Using wearable technology to measure adherence to intervention for upper extremity musculoskeletal conditions: A scoping review
Bibliographic record
Abstract
BACKGROUND: Measures of adherence usually rely on patient self-report, however, objective methods, such as the use of sensors and wearable devices are a growing area in enhancing personalized care and patient engagement in upper limb musculoskeletal intervention. PURPOSE: To identify and summarize evidence on the use of sensors and wearable devices to help us understand adherence to interventions in adults with upper extremity musculoskeletal pathology. STUDY DESIGN: Scoping review. METHODS: Database (CINAHL, Ovid MEDLINE, and Web of Science) and supplementary searches were conducted. Eighteen studies were included following title and abstract screening and full-text review. Studies were included into the final review if they (1) involved musculoskeletal conditions affecting the upper extremity; (2) incorporated the use of sensors and wearable devices; (3) included adult participants; and (4) provide insights into treatment adherence. Data from these studies were then extracted, analyzed, and synthesized as per the review aim. RESULTS: Included papers were published between the years 1985 and 2024, with 12 focusing on shoulder pathology, three on flexor tendon injuries, two on distal radius fractures and one on wrist injuries. Sensors were primarily used for monitoring device wear time and adherence to exercise interventions. CONCLUSIONS: This scoping review maps the relevant literature on how sensors and wearable devices help us understand adherence to interventions for upper extremity musculoskeletal conditions. Findings highlight the value in using standardized tools for measuring and monitoring adherence to provide more consistent data for application in both research and clinical settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".