Home-Based Digital Technologies to Support Aging-in-Place for Rural African American People With Alzheimer Disease and Their Care Partners: Protocol for a Mixed Methods Feasibility Study
Bibliographic record
Abstract
BACKGROUND: Rural, low-income African American people have the highest Alzheimer disease and related dementia (ADRD) incidence and prevalence rates but have the least access to formal dementia care. To support individuals living with ADRD, growing evidence suggests that remote monitoring technologies can augment existing care by facilitating the completion of activities of daily living (ADLs) and maintaining communication between individuals living with ADRD and their care partners. Despite the success of remote technologies, no studies have investigated the usability, acceptability, and feasibility of these technologies among rural, lower-income African American people living with ADRD and their care partners. Understanding the potential impact of remote monitoring technology on this population can guide the development of tailored aging-in-place interventions. OBJECTIVE: Among rural, low-income African American people living with ADRD and their care partners, our study, "Revolutionizing Empowerment of African Americans' Cognitive Health Through Innovative Technology," aims (1) to identify barriers to aging-in-place, current technology use behaviors, and attitudes toward remote monitoring technologies and (2) to examine the usability, acceptability, and feasibility of deploying a remote monitoring system in the homes of this population for supporting ADLs. METHODS: In total, 10 low-income African American people living with ADRD and their care partners will be recruited from rural cities in South Carolina. Participants will complete a short web-based survey to collect demographics and their knowledge, experience, and comfort with using internet-connected devices, followed by 45- to 60-minute in-depth interviews (objective 1). In phase 2 (ie, objective 2), 10 additional pairs of participants will be recruited to use a remote monitoring system for 18 months. "Weekly Health Update" surveys will measure changes in health and time spent at home. In-depth interviews will be used at 18 months to examine the usability and acceptability of the system. Feasibility will be determined by the percentage of days data are collected across all sensors. RESULTS: This study was approved by the institutional review board in June 2024. Recruitment for objective 1 began in January 2025. To date, 5 persons living with ADRD and their care partners have been recruited, surveyed, and interviewed about their challenges to aging with ADRD or caring for someone with the disease, technology use, and openness to remote monitoring technology. Recruitment for objective 2 is anticipated to begin in fall 2025, and data collection will conclude by May 2027. Results for objectives 1 and 2 are expected to be published in fall 2026 and winter 2027, respectively. CONCLUSIONS: Findings from the Revolutionizing Empowerment of African Americans' Cognitive Health Through Innovative Technology study will contribute to the refinement of the Collaborative Aging Research Using Technology platform, a multisensor remote monitoring system to support the ADLs for low-income, rural-dwelling African American people living with ADRD. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/78623.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.058 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".