Quantifying At-Home Physiotherapy Participation: SPARS vs Self-Reported Diaries
Bibliographic record
Abstract
The completion of at-home physiotherapy exercise is key to many rehabilitation protocols. This study compares at-home upper extremity physiotherapy participation as measured based on data captured with a smart watch to that recorded in self-report diaries. Daily at-home exercise participation (sessions) was recorded for 53 patients with rotator cuff pathology during their first 2 weeks of a 12-week physiotherapy rehabilitation program. Exercise participation was measured using a physical therapy monitoring system that uses smart watch (accelerometer/gyroscope) data analyzed via a convolutional neural network trained on labeled patient-specific in-clinic data and compared to patient reported diaries. A high level of agreement between diary exercise participation and the measurements derived from the smart watch data (ICC=0.72, n=53) was found, with an AUROC=0.99 for binary identification of exercise periods on labeled clinic data. However, overall patient diaries reported more exercise performed (0.96 additional days on average) than measured by the ML algorithm. ML and accelerometer/gyroscope data collected by embedded sensors in a smartwatch represents an accurate and objective alternative to self-reported diaries for monitoring patient at-home participation. Lower levels recorded by the ML algorithm may indicate some limitations in the technology to fully capture participation or potential over-reporting of participation within diaries. As self-reported diary completion decreases over time, physical therapy monitoring technology may represent an acceptable method for longer term assessment of exercise participation.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".