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.

据报道，肥胖是美国最重要的医疗和公共卫生问题之一。 最近的全国代表性研究显示，美国有三分之一的成年人患有肥胖症。 以客户为中心和指导性的行为改变咨询方法已被广泛应用于治疗 尽管已发表的荟萃评论中提供的证据表明 MI 在以下方面有效： 激活行为变化，人体测量变化不太显着，同时还有几个。 获得此类行为保健的机会，例如缺乏人类咨询师在某些方面存在障碍 地理区域、漫长的等待时间、成本和对判断的恐惧使得深度学习的最新进展成为可能。 人工智能（AI）方法扩展到医疗保健领域，这些领域以前被认为是 然而，行为健康和心肌梗死等领域是人类专家的专属领域。 尚未从现代人工智能技术中实质性受益的医学领域，例如神经 为了解决这个限制，拟议的项目旨在测试可行性和可用性。 使用神经会话代理进行自动行为咨询，重点关注减肥。 具体来说，我们以深度学习的最新进展为基础，例如对话代理、神经注意力、 变压器、监督策略学习、变分自动编码器和对抗性训练，旨在开发 并验证肥胖动机访谈神经代理 (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，NAOMI 是基于一种新颖的。 神经架构，由可以独立和集体训练的神经网络组成 拟议的学习沟通行为的多阶段程序，应战略性地利用 在 MI 咨询会议的不同阶段，根据观察到的互动，产生 基于会话上下文并反映患者语言的回答我们将招募 40 名肥胖成年人， 他们将与 NAOMI 互动并通过半结构化定性访谈提供反馈。 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 我们将在每个开发周期后进行混合方法的子研究。 NAOMI 的 MI 咨询技能将根据参与者互动的记录进行 编码员接受过使用 MI 治疗完整性 (MITI) 编码系统的培训，这是一种评估标准工具 MI 保真度。将使用框架矩阵分析对参与者进行定性访谈。 该项目提出的方法和技术可以适用于其他类型的心理治疗 除心肌梗死和肥胖以外的其他疾病的干预措施。