Determinants of technology adoption and continued use among cognitively impaired older adults: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Technology offers opportunities to support older adults with mild cognitive impairments to remain independent and socially connected, but is often not used. Although determinants of technology use among older adults in general are well studied, much less is known about how these factors impact technology use behaviour in cognitively impaired older adults. This study aimed to bridge this gap in research by examining the factors underlying technology use in community-dwelling older adults with mild cognitive impairments. METHODS: We applied a generic qualitative design and used 16 semi-structured interviews to collect data from Belgian (Flemish) community-dwelling older adults diagnosed with Mild Cognitive Impairment or dementia and informal caregivers. To get data from different perspectives, a focus group with professional caregivers was added. We used thematic analysis with an inductive approach to identify and select themes from the data. RESULTS: We identified two themes: introduction of technology and determinants of technology adoption and continued use. Successful technology adoption in cognitively impaired older adults is need-driven and subject to individual, technological and contextual characteristics. Specific for older adults with cognitive impairments are the importance of disease awareness and cognitive ability for adoption and continued use, respectively. Although social support can be a valuable alternative to technology, it is an important facilitator of continued technology use in these older adults. Similarly, integration of technologies in daily routines can buffer discontinuation of technologies. CONCLUSIONS: Future research is encouraged to validate our findings in a postpandemic era and to further develop a novel theoretical framework for technology acceptance among older adults with cognitive impairments. Moreover, identification of crucial determinants as well as strategies to remove use barriers are also important future research tasks. Clinical practice should focus on improving disease awareness to facilitate technology adoption and policies should invest in training and support of professional caregivers and in reimbursement strategies to facilitate implementation of technology in practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".