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Record W2735036748 · doi:10.1055/s-0043-110226

Submaximal Markers of Fatigue and Overreaching; Implications for Monitoring Athletes

2017· article· en· W2735036748 on OpenAlexfundno aff
André Siegl, Elisa Kosel, Nicholas Tam, Susanne Koschnick, Nelleke G. Langerak, Sabrina Skorski, Tim Meyer, Robert P. Lamberts

Bibliographic record

VenueInternational Journal of Sports Medicine · 2017
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersUniversity of British ColumbiaJohns Hopkins University
KeywordsMedicineHeart ratePhysical therapyAthletesRating of perceived exertionTreadmillExertionPerceived exertionCardiologyPhysical medicine and rehabilitationInternal medicineBlood pressure

Abstract

fetched live from OpenAlex

The regular monitoring of athletes is important to fine-tune training and detect early symptoms of overreaching. Therefore the aim of this study was to determine if a noninvasive submaximal running test could reflect a state of overreaching. 14 trained runners completed a noninvasive Lamberts Submaximal Running Test, one week before and 2 days after finishing an ultramarathon, and delayed onset of muscle soreness and the daily analysis of life demands for athletes questionnaire were also captured. After the ultramarathon, submaximal heart rate was lower at 70% (-3 beats) and 85% of peak treadmill running speed (P<0.01). Ratings of perceived exertion were higher at 60% (2 units) and 85% (one unit) of peak treadmill running speed, while 60-second heart rate recovery was significantly faster (7 beats, P<0.001). Delayed Onset of Muscle Soreness scores and the number of symptoms of stress (Daily Analysis of Life Demands for Athletes) were also higher after the ultramarathon (P<0.01). The current study shows that the Lamberts Submaximal Running Test is able to reflect early symptoms of overreaching. Responses to acute fatigue and overreaching were characterized by counterintuitive responses, such as lower submaximal heart rates and faster heart rate recovery, while ratings of perceived exertion were higher.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.264

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.380
Teacher spread0.321 · 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 teacher head, not a consensus.

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

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

Citations27
Published2017
Admission routes1
Has abstractyes

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