Using gamification, predictive analytics, artificial intelligence, and Alexa Voice to optimize user experience for individuals living with AD/ADRD and their caregivers

使用游戏化、预测分析、人工智能和 Alexa Voice 来优化 AD/ADRD 患者及其护理人员的用户体验

• 批准号: 10325279
• 负责人: Matthew Golden
• 金额: $ 118.99万
• 依托单位: MAPHABIT, INC.
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10325279
• 关键词: Activities of Daily Living; Address; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease related dementia; American; Area; Artificial Intelligence; Award; Behavior; Behavior Therapy; Behavioral; Biometry; Brain; Caregivers; Caring; Clinical; Clinical Trials; Cognitive; Cohort Studies; Communities; Computer software; Cues; Data; Dementia; Development; Diagnostic; Disease; Early Intervention; Early treatment; Effectiveness; Family; Family member; Friends; Goals; Habits; Health Status; Health system; Healthcare; Home Care Services; Hospitals; Impaired cognition; Independent Living; Individual; Journals; Learning; Legal patent; Link; Maps; Marketing; Memory; Memory impairment; Motivation; Neostriatum; Neuropsychology; Neurosciences; Outpatients; Paper; Patients; Pattern; Peer Review; Pharmacotherapy; Phase; Play; Predictive Analytics; Procedures; Provider; Public Health; Quality of life; Randomized Clinical Trials; Reporting; Rewards; Science; Self Care; Self-Help Devices; Services; Short-Term Memory; Signal Transduction; Small Business Innovation Research Grant; Social Interaction; Social Network; Social isolation; Societies; Standardization; Symptoms; System; Technology; Telephone; Testing; Therapeutic; Time; Translating; United States National Institutes of Health; Visual; Visual system structure; Voice; Work; base; behavioral outcome; clinically relevant; commercialization; cost; daily functioning; dashboard; data streams; decision-making capacity; dementia caregiving; design; evidence base; experience; health goals; human old age (65+); improved; innovation; intrinsic motivation; multiple data sources; novel; patient home care; predictive modeling; pressure; promoter; response; skills; social; social anxiety; tool; user-friendly; virtual reality; vision science; visual map

More than 5.8 million Americans live with dementia and one in 10 Americans over the age of 65 suffer from a diagnosis of Alzheimer’s disease and Alzheimer’s related dementias (AD/ADRD). There are no cures for AD and drug treatments have little overall impact on the course and symptoms of disease. There are no diagnostic tools that can reliably identify people in the early stages of the disease. Individuals living with AD/ADRD have the same quality of life needs as individuals without dementia, including the ability to successful complete routine activities of daily living and maintain satisfying levels of independence and autonomy. However, in the setting of progressively diminishing memory and worsening abilities for self-care, individuals living with AD/ADRD experience progressive losses in independent decision- making capacity and quality of life. Social isolation and anxiety are disabling consequences of this condition. The MapHabitTM system (MHS), is an award-winning (NIH/NIA 1st place Eureka Award) patented, neuroscience-based assistive technology app that helps the memory-impaired accomplish activities of daily living, maintain their independence, and improves overall quality of life for users. The MHS product leverages the science of visual mapping to cue appropriate behavior in the AD setting. In this SBIR Phase II application, MapHabit will further develop the utility and effectiveness of the MHS visual mapping technology to enhance its commercialization and marketing potential by increasing its “stickiness” (i.e., motivation to engage with the app) and to establish a more substantive evidence base for its effects on improved quality of life and daily functioning. We propose two specific aims: Aim 1 – MapHabit will optimize the user experience for individuals with AD/ADRD by supplementing its visual mapping software with gamification technology via a partnership with a virtual reality and in-app games company that serves healthcare needs. Social networking (via gaming with friends and other residents) will be developed to build social connectivity and increase competition, both of which are known to increase “stickiness” and keep users engaged and motivated to adhere to behavioral interventions such as ADLs. We will conduct a 6-month clinical trial to assess behavioral outcomes. Aim 2 – MapHabit will develop a predictive analytics framework using the biometric and behavioral data streams from individuals as they use visual maps to carry out their ADLs. Partnering with a healthcare artificial intelligence company, we will conduct an observational cohort study to collect and integrate 16 months of data streams from 15 dyads from multiple data sources, including gamification and social networking using the MHS, standardized neuropsychological assessments of the patients, and assessments from caregivers. These novel data will be used to explore new predictive models that will potentially indicate early warnings of oncoming cognitive decline.

超过580万美国人患有痴呆症，有十分之一的美国人患有诊断 阿尔茨海默氏病和阿尔茨海默氏症相关的痴呆症（AD/ADRD）。没有用于AD和药物治疗的治疗方法 对疾病的病程和症状的总体影响很小。没有诊断工具可以可靠地识别 处于疾病初期的人们。拥有广告/ADRD的个人的生活质量需求与 没有痴呆症的人，包括成功完成日常生活的常规活动和维持的能力 满足独立性和自主权的水平。但是，在逐渐减少内存和 对自我保健的能力恶化，拥有广告/ADRD的个人在独立决策中经历了渐进的损失 - 提高能力和生活质量。社会隔离和动画是这种情况的残疾后果。这 Maphabittm System（MHS）是屡获殊荣的（NIH/NIA第一名Eureka奖），获得了基于神经科学的专利 辅助技术应用程序，可帮助记忆力受损的日常生活活动，维护其 独立，并改善用户的整体生活质量。 MHS产品利用视觉映射科学为 在广告设置中提示适当的行为。在此SBIR II期应用程序中，Maphabit将进一步开发实用程序和 MHS视觉映射技术的有效性，以增强其商业化和营销潜力 增加其“粘性”（即，与应用程序互动的动机），并建立更具实质性的证据基础 它对改善生活质量和日常运作的影响。我们提出了两个具体目标：目标1 - Maphabit将会 通过补充其视觉映射软件，优化AD/ADRD个人的用户体验 游戏化技术通过与可满足医疗保健需求的虚拟现实和应用内游戏公司的合作伙伴关系。 将开发社交网络（通过与朋友和其他居民进行游戏）来建立社交连通性和 增加竞争，众所周知，这两者都会增加“粘性”，并保持用户的参与和动力 遵守行为干预措施，例如ADL。我们将进行为期6个月的临床试验，以评估行为结果。 AIM 2 - Maphabit将使用来自生物识别和行为数据流的生物识别和行为数据流进行预测分析框架 个人使用视觉地图执行ADL。与医疗保健人工智能公司合作， 我们将进行一项观察队列研究，以收集和整合来自15个二元组的16个月数据流 多个数据源，包括使用MHS的游戏化和社交网络，标准化神经心理学 对患者的评估以及护理人员的评估。这些新颖的数据将用于探索新的预测 可能表明认知能力下降的早期警告的模型。