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; 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，以实现关键行为健康 饮食不良，吸烟，不健康的饮酒或久坐的生活方式等风险。但是，是真的 成功的行为干预科学应符合至少三个关键标准：（1）证明 干预效率的累积提高； （2）提供达到高比例的干预措施 需要（3）证明伯恩疾病的有意义的人口影响/减少。目前 没有遇到这些。这样做的一些原因包括（1）过度依赖对暗示交付机制 （人们；通常是某种治疗师或教练）很难大规模训练忠诚，并且 不能精确地操纵； （2）使用样本量可能远低于所需的样本量 准确表征反应的异质性； （3）治疗师特征的跨研究变异性， 样本特征和测量策略。所有这些组合以创建缺乏证据的科学 先前基准的累积改进。建议的解决方案。移动技术显示出大量 承诺是一种可复制，透明，模块化和精确的干预输送机制。然而， 移动干预措施开发和实施的进展已被诸如诸如 开发干预所需的巨大时间和金钱；跨平台兼容性的限制 和互操作性；并且缺乏围绕协作的一致系统。为了满足这些需求， PI开发了计算机干预创作系统（CIAS），该系统的行为行为 干预科学通过允许研究人员直接开发复杂而互动的移动 没有编程的申请。 PI自己实验室外的调查人员已经在使用CIA， 这个过程揭示了来自广泛的NIH资助的研究人员对该软件的浓厚兴趣， 以及重大限制。当前目标。拟议的申请将解决这些限制，使 CIAS成为重要的，开源的，几乎是独特的非商业研究资源。在AIM 1中，我们将 参与持续的用户体验测试，旨在使工具更加直观。这个过程 预计将导致研究者界面的戏剧性重组，以及一组完整的 循证用户培训和支持材料。在AIM 2中，我们将添加一系列功能 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的开放规格，以促进与电子健康记录的整合）。在AIM 3中，我们将 参与促进CIAS作为多LAB合作的一部分，使用开放科学的一部分 实践（例如，通过与开放科学框架集成）。重要的是，我们将参与所有 上面的顾问小组的协助下，将有助于确保最终 产品广泛相关，面向未来，可用于广泛的行为科学家。