A scoping review of the use of intelligent assistive technologies in rehabilitation practice with older adults
Bibliographic record
Abstract
Purpose There is growing interest in intelligent assistive technologies (IATs) in the rehabilitation and support of older adults, however, the factors contributing to or preventing their use in practice are not well understood. This study aimed to develop an overview of current knowledge on barriers and facilitators to the use of smart technologies in rehabilitative practice with older adults.Materials and Methods We undertook a scoping review following guidelines proposed by Arksey and O’Malley (2005) and Levac et al. (2010). A computerised literature search was conducted using the Scopus and Ovid databases, yielding 7995 citations. Of these, 94 studies met inclusion criteria. Analysis of extracted data identified themes which were explored in semi-structured interviews with a purposefully selected sample of seven clinical rehabilitation practitioners (three physical therapists, two occupational therapists, and two speech-language pathologists).Results Barriers and facilitators to using these technologies were associated with accessibility, reported effectiveness, usability, patient-centred considerations, and staff considerations.Conclusions Collaborative efforts of policy-makers, researchers, manufacturers, rehabilitation professionals, and older persons are needed to improve the design of technologies, develop appropriate funding and reimbursement strategies, and minimise barriers to their appropriate use to support independence and quality of life. Any strategies to improve upon barriers to prescribing smart technologies for older people should leverage the expertise of rehabilitation professionals operating at the interface between older people; their health/mobility; their families; and technology-based solutions.Implications for rehabilitationThere is growing interest in intelligent assistive technologies (IATs) in the rehabilitation of older adults, as well as barriers to their use in practice.Rehabilitation professionals can play a key role in enabling access to IATs by recommending or prescribing their use to their older clients. Strategies to address barriers to the use of IATs for older people should leverage the expertise of rehabilitation professionals operating at the interface between older people, their families, and technology-based solutions.Older people and their families require technical support to initiate and continue to use IATs for rehabilitation. While rehabilitation providers may be well-placed to offer this support, they may require time and organizational support to build and maintain expertise in the fast-advancing field of smart technologies for rehabilitation.Cost and usability are universal challenges across the types of smart technologies considered in this review. Participatory approaches to involving older people in the design and development of smart assistive technologies contribute to better usability of these technologies. Devices and interventions that leverage more readily available devices and lower-cost components may overcome cost barriers to accessibility.
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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 | 0.021 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| 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".