I-Corps: A Wearable, Biomarker-Tracking Device Platform Using Machine Learning and Predictive Technology for Positive Behavior Change

I-Corps：可穿戴生物标记跟踪设备平台，利用机器学习和预测技术实现积极行为改变

• 批准号: 1850040
• 负责人: Christina Mair
• 金额: $ 5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-15 至 2020-02-29
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1850040&HistoricalAwards=false
• 关键词: Corps; Wearable; Biomarker; Tracking; Device

The broader impact/commercial potential of this I-Corps project revolves around the human brain?s proclivity towards substances detrimental to human health and wellbeing, such as dangerous amounts of sugar, salt, and drugs, which is causing global health and safety risks. Inability to avoid these temptations shortens lifespans, increases healthcare costs, and strains resources. This team will use machine learning and pattern recognition AI to identify and react to factors that result in destructive human behaviors. While the initial intent of our product is to provide potential customers with a tool to detect and prevent addiction overdose and relapse, offer interventions of supervised individuals, conserve emergency response resources, and save taxpayer money, this unsupervised learning AI could, in subsequent iterations, be used to identify and react to any behavior -- such as unsafe driving, binge eating, or anger management. Helping people identify the precursors to their behaviors could serve as a type of biofeedback that gives them and their support networks timely and individualized insights, preventions, and interventions -- some of which could be implemented by Internet of Things enabled devices (e.g. locking a car?s ignition; calling a sponsor; playing calm music; etc.), resulting in cost and health benefits across many populations.This I-Corps project is focused on a patent-pending platform to predict and prevent addiction relapses and overdoses by treating those in recovery with timely interventions using wearable devices and artificial intelligence. Detecting craving states is supported by past and current research, and there is scientific justification for gathering smartphone usage and biometric wearable data on drug users. Researchers have used the combination of smartphone usage and wearables data to predict binge drinking behavior and we plan to apply this research to opioid use. Additionally, cocaine and opioid research uses the same wearable device as our studies. We expand upon current research by combining just-in-time smartphone-based interventions for people in recovery for opioid use disorders with pattern-detection predictive models that will be trained at the population level and refined at the individual level. We will gather data on all relevant factors that determine drug relapse risk state: physiology, smartphone usage, location data, self-reports, and support-network-reports. Once a high-risk state is detected, a smartphone app-based intervention focused on behavior change will then be implemented in a timely and customizable manner.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这个I-Corps项目的更广泛的影响/商业潜力围绕着人脑？世界卫生组织对有害于人类健康和福祉的物质的倾向，如危险量的糖，盐和药物，这导致全球健康和安全风险。无法避免这些诱惑会缩短寿命，增加医疗成本，并使资源紧张。该团队将使用机器学习和模式识别AI来识别和应对导致破坏性人类行为的因素。虽然我们产品的最初目的是为潜在客户提供一种工具来检测和预防成瘾过量和复发，提供受监督的个人干预措施，节省应急响应资源，并节省纳税人的钱，但这种无监督的学习AI可以在随后的迭代中用于识别和应对任何行为-例如不安全驾驶，暴饮暴食或愤怒管理。帮助人们识别其行为的前兆可以作为一种生物反馈，为他们及其支持网络提供及时、个性化的见解、预防和干预措施--其中一些可以通过物联网设备来实施（例如锁车？点火;打电话给赞助商;播放平静的音乐;等等），这个I-Corps项目的重点是一个正在申请专利的平台，通过使用可穿戴设备和人工智能及时干预治疗康复中的人，预测和预防成瘾复发和过量。 检测渴望状态得到了过去和当前研究的支持，收集吸毒者的智能手机使用和生物识别可穿戴数据也有科学依据。研究人员已经使用智能手机使用和可穿戴设备数据的组合来预测酗酒行为，我们计划将这项研究应用于阿片类药物的使用。此外，可卡因和阿片类药物的研究与我们的研究使用相同的可穿戴设备。我们扩展了目前的研究，将基于智能手机的即时干预措施与模式检测预测模型相结合，这些模型将在人群水平上进行训练，并在个人水平上进行改进。我们将收集有关决定药物复发风险状态的所有相关因素的数据：生理学，智能手机使用，位置数据，自我报告和支持网络报告。一旦检测到高风险状态，将以及时和可定制的方式实施基于智能手机应用程序的干预措施，重点关注行为改变。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。