The relationship between neurocognitive impairment, technology experience, and mobile device proficiency: A cross-sectional exploratory study in older adults
Bibliographic record
Abstract
Purpose To explore the technological experience and competence with mobile devices in a group of older adults from the province of Quebec, Canada.Understanding current use of technology can help researchers, scientists, clinicians, and the industry to promote their use among dyads of family caregivers and older adults with unimpaired cognition (Moreno et al., 2024a) and individuals with dementia (Moreno et al., 2024b).Method A total of 194 older adults were recruited from the CIMA-Q study (Consortium for the Early Identification of Alzheimer's Disease -Quebec, Belleville et al., 2019).They were mostly women (62.9%), with university degrees (56.2%), and a mean age of 65.4 (SD=5.9)years.The sample included individuals with different degrees of cognitive functioning with an average Montreal Cognitive Assessment (MoCA) score of 25.7 (SD = 3.5, range = 7 -30).The group included people with a confirmed clinical diagnosis of normal cognition or neurocognitive impairment by expert physicians or a memory clinic as follows: a) Unimpaired cognition (n = 12), b) Subjective cognitive decline without dementia (n = 120), c) Mild Cognitive Impairment (n = 59), and d) Major Neurocognitive Disorders (n = 3).Measures included the Technology Experience Profile (TEP) and the Mobile Device Proficiency Questionnaire -Short Form (MDPQ). Results and DiscussionMoCA scores were inversely associated with age (r = -3.8,p < .01),but positively associated with technological experience (r = 3.7, p < .01)and mobile device proficiency (r = 3.5, p < .01).Age was negatively associated with technological experience (r = -3.8,p < .01)and mobile device proficiency (r = -4.8,p < .01).Although they are medium correlations, the visual representation indicates that mobile device proficiency seems to be more affected by cognitive impairment than technology experience (Figure 1).Older adults with subjective or objective cognitive impairment report using technologies less frequently and mention more difficulties using mobile devices.AgeTech solutions must adapt technologies to effectively respond to the challenges that older adults may encounter when their cognition is subjectively or objectively affected.As such, flexibility and personalization are key issues when designing technologies for older adults.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".