Creating adaptive, wearable technologies to assess and intervene for individuals with ADRDs

创建自适应可穿戴技术来评估和干预 ADRD 患者

• 批准号: 10390367
• 负责人: Diane Joyce Cook
• 金额: $ 89.69万
• 依托单位: WASHINGTON 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: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10390367
• 关键词: Address; Alzheimer' s disease related dementia; Area; Behavior; Clinical Research; Cognitive; Collaborations; Computing Methodologies; Data; Detection; Environment; Ethnic Origin; Foundations; Generations; Genetic; Health; Health Care Costs; Health behavior; Human; Individual; Interdisciplinary Study; Intervention; Link; Machine Learning; Measures; Memory; Mentors; Methods; Modeling; Neuropsychology; Pattern; Performance; Persons; Population Heterogeneity; Quality of Care; Quality of life; Recording of previous events; Research; Research Personnel; Resources; Series; Source; Technology; Training; Underrepresented Populations; United States National Institutes of Health; Work; built environment; career; computer science; cost; design; digital; experience; functional disability; graduate student; health assessment; improved; innovation; insight; medication compliance; multidisciplinary; novel; predictive modeling; programs; recruit; sensor; smart home; student training; wearable device; wearable sensor technology; webinar

PROJECT SUMMARY / ABSTRACT Advances in machine learning and low-cost, wearable sensors offer a practical method for understanding, assessing, and intervening for Alzheimer's Disease and Related Dementias (ADRDs) in everyday spaces. We propose to create a Behaviorome research program that will create ground-breaking methods for building health-predictive models from wearable sensor data by mapping patterns of behavior using machine learning and pervasive computing technologies. This program will create innovative multidisciplinary ideas to address NIH ADRD Milestone 11.c, Embed wearable technologies/pervasive computing in existing and new clinical research. Our research program builds on a history of interdisciplinary research contributions in areas including human behavior modeling from longitudinal sensor data and design of novel assessment and intervention mechanisms. We propose to design and validate methods for mapping a human behaviorome “in the wild”, automatically assessing cognitive and functional health from behavior markers, scaling technologies through machine learning, linking health and behavior with their influences, and closing the loop with automated interventions. Similarly, our mentoring program builds on experience training students and early- career investigators to become leaders in the field of gerontechnology. We will recruit and train graduate students and early-stage researchers, including those from underrepresented groups, to grow an institutional multidisciplinary Behaviorome research program and to establish new research programs that contribute to the targeted Milestone. We will scale the impact of mentoring by establishing a webinar series and creating youtube videos that highlight and explain breakthroughs in the design and application of Behaviorome research. Results of this program will include scripts and templates to construct a behaviorome with resource- limited wearable devices, scale data and models to large diverse populations, integrate data with multiple information sources (e.g., genetics), automate health assessment and intervention, and create understandable explanations of data and models. These will contribute to existing clinical studies such as the clinician-in-the- loop smart home, digital memory notebook, and pervasive computing measures of functional performance. Furthermore, they will lead to new clinical studies that formalize connections between health and its influences, exploration of the impact of ethnicity and the built environment on health, and the design of ADRD interventions for medication adherence, task prompting, and negative interaction de-escalation. The proposed contributions are significant because they will provide insights on detecting and assessing ADRDs within a person's everyday environment using wearable sensing and pervasive computing methods that have not been investigated in prior work. Additionally, the mentoring steps will pave the way for a new generation of researchers to offer improved methods of addressing the need to understand, assess, and intervene for ADRDs in everyday settings, thereby improving quality of life and reducing health care costs.

项目概要/摘要 机器学习和低成本可穿戴传感器的进步为理解、 评估和干预日常生活中的阿尔茨海默病和相关痴呆症 (ADRD)。 提议创建一个行为组研究计划，该计划将创造突破性的建筑方法 通过使用机器学习映射行为模式，根据可穿戴传感器数据建立健康预测模型 该计划将创造创新的多学科想法来解决。 NIH ADRD 里程碑 11.c，将可穿戴技术/普适计算嵌入现有和新的临床中 我们的研究计划建立在跨学科研究贡献的历史基础上。 包括根据纵向传感器数据进行人类行为建模以及新颖的评估和设计 我们建议设计和验证绘制人类行为组的方法。 野外”，通过行为标记、扩展技术自动评估认知和功能健康 通过机器学习，将健康和行为与其影响联系起来，并与 同样，我们的指导计划建立在培训学生和早期干预的经验之上。 职业调查员成为老年科技领域的领导者我们将招募和培训毕业生。 学生和早期研究人员，包括来自代表性不足群体的研究人员，以发展一个机构 多学科行为组学研究计划，并建立新的研究计划，有助于 我们将通过建立网络研讨会系列并创建目标里程碑来扩大指导的影响。 YouTube 视频突出并解释了Behaviorome 设计和应用方面的突破 该计划的研究结果将包括用于构建具有资源的行为组的脚本和模板。 有限的可穿戴设备，将数据和模型扩展到大量不同的人群，将数据与多个 信息源（例如遗传学），自动化健康评估和干预，并创建可理解的 这些将有助于现有的临床研究，例如临床医生。 循环智能家居、数字内存笔记本以及功能性能的普适计算测量。 此外，它们还将带来新的临床研究，使健康与其健康之间的联系正式化。 影响，探索种族和建筑环境对健康的影响，以及 ADRD 的设计 药物依从性、任务提示和负面相互作用降级的干预措施。 贡献是重要的，因为它们将提供有关在一定范围内检测和评估 ADRD 的见解。 人们的日常环境使用尚未被广泛应用的可穿戴传感和普适计算方法 此外，指导步骤将为新一代铺平道路。 研究人员提供改进的方法来满足理解、评估和干预 ADRD 的需求 在日常生活中，从而提高生活质量并降低医疗保健成本。