Evidence map›Paper›PMID 37204619›Full record

ReviewSports medicine (Auckland, N.Z.)2023

Blood-Based Biomarkers for Managing Workload in Athletes: Considerations and Recommendations for Evidence-Based Use of Established Biomarkers.

Nils Haller, Michael Behringer, Thomas Reichel, Patrick Wahl, Perikles Simon, Karsten Krüger, Philipp Zimmer, Thomas Stöggl

Open access · hybridAbstract readReview
In one paragraph

Review in Sports medicine (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
14.2field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 6 institutions in 2 countries.

Nils HallerDepartment of Sports Medicine, Rehabilitation and Disease Prevention, Johannes Gutenberg University of Mainz, Mainz, Germany.ORCID 0000-0002-7066-7615
Michael BehringerDepartment of Sports Sciences, Goethe University Frankfurt, Frankfurt am Main, Germany.ORCID 0000-0002-9503-9965
Thomas ReichelDepartment of Exercise Physiology and Sports Therapy, Institute of Sports Science, Justus-Liebig-University Gießen, Gießen, Germany.
Patrick WahlDepartment of Exercise Physiology, German Sport University Cologne, Cologne, Germany.ORCID 0000-0001-8682-5916
Perikles SimonDepartment of Sports Medicine, Rehabilitation and Disease Prevention, Johannes Gutenberg University of Mainz, Mainz, Germany.ORCID 0000-0002-7996-4034
Karsten KrügerDepartment of Exercise Physiology and Sports Therapy, Institute of Sports Science, Justus-Liebig-University Gießen, Gießen, Germany.ORCID 0000-0003-1506-8254
Philipp ZimmerDivision of Performance and Health (Sports Medicine), Institute for Sport and Sport Science, TU Dortmund University, Dortmund, Germany.ORCID 0000-0001-9631-4503
Thomas StögglDepartment of Sport and Exercise Science, University of Salzburg, Schlossallee 49, Salzburg, 5400 Hallein-Rif, Austria. thomas.stoeggl@plus.ac.at.ORCID 0000-0002-6685-1540
Justus-Liebig-Universität Gießen · DEUniversity of Salzburg · ATGerman Sport University Cologne · DEGoethe University Frankfurt · DEJohannes Gutenberg University Mainz · DETU Dortmund University · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Blood-based biomarkers can provide an objective individualized measure of training load, recovery, and health status in order to reduce injury risk and maximize performance. Despite enormous potentials, especially owing to currently evolving technology, such as point-of-care testing, and advantages, in terms of objectivity and non-interference with the training process, there are several pitfalls in the use and interpretation of biomarkers. Confounding variables such as preanalytical conditions, inter-individual differences, or an individual chronic workload can lead to variance in resting levels. In addition, statistical considerations such as the detection of meaningful minimal changes are often neglected. The lack of generally applicable and individual reference levels further complicates the interpretation of level changes and thus load management via biomarkers. Here, the potentials and pitfalls of blood-based biomarkers are described, followed by an overview of established biomarkers currently used to support workload management. Creatine kinase is discussed in terms of its evidence for workload management to illustrate the limited applicability of established markers for workload management to date. We conclude with recommendations for best practices in the use and interpretation of biomarkers in a sport-specific context.

Indexed as

Athletic InjuriesSportsAthletesBiomarkersHumansWorkloadBiomarkers

Identifiers

PMID37204619
PMCPMC10197055
OpenAlexW4377093189

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.