Validity of the Handheld Dynamometer Compared with an Isokinetic Dynamometer in Measuring Peak Hip Extension Strength
Bibliographic record
Abstract
PURPOSE: To determine whether the handheld dynamometer (HHD) is an appropriate tool to assess and quantify peak hip extension strength in prone standing position by (1) evaluating the concurrent validity of the HHD versus an isokinetic dynamometer (IKD), (2) establishing the minimal detectable change (MDC), and (3) determining the validity of single-trial versus multi-trial measures. METHOD: A convenience sample of 20 healthy adults was recruited for this cross-sectional study. Measures of peak hip extension strength were collected in prone standing position with both the HHD and the IKD and in supine position with the IKD. RESULTS: Values of r were 0.37 for HHD versus IKD prone standing and 0.51 for HHD versus IKD supine. MDC was 14.8 Nm for the HHD, 25.6 Nm for IKD prone standing, and 41.5 Nm for IKD supine. High correlations (r values of 0.92-0.94) were observed between trial 1 and the mean of three trials. CONCLUSIONS: The HHD has moderate concurrent validity in measuring peak hip extension strength in the prone standing position in healthy adults. MDC for hip extension strength was lowest using the HHD. Single-trial values showed a high correlation with three-trial mean values.
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 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 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 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".