Smart Home Devices for Supporting Older Adults: A Systematic Review
Bibliographic record
Abstract
Smart home devices have great potential for supporting older adults’ health, safety, and independent living. Past reviews have identified only a few studies on the use of smart home devices for older adults and reported low technology readiness levels for the devices. This article presents a systematic literature review to identify the devices that have been used in studies with older adults, the setting in which those devices have been tested, the evaluation methods of the existing user studies, and the limitations. [Method] ACM DL, Scopus, PubMed, and IEEE Xplore were searched for a set of different keywords that included smart home sensors and older adults. The search was limited to “past ten years" (from the search date). Articles written in English that included user studies evaluating smart home devices with older adults were included. PRISMA guidelines were followed. [Results] 3847 unique articles were identified, 48 of which were included in the review. The articles represented research from a large range of countries. The majority of the studies evaluated the devices in participants’ homes, followed by research lab settings. A few articles used other settings such as care centres and hospitals. The studies mainly evaluated the performance of the systems, followed by users’ evaluations, such as perceptions and acceptance. Many studies had long-term interactions (more than a month). [Conclusion] there are still limited studies on the impact and benefits of smart home devices on older adults’ quality of life, health, or well-being. Future studies are needed to better understand these benefits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".