Smart environment technology for longitudinal behavior analysis and intervention

纵向行为分析与干预的智能环境技术

• 批准号: 8402015
• 负责人: Diane Joyce Cook
• 金额: $ 48.11万
• 依托单位: WASHINGTON STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-30 至 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8402015
• 关键词: Activities of Daily Living; Acute; Address; Adult; Aging; Algorithms; Behavior; Behavioral; Caring; Chronic Disease; Clinical Data; Cognitive; Computer software; Data; Dementia; Detection; Disease Progression; Early treatment; Elderly; Environment; Exercise; Funding; Gerontology; Health; Health Status; Healthcare; Home environment; Human; Impaired cognition; Individual; Infection; Injury; Interdisciplinary Study; Intervention; Interview; Investigation; Label; Laboratories; Lead; Life; Life Style; Longitudinal Studies; Medical Records; Memory; Modeling; Monitor; Natural History; Nursing Homes; Pattern; Performance; Personal Satisfaction; Pilot Projects; Population; Prevalence; Prevention; Psychological Transfer; Public Health; Quality of life; Research; Research Methodology; Research Personnel; Risk; Signal Transduction; Sleep; Societies; Software Design; Solid; Supervision; Technology; Time; Vision; Work; activity marker; base; cognitive rehabilitation; cost; design; functional decline; functional status; health care service utilization; improved; innovation; new technology; sensor; social; success; trend

DESCRIPTION (provided by applicant): The world's population is aging and the resulting prevalence of chronic illnesses is a challenge that our society must address. Our vision is to address this challenge by designing smart environment technologies that keep older adults functioning independently in their own homes as long as possible. Smart environments have been used as the basis of monitoring activities for residents with health conditions. However, there is currently a lack of large scale, longitudinal research to identify early markers of dementia and other health status changes and to predict functional decline. The objective of this project is to perform a 5-year longitudinal study of older adults performing daily activities in thir own smart homes. By tracking residents' daily behavior over a long period of time our intelligent software can perform automated functional assessment and identify trends that are indicators of acute health changes (e.g., infection, injury) and slower progressive decline (e.g., dementia). By implementing prompt-based interventions that support functional independence and promote healthy lifestyle behaviors (e.g., social contact, exercise, regular sleep), we can improve overall health and well- being. We hypothesize that smart home technologies can be used to detect and predict functional change, to slow functional change and extend functional independence, and to improve quality of life in elderly individuals who are at risk of transitioning to MCI and t dementia. This hypothesis has been formulated on the basis of preliminary data produced by the applicants which supports the efficacy of using smart home technologies for both functional status assessment and for prompting the initiation and completion of activities in individuals with MCI and dementia. The rationale of the proposed work is that understanding the natural history of functional change between aging and dementia will lead to early prevention and proactive interventions that will slow functional change, thereby delaying nursing home placement and cost of care to society. We plan to pursue the following specific aims: (1) Characterize the daily lifestyle of smart environment residents through minimal-supervision activity recognition and activity discovery, (2) Design software algorithms that detect trends in behavioral data, and (3) Evaluate the efficacy of activity-aware automated prompting technology for extending functional independence and improving quality of life. The proposed work is innovative because it will track a large number of individuals longitudinal in their own homes and determine whether this technology can be used to promote healthy lifestyle behaviors and detect health care changes that may lead to early interventions, improved quality of life, and decreased health care utilization. The project is significant because it will introduce new technologies for activity discovery and tracking that require minimal- supervision, contribute algorithms that predict cognitive decline and signal more acute health status change, and demonstrate for the first time that activity-aware automated prompting technologies can be used to support and/or slow functional change and to increase quality of life in elderly individuals. PUBLIC HEALTH RELEVANCE: The proposed study represents the first large-scale, longitudinal investigation of smart environment technologies for in-home functional assessment and intervention. The result of this research will be software algorithms embedded in everyday environments that monitor and assess trends in functional performance as well as automated technologies for activity-aware prompting interventions. This work is relevant to public health because understanding early indicators and predictors of health status change and developing activity- aware automated technologies will have important implications for early prevention, proactive intervention, and treatment that will extend the amount of time individuals can live independently in their own homes.

描述（由申请人提供）：世界人口正在老龄化，由此导致的慢性疾病的流行是我们社会必须应对的挑战。我们的愿景是通过设计智能环境技术来应对这一挑战，让老年人尽可能长时间地在自己的家中独立生活。智能环境已被用作对有健康状况的居民进行监测活动的基础。然而，目前缺乏大规模的纵向研究来识别痴呆症和其他健康状况变化的早期标志并预测功能衰退。该项目的目标是对老年人在自己的智能家居中进行日常活动进行为期 5 年的纵向研究。通过长期跟踪居民的日常行为，我们的智能软件可以执行自动功能评估，并识别作为急性健康变化（例如感染、受伤）和缓慢进行性衰退（例如痴呆）指标的趋势。通过实施支持功能独立和促进健康生活方式行为（例如社交接触、锻炼、规律睡眠）的即时干预措施，我们可以整体改善 健康和福祉。我们假设智能家居技术可用于检测和预测功能变化，减缓功能变化并延长功能独立性，并改善有发展为 MCI 和 t 型痴呆风险的老年人的生活质量。该假设是根据申请人提供的初步数据制定的，该数据支持使用智能家居技术进行功能状态评估以及提示患有以下疾病的个人开始和完成活动的功效 MCI 和痴呆症。拟议工作的基本原理是，了解衰老和痴呆之间功能变化的自然史将导致早期预防和主动干预，从而减缓功能变化，从而延迟疗养院的安置和社会护理成本。我们计划实现以下具体目标：（1）通过最小监督活动识别和活动发现来表征智能环境居民的日常生活方式，（2）设计检测行为数据趋势的软件算法，以及（3）评估活动感知自动提示技术在扩展功能独立性和提高生活质量方面的功效。拟议的工作具有创新性，因为它将在自己的家中纵向跟踪大量个人，并确定该技术是否可用于促进健康的生活方式行为并检测可能导致早期干预、提高生活质量和降低医疗保健利用率的医疗保健变化。该项目意义重大，因为它将引入需要最少监督的活动发现和跟踪新技术，提供预测认知能力下降和发出更剧烈的健康状况变化信号的算法，并首次证明活动感知自动提示技术可用于支持和/或减缓功能变化并提高老年人的生活质量。 公共健康相关性：拟议的研究代表了对用于家庭功能评估和干预的智能环境技术的首次大规模纵向调查。这项研究的结果将是嵌入日常环境中的软件算法，用于监控和评估功能性能的趋势，以及用于活动感知提示干预的自动化技术。这项工作与公共卫生相关，因为了解健康状况变化的早期指标和预测因素以及开发活动感知自动化技术将对早期预防、主动干预和治疗产生重要影响，从而延长个人在家中独立生活的时间。