Physiotherapists’ User Acceptance of a Lower Limb Robotic Exoskeleton in Specialized Rehabilitation: Qualitative Exploratory Study
Bibliographic record
Abstract
BACKGROUND: Robotic lower limb exoskeletons have emerged as promising tools in the clinical rehabilitation of patients with lower limb paralysis due to neurological disease, stroke, or spinal cord injury. Identified benefits in gait function rehabilitation include improved gait function, cardiovascular effects, enhanced training quality, patient motivation, and reduced physical and psychological workload for therapists. Despite the identified benefits, the successful adoption of this technology largely depends on therapists' user acceptance. OBJECTIVE: This study aims to explore physiotherapists' perceptions of using robot-assisted lower-limb gait training in specialized neurological rehabilitation using the unified theory of acceptance and use of technology framework. METHODS: A qualitative, exploratory research design with a deductive approach was used. Semistructured interviews were conducted with 7 expert physiotherapists in a Norwegian specialized rehabilitation hospital. Data collection and analysis were guided by the unified theory of acceptance and use of technology framework. RESULTS: The physiotherapists' use of lower limb exoskeletons was greatly influenced by perceived benefits for patients or challenges, such as usability issues, the time required for adjustment to each patient, and the lack of personnel resources to facilitate their use. Thus, perceived usefulness and facilitating conditions (or lack thereof) had a great influence on the physiotherapists' intentions to use and the actual use of the exoskeleton. CONCLUSIONS: This study identified several factors influencing the physiotherapists' acceptance and integration of the lower limb exoskeleton. Available resources, such as time and personnel, were emphasized as important factors to increase the use of the exoskeleton in specialized rehabilitation. Our findings may inform service providers and engineers in specialized neurological rehabilitation settings.
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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".