Smart Home Technology Integration in Home Modification Programs Serving Older Adults: A Focus Group Study with Program Grantees (Preprint)
Bibliographic record
Abstract
Background: As people age over the coming decades, demand for in-home support and other interventions, such as home modifications, to help older adults age in place successfully, is also expected to rise. Smart home technologies have the potential to enhance aging in place by complementing traditional home modifications; however, adoption within federally funded home modification programs remains limited. Objective: This study explored grantees' perspectives on the Older Adults Home Modification Program (OAHMP) to understand current practices, perceived benefits, barriers, and strategies for integrating smart home technologies into home modification services. Methods: An exploratory qualitative study was conducted with staff, occupational therapists, and home builders from 3 OAHMP grantee organizations. A 1.5-hour virtual focus group was held and thematically analyzed using a deductive approach grounded in the discussion agenda. Results: A total of 11 participants reported early adoption of select smart devices-most commonly smart speakers, doorbell cameras, motion-activated lights, and smart plugs-to enhance home safety, communication, and independence. Barriers to broader implementation emerged at three levels: (1) older adults' digital literacy, privacy concerns, and device maintenance burden; (2) contextual constraints, such as unreliable internet in rural areas; and (3) organizational limitations, including training needs, staffing capacity, and funding challenges. Participants emphasized the importance of progressive adoption, hands-on training, and low-maintenance technology. In addition, partnerships with university educational programs were well established among selected grantees to provide technology training resources for older adults. To inform a reasonable technology selection for home modifications, criteria were proposed across 5 domains: installation, usability, accessibility, sustainability, and security and privacy. Conclusions: Integrating smart home technologies into home modification programs provides a scalable, cost-effective opportunity to improve aging in place outcomes. Policy support, workforce training, valid selection criteria, and sustainable funding models are needed to promote equitable adoption across OAHMP and similar federally supported programs.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".