Perspectives of older adults on the ethics of active assisted living technologies: A Scoping review and conceptual framework
Bibliographic record
Abstract
Introduction Active Assisted Living (AAL) technologies have emerged as a multidisciplinary endeavor driven by the imperative to enhance the lives of older adults. However, users’ perspectives on the ethics of AAL technology use are often secondary considerations. Objectives The objective of this scoping review was to synthesize the literature on users’ perspectives on the ethics of AAL technologies to bridge the gap in both academic and non-academic contexts. Methods This scoping review was conducted comprehensively using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses-Scoping Reviews (PRISMA-ScR) in Covidence, focusing on peer-reviewed publications from 2000 to 2023. Our search strategies integrated concepts of AAL technologies, ethics, and user perspectives. Inclusion criteria encompassed qualitative and mixed methods that obtained users’ perspectives on AAL technologies in smart home communities. A gray literature review was also conducted to retrieve documents for analysis that incorporated guidelines, standardization, and/ or recommendations. Results Five relevant articles utilized qualitative methods ( n = 3) and mixed methods ( n = 2). Privacy and data protection were highlighted, with users expressing concerns about data tracking ( n = 4). Respect for autonomy was emphasized in decision-making regarding technology use ( n = 3). Accessibility issues pertained to missing accommodations for some physical abilities ( n = 2). Diversity and social inclusion were important for social engagement and mental health support ( n = 2). Perceived beneficence was also cited as a factor in improving health outcomes ( n = 3). Transparency and accountability by the AAL technologies implementers were deemed essential for building trust ( n = 1). The gray literature review yielded six relevant documents that included discussions on ethics and the use of AAL-related technologies for older adults. Conclusions Older adults are willing to include wearables and Internet of Things devices in their AAL technologies ecosystem, especially when they co-design the technology, but it is contingent on the implementation of ethical principles such as safety, security, and privacy to counter intrusive monitoring systems. This scoping review has the potential to offer valuable insights to various stakeholders, including tech developers and designers, providers, health policymakers, providers, and regulatory agencies.
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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 | 0.086 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".