Exploring the Role of Active Assisted Living in the Continuum of Care for Older Adults
Bibliographic record
Abstract
Abstract Background and objectives Active Assisted Living (AAL) refers to internet-connected systems designed to improve quality of life, aid in independence, and create healthier lifestyles. As the population of older adults grows, there is a pressing need for additional supports in their daily lives and for non-intrusive, continuous, adaptable, and reliable health monitoring tools. AAL has great potential to support these efforts, but additional work is required to address the feasibility of the integration of AAL into care. The objective of this project is to address core issues with AAL system implementation, including user concerns, data governance, and clinical considerations. Methods To understand the concerns and opportunities regarding AAL, 18 group interviews were held with stakeholders representing different parts of an AAL ecosystem. Each group comprising several participants from the same organization. These were categorized as (1) care organizations, (2) tech developers, (3) tech integrators, and (4) potential care recipients or patient advocacy groups. Thematic analysis was then performed to identify key concerns. Results AAL systems may lead to improved support for care recipients through more comprehensive monitoring and alerting, greater confidence in aging-in-place, and increased care recipient empowerment. However, participants also raised concerns regarding the management and monetization of data emerging from AAL systems, as well as general accountability and liability. Conclusions Better role definition is needed regarding who can access data and who is responsible for acting on it. It is important for stakeholders to understand the trade-off between using AAL technologies in care settings and their costs, including loss of patient privacy and control. Key messages • AAL has excellent potential to support confidence in aging-in-place for older adults and their caregivers. • It is critical to acknowledge the trade-off inherent in smart technology use between utility, cost, and encroachment on privacy.
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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 | 0.029 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.003 |
| 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".