Training Volume and Training Frequency Changes Associated with Boston Marathon Race Performance
Bibliographic record
Abstract
BACKGROUND: Physical training influences competitive marathon performance, including training volume and training frequency changes (TFCs) pre-race. Training intensity distribution (i.e., steady-state, quality sessions, interval training) and cross-training contribute to volume and TFCs that may influence performance. OBJECTIVE: The aim of this study is to assess the relationships among training and TFCs preceding the 2022 Boston Marathon and race performance. METHODS: Adult 2022 Boston Marathon registrants were contacted via email 1 month pre-race. Athletes reported demographics, training/racing experience, and training pre-race. TFCs were calculated by comparing two timeframes: 12-4 and 4-0 month pre-race training. Official race performance was obtained from chip timing data and demographics. Separate linear regressions were used to assess the effects of training and cross-training in 12-4 and 4-0 months pre-race and TFCs on performance, accounting for experience and demographics. RESULTS: In total, 917 athletes were included (female: n = 495, 3:53 ± 0:37 h race times, 64.4 ± 24 km/week weekly distance; male: n = 422; 3:35 ± 0:39 h race times, 67.6 ± 26.2 km/week weekly distance). Higher running distance/week, running sessions/week (n), quality sessions/week ("hard sessions"; n), average distance in the 12-4 and 4-0 months pre-race (p ≤ 0.050), and more cross-training (p < 0.001) in the 4-0 months pre-race were associated with faster times and performance. Runners with TFCs of decreased running sessions/week (p = 0.035) had faster times and better performance versus athletes who maintained/increased volume. CONCLUSION: Habitually higher training exposure 12-4 and 4-0 months, but relatively reduced training frequency 4-0 months pre-race, contributed to better marathon performance.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".