Can a Patient use an App at Home to Measure Knee Range of Motion? Utilizing a Mobile App, Curovate, to Improve Access and Adherence to Knee Range of Motion Measurements
Bibliographic record
Abstract
Introduction: Knee range of motion is a critical measure of progress after knee injury and knee surgery. However, many patients do not understand the importance of knee range of motion and most do not have a way to self-monitor their knee range of motion at home. The patient being able to measure their own range of motion can provide improved access to this critical health metric, and could improve adherence with their daily knee range of motion exercises. The purpose of this technical report is to determine if a mobile app, Curovate, can provide reliable measures of knee range of motion compared to standard goniometric measurements. Procedures: There were four positions of knee flexion and four positions of knee extension each measured twice with a standard goniometer and four different mobile devices with the app Curovate. The reliability and validity of the Curovate app was tested across mobile devices and operating systems and compare to goniometric knee range of motion measurements. A total of 80 measurements were taken. All testing was completed on a healthy 23-year-old male with no knee pathology. Results: A strong positive correlation, Pearson's r > = 0.9985, for all positions of knee flexion and extension across all four mobile devices as well as each mobile device compared to standard goniometric measurements. Conclusions: This article presents a unique method for patients to measure their knee range of motion using the mobile app Curovate. Overall, the mobile app, Curovate, was found to have a strong positive correlation across four mobile devices with varying operating systems and compared to goniometric measurements. Level of evidence: 4.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".