Perspectives of Older Adults on Assistive Technology: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: The aging population presents challenges for healthcare systems. Assistive technologies (ATs) like telemonitoring, fall detection, and self-monitoring devices offer potential solutions to support older adults and their care. However, successful implementation relies on their acceptance, which remains poorly understood, particularly among non-users. OBJECTIVE: This study explores older adults' perceptions of ATs, including perceived benefits, adoption barriers, and factors influencing willingness to use these technologies. METHODS: A qualitative study was conducted with 31 participants (aged 65+) with varying levels of health and care needs. Data were collected through six focus groups and six in-depth interviews, then analyzed thematically using NVivo software. RESULTS: Seven themes emerged: 1) Limited familiarity, with greater recognition of fall detection and self-monitoring devices compared to telemonitoring; 2) Perceived benefits, include safety, independence, and chronic disease management; 3) Key concerns include usability, cost, reliability, privacy, and psychological impacts; 4) Suggested improvements comprise user-friendly designs and training programs; 5) Contextual influences identified with independent older adults perceiving greater utility; 6) Strategies for ATs' promotion proposed such as media campaigns, government subsidies, and healthcare endorsements; and 7) Overall willingness to adopt ATs, driven by perceived need, social and healthcare influence, and ease of use. CONCLUSIONS: While ATs offer clear benefits, adoption remains limited due to usability, cost, and psychological concerns. Improving accessibility, training, and integration into traditional healthcare services delivery may facilitate acceptance and use. Future research should focus on inclusive designs and policy interventions to maximize ATs' potential in aging populations.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".