Independent Living for Older Adults with Cognitive Impairment: A Narrative Review of Stakeholder Perceptions and Experiences with Assistive and Socially Assistive Robots
Bibliographic record
Abstract
Background: (1)Alzheimer's disease and related dementias (ADRD) are a major cause of mortality among older adults globally. The cognitive decline associated with ADRD often reduces individuals' ability to live independently over time, increasing reliance on caregivers. Assistive and socially assistive robots offer a promising means of supporting independent living. This narrative review examined how older adults with ADRD, their caregivers, and healthcare providers perceive and experience interactions with robots. Methods: (2)Guided by the Population, Phenomenon of Interest, and Context (PICo) framework, five databases were searched. Sixteen studies met the inclusion criteria. Extracted data were summarized, and a convergent synthesis integrated qualitative and quantitative findings. Results: (3)Drawing on content analysis, the qualitative findings were organized into three domains: user perceptions and experiences, barriers to adoption, and suggestions for improvement. Quantitative results emphasized usability, usefulness, acceptance, satisfaction, feature preferences, and barriers. While most stakeholders viewed robots as beneficial, acceptance was shaped by factors such as design features, timing of introduction, familiarity with technology, and perceived need. Conclusions: (4)This review highlights priorities for future research and development, including personalization, ethical safeguards, and caregiver integration, to improve the acceptance and effectiveness of robot-assisted support for individuals with cognitive impairment.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".