Automated Coding of eCoaching Exchanges to Promote Healthier Eating

电子教练交流的自动编码以促进健康饮食

• 批准号: 9336295
• 负责人: April Marie Idalski Carcone
• 金额: $ 18.72万
• 依托单位: WAYNE STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9336295
• 关键词: 21 year old; Adolescent; Adult; Alcohol consumption; Area; Artificial Intelligence; Automation; Behavior; Caring; Child; Chronic; Chronic Disease; Classification; Clinical; Clinical Services; Code; Communication; Communication Research; Computer software; Computers; Counseling; Data; Data Analyses; Development; Diet; Diet Habits; Dietary Practices; Eating; Effectiveness; Electronic Mail; Food; Forensic Medicine; Future Generations; Gender; Generations; Goals; Health Promotion; Health Status; Health behavior change; Healthy Eating; Human; Image; Individual; Intake; Intervention; Investigator-Initiated Research; Judgment; Lead; Life Expectancy; Link; Machine Learning; Manuals; Mediator of activation protein; Medical; Modeling; Motivation; National Institute of Child Health and Human Development; Non-Insulin-Dependent Diabetes Mellitus; Obesity; Outcome; Outcome Measure; Parents; Patients; Pattern; Pennsylvania; Performance; Pharmacy facility; Physical activity; Population; Process; Professional counselor; Provider; Public Health; Qualitative Methods; Randomized Clinical Trials; Research; Research Methodology; Research Project Grants; Resources; Rural; Sampling; Scheme; Self Efficacy; Smoking; Social Work; Specific qualifier value; Substance abuse problem; Sum; Techniques; Technology; Testing; Text; Time; Visit; Work; age group; base; behavior change; clinical practice; cognitive task; communication behavior; computer science; data mining; digital; effective intervention; emerging adulthood; evidence base; fruits and vegetables; good diet; improved; inner city; innovation; interest; intrinsic motivation; member; metropolitan; motivational enhancement therapy; new technology; point of care; primary outcome; public health intervention; rural setting; satisfaction; secondary analysis; sugar; sweetened beverage; urban setting; young adult

Poor eating habits, particularly low fruit and vegetable intake, is a growing, serious public health concern, particularly among young adults age 21-30, referred to as Generation Y (GenY). GenY's poor dietary practices are associated with the onset of obesity and many chronic diseases, such as type 2 diabetes, as well as declines in predicted health status and life expectancy. Thus, there is a need to develop effective interventions to improve GenY's eating habits. MENU GenY is a computer-based intervention to encourage increased fruit and vegetable intake among GenY. A critical component of MENU GenY is personalized eCoaching. eCoaches use email to deliver motivation-enhancing coaching to encourage healthy eating, grounded in the principles of Motivational Interviewing (MI), an evidence-based communication technique to increase intrinsic motivation and self-efficacy for behavior change. The MI model posits that counselor's use of “MI-consistent” communication techniques are responsible for eliciting behavior change through patient “change talk” (i.e., statements about one's own desire, ability, reasons, need for or commitment to behavior change). A growing body of empirical evidence links change talk to behavior change, but research identifying the specific provider behaviors that elicit patient change talk is limited to specific populations (mainly adults who abuse substances and a couple studies of adolescents). Identifying specific communication strategies linked to behavior change and integrating these strategies into communication-based interventions (e.g., brief, motivation-enhancing interventions delivered in a variety of settings or public health initiatives) can increase these interventions' potency. However, a significant barrier to this research is the qualitative methods traditionally used to analyze the communication process which are resource-intensive, requiring an iterative process of human (subjective) interpretation of text. Rapidly developing computational technologies, specifically machine learning combined with classification models, offer a unique opportunity to accelerate this process. Our research group has recently applied machine learning-based data mining models to similar communication data. We automated a simple communication code scheme to characterize patient communication and achieved accuracy comparable to human coders. The goals of this study are to leverage innovative computer science machine learning and classification models to fully automate the communication coding process and link patterns in eCoach-patient communication to increases in fruit and vegetable intake. We propose a secondary analysis of data collected for a NICHD randomized clinical trial (R01 HD067314). The sample is 160 members of GenY drawn from both urban and rural settings (Detroit metropolitan area and rural Pennsylvania) with outcomes measured at baseline and 3 months. Our validated approach will accelerate the pace of outcomes-oriented communication research and identify effective communication strategies linked to healthy eating. These findings will be used to tailor interventions and public health messages and develop automated eCoaching.

