Uptake of Technology for Neurorehabilitation in Clinical Practice: A Scoping Review
Bibliographic record
Abstract
OBJECTIVE: Technology-based interventions offer many opportunities to enhance neurorehabilitation, with associated research activity gathering pace. Despite this fact, translation for use in clinical practice has lagged research innovation. An overview of the current "state of play" regarding the extent of clinical uptake and factors that might influence use of technologies is required. This scoping review explored the uptake of technologies as neurorehabilitation interventions in clinical practice and factors that are reported to influence their uptake. METHODS: This systematic scoping review was conducted with narrative synthesis and evidence mapping. Studies of any design reporting uptake or implementation of technology (wearable devices, virtual reality, robotics, and exergaming) for movement neurorehabilitation after stroke and other neurological conditions were sought via a formal search strategy in MEDLINE (Ovid), CINAHL, AMED, and Embase. Full-text screening and data extraction were completed independently by 2 reviewers. RESULTS: Of 609 studies returned, 25 studies were included after title, abstract, and full-text screening. Studies investigated a range of technologies at various stages of development. Only 4 of the included studies explored the sustained use of technology in practice. The following 5 themes representing experiences of technology use emerged: perceived usefulness, technology design, social interaction, integration with services, and suggested improvements to enhance uptake. CONCLUSION: Reporting of uptake and use of neurorehabilitation technologies in clinical practice is limited. The synthesis provided comprehensive knowledge of barriers to and facilitators of uptake to be considered in future protocols, including a steep learning curve required to engage with technology, a need for a supportive organizational culture, and a need for user involvement in both design and development. IMPACT: This scoping review has provided indicators from current evidence of important factors to consider in the planning of research into and clinical implementation of technologies for neurorehabilitation. It serves to support an evidence-based, user-centered platform for improved research on and translation of technologies in neurorehabilitation clinical practice.
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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 | 0.039 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.024 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| 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".