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，全国代表性的研究，美国的每三个成年人都是肥胖。激励访谈（MI）， 以客户为中心的指示方法来改变行为改变咨询，已广泛适合治疗 肥胖。尽管已发表的元评论中提供了证据，表明MI有效 激活行为变化，人体测量值的变化不那么显着。同时，有几个 访问这种行为保健的障碍，例如某些人的人类查询不足 地理区域，漫长的等待时间，成本和对判断力的恐惧。深度学习的最新进展允许 人工智能（AI）方法扩展到以前被认为是的医疗保健领域 人类专家的独家省，例如临床诊断。但是，行为健康和MI是 尚未从现代AI技术中受益的医学领域，例如中立 会话代理。为了解决此限制，拟议的项目旨在测试可行性和可用性 使用神经对话剂进行自动行为咨询，重点是减肥。 具体来说，我们以深度学习的最新进展为基础，例如对话剂，神经关注， 变形金刚，有监督的政策学习，各种自动编码器和对抗培训，旨在发展 并验证肥胖动机访谈（NAOMI）的神经药物（一种移动设备）（智能手机或 平板电脑）应用于重量减轻的自动MI咨询。 Naomi是基于小说的 神经结构，由可以独立和统称使用的神经网络组成 提出的学习沟通行为的多阶段程序，应在战略上使用 在MI咨询会议的不同阶段，具体取决于观察到的互动并产生 在会话环境中基于的响应并反映了患者的语言。我们将招募40名肥胖成年人， 谁将与Naomi互动，并通过半结构化定性访谈提供反馈。我们 计划在NAOMI的最多4个迭代发展周期，每个患者参与每个患者 循环。每个开发周期后，我们将进行混合方法子研究。定量评估 NAOMI的MI咨询技能将根据参与者互动的成绩单进行 使用MI处理完整性（MITI）编码系统培训的编码器，这是一种评估的标准工具 mi忠诚。将使用框架矩阵分析分析与参与者的定性访谈。这 该项目中提出的方法和技术可以适应其他类型的心理治疗 除了MI和肥胖症外，其他条件以外的干预措施。