POWER ASSIST ADD-ONS FOR OLDER ADULT MANUAL WHEELCHAIR USERS: A SCOPING REVIEW
Bibliographic record
Abstract
Abstract Manual wheelchairs can promote independence among users of all ages. However, there are age-related differences in the level of disability, strength, stamina, and the environmental conditions within which the wheelchair is used. Using power assist add-ons may mitigate these limitations and help individuals in independent mobility. A gap in the literature is an understanding of the ways in which aging may limit the functionality of power assists on wheelchairs and the impact on independence and active aging. This scoping review analyzes scientific and gray literature to examine the use of power assist add-ons among older adults who use manual wheelchairs, as well as their advantages, limitations, and potential benefits in promoting independence and active aging. This review was guided by the PRISMA checklist for scoping reviews, and the Arksey and O’Malley review methodology. The literature search was carried out in two phases: 1) a keyword and MeSH search of electronic databases, proceedings, as well as symposia for relevant titles/abstracts; and 2) a search of Google and Google Scholar, as well as hand searches. After applying the inclusion and exclusion criteria, we included 20 publications with a focus on power-assist wheelchair technology for full review. Results indicate that power-assist add-ons for manual wheelchairs show promise in improving mobility and reducing user exertion for older adults. However, concerns regarding safety, indoor maneuverability, and user preferences highlight the need for specialized training and retrofitting power assist add-ons, especially among older users. Further research on user-centered design, and adherence to safety standards are warranted.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".