Characterization of Hormonal, Metabolic, and Inflammatory Responses in CrossFit® Training: A Systematic Review
Bibliographic record
Abstract
Background: CrossFit® training is a high-intensity functional training program that aims to increase physical functional performance through biochemical responses, i.e., hormonal, metabolic and inflammatory responses. Most hormonal, metabolic and inflammatory changes induced by CrossFit® training have been reported in isolated clinical studies. The purpose of this review was to systematically explore the existing literature on characterization of hormonal, metabolic, and inflammatory responses resulting from CrossFit® training. Methods: A systematic search of the literature was conducted in PubMed, Web of Science and Scopus from August 2019 to October 2019. Studies were selected through critical review of the content. Using specific keywords, 623 articles were found, of which 597 were excluded for ineligibility, and 25 were eligible. The papers were separated according to subject area: hormonal (n = 8), metabolic (n = 19), and inflammatory (n = 6) changes. All were published between 2015 and 2019. Results: This review reveals potential effects of CrossFit® training on hormonal, metabolic and inflammatory parameters. However, studies had low levels of evidence and reliability due to methodological limitations. Conclusion: In summary, the results are of paramount importance for improving understanding of the effects of CrossFit® training and serve as a basis for prescribing future exercise protocols.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".