ArticleScientific reports2015
Complex network models reveal correlations among network metrics, exercise intensity and role of body changes in the fatigue process.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Does Acute Fatigue Negatively Affect Intrinsic Risk Factors of the Lower Extremity Injury Risk Profile? A Systematic and Critical Review.Sports medicine (Auckland, N.Z.) · 2020Pooled it
- Complex networks analysis reinforces centrality hematological role on aerobic-anaerobic performances of the Brazilian Paralympic endurance team after altitude training.Scientific reports · 2022Trial
- Multivariate dynamic association analysis between sprint interval training and adolescent freestyle swimming performance.BMC sports science, medicine & rehabilitation · 2026Article
- Development of a Dynamic Network Model to Identify Temporal Patterns of Structural Malformations in Zebrafish Embryos Exposed to a Model Toxicant, Tris(4-chlorophenyl)methanol.Journal of xenobiotics · 2023Article
- Effects of different inspiratory muscle warm-up loads on mechanical, physiological and muscle oxygenation responses during high-intensity running and recovery.Scientific reports · 2022Article
- Article
- Associations among sleep, hematologic profile, and aerobic and anerobic capacity of young swimmers: A complex network approach.Frontiers in physiology · 2022Article
- Complex network model indicates a positive effect of inspiratory muscles pre-activation on performance parameters in a judo match.Scientific reports · 2021Article
- Corresponding Assessment Scenarios in Laboratory and on-Court Tests: Centrality Measurements by Complex Networks Analysis in Young Basketball Players.Scientific reports · 2020Article
- New Insights into Mechanical, Metabolic and Muscle Oxygenation Signals During and After High-Intensity Tethered Running.Scientific reports · 2020Article
- Clarifying the structure of serious head and spine injury in youth Rugby Union players.PloS one · 2020Article
- Complex Networks Models and Spectral Decomposition in the Analysis of Swimming Athletes' Performance at Olympic Games.Frontiers in physiology · 2019Article
- Computational and Complex Network Modeling for Analysis of Sprinter Athletes' Performance in Track Field Tests.Frontiers in physiology · 2018Article
- The 3-min all-out test is valid for determining critical power but not anaerobic work capacity in tethered running.PloS one · 2018Article
- Relationship between anaerobic capacity estimated using a single effort and 30-s tethered running outcomes.PloS one · 2017Article
- Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aims of the present study were analyze the fatigue process at distinct intensity efforts and to investigate its occurrence as interactions at distinct body changes during exercise, using complex network models. For this, participants were submitted to four different running intensities until exhaustion, accomplished in a non-motorized treadmill using a tethered system. The intensities were selected according to critical power model. Mechanical (force, peak power, mean power, velocity and work) and physiological related parameters (heart rate, blood lactate, time until peak blood lactate concentration (lactate time), lean mass, anaerobic and aerobic capacities) and IPAQ score were obtained during exercises and it was used to construction of four complex network models. Such models have both, theoretical and mathematical value, and enables us to perceive new insights that go beyond conventional analysis. From these, we ranked the influences of each node at the fatigue process. Our results shows that nodes, links and network metrics are sensibility according to increase of efforts intensities, been the velocity a key factor to exercise maintenance at models/intensities 1 and 2 (higher time efforts) and force and power at models 3 and 4, highlighting mechanical variables in the exhaustion occurrence and even training prescription applications.
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Registered trials
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.