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.
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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 | 0.069 | 0.247 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".