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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".