Case Study: Body Composition Periodization in an Olympic-Level Female Middle-Distance Runner Over a 9-Year Career
Bibliographic record
Abstract
This case study features an Olympic-level female middle-distance runner implementing a science-based approach to body composition periodization. Data are emerging to suggest that it is not sustainable from a health and/or performance perspective to be at peak body composition year-round, so body composition needs to be strategically periodized. Anthropometric (n = 44), hematological, other health measures, and 1,500-m race performances (n = 83) were periodically assessed throughout a 9-year career. General preparation phase (September to April) featured the athlete at ∼2-4% over ideal competition phase body weight (BW) and body fat (%), with optimal energy availability being prioritized. The competition body composition optimization phase (May to August) included creating an individualized time frame and caloric deficit with various feedback metrics (BW, performance, and hunger) to guide the process. There were significant seasonal fluctuations in anthropometric outcomes between phases (47.3 ± 0.8 vs. 48.3 ± 0.9 kg BW; 53.6 ± 7.8 vs. 61.6 ± 9.7 mm International Society for the Advancement of Kinanthropometry sum of 8 [So8] skinfolds; p < .01), and a significant correlation of decreasing So8 during the peak competition period over her career (r = -.838; p = .018). The range of body composition during the competition period was 46.0-48.0 kg BW and a So8 range was 42.0-55.9 mm. There were also significant positive correlations between slower 1,500-m race times and increasing So8 (r = .437; p < .01), estimated fat mass (r = .445; p < .01), and BW (r = .511; p < .0001). The athlete only had two career injuries. This case study demonstrates a body composition periodization approach that allowed for targeted peak yearly performances, which improved throughout her career, while maximizing training adaptation and long-term athlete health through optimal energy availability.
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
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".