Automated Coding of eCoaching Exchanges to Promote Healthier Eating

不良的饮食习惯，特别是水果和蔬菜摄入量低，是一个日益严重的公共卫生问题， 特别是在21-30岁的年轻人中，被称为Y世代（GenY）。GenY的不良饮食习惯 与肥胖症和许多慢性疾病的发病有关，如2型糖尿病， 预测健康状况和预期寿命。因此，有必要制定有效的干预措施， 改善Geny的饮食习惯。MENU GenY是一种基于计算机的干预措施，旨在鼓励增加水果产量， 蔬菜的摄入量。MENU GenY的一个关键组成部分是个性化的eCoaching。eCoaches使用 电子邮件提供激励增强辅导，以鼓励健康饮食，以原则为基础， 动机访谈（MI），一种基于证据的沟通技术，以增加内在动机 和自我效能感来改变行为MI模型假定咨询师使用“MI一致” 通信技术负责通过患者“改变谈话”（即， 关于自己的愿望，能力，原因，需要或承诺改变行为的陈述）。越来越 大量的经验证据将改变谈话与行为改变联系起来，但研究确定了具体的提供者， 引发患者改变谈话的行为仅限于特定人群（主要是滥用药物的成年人 和一些青少年的研究）。确定与行为改变相关的具体沟通策略 并将这些策略整合到基于沟通的干预措施中（例如，简短的，增强动力的 在各种环境或公共卫生倡议中提供的干预措施）可以增加这些干预措施的 力量然而，这项研究的一个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型的，需要人为（主观）的迭代过程 文本的解释。快速发展的计算技术，特别是机器学习结合 与分类模型，提供了一个独特的机会，以加快这一进程。我们的研究小组 最近将基于机器学习的数据挖掘模型应用于类似的通信数据。我们自动化了 简单的通信编码方案，以表征患者通信，并实现了可比的准确性 人类程序员这项研究的目标是利用创新的计算机科学机器学习， 分类模型，以完全自动化eCoach-patient中的通信编码过程和链接模式 增加水果和蔬菜的摄入量。我们建议对收集的数据进行二次分析 NICHD随机临床试验（R 01 HD 067314）。样本是160名GenY成员， 城市和农村环境（底特律大都市区和宾夕法尼亚州农村）， 基线和3个月。我们经过验证的方法将加快以结果为导向的沟通的步伐 研究并确定与健康饮食相关的有效沟通策略。这些发现将用于 定制干预措施和公共卫生信息，并开发自动化电子辅导。

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.