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，每10名65岁以上的美国人中就有一人被诊断患有痴呆症 阿尔茨海默病和阿尔茨海默病相关痴呆(AD/ADRD)。阿尔茨海默病没有治愈的方法和药物治疗 对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 处于疾病早期阶段的人。AD/ADRD患者的生活质量需求与 无痴呆症的个人，包括成功完成日常生活和维持日常活动的能力 令人满意的独立性和自主性。然而，在记忆逐渐减少的背景下， 自理能力恶化，患有AD/ADRD的患者在独立决策方面逐渐丧失- 创造能力和生活质量。社会孤立和焦虑是这种情况的令人无法接受的后果。这个 MapHabitTM系统(MHS)，是一个获奖的(NIH/NIA第一名尤里卡奖)专利，以神经科学为基础 帮助记忆力受损的人完成日常生活活动的辅助技术应用程序，保持他们的 独立性，并提高用户的整体生活质量。MHS产品利用视觉映射的科学来 在AD设置中提示适当的行为。在此SBIR第二阶段应用程序中，MapHabit将进一步开发实用程序和 MHS视觉地图技术通过以下方式提高其商业化和营销潜力的有效性 增加其“粘性”(即参与应用程序的动机)，并为以下方面建立更实质性的证据基础 它对改善生活质量和日常功能的影响。我们提出两个具体目标：目标1-MapHabit将 通过为其可视化地图软件添加以下功能，优化AD/ADRD患者的用户体验 通过与一家服务于医疗保健需求的虚拟现实和应用内游戏公司合作，实现游戏化技术。 将开发社交网络(通过与朋友和其他居民进行游戏)，以建立社交连接和 增加竞争，众所周知，这两种方式都会增加“粘性”，并保持用户的参与度和积极性 坚持ADLS等行为干预措施。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 个人使用视觉地图来执行他们的ADL。与一家医疗保健人工智能公司合作 我们将进行一项观察性队列研究，以收集和整合来自15个二元组的16个月的数据流 多个数据来源，包括使用MHS的游戏化和社交网络，标准化了神经心理学 对病人的评估，以及来自护理人员的评估。这些新颖的数据将被用来探索新的预测 这些模型可能预示着即将到来的认知衰退的早期预警。