Assistive Technology to Support Dementia Management: A Scoping Review of Reviews
Bibliographic record
Abstract
BACKGROUND: More than 60% of Canadians living with dementia reside in their own homes, and over 25% rely on care partners (family members, friends) for daily activity assistance. Assistive technology (AT) helps to maintain health, social support, and autonomy. AT comprises assistive products and services which are required for safe and effective product use. Persons with dementia and care partners often require multiple AT types. AT for dementia management is rapidly developing with abundant scientific literature, presenting challenges when efficiently navigating and extracting insights for policy and personal decision-making. The aim of this scoping review is to synthesize review-level evidence on AT to support dementia management for persons with dementia and care partners living at home. The range of AT types and characteristics, outcomes and conclusions from review-level evidence are examined. Knowledge gaps and areas for further investigation regarding use and access to AT are identified. METHOD: The Joanna Briggs Institute's framework for conducting scoping reviews and the PRISMA-ScR guidelines were applied. Six electronic databases were searched. Selection criteria followed the PCC framework: Population (persons with dementia, care partners, health care professionals (e.g., therapists who recommend AT), Concept (AT), and Context (home and community settings). A data charting template guided data extraction, numerical summarization, and content analysis. RESULT: Out of 10,978 unique citations identified, 39 articles met the inclusion criteria. Preliminary results show that AT types ranged from virtual reality systems (e.g., immersive goggles, game-based platforms) for motor and cognitive training, voice-activated technologies (e.g., Amazon, Alexa) for reminders and speech assistance, wearable devices for fall detection, and smartphones for communication. Key AT characteristics include portability, ease of learning, and task-specific adaptability. However, knowledge gaps in distribution, assessment, and use of AT, plus small sample sizes, inconsistent outcome measure use, and limited economic analyses, restrict definitive conclusions to support decisions for AT recommendations. CONCLUSION: This is the first scoping review of reviews on this topic, providing comprehensive insights into AT types, characteristics, uses, and access for dementia management. This review charts a path toward creating more impactful AT solutions for persons with dementia and their care partners in their own homes and community settings.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.029 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".