New Opportunities to Advance the Field of Sports Nutrition
Bibliographic record
Abstract
Sports nutrition is a relatively new discipline; with ~100 published papers/year in the 1990s to ~3,500+ papers/year today. Historically, sports nutrition research was primarily initiated by university-based exercise physiologists who developed new methodologies that could be impacted by nutrition interventions (e.g., carbohydrate/fat oxidation by whole body calorimetry and muscle glycogen by muscle biopsies). Application of these methods in seminal studies helped develop current sports nutrition guidelines as compiled in several expert consensus statements. Despite this wealth of knowledge, a limitation of the current evidence is the lack of appropriate intervention studies (e.g., randomized controlled clinical trials) in elite athlete populations that are ecologically valid (e.g., in real-life training and competition settings). Over the last decade, there has been an explosion of sports science technologies, methodologies, and innovations. Some of these recent advances are field-based, thus, providing the opportunity to accelerate the application of ecologically valid personalized sports nutrition interventions. Conversely, the acceleration of novel technologies and commercial solutions, especially in the field of biotechnology and software/app development, has far outstripped the scientific communities' ability to validate the effectiveness and utility of the vast majority of these new commercial technologies. This mini-review will highlight historical and present innovations with particular focus on technological innovations in sports nutrition that are expected to advance the field into the future. Indeed, the development and sharing of more "big data," integrating field-based measurements, resulting in more ecologically valid evidence for efficacy and personalized prescriptions, are all future key opportunities to further advance the field of sports nutrition.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".