Non-exhaustive double effort test is reliable and estimates the first ventilatory threshold intensity in running exercise
Bibliographic record
Abstract
Purpose The present study aimed to investigate the reliability of the non-exhaustive double effort (NEDE) test in running exercise and its associations with the ventilatory thresholds (VT 1 and VT 2 ) and the maximal lactate steady state (MLSS). Methods Ten healthy male adults (age: 23 ± 4 years, height: 176.6 ± 6.4 cm, body mass: 76.6 ± 10.7 kg) performed 4 procedures: (1) a ramp test for VT 1 and VT 2 determinations measured by ratio of expired ventilation to O 2 uptake (VE/VO 2 ) and expired ventilation to CO 2 output (VE/VCO 2 ) equivalents, respectively; (2) the NEDE test measured by blood lactate concentration (NEDE LAC ) and heart rate responses (NEDE HR ); (3) a retest of NEDE for reliability analysis; and (4) continuous efforts to determine the MLSS intensity. The NEDE test consisted of 4 sessions at different running intensities. Each session was characterized by double efforts at the same running velocity (E1 and E2, 180 s), separated by a passive recovery period (90 s rest). LAC and HR values after E1 and E2 (in 4 sessions) were used to estimate the intensity equivalent to “null delta” by linear fit. This parameter represents, theoretically, the intensity equivalent to maximal aerobic capacity. Results The intraclass correlation coefficient indicated significant reliability for NEDE LAC (0.93) and NEDE HR (0.79) (both p < 0.05). There were significant correlations, no differences, and strong agreement with the intensities predicted by NEDE LAC (10.1 ± 1.9 km/h) and NEDE HR (9.8 ± 2.0 km/h) to VT 1 (10.2 ± 1.1 km/h). In addition, despite significantly lower MLSS intensity (12.2 ± 1.2 km/h), NEDE LAC and NEDE HR intensities were highly correlated with this parameter (0.90 and 0.88, respectively). Conclusion The NEDE test applied to running exercise is reliable and estimates the VT 1 intensity. Additionally, NEDE intensities were lower but still correlated with VT 2 and MLSS.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".