Integrative Field-Based Health and Performance Research: A Narrative Review on Experimental Methods and Logistics to Conduct Competition and Training Camp Studies in Athletes
Bibliographic record
Abstract
Field-based sport research involves studies that collect data from athletes and/or teams during competition and/or their daily training environments. Over the last decade, sport-specific field-based research projects have significantly increased in number and complexity, partially owing to the further development of more portable measurement equipment (e.g., indirect calorimetry, desktop blood/gas analyzers, portable laboratories, etc.) and/or wearable or consumable technologies (e.g., smart watches, sensors, core temp pills, etc.). However, given these rapid advances and novelty, challenges remain in the validity and applicability of these devices. Unfortunately, there are no global ethical or best-practice standards for the use of portable devices and/or wearables in sport; however, this review will outline various opportunities and challenges. Many decision trade-offs are required when designing field-based research studies to balance gold-standard scientific rigor and strict research control with highly applied, but less-controlled, "real-world" conditions. To our knowledge, there are no narrative reviews that take a wholistic view of the logistical and methodological considerations of field-based research in athletes. Accordingly, this review takes a multi-disciplinary methodological approach (physiological, nutritional/energetic, biomechanical, musculoskeletal, cognitive, and psychosocial factors), along with the logistical considerations involved in project planning, research design, and ethics of field-based research with elite athletes and/or teams. We also provide practical guidance for characterizing the extreme demands of elite training and competition to support research that ultimately catalyzes improved understanding of the limits of human capacity. We hope this review can serve as a practical guide for researchers undertaking elite athlete field-based research.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".