Validity And Reliability Of The Computrainer® During 40 km And 100 km Time-trials
Bibliographic record
Abstract
The ecological validity of findings can substantially be improved when laboratory-based research studies use experimental designs attempting to emulate real-word exercise conditions. The exercise science literature contains ample of research that looked at the impact of various interventions using running and cycling time-trial (TT) protocols. Regarding cycling TT performances, the Computrainer®(CT), an electromagnetically brake bike trainer applying resistance to the rear wheel of a standard bike, has been extensively used for over a decade. Yet, it is unknown whether the CT provides valid and reliable power output data under TT conditions. PURPOSE: Determine the validity and reliability of two CTs during 40 km and 100 km TTs. METHODS: Power output data of two CT Lab® were compared against those of a calibration rig (Drouet, J.M. et al. Sports Eng. 2008. 15-22.) connected to left side of the crank axle of a road bike with a driving shaft, allowing direct measurement of the true workloads generated by both CTs. The measurement process consisted of comparing the power delivered by the calibration rig to the power displayed by the CTs. Power uncertainty delivered by the calibration rig is ± 0.9%. Each TT was performed under standardized conditions on two occasions with both CTs®. TTs were completed on a flat course and designed upon previously published results in elite/highly-trained cyclists. RESULTS: Mean power outputs generated by the two CTs for the 40 km and 100 km TTs were respectively of 359 ± 38 and 282 ± 56 watts. Mean biases between the CT 1 and the rig for the 40 km TTs varied from -0.8 to -0.7%, and between the CT 2 and the rig for the same distance from -2.7 to 3.6%. For the 100 km TTs, biases between the CT 1 and the rig ranged from -1.4 to -0.4%, whereas for the CT 2 from -5.9 to -3.4%. For repeated trials, biases within 40 and 100 km TTs for CT 1 were < 1% whereas, for CT 2, bias was also < 1% for the 40 km TTs, but reached 2.5% for the 100 km TTs. CONCLUSIONS: Our results indicate that accuracy differs between CTs during 40 and 100 km TTs, suggesting that CTs should not be used interchangeably. Both CTs were shown to provide repeatable data for the 40 km TTs. Whereas for one CT this was also the case for the 100 km TT, for the other CT it was observed that the rig had to produce less torque on one of the two trials to keep the power output generated by CT constant.
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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 | 0.031 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".