Proceedings of the Canadian Society for Exercise Physiology Annual General Meeting – Zooming into the Future: Exercise Science in the Virtual Age
Bibliographic record
Abstract
Commercially available wearable technology may provide practical and accurate assessments of exercise intensity with respect to exercise thresholds.The purpose of this study was to determine whether the Coros Pace 2-a wrist-worn sports watch that provides a measure of running power output (PO)-can accurately detect changes in treadmill running speeds (+5% and À5%) relative to the maximal lactate steady state (MLSS).Thirteen male and female trained runners (3065 years; 57.065.7 mL•kg À1 •min À1 [mean6SD]) completed, on separate days, a maximal incremental treadmill test, three to four 30-minute constant-speed exercise tests to determine the PO associated with MLSS, and a repeat trial at MLSS.Constantspeed treadmill running 5% above and 5% below MLSS resulted in significantly higher (217.1633.3W; p<0.001) and lower (207.6632.1 W; p<0.05) average running POs, respectively, compared to running PO at MLSS (212.2633.2W).The PO at MLSS was not significantly different than the PO during the repeat trial at MLSS (212.16 31.1 W; p>0.05) and were strongly correlated (ICC=0.99).For each running speed, the average POs for the first and last 5 minutes were not significantly different (p>0.05).Running economy at MLSS was 15.76 1.7 mL O 2 /W.Based on these results, the running PO measured by the Coros Pace 2 on a treadmill was sensitive, stable, repeatable, and physiologically reasonable.Overall, this device seems suitable for measuring the intensity of exercise near the MLSS.(Supported by the Natural Sciences and Engineering Research Council of Canada (NSERC), NSERC CREATE We-TRAC training program, and Alberta Innovates.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.101 | 0.022 |
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".