Interinstrument Reliability Between the Squegg® Smart Dynamometer and Hand Grip Trainer and the Jamar® Hydraulic Hand Dynamometer: A Pilot Study
Bibliographic record
Abstract
IMPORTANCE: Occupational therapists need dependable and accurate instruments for remote assessments and monitoring of hand functionality. These assessments monitor progress, evaluate interventions, and guide independence goals. OBJECTIVE: To assess the interinstrument reliability and concurrent validity of the Squegg® Smart Dynamometer and Hand Grip Trainer and the Jamar® Hydraulic Hand Dynamometer. DESIGN: Repeated-measures design. SETTING: Individual clinic in Bucharest, Romania. PARTICIPANTS: Forty middle-age and older adult volunteers, healthy and free from any neuromuscular, orthopedic dysfunction that affected hand strength. OUTCOMES AND MEASURES: Participants' maximal grip strength (MGS) for both their dominant and nondominant hands was measured with both devices. Participants with odd-numbered IDs were measured with the Squegg first and the Jamar second, and those with even-numbered IDs were measured in opposite sequence. RESULTS: Paired-samples t tests on overall mean MGS and mean MGS (three measures on each hand) showed no statistically significant differences between the two devices. Intraclass correlation analysis showed good to excellent interinstrument agreement. Pearson correlations between measurements across all participants, and hands, indicated strong agreement. CONCLUSIONS AND RELEVANCE: The Squegg shows promise for health care professionals, including occupational therapists, for grip strength assessment in clinical contexts. What This Article Adds: These results offer initial psychometric data for a new remote MGS measurement device. MGS is crucial for assessing the physical function of aging adults. Reliable measurements from such a device are vital for occupational therapists to guide treatment interventions and assess hand function's impact on daily activities.
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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 | 0.013 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".