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Record W3150555068 · doi:10.1080/17461391.2021.1915391

Low energy availability in female athletes: From the lab to the field

2021· review· en· W3150555068 on OpenAlexaff
Ida A. Heikura, Trent Stellingwerff, José L. Areta

Bibliographic record

VenueEuropean Journal of Sport Science · 2021
Typereview
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsCanadian Sport Centre PacificUniversity of Victoria
Fundersnot available
KeywordsAthletesFemale athlete triadMedicineField (mathematics)Energy (signal processing)Set (abstract data type)Physical therapyPsychologyClinical psychologyComputer scienceStatisticsMathematics

Abstract

fetched live from OpenAlex

Decades of laboratory research have shown impairments to several body systems after only 4-5 days of strictly controlled consistent low energy availability (LEA); where energy availability (EA) = Energy Intake (EI) - Exercise Energy Expenditure (EEE)/Fat-Free Mass. Meanwhile, cross-sectional reports exist on the interrelatedness of LEA, menstrual dysfunction and impaired bone health in females (the Female Athlete Triad). These findings have demonstrated that LEA is the key underpinning factor behind a broader set of health and performance outcomes, recently termed as Relative Energy Deficiency in Sport (RED-S). There is utmost importance of early screening and diagnosis of RED-S to avoid the development of severe negative health and performance outcomes. However, a significant gap exists between short-term laboratory studies and cross-sectional reports, or clinically field-based situations, of long-term/chronic LEA and no definitive, validated diagnostic tests for RED-S exist. This review aims to highlight methodological challenges related to the assessment of the components of EA equation in the field (e.g. challenges with EI and EEE measures). Due to the uncertainty of these parameters, we propose the use of more chronic "objective" markers of LEA (i.e. blood markers). However, we note that direct extrapolations of laboratory-based outcomes into the field are likely to be problematic due to potentially poor ecological validity and the extreme variability in most athlete's daily EI and EEE. Therefore, we provide a critical appraisal of the scientific literature, highlighting research gaps, and a potential set of leading objective RED-S markers while working in the field. HighlightsDirect application of short-term laboratory-based findings in the field is problematic.Calculation of energy availability (EA) in the field is methodologically challenging and prone to errors.The use of several biomarkers may allow the detection of early exposure to low EA in the female athlete.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.328
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
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

Citations75
Published2021
Admission routes1
Has abstractyes

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