Artificial neural network predictive models for optimizing the training process in race walking: a longitudinal observational study.

Dariusz Skalski, Magdalena Prończuk, Kinga Łosińska, Petr Stastny, Adam Maszczyk, Adam Zajac

Abstract readObservational Study

In one paragraph

Observational study in PeerJ, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Dariusz SkalskiGdańsk University of Physical Education and Sport, Gdansk, Poland.

Magdalena PrończukGdańsk University of Physical Education and Sport, Gdansk, Poland.

Kinga ŁosińskaGdańsk University of Physical Education and Sport, Gdansk, Poland.

Petr StastnyDepartment of Sport Games, Faculty of Physical Education and Sport, Charles University in Prague, Prague, Czech Republic.

Adam MaszczykGdańsk University of Physical Education and Sport, Gdansk, Poland.

Adam ZajacInstitute of Sport Science, The Jerzy Kukuczka Academy of Physical Education, Katowice, Poland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study identified key physiological, biomechanical, and strength-related predictors of competitive performance in elite female race walkers and evaluated the effectiveness of classical and machine-learning models for individualized training optimization. Thirty nationally ranked female race walkers (25 ± 3 years) were assessed over four seasons (2021-2024). Laboratory and field tests included ergospirometry (VO

Indexed as

Neural Networks, ComputerWalkingAdultBiomechanical PhenomenaFemaleGaitHeart RateHumansLongitudinal StudiesMultilayer PerceptronsPrediction AlgorithmsPredictive Learning ModelsRadial Basis Function NetworksMachine learningPhysiological monitoringRace walking performanceTraining adaptation

Identifiers

PMID42199838

DOI10.7717/peerj.21224

PMCPMC13200623

What OpenQuestion holds

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Registered trials

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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.