Patients’ and therapists’ experience and perception of exoskeleton-based physiotherapy during subacute stroke rehabilitation: a qualitative analysis
Bibliographic record
Abstract
To explore the experience and acceptability of an exoskeleton-based physiotherapy program for non-ambulatory patients during subacute stroke rehabilitation from the perspective of patients and therapists. This was a qualitative descriptive study using semi-structured interviews and thematic analysis. Fourteen patients with stroke who participated in the experimental arm of a randomized controlled trial investigating the efficacy of exoskeleton-based physiotherapy were recruited. Six physiotherapists who provided the intervention were also recruited. Three themes were identified relating to the experience and acceptability of an exoskeleton-based physiotherapy program: (1) <i>A matter of getting into the swing of things</i> depicted the initial and ongoing learning process of using an exoskeleton; (2) <i>More of a positive experience than anything else</i> described the participants’ mostly favorable attitude toward exoskeleton-based gait training; and (3) <i>The best step forward</i> captured participant-identified recommendations and considerations for the future integration of exoskeleton training into stroke rehabilitation. Patients with stroke were even more optimistic than therapists toward the experience and benefits of exoskeleton-based gait training during subacute stroke rehabilitation. Future clinical practice should consider the balance between actual and perceived benefits, as well as the potential barriers to integrating an exoskeleton into stroke rehabilitation.IMPLICATIONS FOR REHABILITATIONPowered robotic exoskeletons can be used to provide higher duration and more repetitious walking practice for non-ambulatory patients with stroke.Patients with stroke view exoskeleton-based physiotherapy highly favorably, attributing greater opportunity and benefit to using the device during subacute rehabilitation.Physiotherapists should consider learning challenges, patient characteristics, and implementation barriers when integrating exoskeleton-based training within a treatment program. Powered robotic exoskeletons can be used to provide higher duration and more repetitious walking practice for non-ambulatory patients with stroke. Patients with stroke view exoskeleton-based physiotherapy highly favorably, attributing greater opportunity and benefit to using the device during subacute rehabilitation. Physiotherapists should consider learning challenges, patient characteristics, and implementation barriers when integrating exoskeleton-based training within a treatment program.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.025 | 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 teacher head, 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".