Power assist add-ons for adult manual wheelchair users: A scoping review
Bibliographic record
Abstract
Manual wheelchairs can promote independence among users. However, the user's level of disability, strength, stamina, and the environmental conditions within which the wheelchair is used may limit manual wheelchair functionality. The use of power assist add-ons may mitigate these limitations and help individuals to age in place. This scoping review analyzes scientific and gray literature to examine the use of power assist add-ons among adults across the life course who use manual wheelchairs, as well as their advantages and limitations 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 involved a keyword and MeSH search of electronic databases, proceedings, Google, Google Scholar and symposia. Articles were selected based on pre-defined inclusion criteria. Of the 945 unique titles returned, 17 articles were included. PADs such as rear-mounted power assist devices, powered main wheels, and front-end attachments were identified. Power-assist add-ons for manual wheelchairs show promise in improving mobility and reducing exertion for users. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".