Normative values for isometric quadriceps peak torque using fixed dynamometry in healthy young adults
Bibliographic record
Abstract
RATIONALE: Assessment of muscle strength is recommended to measure weakness and inform exercise prescription in adults undergoing pulmonary rehabilitation. There is a lack of normative data for lower extremity muscle strength using fixed dynamometry in the clinical setting in young adults.OBJECTIVES: Primary objectives were to determine (i) normative values for isometric quadriceps peak torque (QT) using fixed dynamometry and (ii) the relationship between isometric QT with demographics, body composition, habitual physical activity and one-repetition maximum (1-RM) for knee extension. A secondary objective was to develop a preliminary equation to predict 1-RM knee extension using isometric QT.METHODS This was a cross-sectional study of healthy adults aged 18–39 years. Isometric QT was assessed using fixed dynamometry (MedUp®). A 1-RM test was performed in the same setting. Age, sex and body composition using bioelectiral impedance analysis (Tanita, DC-430U) were collected. Physical activity was assessed by the Rapid Assessment of Physical Activity (RAPA).RESULTS Eighty-two participants were included (44 females, 54%). Isometric QT was 89 (77-132) Nm for females and 158 (133-187) Nm for males (p < 0.0001). There was a strong correlation between isometric QT and 1-RM (r = 0.85, p < 0.0001). The multi-linear regression model to predict 1-RM included sex, isometric QT and BMI (F(3, 78) = 60.819; p < 0.001; R2 = 0.701).CONCLUSION Normative isometric QT values to quantify lower limb strength impairment and a preliminary equation using isometric QT to predict 1-RM and prescribe resistance quadriceps exercise training may inform pulmonary rehabilitation programs for a younger respiratory population. Validation of the 1-RM equation in clinical populations is needed.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".