Neural Conversational Agent for Automated Weight Loss Counseling

用于自动减肥咨询的神经对话代理

• 批准号: 10668094
• 负责人: Alexander Kotov
• 金额: $ 22.12万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-08 至 2025-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10668094
• 关键词: Address; Adult; Architecture; Area; Artificial Intelligence; Assessment tool; Attention; Behavior; Behavior Therapy; Behavioral; Body Weight decreased; Cellular Phone; Client; Code; Collection; Communications Media; Complement; Counseling; Data Analyses; Development; Family Practice; Feedback; Foundations; Fright; Future; Geographic Locations; Gestures; Health; Health Personnel; Health Sciences; Health education; Healthcare; Human; Image; Intervention; Interview; Judgment; Language; Learning; Life; Literature; Medical; Medicine; Methods; Modernization; Music; Obesity; Participant; Patient Participation; Patients; Policies; Posture; Procedures; Professional counselor; Province; Public Health; Publishing; Quantitative Evaluations; Research Project Grants; Service delivery model; Services; Speech; Standardization; Structure; Tablets; Techniques; Technology; Text; Time; Training; Transcript; United States; Universities; Validation; Visual; Wait Time; Weight; Work; adult obesity; animation; artificial intelligence method; autoencoder; barrier to care; behavior change; behavioral health; clinical diagnosis; clinical diagnostics; communication behavior; computer science; computerized; cost; deep learning; feasibility testing; gaze; handheld mobile device; improved; innovation; intelligent agent; learning network; motivational enhancement therapy; neural; neural network; neurodevelopment; novel; obese patients; patient engagement; practice-based research network; primary care practice; provider adoption; recruit; response; skills; theories; therapeutically effective; usability; virtual coach; weight loss intervention

Obesity is one of the most important medical and public health problems in the United States. According to recent nationally representative studies, every third adult in the U.S. is obese. Motivational Interviewing (MI), a client-centered and directive approach to behavior change counseling, has been widely adapted for treating obesity. Despite the evidence presented in published meta-reviews that suggests that MI is effective at activating behavioral changes, anthropometric changes are less significant. At the same time, there are several access barriers to this type of behavioral health care, such as shortage of human counselors in certain geographical areas, long wait times, cost, and fear of judgment. Recent advances in deep learning have allowed artificial intelligence (AI) methods to expand into the areas of health care that were previously thought to be the exclusive province of human experts, such as clinical diagnostics. Behavioral health and MI, however, are the areas of medicine that have not yet substantially benefitted from modern AI technologies, such as neural conversational agents. To address this limitation, the proposed project aims to test the feasibility and usability of using neural conversational agents for automated behavioral counseling with a focus on weight loss. Specifically, we build on recent advances in deep learning, such as conversational agents, neural attention, transformers, supervised policy learning, variational autoencoders and adversarial training, and aim to develop and validate Neural Agent for Obesity Motivational Interviewing (NAOMI), a mobile device (smartphone or tablet) application to conduct automated MI counseling focused on weight loss. NAOMI is based on a novel neural architecture, which consists of neural networks that can be independently and collectively trained using the proposed multi-stage procedure to learn communication behaviors, which should be strategically utilized during different stages of an MI counseling session depending on the observed interactions and generate responses that are grounded in session context and reflect patient’s language. We will recruit 40 obese adults, who will interact with NAOMI and provide their feedback through semi-structured qualitative interviews. We plan on conducting at most 4 iterative development cycles of NAOMI with 10 patients participating in each cycle. We will conduct a mixed-methods sub-study after each development cycle. Quantitative evaluation of NAOMI’s MI counseling skills will be conducted based on the transcripts of participants’ interactions by a coder trained in using the MI Treatment Integrity (MITI) coding system, a standard instrument for assessing MI fidelity. Qualitative interviews with the participants will be analyzed using Framework Matrix Analysis. The methods and techniques proposed in this project can be adapted to other types of psychotherapeutic interventions besides MI and to other conditions besides obesity.

肥胖是美国最重要的医疗和公共卫生问题之一。根据 最近具有全国代表性的研究表明，美国每三个成年人中就有一个肥胖。动机访谈(MI)， 一种以客户为中心的指导行为改变咨询的方法，已被广泛用于治疗 肥胖。尽管在已发表的元评中提出的证据表明MI在 激活行为变化，人体测量学变化就不那么显著了。同时，还有几个 获得这种类型的行为保健的障碍，例如在某些情况下缺乏人类咨询师 地理位置、漫长的等待时间、成本和对判断的恐惧。深度学习的最新进展使 人工智能(AI)方法扩展到以前认为的医疗保健领域 人类专家的专属领域，如临床诊断学。然而，行为健康和MI是 尚未从现代人工智能技术中实质性受益的医学领域，如神经 对话代理。为了解决这一限制，拟议的项目旨在测试可行性和可用性 使用神经对话代理进行自动化行为咨询，重点是减肥。 具体地说，我们建立在深度学习的最新进展基础上，例如对话代理、神经注意力、 Transformers、有监督的策略学习、可变自动编码器和对抗性培训，目标是开发 和验证肥胖神经代理动机访谈(Naomi)、移动设备(智能手机或 平板电脑)应用程序进行专注于减肥的自动化MI咨询。娜奥米是根据一部小说改编的 神经体系结构，它由神经网络组成，这些神经网络可以使用 建议的学习沟通行为的多阶段程序，应该战略性地利用 在MI咨询会话的不同阶段期间，根据观察到的交互并生成 以会议背景为基础并反映患者语言的回应。我们将招募40名肥胖成年人， 世卫组织将与娜奥米互动，并通过半结构化的定性访谈提供他们的反馈。我们 计划进行最多4个Naomi迭代开发周期，每个周期有10名患者参与 周而复始。我们将在每个开发周期之后进行混合方法的子研究。定量化评价 Naomi的MI咨询技能将基于参与者的互动记录进行 接受过使用MI治疗完整性(MITI)编码系统的培训的编码员，这是评估的标准工具 我的忠诚。对参与者的定性访谈将使用框架矩阵分析进行分析。这个 本课题提出的方法和技术可适用于其他类型的心理治疗 除心肌梗死外的干预措施，以及除肥胖以外的其他情况。