Establishing an Artificially Intelligent Framework for Improving Therapeutic Alliance with Obese African American Youth and Caregivers through Multimodal Monitoring of Empathetic Accuracy and Interper

建立人工智能框架，通过共情准确性和 Interper 的多模式监测来改善肥胖非裔美国青年和护理人员的治疗联盟

• 批准号: 10710204
• 负责人: PHILLIPPE Belton CUNNINGHAM
• 金额: $ 52.26万
• 依托单位: MEDICAL UNIVERSITY OF SOUTH CAROLINA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710204
• 关键词: Acceleration; Accounting; Address; Adolescent; Affect; African American; African American population; Age; Agreement; Applied Research; Artificial Intelligence; Basic Science; Behavior; Behavior Therapy; Caregivers; Classification; Client; Clinical; Clinical Skills; Clinical Trials; Community Health Aides; Competence; Cultural Backgrounds; Data; Deterioration; Development; Diabetes Mellitus; Discipline; Disparity; Dropout; Effectiveness; Elements; Emotional Bonds; Empathy; Ensure; Ethnic Population; Evidence based treatment; Family; Feedback; Future; Galvanic Skin Response; Gender; Geographic Locations; Goals; Health; Health Promotion; Health behavior change; Heart Rate; Influentials; Interpersonal Relations; Intervention; Intervention Trial; Laboratories; Link; Maintenance; Measures; Mediating; Methodology; Minority Groups; Modeling; Monitor; Natural Language Processing; Not Hispanic or Latino; Obesity; Outcome; Patient Self-Report; Personality; Physiological; Physiology; Prediction of Response to Therapy; Process; Process Assessment; Provider; Psychotherapy; Public Health; Race; Religion; Research; Research Design; Role; Rupture; Science; Scientist; Services; Sex Orientation; Signal Transduction; Therapeutic; Time; Time Series Analysis; Training; Treatment outcome; Voice; Wireless Technology; Work; Youth; analytical tool; behavior change; biopsychosocial; clinical encounter; clinical translation; cultural values; design; ethnic minority; ethnic minority population; heart rate variability; improved; innovation; machine learning model; minority children; multimodal data; multimodality; obesity treatment; practice setting; premature; primary caregiver; programs; racial population; randomized trial; recruit; repaired; response; skills; therapeutic development; therapy outcome; wearable device; wearable sensor technology

PROJECT SUMMARY/ABSTRACT. Obesity is a significant public health concern among all racial/ethnic groups, ages, gender, and geographic areas, but some groups are more disproportionately affected than others. Ethnic minority adolescents, especially AAs, are disproportionately likely to be obese relative to non-Hispanic White adolescents. The proposed study is designed to improve a comprehensive multicomponent family- based behavioral intervention (FIT Families) delivered by Community Health Workers (CHWs; therapists) for African American (AA) adolescents with obesity (AAAO). This application expands a program of research that has followed the Obesity Related Behavioral Intervention Trials (ORBIT) for developing behavioral treatments, by capitalizing on our recent work using innovative machine-learning (ML) models to thoroughly examine within-session therapy processes, specifically the role of CHW empathy in the development and maintenance of the therapeutic alliance (TA) while treating AAAO and their primary caregivers. TA is considered an essential element of therapy. The TA is composed of three features: 1. Emotional bond between therapist and client; 2. Agreement between the client and therapist on the goals of treatment; and 3. Agreement on the tasks of therapy. TA is a dynamic, bidirectional, and synergistic element of therapy with implications across differences in race, age, and gender of the client and therapist. Therapist empathy is critical in establishing and maintaining a strong TA, with outcomes mediated by the therapeutic alliance. Empathy accounts for more variance in therapy outcome than specific interventions and consistently predicts client change. Unfortunately, analysis of crucial facilitative interpersonal skills such as therapist empathy is technically challenging as the intent of another's behavior is nuanced, contextual (e.g., therapists and client differ in personality, background, cultural values, etc.), and interactive (bidirectional and synergistic); and can be expressed in multiple ways (voice tone, physiology]). To better understand empathy, especially in a cross-race context, requires empirical approaches and analytical tools that can discern empathetic behaviors in actual clinical encounters that produce behavior change. We will operationalize empathy through two measures that are each associated with empathy and treatment outcomes: 1) Empathic accuracy, defined as the CHW's accurate understanding of the client's state; and 2) Interpersonal physiological synchrony (IPS), defined as the correlation between therapist and client IPS over time (i.e., heart rate [HR], HR variability, galvanic skin response, voice features). This study uses these two empathy measures to understand the relationship between empathy, therapeutic alliance, and treatment outcomes. Further, the study will explore how empathy effects change based on the race and gender concordance/discordance of therapist-client dyads. Toward future clinical translation, the audio and physiological data collected will be used to build a M-L classification model for differing degrees of empathy. This model could then be used to provide real-time feedback for therapists training to improve their empathic responses.

项目总结/摘要。肥胖是所有种族/民族的重大公共卫生问题 群体、年龄、性别和地理区域，但有些群体比其他群体受到的影响更大。少数民族青少年，尤其是AA，相对于非西班牙裔白色青少年肥胖的可能性不成比例。拟议的研究旨在改善由社区卫生工作者（CHW;治疗师）为非洲裔美国人（AA）肥胖青少年（AAAO）提供的综合多组分家庭行为干预（FIT家庭）。该应用程序扩展了一项研究计划，该计划遵循肥胖相关行为干预试验（ORBIT），用于开发行为治疗，通过利用我们最近的工作，使用创新的机器学习（ML）模型来彻底检查会话内治疗过程，特别是CHW同理心在治疗AAAO及其主要照顾者的治疗联盟（TA）的发展和维持中的作用。TA被认为是治疗的基本要素。TA由三个特征组成：1.治疗师和客户之间的情感纽带; 2。客户和治疗师之间对治疗目标的协议;和3。就治疗任务达成一致。TA是一种动态的、双向的、协同的治疗元素，其影响跨越了来访者和治疗师的种族、年龄和性别差异。治疗师同理心在建立和维持强大的TA方面至关重要，其结果由治疗联盟介导。共情占更多的方差比具体的干预治疗结果，并始终预测客户的变化。不幸的是，分析关键的促进人际交往技能，如治疗师同情，在技术上是具有挑战性的，因为另一个人的行为的意图是微妙的，上下文（例如，治疗师和来访者在个性、背景、文化价值观等方面不同），和互动（双向和协同）;并可以多种方式表达（语音语调，生理学）。为了更好地理解共情，特别是在跨种族的背景下，需要实证方法和分析工具，可以在实际的临床接触中识别共情行为，从而产生行为改变。我们将通过两个与同理心和治疗结果相关的指标来操作同理心：1）同理心准确性，定义为CHW对客户状态的准确理解; 2）人际生理同步性（IPS），定义为治疗师和客户IPS之间的相关性（即，心率[HR]、HR变异性、皮肤电反应、语音特征）。本研究使用这两个共情措施，以了解共情，治疗联盟和治疗结果之间的关系。此外，该研究将探讨共情如何影响基于种族和性别的一致性/不一致性的治疗师-客户二人组的变化。为了未来的临床翻译，收集的音频和生理数据将用于建立不同程度的同理心的M-L分类模型。然后，该模型可以用于为治疗师培训提供实时反馈，以改善他们的移情反应。