Complex networks analysis reinforces centrality hematological role on aerobic–anaerobic performances of the Brazilian Paralympic endurance team after altitude training

Abstract

This study investigated the 30-days altitude training (2500 m, LHTH-live and training high) on hematological responses and aerobic–anaerobic performances parameters of high-level Paralympic athletes. Aerobic capacity was assessed by 3000 m run, and anaerobic variables (velocity, force and mechanical power) by a maximal 30-s semi-tethered running test (AO30). These assessments were carried out at low altitude before (PRE) and after LHTH (5–6 and 15–16 days, POST1 and POST2, respectively). During LHTH, hematological analyzes were performed on days 1, 12, 20 and 30. After LHTH, aerobic performance decreased 1.7% in POST1, but showed an amazing increase in POST2 (15.4 s reduction in the 3000 m test, 2.8%). Regarding anaerobic parameters, athletes showed a reduction in velocity, force and power in POST1, but velocity and power returned to their initial conditions in POST2. In addition, all participants had higher hemoglobin (Hb) values at the end of LHTH (30 days), but at POST2 these results were close to those of PRE. The centrality metrics obtained by complex networks (pondered degree, pagerank and betweenness) in the PRE and POST2 scenarios highlighted hemoglobin, hematocrit (Hct) and minimum force, velocity and power, suggesting these variables on the way to increasing endurance performance. The Jaccard’s distance metrics showed dissimilarity between the PRE and POST2 graphs, and Hb and Hct as more prominent nodes for all centrality metrics. These results indicate that adaptive process from LHTH was highlighted by the complex networks, which can help understanding the better aerobic performance at low altitude after 16 days in Paralympic athletes.

Complex Network Model Reveals the Impact of Inspiratory Muscle Pre-Activation on Interactions among Physiological Responses and Muscle Oxygenation during Running and Passive Recovery

“…Although there are many measures of centrality [ 70 ], we chose four of them for the current investigation (Degree, Betweenness, Eigenvector and Pagerank centralities). Our option was based on previous studies involving exercise physiology [ 21 , 40 , 41 , 42 , 43 ] and a recent paper that discussed the consistency and differences between centrality measures across distinct classes of networks [ 52 ].…”

“…In this study, we used four centrality metrics to provide the complex network interpretation: Degree, Betweenness Centrality, Eigenvector Centrality and Pagerank. The use of these metrics was based on previous studies by our group in exercise investigations [ 21 , 40 , 41 , 42 , 43 ] and on Oldham et al [ 52 ]. The methodological details about the metrics adopted here are visualized in Supplementary File S1 -Complex network centrality metrics: Degree, Betweenness, Eigenvector, and Pagerank.…”

“…So, it is possible that important physiological changes occur in athletes or non-athletes submitted to IM PA prior to different exercise types but are not always detectable by statistical analyses based on “cause-effect” approaches. In previous studies, we have worked with more integrated models to investigate the physical exercise [ 40 , 41 ] and sports-related context [ 21 , 42 , 43 ], applying the concepts of complex networks [ 44 , 45 ] for biological data interpretation [ 46 , 47 ]. This line of investigation has contributed strongly to understanding biological responses that do not depend only on an isolated factor [ 46 , 47 , 48 , 49 , 50 , 51 ], which occurs with physiological adjustments in effort and recovery.…”

“…Centrality measures are capable of quantifying the capacity of a node to influence or be influenced by other parameters in a connection topology [ 52 ]. Among the many available centrality metrics, we highlight here the analysis of the Degree, Betweenness, Eigenvector and Pagerank, each with a purpose in studies with complex networks [ 21 , 42 , 43 , 52 , 53 , 54 , 55 , 56 ].…”

Associations among sleep, hematologic profile, and aerobic and anerobic capacity of young swimmers: A complex network approach

“…In the sports science field, the complex networks elucidated variables affecting the fatigue process related to physical exercise ( Pereira et al, 2015 ) and also identify critical components for different sports modalities ( Pereira et al, 2018 ; Ribeiro et al, 2019 ; Cirino et al, 2021 ; Breda et al, 2022 ), including swimming ( Pereira-Ferrero et al, 2019 ; Fiori et al, 2022 ). However, the relationship between sleep and hematological variables, as well as their impact on the aerobic/anerobic capacity of young swimmers, was not explored.…”