Assessing Shoulder Proprioceptive Sense of Force: Hand-Held Dynamometer Reliability and Comparison with Isokinetic Protocols
Bibliographic record
Abstract
Background: Proprioception is crucial for shoulder stability, yet clinical methods for assessing all aspects, particularly the sense of force (SOF) -the ability to perceive, interpret, and reproduce force at a joint-are limited. The purpose of this study was to test a new SOF protocol with a handheld dynamometer (HHD) and examine its agreement with an isokinetic dynamometer (IKD), as well as its reliability and the effect of contraction intensity. Design: Cross-sectional measurement study. Methods: Fifty-one healthy participants were assessed for SOF using an Isokinetic dynamometer (IKD) and a HHD to evaluate the agreement between the two methods. Of the initial sample, 25 participants completed a second session with the HHD, enabling the evaluation of the protocol's reliability exclusively with this device. Error score were also compared between three different contraction intensities: 10%, 30% and 50% of maximal voluntary isometric contraction (MVIC). Intra-class correlation coefficients (ICCs), standard error of measurement (SEM), and minimal detectable change (MDC) for intra-rater (within-day and between-day) and inter-rater (within-day) reliability while agreement between the tools was assessed using regression line method. Results: Agreement between devices was low with a poor correlation observed between measurements. The HHD SOF protocol showed low to moderate reliability for intra-rater between-day assessments, with ICCs from 0.44 to 0.64. The absolute reliability MDC95 ranged from 12% to 42%. Inter-rater within-day reliability was low, with ICCs from 0.007 to 0.43. Significant differences in error scores were observed between the HHD and IKD at 30% and 50% MVIC, and higher error scores were noted at Target 10% MVIC compared to 30% and 50%. Conclusions: The SOF protocol using HHD demonstrates moderate reliability but low inter-rater reliability. Different tools yield varying results, with force intensity impacting SOF error scores, while rotation does not. Level of evidence: 2b.
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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 | 0.013 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".