Exploring factors influencing willingness of older adults to use assistive technologies: evidence from the cognitive function and ageing study II
Bibliographic record
Abstract
Technology is widely promoted as a solution to greater independence and better health for the rapidly growing UK older population. If this is to be realised, we need to understand barriers and facilitators to uptake and investigate who wants this technology and who does not express an interest in use. This analysis is based on data from a population-based cohort study, the Cognitive Function and Ageing Study (CFAS)-II, which focused on brain health in older people and included questions about access to- and interest in- internet technologies. The factors affecting willingness to use technologies that support memory and ADL were identified using binary logistic regression analysis. 541 people aged 75 years and older from Cambridgeshire, Nottingham and Newcastle responded. Older adults were more willing to use technologies directed towards improving memory (65%) than towards ADL supportive technologies (38%). Regression analysis showed that an older age (OR = 0.64, 95% CI = 0.34–0.98), female gender (OR = 0.64, 95% CI = 0.42–0.99), no access to technology including laptops and tablets (OR = 0.48, 95% CI = 0.32–0.72), and self-reported physically less slowing down (but no objective health indicators) (OR = 0.57, 95% CI = 0.36–0.88) were strongly associated with UK older adults’ lesser willingness to use memory assistive technologies while not having access to laptops and tablets (OR = 0.57, 95% CI = 0.39–0.84) was associated with willingness to use ADL supportive technologies. Older people, females and those with less access to technologies should be considered as target groups by healthcare providers, policymakers, and technology producers to promote technology and support healthy and independent ageing.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".