity of older adults’ lives. Aim of Pilot #1 is to efficiently identify the distinct behavior change techniques (i.e., building blocks) that are preferred and effective for improving low intensity physical activity (walking) in older, otherwise healthy, community-dwelling adults to be included in later-stage multi-behavior change technique interventions. Aim of Pilot #2 is to formally test the Habit Formation theory to improve walking in a second sample of older, otherwise healthy, community-dwelling adults, by employing its behavior change techniques and hypothesized mechanism of action—cognitive automaticity. If detected, heterogeneity in attaining a walking habit will inform later stage habit intervention studies. We will advance translational knowledge about preferences and effectiveness of behavior change techniques to promote physical activity in older adults. We will have a Network of international investigators who disseminate behavior change intervention development activities into their networks, and their programs of research. We will have conducted 18 pilot studies. We will have provided useful tools to create behavior change interventions and decide whether Personalized or N-of-1 methods are useful for Stage I intervention development for other physical activity interventions. We will provide a repository of useful behavior change techniques and mechanisms of action, so that we have a large impact upon physical activity interventions development with other older adult populations, and for other physical activity interventions. We will be successful if we have created practical, science-based, prevention-driven suite of behavioral interventions that are successfully implemented in the real world, to improve the health and vitality of older adults.
期刊论文(0)
专著(0)
科研奖励(0)
会议论文
Optimization of monitoring, prediction and phenotyping of deterioration of inhospital patients using machine learning and multimodal real time data
海外基金