Co-Designed Digital Device for Tracking Rehabilitation Dosage in a Clinical Environment After Stroke: Mixed Methods Validity and Feasibility Study
Bibliographic record
Abstract
Background: In 2023, the National Clinical Guidelines for Stroke revised the recommended daily multidisciplinary therapy dose from 45 minutes per therapy to 3 hours of therapy overall. To monitor the achievement of these guidelines, there is a need for accurate measurement. This study introduces a novel co-designed digital dosage tracking system that uses Near Field Communication technology to log rehabilitation activities and demonstrates its feasibility and accuracy in a clinical setting through comparison with the current clinical method of manual recording. Objective: This study aimed to assess the validity, feasibility, and usability of a novel co-designed digital tracker using Near Field Communication technology to automatically log rehabilitation dosage in people with stroke history, providing an objective and low-burden solution for clinical environments. Methods: This pilot mixed methods study included 2 phases. Phase 1 involved a usability trial with 9 participants conducted at a university research center, assessing usability with the System Usability Scale (SUS) and Intrinsic Motivation Inventory (IMI). Phase 2 consisted of a clinical trial in a National Health Service stroke ward with 15 inpatients, comparing the digital tracker with manual therapist recordings for validity and feasibility using paired t tests, Cohen d, and Bland-Altman plots. An acceptable discrepancy range was set at ±5%-10%. Results: The digital tracker demonstrated high usability with a mean SUS score of 91.43 (SD 9.53) and strong user satisfaction (IMI score 6.29/7, SD 1.50). Clinical trial results showed a strong agreement between the digital and manual methods (t206=-1.60; P=.11; Cohen d=-0.06), with a small mean time discrepancy of 1.23 (SD 11.01) minutes across 207 activities. The Bland-Altman plot indicated good accuracy and consistency between methods, with limits of agreement within the clinically acceptable range. Conclusions: The co-designed digital tracker has been shown to agree with a manual method for recording rehabilitation dosage. This development presents the opportunity for objective, automated, and low-burden recording of rehabilitation dose to support prescription, monitoring, and research.
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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 | 0.036 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".