The Single-Bout Forearm Critical Force Test: A New Method to Establish Forearm Aerobic Metabolic Exercise Intensity and Capacity
Bibliographic record
Abstract
No non-invasive test exists for forearm exercise that allows identification of power-time relationship parameters (W', critical power) and thereby identification of the heavy-severe exercise intensity boundary and scaling of aerobic metabolic exercise intensity. The aim of this study was to develop a maximal effort handgrip exercise test to estimate forearm critical force (fCF; force analog of power) and establish its repeatability and validity. Ten healthy males (20-43 years) completed two maximal effort rhythmic handgrip exercise tests (repeated maximal voluntary contractions (MVC); 1 s contraction-2 s relaxation for 600 s) on separate days. Exercise intensity was quantified via peak contraction force and contraction impulse. There was no systematic difference between test 1 and 2 for fCF(peak) force (p = 0.11) or fCF(impulse) (p = 0.76). Typical error was small for both fCF(peak force) (15.3 N, 5.5%) and fCF(impulse) (15.7 N ⋅ s, 6.8%), and test re-test correlations were strong (fCF(peak force), r = 0.91, ICC = 0.94, p<0.01; fCF(impulse), r = 0.92, ICC = 0.95, p<0.01). Seven of ten subjects also completed time-to-exhaustion tests (TTE) at target contraction force equal to 10%<fCF(peak force) and 10%>fCF(peak force). TTE predicted by W' showed good agreement with actual TTE during the TTE tests (r = 0.97, ICC = 0.97, P<0.01; typical error 0.98 min, 12%; regression fit slope = 0.99 and y intercept not different from 0, p = 0.31). MVC did not predict fCF(peak force) (p = 0.37), fCF(impulse) (p = 0.49) or W' (p = 0.15). In conclusion, the poor relationship between MVC and fCF or W' illustrates the serious limitation of MVC in identifying metabolism-based exercise intensity zones. The maximal effort handgrip exercise test provides repeatable and valid estimates of fCF and should be used to normalize forearm aerobic metabolic exercise intensity instead of MVC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".