Evidence map›Paper›PMID 33732550›Full record

ArticlePeerJ2021

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach.

Nicos Haralabidis, Gil Serrancolí, Steffi Colyer, Ian Bezodis, Aki Salo, Dario Cazzola

Abstract read
In one paragraph

Article in PeerJ, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–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

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

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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

6 authors.

Nicos HaralabidisDepartment for Health, University of Bath, Bath, UK.
Gil SerrancolíDepartment of Mechanical Engineering, Universitat Politècnica de Catalunya, Barcelona, Spain.
Steffi ColyerDepartment for Health, University of Bath, Bath, UK.
Ian BezodisCardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff, UK.ORCID 0000-0002-0250-032X
Aki SaloDepartment for Health, University of Bath, Bath, UK.ORCID 0000-0002-8055-2854
Dario CazzolaDepartment for Health, University of Bath, Bath, UK.ORCID 0000-0001-7877-6755

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

Indexed as

AthleticsHuman locomotionModellingNonlinear programmingOptimisationParameter estimationRunningSports biomechanicsTrack and field

Identifiers

PMID33732550
PMCPMC7950206

What OpenQuestion holds

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LicenceCC BY
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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.