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Record W4409623799 · doi:10.1007/s40279-025-02227-0

Integrative Field-Based Health and Performance Research: A Narrative Review on Experimental Methods and Logistics to Conduct Competition and Training Camp Studies in Athletes

2025· review· en· W4409623799 on OpenAlexaff
Trent Stellingwerff, Louise M. Burke, Hannah G. Caldwell, Rob Gathercole, Chris J. McNeil, Christopher Napier, Sarah A. Purcell, Susan Boegman, Elizabeth A. Johnson, Sharleen Hoar, Alexandra M. Coates, Erica Bennett, Alannah K. A. McKay, Ida A. Heikura, Michael J. Joyner, Jamie F. Burr

Bibliographic record

VenueSports Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsKelowna General HospitalSimon Fraser UniversityCanadian Sport Centre PacificUniversity of VictoriaOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of GuelphUniversity of British Columbia
Fundersnot available
KeywordsAthletesWearable computerField (mathematics)Computer scienceRigourWearable technologyInteroperabilityEngineering ethicsApplied psychologyManagement sciencePsychologyKnowledge managementMedicineEngineering

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.054
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0060.006
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.487
GPT teacher head0.596
Teacher spread0.108 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
GenreReview

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".

Quick stats

Citations17
Published2025
Admission routes1
Has abstractyes

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