Barriers and Facilitators to Older Adults’ Acceptance of Camera-Based Active and Assisted Living Technologies: A Scoping Review
Bibliographic record
Abstract
Background and Objectives: Camera-based active and assisted living (AAL) technologies are an eminent solution to population aging but are frequently rejected by older adults. The factors that influence older adults' acceptance of these technologies remain poorly understood, which may account for their lagging diffusion. This scoping review aimed to identify the barriers and facilitators to older adults' acceptance of camera-based AAL technologies, with a view to facilitating their development and widespread dissemination. Research Design and Methods: MEDLINE, CINAHL, Embase, IEEE Xplore Digital Library, ACM Digital Library, Web of Science, and gray literature databases were searched from inception to June 2024. Publications that reported data on barriers and facilitators to the acceptance of camera-based AAL technologies among community-dwelling older adults aged 60 and above were eligible. Barriers and facilitators were extracted and mapped to the theoretical domains framework, thematically clustered, and narratively summarized. Results: A total of 28 barriers and 19 facilitators were identified across 50 included studies. Dominant barriers concerned the technology's privacy-invasive, obtrusive, and stigmatizing qualities. Salient facilitators included the perceived usefulness of, and older adults' perceived need for, the technology. Discussion and Implications: Results inform practitioners' selection of strategies to promote older adults' acceptance of camera-based AAL technologies. These efforts should transcend the conventional focus on pragmatics and give credence to psychological, social, and environmental influences on technology acceptance.
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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 | 0.023 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".