Investigating the Test–Retest Reliability and Validity of Hand-Held Dynamometry for Measuring Knee Strength in Older Women with Knee Osteoarthritis
Bibliographic record
Abstract
Purpose: Hand-held dynamometry (HHD) can be used to evaluate strength when gold-standard isokinetic dynamometry (IKD) is not feasible. HHD is useful for measuring lower limb strength in a healthy population; however, its reliability and validity in individuals with knee osteoarthritis (OA) has received little attention. In this research, we examined the test–retest reliability and validity of HHD in older women with knee OA. We also examined the associations between reliability and symptom and disease severity. Method: A total of 28 older women with knee OA completed knee extension and flexion exertions measured using HHD and IKD. Intra-class correlation coefficients (ICC 2,3 ), standard error of measurement, and minimal detectable change were calculated. Correlation coefficients and regressions evaluated the relationships between inter-trial differences and symptom and disease severity. Results: High test–retest reliability was demonstrated for both exertions with each device (ICC 2,3 = 0.83–0.96). Variance between trials was not correlated with OA symptoms. Criterion validity was good (ICC 2,3 = 0.76), but extension yielded lower agreement than flexion. Regression analysis demonstrated that true strength can be predicted from HHD measurements. Conclusions: HHD is a reliable tool for capturing knee extension and flexion in individuals with OA. Because of lower agreement, HHD might be best suited for evaluating within-subject strength changes rather than true strength scores. However, gold-standard extension strength magnitudes may reasonably be predicted from regression equations ( r 2 = 0.82).
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".