Ethical considerations in home monitoring technologies for persons living with cognitive impairment: a scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: As the global population of people living with cognitive impairment grows, Home Monitoring Technologies (HMTs), such as cameras, motion sensors, wearable trackers, and artificial intelligence enabled ones are increasingly used to enhance safety and support aging in place. However, these technologies raise ethical concerns, particularly regarding privacy, autonomy, trust, and transparency. This scoping review explores these ethical implications and identifies key themes to inform future research, practice, and policy development. RESEARCH DESIGN AND METHODS: Following Arksey and O'Malley's scoping review framework, systematic searches were conducted in PubMed, EMBASE, CINAHL, and PsycINFO (Arksey & O'Malley (2005). Scoping studies: Towards a methodological framework. International Journal of Social Research Methodology, 8, 19-32). Studies were included if they examined HMTs for people living with cognitive impairment and addressed ethical concerns. Our eight central themes were derived inductively during data synthesis, and the Rubeis' 4D Risks Framework offered a valuable conceptual scaffold to organize and interpret the broader patterns of ethical risk. RESULTS: A total of 110 publications from 30 countries were reviewed. Ethical concerns were identified in each of the 4 areas of the framework, including privacy violations, loss of autonomy, erosion of trust, and unintended consequences such as social isolation and reduced human interaction. Person-centered design approaches, which engage both people with cognitive impairment and caregivers, were identified as crucial for mitigating risks and fostering ethical implementation. DISCUSSION AND IMPLICATIONS: Findings underscore the need for evidence-informed guidelines that explicitly incorporate ethical frameworks to ensure consideration of the balance of health and safety with autonomy and dignity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".