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

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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.