ROBAS: A Multimodal Sensor System for Remote Assessment of Oral Health Behaviors

 描述(申请人提供)：牙病是美国最常见的慢性疾病之一，对个人和社会造成巨大的损失。大量研究将牙病与不良的口腔卫生行为联系起来，如刷牙和用牙线清洁牙齿，但未能充分解释这些假定的因果关系是如何产生的或是如何调节的。传统的回溯性自我报告容易受到许多偏见的影响，缺乏严格测试因果路径假说所需的粒度和科学严谨性。为了解决口腔行为测量的难题，我们组建了一个由科学家和工程师组成的多学科团队，开发了一个远程口腔行为评估系统(ROBAS)，它可以提供客观的、个人水平的和生态有效的口腔卫生行为数据。在我们团队开发的大量研究经验和创新技术的基础上，我们的学术和行业合作伙伴关系将分三个顺序阶段建立、测试、完善和现场验证ROBAS，并实现相应的具体目标。在第一阶段，我们将开发ROBAS，将传感器牙刷与手腕活动记录仪和数据分析系统集成在一起，然后使用计算机化的模拟器和测试对象对其进行台架测试和迭代优化(目标1)。在第二阶段，我们将在32名健康受试者中对ROBAS进行试点测试，以优化其在自然环境中的性能和可用性(目标2)。收集到的关于客观(精确度、有效性、可靠性)、主观(用户满意度、可接受性)和用户表现参数的数据将用于进一步完善ROBAS的有效性、可靠性和功能性。在第三阶段，我们将使用一项随机、对照、交叉研究，将60名高危(物质使用)个人与60名可比对照进行对比，以评估ROBAS在自然环境下对口腔健康行为(OHBs)进行纵向(6个月)评估的能力。具体地说，我们将通过回顾自我报告和生态瞬时评估(EMAS)来测试ROBAS的临床实用性和增量a有效性(目标3)。此外，我们将使用一个明确的理论框架来调查社会行为决定因素、OHBs(通过自我报告、EMAS和ROBAS来衡量)和牙病指标(牙龈炎和牙菌斑)之间的关联(目标4)。在具有不同牙病风险水平的社区受试者(物质使用者和非使用者)中评估ROBAS将有助于我们研究的可译性。紧密协调的学术-产业联盟将确保ROBAS(项目交付成果)的快速产品化和可用性。长期收集颗粒状、生态有效的数据的能力将提供前所未有的机会来开发和严格测试更复杂的口腔健康行为因果模型，并收集关于行为变化过程的基本见解。从传统的救援驱动的牙科护理模式向基于传感器启用的行为分析的主动预防方法的范式转变，有可能从根本上改变牙科护理的提供方式。同样重要的是，我们项目的技术创新将在各种社会、行为和临床环境中得到广泛应用。

 DESCRIPTION (provided by applicant): Dental disease is one of the most common chronic diseases in the United States and exacts a substantial personal and societal toll. A large body of research links dental disease to poor oral hygiene behaviors such as inadequate brushing and flossing, but fails to explain adequately how these putative causal relationships arise or are mediated. Traditional retrospective self-reports are susceptible to numerous biases and lack the granularity and scientific rigor needed to rigorously test hypotheses about causal pathways. To solve the vexing challenges of oral behavior measurement, we have assembled a multidisciplinary team of scientists and engineers to develop a Remote Oral Behaviors Assessment System (ROBAS) that can provide objective, individual-level and ecologically-valid data on oral hygiene behaviors. Building on a strong body of research priors and innovative technologies developed by our team, our academic-industry partnership will build, test, refine and field-validate ROBAS in three sequential stages with their corresponding specific aims. In Stage 1, we will develop ROBAS by integrating a sensor-enabled toothbrush with a wrist actigraph and a data analytics system and then bench-test and iteratively refine it using computerized simulators and test subjects (Aim 1). In Stage 2, we will pilot-test ROBAS in a cohort of 32 healthy subjects to optimize its performance and usability in naturalistic settings (Aim 2). Data collected on the objective (precision, validity, reliability), subjective (user satisfaction, acceptability) and user performance parameters will be used to further refine ROBAS validity, reliability and functionality. In Stage 3, we will use a randomized, controlled, crossover study contrasting 60 at-risk (substance using) individuals with 60 comparable controls to evaluate ROBAS's capacity for longitudinal (6 months) assessment of oral health behaviors (OHBs) in naturalistic settings. Specifically, we will test ROBAS's clinical utility and incrementa validity over retrospective self-reports and Ecological Momentary Assessments (EMAs) (Aim 3). Additionally, we will use an articulated theoretical framework to investigate the associations among sociobehavioral determinants, OHBs (measured by self-reports, EMAs and ROBAS), and dental disease indicators (gingivitis and dental plaque) (Aim 4). Evaluating ROBAS in community subjects with different levels of risk for dental disease (substance users and non-users) will facilitate translatability of our research. The tightly coordinated academic-industry alliance will ensure rapid productization and availability of ROBAS (project deliverable). The ability to collect granular, ecologically-valid data for long periods of time will afford unprecedented opportunity to develop and rigorously test more sophisticated causal models of oral health behaviors and glean fundamental insights regarding behavior change processes. The paradigm shift from the conventional rescue- driven model of dental care to a proactive preventive approach, based on sensor-enabled behavioral analysis, has the potential to fundamentally transform the delivery of dental care. Equally important, the technological innovations of our project will have broad application in a variety of social, behavioral and clinical settings.