Accelerating collaborative, cumulative, and open intervention science with an e-intervention authoring platform

通过电子干预创作平台加速协作、累积和开放的干预科学

• 批准号: 10271571
• 负责人: STEVEN J. ONDERSMA
• 金额: $ 37.75万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10271571
• 关键词: Address; Adult; Apple; Area; Behavior; Behavior Therapy; Behavioral; Benchmarking; Budgets; Cessation of life; Characteristics; Code; Collaborations; Computer software; Computers; Consumption; Development; Documentation; Eating; Ecosystem; Electronic Health Record; Elements; Ensure; Evaluation; Evidence based intervention; Fast Healthcare Interoperability Resources; Funding; Future; Health behavior; Heterogeneity; Human; Intervention; Intervention Studies; Interview; Intuition; Investments; Language; Machine Learning; Measurement; Methods; Modification; National Institute of Biomedical Imaging and Bioengineering; Operating System; Outcome; Persons; Population; Process; Public Health Informatics; Publications; Research; Research Personnel; Resources; Risk; SMART health; Sample Size; Sampling; Science; Scientist; Smoking; Support System; System; Technology; Testing; Text Messaging; Time; Training; Training Support; Translations; Treatment Efficacy; Unhealthy Diet; United States National Institutes of Health; Work; alcohol misuse; base; behavioral health; brief intervention; burden of illness; comparison intervention; computerized; cost; design; diet and exercise; evidence base; experience; flexibility; good diet; handheld mobile device; healthy weight; interest; interoperability; intervention effect; mHealth; medication compliance; mobile application; mobile computing; open data; open source; prevent; reduced substance use; repository; response; sedentary lifestyle; software development; substance use; symposium; therapy design; therapy development; tool; user centered design; virtual; web site

Background. Research has identified a wide range of evidence-based interventions for key behavioral health risks such as poor diet, smoking, unhealthy alcohol use, or a sedentary lifestyle. However, to be truly successful, behavioral intervention science should fulfill at least three key criteria: (1) demonstration of cumulative increases in intervention efficacy; (2) provision of interventions that reach a high proportion of those in need; and (3) demonstration of a meaningful population impact/reduction in disease burden. It currently meets none of these. Some reasons for this include (1) over-reliance on an imprecise delivery mechanism (people; usually therapists or coaches of some kind) that is difficult to train to fidelity on a large scale, and that cannot be manipulated with precision; (2) use of sample sizes that may be far below what is needed to accurately characterize heterogeneity of response; and (3) cross-study variability in therapist characteristics, sample characteristics, and measurement strategy. All of this combines to create a science that lacks evidence of cumulative improvements upon prior benchmarks. Proposed solution. Mobile technology shows significant promise as an intervention delivery mechanism that is replicable, transparent, modular, and precise. However, progress in the development and implementation of mobile interventions has been slowed by factors such as the tremendous time and money needed to develop an intervention; limitations in cross-platform compatibility and interoperability; and lack of a consistent system around which to collaborate. To address these needs, the PI developed the Computerized Intervention Authoring System (CIAS), which facilitates behavioral intervention science by allowing investigators to directly develop sophisticated and interactive mobile applications without programming. CIAS is already being used by investigators outside of the PI’s own lab, a process that has revealed significant interest in this software from a wide range of NIH-funded investigators, as well as significant limitations. Current aims. The proposed application will address these limitations, making CIAS into a significant, open-source, and virtually unique non-commercial research resource. In Aim 1, we will engage in sustained user experience testing designed to make the tool far more intuitive to use. This process is expected to result in a dramatic reorganization of the investigator interface, as well as in a complete set of evidence-based user training and support materials. In Aim 2, we will add a range of features and capabilities to make CIAS more powerful, flexible, and interoperable (e.g., by building to SMART Health IT standards, as well as FHIR open specifications to facilitate integration with Electronic Health Records). In Aim 3, we will engage in focused efforts to promote the use of CIAS as part of multi-lab collaborations using open science practices (e.g., via integration with the Open Science Framework). Importantly, we will engage in all of the above with the assistance of a highly accomplished panel of advisors who will help ensure that the final product is broadly relevant, future-facing, and usable to a broad range of behavioral scientists.

背景研究已经确定了广泛的基于证据的干预措施，用于关键的行为健康 不良饮食、吸烟、不健康的饮酒或久坐不动的生活方式等风险。然而，要真正 成功的行为干预科学至少应满足三个关键标准：（1）证明 干预效果的累积增加;（2）提供干预措施，达到高比例的人， 需要;和（3）证明疾病负担的有意义的人口影响/减少。它目前 这些都不符合。其原因包括：（1）过度依赖不精确的交付机制 （人;通常是治疗师或教练的某种），这是很难训练到大规模的忠诚， 无法精确操作;（2）使用的样本量可能远远低于需要的样本量， 准确地表征反应的异质性;和（3）治疗师特征的交叉研究变异性， 样本特征和测量策略。所有这些结合在一起创造了一种缺乏证据的科学 在先前基准的基础上累积改进。建议的解决方案。移动的技术显示出 承诺作为一种干预交付机制，是可复制的，透明的，模块化的和精确的。然而，在这方面， 移动的干预措施的开发和实施进展缓慢， 开发干预措施所需的大量时间和金钱;跨平台兼容性的限制 和互操作性;以及缺乏一个一致的系统来进行协作。为了满足这些需求， PI开发了计算机化干预创作系统（CIAS），该系统有助于行为 干预科学，允许研究人员直接开发复杂的和互动的移动的 没有编程的应用程序CIAS已经被PI自己实验室之外的调查人员使用， 这一过程显示了NIH资助的广泛研究人员对该软件的极大兴趣， 以及显著的局限性。目前的目标。拟议的应用程序将解决这些限制， CIAS成为一个重要的，开源的，几乎是唯一的非商业研究资源。在目标1中，我们 参与持续的用户体验测试，旨在使工具使用起来更加直观。这个过程 预计将导致研究者界面的戏剧性重组，以及一套完整的 循证用户培训和辅助材料。在目标2中，我们将添加一系列特性和功能 为了使CIAS更强大、灵活和可互操作（例如，通过构建智能健康IT标准， 以及FHIR开放规范，以促进与电子健康记录的集成）。在目标3中，我们 作为多实验室合作的一部分，利用开放科学，集中精力促进CIAS的使用 实践（例如，通过与开放科学框架的集成）。重要的是，我们将参与所有 在一个非常有成就的顾问小组的协助下，他们将帮助确保最后的 产品具有广泛的相关性，面向未来，并可用于广泛的行为科学家。