Tailoring Responses to ADRD Caregivers' InfOrmation wants (TRACO) through human-machine collaboration

通过人机协作定制响应 ADRD 护理人员的信息需求 (TRACO)

• 批准号: 10670479
• 负责人: Daqing He
• 金额: $ 56.14万
• 依托单位: UNIVERSITY OF TEXAS AT AUSTIN
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2024-09-29
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10670479
• 关键词: Address; Affect; Alzheimer' s Disease; Alzheimer' s disease related dementia; Belief; COVID-19 pandemic; Caregiver support; Caregivers; Caring; Characteristics; Collaborations; Communication; Data Sources; Decision Making; Dementia; Dementia caregivers; Development; Dimensions; Education; Elderly; Ensure; Ethnic Origin; Family member; Gender; Goals; Health; Health Technology; Health education; Healthcare; Hispanic; Hour; Human; Human Rights; Information Resources; Internet; Intervention; Knowledge; Language; Left; Literature; Living Arrangement; Mental Depression; Meta-Analysis; Not Hispanic or Latino; Output; Patients; Personal Satisfaction; Persons; Pilot Projects; Privacy; Process; Public Health; Quality of Care; Race; Reporting; Research; Resources; Risk; Sampling Studies; Security; Source; Stress; Symptoms; System; Technology; Testing; Work; base; caregiver stress; caregiving; dementia care; dementia caregiving; design; digital; digital health; diversity and equity; diversity and inclusion; efficacy evaluation; ethnic minority; ethnic minority population; experience; handheld mobile device; health care availability; improved; innovation; knowledge base; large scale data; literacy; mHealth; machine learning algorithm; mobile application; pandemic disease; preference; prototype; public health relevance; racial and ethnic; response; social media; usability; user-friendly

Abstract Alzheimer’s disease and its related dementias (ADRD) are a major public health concern. Caregiving for persons with ADRD is stressful and can severely affect the caregiver’s health and well-being. Yet caregivers report that they have been unable to obtain sufficient information about challenges or care options through conventional sources. Existing studies’ samples consisted of predominately Whites. Yet people from ethnic minority groups may prefer different care, and their caregiving experiences differ. Digital technology has increasingly been used in health care, and the COVID-19 pandemic has made such use of technology even more necessary than before. As internet access is arguably becoming a basic human right, it is critical that the needs and preferences of diverse caregivers are well understood and fully integrated into digital health technology’s design and development to ensure equity and inclusion. Our long-term goal is to help diverse caregivers obtain information tailored to their needs and situations to enhance the quality of care and reduce caregivers’ stress. Toward this goal, we propose to develop the Tailoring Responses to ADRD Caregivers' InfOrmation wants (TRACO) system through human-machine collaboration. TRACO will include two components: (1) a backend that handles computation and storage (i.e., a tailoring engine), and (2) a frontend installed on caregivers’ mobile devices, that is, a mobile application (app) interface. Our specific aims are: · Aim 1: To develop an ADRD caregiving knowledge base and validate it with clinicians through human- machine collaboration. This knowledge base will feature the integration of input from 2 large-scale data sources: (1) the types of information that caregivers want to have as extracted and organized via a large number of social media posts; and (2) existing high-quality information resources for caregivers. · Aim 2: To develop and validate a tailoring engine that uses the generic knowledge presented in the knowledge base developed in Aim 1 as input to provide tailored responses as output. Tailoring will be based on factors that promote diversity and inclusion; these include: (1) caregivers’ characteristics (e.g., age, race/ethnicity, gender, education, relationship), (2) caregiving scenarios (e.g., patients’ stages, symptoms, and living arrangements), and (3) caregivers’ expressed desire for types of health information. · Aim 3: To develop TRACO prototype as a mobile app interface on top of the tailoring engine and evaluate its quality and feasibility. We will use the Mobile App Development and Assessment Guide (MAG)23 to guide the development of our prototype and assessment of its quality (along the dimensions of usability, privacy, security, etc.; see Approach for more details). We will also assess the feasibility of diverse caregivers’ daily use of TRACO in their natural settings. Based on results of these assessments, we will revise the prototype to ensure user-friendly, effective, efficient tailoring.

摘要 阿尔茨海默病及其相关痴呆症（ADRD）是一个主要的公共卫生问题。照顾 患有ADRD的人压力很大，可能严重影响护理人员的健康和福祉。然而， 报告说，他们无法通过以下途径获得有关挑战或护理方案的充分信息： 传统来源。现有研究的样本主要由白人组成。然而， 少数群体可能喜欢不同的护理，他们的生活经历也不同。数字技术 越来越多地用于医疗保健，COVID-19大流行甚至使这种技术的使用 比以前更有必要。由于互联网接入可以说是一项基本人权， 充分了解不同护理人员的需求和偏好，并将其完全融入数字医疗 技术的设计和开发，以确保公平和包容。我们的长期目标是帮助多样化的 护理人员获得适合其需要和情况的信息，以提高护理质量， 照顾者的压力为了实现这一目标，我们建议制定针对ADRD护理人员的定制响应， 信息需求（TRACO）系统通过人机协作。TRACO将包括两个 组件：（1）处理计算和存储的后端（即，裁剪引擎），以及（2）前端 安装在护理人员的移动的设备上，即移动的应用（app）界面。我们的具体目标是： ·目标1：开发ADRD诊断知识库，并通过人类- 机器协作。该知识库将整合来自2个大规模数据的输入 来源：（1）护理人员希望通过大型 社交媒体帖子的数量;（2）现有的高质量信息资源。 ·目标2：开发和验证一个裁剪引擎，该引擎使用 在目标1中建立知识库，作为投入，提供有针对性的回应作为产出。裁缝将是 基于促进多样性和包容性的因素;这些因素包括：（1）照顾者的特征（例如， 年龄、种族/民族、性别、教育、关系），（2）两种情况（例如，患者的阶段， 症状，和生活安排），和（3）照顾者表达的愿望类型的健康信息。 ·目标3：开发TRACO原型，作为定制引擎之上的移动的应用程序界面，并评估 质量和可行性。我们将使用《移动的应用程序开发和评估指南》（MAG）23， 指导我们的原型开发和质量评估（沿着可用性的维度， 隐私、安全等;更多细节见方法）。我们还将评估各种 护理人员在自然环境中日常使用TRACO。根据这些评估结果，我们将 修改原型，以确保用户友好，有效，高效的剪裁。