Reliability and Validity of a Novel Wearable Device for Measuring Elbow Strength
Bibliographic record
Abstract
Muscle strength is an important clinical outcome in rehabilitation and sport medicine, but options are limited to expensive but accurate isokinetic dynamometry (IKD) or inexpensive but less accurate hand-held dynamometers (HHD). A wearable, self-stabilizing, limb strength measurement device (LSMD) was developed to fill the current gap in portable strength measurement devices. The purpose of this study was to evaluate the reliability and validity of the LSMD in healthy adults. Twenty healthy adults were recruited to attend two strength testing sessions where elbow flexor and extensor strength was measured with the LSMD, with HHD and with IKD in random order, by two raters. Outcomes were intra-rater repeatability, inter-rater reproducibility and inter-session reproducibility using intra-class correlation coefficients (ICC). Limits of agreement and weighted least products regression were used to test the validity of the LSMD relative to the criterion standard (IKD), and calibration formulas derived to improve measurement fidelity. ICC values for the LSMD were >0.90 for all measures of reliability and for both muscle groups, but over-predicted extensor strength and under-predicted flexor strength. Validity was established by transforming the data with the criterion standard-based calibration. These data indicate that the LSMD is reliable and conditionally valid for quantifying strength of elbow flexors and extensors in a healthy adult population.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".