Energy balance dynamics during short-term high-intensity functional training
Bibliographic record
Abstract
CrossFit (CF; CrossFit Inc., Washington, DC, USA) is a form of high-intensity functional training that focuses on training across the entire spectrum of physical fitness. CF has been shown to improve a number of indicators of health but little information assessing energy balance exists. The purpose of the present study was to investigate energy balance during 1 week of CF training. Men and women (n = 21; mean ± SD; age, 43.5 ± 8.4 years; body mass index, 27.8 ± 4.9 kg·m−2), with ≥3 months CF experience, had body composition assessed via air displacement plethysmography before and after 1 week of CF training. Participants wore ActiHeart monitors to assess total energy expenditure (TEE), activity energy expenditure, and CF energy expenditure (CF EE). Energy intake was assessed from TEE and Δ body composition. CF EE averaged 605 ± 219 kcal per 72 ± 10 min session. Weekly CF EE was 2723 ± 986 kcal. Participants were in an energy deficit (TEE: 3674 ± 855 kcal·day−1; energy intake: 3167 ± 1401 kcal·day−1). Results of the present study indicate that CF training can account for a significant portion of daily activity energy expenditure. The weekly expenditure is within levels shown to induce clinically meaningful weight loss in overweight/obese populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".