Incidence of nonresponse and individual patterns of response following sprint interval training
Bibliographic record
Abstract
The current study sought to explore the incidence of nonresponders for maximal or submaximal performance following a variety of sprint interval training (SIT) protocols. Data from 63 young adults from 5 previously published studies were utilized in the current analysis. Nonresponders were identified using 2 times the typical error (TE) of measurement for peak oxygen uptake (2 × TE = 1.74 mL/(kg·min)), lactate threshold (2 × TE = 15.7 W), or 500 kcal time-to-completion (TTC; 2 × TE = 306 s) trial. TE was determined on separate groups of participants by calculating the test-retest variance for each outcome. The overall rate of nonresponders for peak oxygen uptake across all participants studied was 22% (14/63) with 4 adverse responders observed. No nonresponders for peak oxygen uptake were observed in studies where participants trained 4 times per week (n = 18), while higher rates were observed in most studies requiring training 3 times per week (30%-50%; n = 45). A nonresponse rate of 44% (8/18) and 50% (11/22) was observed for the TTC test and lactate threshold, respectively. No significant correlations were observed between the changes in peak oxygen uptake and TTC (r = 0.014; p = 0.96) or lactate threshold (r = 0.17; p = 0.44). The current analysis demonstrates a significant incidence of nonresponders for peak oxygen uptake and heterogeneity in the individual patterns of response following SIT. Additionally, these data support the importance of training dose and suggest that the incidence of nonresponse may be mitigated by utilizing the optimal dose of SIT.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".