Neural Conversational Agent for Automated Weight Loss Counseling

肥胖是美国最重要的医学和公共卫生问题之一。根据 最近的全国代表性研究表明，美国三分之一的成年人肥胖。动机面试（MI）， 一个以客户为中心和指导性的行为改变咨询方法，已被广泛适用于治疗 肥胖尽管发表的荟萃综述中提出的证据表明，MI在以下方面是有效的： 激活行为变化，人体测量变化不太显著。与此同时， 获得这种类型的行为卫生保健的障碍，例如某些地区缺乏人类咨询师， 地理区域、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得 人工智能（AI）方法扩展到以前被认为是 人类专家的专属领域，如临床诊断。然而，行为健康和MI是 尚未从现代人工智能技术中获益的医学领域，如神经 会话代理。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行为咨询，重点是减肥。 具体来说，我们建立在深度学习的最新进展，如会话代理，神经注意力， transformers，监督策略学习，变分自编码器和对抗训练，旨在开发 并验证神经代理肥胖动机面试（NAOMI），一个移动终端（智能手机或 平板电脑）应用程序进行自动化MI咨询，重点是减肥。NAOMI是根据小说改编的 神经架构，由神经网络组成，可以使用 建议的多阶段程序，以学习沟通行为，这应该是战略性的利用 在MI咨询会话的不同阶段，根据观察到的相互作用， 基于会话上下文并反映患者语言的响应。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。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 周期我们将在每个开发周期后进行混合方法子研究。定量评价 NAOMI的MI咨询技能将根据参与者的互动记录进行， 编码员接受过使用MI治疗完整性（MITI）编码系统的培训，MITI编码系统是一种用于评估 我的忠诚。与参与者的定性访谈将使用框架矩阵分析进行分析。的 本项目提出的方法和技术可以适用于其他类型的心理治疗。 除了MI和肥胖症以外的其他疾病。

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.