Telerehabilitation Technology Used for Remote Wrist/Finger Range of Motion Evaluation: A Scoping Review
Bibliographic record
Abstract
Background: Monitoring finger/wrist range of motion (ROM) is an important component of routine hand therapy after surgery. Telerehabilitation is a field that may potentially address various barriers of in-person hand therapy appointments. Therefore, the purpose of this scoping review is to identify telerehabilitation technologies that can be feasibly used in a patient's home to objectively measure finger/wrist ROM. Methods: Following PRISMA-ScR guidelines for scoping reviews, we systematically searched MEDLINE and Embase electronic databases using alternative word spellings for the following core concepts: "wrist/hand," "rehabilitation," and "telemedicine." Studies were imported into Covidence, and systematic two-level screening was done by two independent reviewers. Patient demographics and telerehabilitation information were extracted from the selected articles, and a narrative synthesis of the findings was done. Results: There were 28 studies included in this review, of which the telerehabilitation strategies included smartphone angle measurement applications, smartphone photography, videoconference, and wearable or external sensors. Most studies measured wrist ROM with the most accurate technologies being wearable and external sensors. For finger ROM, the smartphone angle application and photography had higher accuracy than sensor systems. The telerehabilitation strategies that had the highest level of usability in a remote setting were smartphone photographs and estimation during virtual appointments. Conclusions: Telerehabilitation can be used as a reliable substitute to in-person goniometer measurements, particularly the smartphone photography and motion sensor ROM measurement technologies. Future research should investigate how to improve the accuracy of motion sensor applications that are available on easy-to-access devices.
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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 | 0.010 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".