Therapist perspectives on the clinical utility of hand performance information from at-home egocentric video in outpatient neurorehabilitation: A multi-methods evaluation study
Bibliographic record
Abstract
Abstract Background Restoring hand function is a primary focus of rehabilitation after neurological injuries, such as stroke and spinal cord injury. However, monitoring hand use outside the clinic remains challenging. This study aims to evaluate how therapists perceive and would utilise information from a clinical decision support system (CDSS) that uses egocentric video to monitor patients’ hand use at home. Methods Five patient-therapist dyads were recruited. Patients recorded daily activities using head-mounted cameras. Therapists reviewed dashboards of processed video data from their patients and completed semi-structured interviews and structured questionnaires. A multi-methods approach with thematic analysis was used to evaluate the CDSS’s clinical usefulness. Results Qualitative analyses revealed four main themes: therapists strongly preferred observational video data over quantitative metrics, valued insights into home environments that cannot be captured in-clinic, identified ways the information could tailor therapy to real-world activities, but noted practical implementation challenges including time constraints and patient burden. The system achieved high usability scores (mean SUS: 80.0, 85-89th percentile). Conclusions This study demonstrates the potential of using egocentric video to inform clinical decision-making in neurorehabilitation, particularly for hand function. The strong preference for video over metrics suggests clinical decision support systems should prioritize interpretable, observation-based information aligned with clinical reasoning processes. Despite implementation challenges, therapists across technical familiarity levels expressed trust in the system and willingness to use it regularly. These findings indicate that egocentric video systems can bridge the clinic-home divide when designed to match interest-holder priorities.
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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 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".