A comparison of critical speed and critical power in runners using Stryd running power
Bibliographic record
Abstract
Abstract Purpose Although running traditionally relies on critical speed (CS) as an indicator of critical intensity, portable inertial measurement units (IMUs) offer a potential solution for estimating running mechanical power to assess critical power (CP) in runners. The purpose of this study was to determine whether CS and CP differ when assessed using the Stryd device, a portable IMU, and if two running bouts are sufficient to determine CS and CP. Methods On an outdoor running track, ten trained runners ( , 59.0 [4.2] ml·kg -1 ·min -1 ) performed three running time-trials (TT) between 1200 and 4400m on separate days. CS and CP were derived from two-parameter hyperbolic speed-time and power-time models, respectively, using two (CS 2TT CP 2TT ) and three (CS 3TT CP 3TT ) time trials. Subsequently, runners performed constant intensity running for 800m at their calculated CS 3TT and CP 3TT . Results Running at the calculated CS 3TT speed (3.88 [0.44] m·s -1 ) elicited an average Stryd running power (271 [28] W) not different from the calculated CP 3TT (270 [28]; p=0.940; d=0.02), with excellent agreement between the two values (ICC=0.980). The CS 2TT (3.97 [0.42] m·s -1 ) was not significantly higher than CS 3TT (3.89 [0.44] m·s -1 ; p=0.178; d=0.46); however, CP 2TT (278 [29] W) was significantly greater than CP 3TT (p=0.041; d=0.75). Conclusion The running intensities at CS and CP were similar, supporting the use of running power (Stryd) as a metric of aerobic fitness and exercise prescription, and two trials provided a reasonable, albeit higher, estimate of CS and CP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".