MAPPING ASSISTIVE TECHNOLOGIES ON THE PROGRESSION OF ALZHEIMER’S DISEASE: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract A growing population of people living with Alzheimer’s Disease urges improved support for aging in place and with dignity. Assistive technologies (ATs) can be used to delay institutionalization, reduce caregiver burden, and improve quality of life for this population and their care partners. The abilities and needs of this population change during disease progression, requiring a better understanding of which ATs could be used at each stage of the disease. The purpose of this scoping review is to generate knowledge on how ATs can be mapped on seven stages of Alzheimer’s Disease progression described by the Global Deterioration Scale. The review follows the Arksey and O’Malley framework to identify and harvest information from Medline OVID, Scopus, CINAHL, and Embase OVID databases. Inclusion criteria were Alzheimer’s Disease, technology interventions of any type and duration, English language, and the time frame between 2000-2023. Data was extracted and thematically analyzed using six predetermined domains of ATs for dementia, namely safety devices, clinical devices, memory aids, ATs for preventing social isolation, ATs for leisure activities, and ATs for supporting everyday tasks. A total of 53 articles were included. Findings show that a variety of ATs are available throughout the disease progression. High technology (e.g., tracking devices) mainly target early stages, while low technology (e.g., weighted blanket) target later stages. Some, such as music therapy, are present at every stage of disease. The map has the potential to inform people with dementia, care partners, technology companies, distributors, policy makers and service providers.
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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 | 0.008 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".