饮食习惯不良，尤其是水果和蔬菜摄入量，是一个日益严重的公共健康问题， 特别是在21-30岁的年轻人中，被称为Y一代（基因）。 Geny的饮食习惯不佳 与肥胖症和许多慢性疾病（例如2型糖尿病）有关，并下降 在预测的健康状况和预期寿命中。那是有必要制定有效的干预措施 改善吉尼的饮食习惯。菜单基因是一种基于计算机的干预措施，可鼓励增加水果和 基因中的蔬菜摄入量。菜单基因的关键组成部分是个性化的生态修复。生态可持续使用 通过电子邮件提供增强动力的教练以鼓励健康的饮食，以此为基础 动机访谈（MI），这是一种基于证据的沟通技术，以增加内在动机 和行为改变的自我效能。 MI模型认为辅导员使用“ MI一致” 沟通技术负责通过患者“变化谈话”引发行为改变（即 关于自己的愿望，能力，原因，需求或对行为改变的承诺的陈述）。成长 经验证据的身体链接将谈话与行为改变改变，但研究确定特定提供者 引起患者变更谈话的行为仅限于特定人群（主要是滥用物质的成年人 以及一些对青少年的研究）。确定与行为改变相关的特定交流策略 并将这些策略整合到基于交流的干预措施中（例如，简短的增强动机 在各种环境或公共卫生计划中提供的干预措施可以增加这些干预措施” 效力。但是，这项研究的重大障碍是传统上用于分析的定性方法 沟通过程是资源密集的，需要人类的迭代过程（主观） 文字的解释。快速开发的计算技术，特别是机器学习的组合 借助分类模型，提供了一个独特的机会来加速这一过程。我们的研究小组有 最近应用机器学习的数据挖掘模型用于类似的通信数据。我们自动化a 简单的通信代码方案以表征患者沟通并获得准确性可比性 给人类编码人员。这项研究的目标是利用创新的计算机科学机器学习和 分类模型以充分自动化通信编码过程和链接ecoach患者中的链接模式 沟通以增加水果和蔬菜摄入量。我们建议对收集的数据进行二次分析 用于NICHD随机临床试验（R01 HD067314）。样本是从两者中抽出的160个基因成员 城市和粗糙的环境（底特律大都会地区和宾夕法尼亚州粗糙的宾夕法尼亚州）的结果 基线和3个月。我们经过验证的方法将加速以结果为导向的交流空间 研究并确定与健康饮食有关的有效沟通策略。这些发现将用于 量身定制干预措施和公共卫生信息，并开发自动生态缓解。

项目成果

Identifying Effective Motivational Interviewing Communication Sequences Using Automated Pattern Analysis.

使用自动模式分析识别有效的动机访谈沟通序列。

• DOI: 10.1007/s41666-018-0037-6
• 发表时间: 2019
• 期刊: Journal of healthcare informatics research
• 影响因子: 5.9
• 作者: Hasan,Mehedi;Carcone,AprilIdalski;Naar,Sylvie;Eggly,Susan;Alexander,GwenL;Hartlieb,KathrynEBrogan;Kotov,Alexander
• 通讯作者: Kotov,Alexander

Deep Neural Architectures for Discourse Segmentation in E-Mail Based Behavioral Interventions.

基于电子邮件的行为干预中的话语分割的深层神经架构。

• 发表时间: 2019
• 期刊: AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
• 作者: Hasan,Mehedi;Kotov,Alexander;Naar,Sylvie;Alexander,GwenL;Carcone,AprilIdalski
• 通讯作者: Carcone,AprilIdalski