The Multimodal Nature of High-Intensity Functional Training: Potential Applications to Improve Sport Performance
Bibliographic record
Abstract
Training for sports performance requires the development of multiple fitness components within the same program. In this context, training strategies that have the potential to concomitantly enhance metabolic and musculoskeletal fitness are of great value for athletes and coaches. The purpose of this manuscript is to review the current studies on high-intensity functional training (HIFT) and to assess how HIFT could be utilized in order to improve sport-specific performance. Studies on untrained and recreationally-active participants have led to positive results on aerobic power and anaerobic capacity, and muscular endurance, while results on muscular strength and power are less clear. Still, HIFT sessions can elicit high levels of metabolic stress and resistance training exercises are prescribed with parameters that can lead to improvements in muscular endurance, hypertrophy, strength, and power. As similar training interventions have been shown to be effective in the athletic population, it is possible that HIFT could be a time-efficient training intervention that can positively impact athletes' performances. While the potential for improvements in fitness and performance with HIFT is promising, there is a clear need for controlled studies that employ this training strategy in athletes in order to assess its effectiveness in this population.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".