TECHNOLOGY-BASED INTERVENTIONS AND ASSESSMENT FOR OLDER ADULTS WITH COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract During the COVID-19 pandemic, older adults with cognitive impairment experienced social isolation, stress, and challenges to stay healthy at home or in a long-term care facility. Technology-based interventions and assessment can be valuable in managing dementia at home before a crisis situation occurs, which can lessen caregiver burden and stress and improve quality of life for older adults with cognitive impairment. In the symposium, specific technology-based interventions (telepresence robot, online chair yoga, exergames, virtual cycling, video-conferencing platforms) and assessment (IOM2 biofeedback device) were used for older adults with cognitive impairment. We cultivated a novel interdisciplinary approach to emerging clinical entities of technology-based intervention and assessment for older adults with cognitive impairment. In the symposium, we will present a variety of technology-based clinical interventions. Our first study explored the experiences of virtual family visits in four Canadian long-term care homes, using a telepresence robot. Online survey, interviews, focus groups, and observations were conducted to explore the experience. The second study assessed feasibility of a remotely supervised online chair yoga (CY) intervention for older adults with dementia in Florida and explored the preliminary effects of CY on psychosocial outcomes in this population. The third study evaluated the ease of use and quality of cardiac data using IOM2 biofeedback device for older adults with dementia. Cardiac rhythms were analyzed from pulse data measured using the IOM2 biofeedback device (UNYTE). The fourth study was a scoping review to analyze evidence about online group-based exercise programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".