ECSS Paris 2023: OP-AP25
INTRODUCTION: During prolonged continuous exercise, the relationship between external load (e.g. running speed) and internal load [e.g. heart rate (HR)] shifts [1,2,3]. This means, for example, that at a given (vigorous) running speed, the HR will continuously increase, whereas if the goal of the exercise is to maintain a constant HR, the running speed will decrease over time. The time dependence of the internal/external load relationship could lead to different acute responses and chronic adaptations depending on the exercise intensity reference method. This hypothesis was tested in the present study. METHODS: Twenty-four previously inactive individuals (8 men, 16 women) were randomized into two groups: One group (SPEED-C) trained at a speed halfway between the first and second lactate thresholds, whereas the other group (HR-C) trained at an HR halfway between the thresholds. Both groups underwent 30 min of continuous endurance exercise 3 times per week for 8 weeks. Maximal oxygen consumption (VO2max) and peak treadmill speed (Vpeak) were determined by an incremental treadmill test followed by a verification test before (PRE) and after (POST) the 8 weeks of training. VO2, HR, speed, and perceived exertion measured with the Borg CR10 were recorded during the first and last treadmill training sessions performed in the lab. After POST, an additional exercise session was performed in the lab with the intensity adjusted to the new training level. RESULTS: SPEED-C showed higher speed (+1.6 km/h, p<0.001), VO2 (+7.0 mL/kg/min, p<0.001), HR (+21 bpm, p<0.001) and perceived exertion (+1.6 points, p=0.026) than HR-C during the first training session. The differences in speed (-0.7 km/h, p=0.018), VO2 (-4.2 mL/kg/min, p=0.004) and HR (-13 bpm, p<0.001) but not perceived exertion (-1.0 point, p=0.092) were reduced after 8 weeks of training. However, statistically significant reductions were no longer observed when the intensity was adjusted to the new training level. VO2max (+1.2 mL/kg/min, p=0.047) and Vpeak (+0.9 km/h, p=0.002) improved more in SPEED-C than in HR-C. Among the other physiological characteristics investigated in this study, speed at the second lactate threshold (+1.2 km/h, p<0.001) and speed halfway between the thresholds (+0.4 km/h, p=0.025) also improved more in SPEED-C than in HR-C, whereas HR at the second lactate threshold improved more in HR-C than in SPEED-C (-9 bpm, p=0.020). CONCLUSION: The exercise intensity reference method (speed vs HR) affects the acute responses and chronic adaptations in previously inactive individuals. However, training conditioning may mitigate some of these differences. 1. Zuccarelli et al. (2018) 2. Zuccarelli et al. (2021) 3. Baldassarre et al. (2022)
Read CV Raffaele MazzolariECSS Paris 2023: OP-AP25
INTRODUCTION: Acute bouts of exercise can affect the functional organization of the brain and the neural processes contributing to sports performance. Exercise duration and intensity are accepted as key factors in this interplay through their association with metabolic processes which modulate neural activity [1]. Further, qualitative characteristics of exercise bouts such as the mode [2] or setting (indoors vs. outdoors) [3] also seem to modulate acute changes in brain function due to altered sensorimotor demands. This study aimed to explore the effects of exercise duration and setting on acute changes in electroencephalography (EEG) resting state networks (RSNs). METHODS: Eleven male recreational runners (29.7±6.9 years, 75.3±4.4 kg, 184.8±5.2 cm) performed a field lactate test to obtain individual aerobic threshold (IAT). Each athlete performed two running protocols at IAT speed (3 x 30 mins) within one week, once in indoors (treadmill, IN) and once outdoors (running track, OUT). EEG RSN data (64 channel) was recorded before exercise, immediately after each block and 15 minutes after exercise cessation. Brain graphs were reconstructed to compute whole-brain small world index (SWI, network efficiency), clustering coefficient (CC, network segregation) and path length (PL, network integration) in the theta, alpha-1, and alpha-2 frequency bands. Blood lactate concentration (Lac), heart rate (HR), and Borg scale (BS) were assessed as physiological markers. A two-way repeated measures ANOVA was performed to explore the effects of exercise duration (PRE, 30, 60, 90, POST) and environment (IN vs OUT) on RSNs and physiological parameters. RESULTS: ANOVA yielded main effects of exercise duration in the alpha-1 network, indicating increases in SWI (p < .001) and CC (p < .001) and reductions in PL (p = .02) after 90 mins of exercise. Physiological outcomes were also modulated by duration and yielded increased HR and BS after 30, 60 and 90 minutes of exercise (p < .001). Main effects of exercise setting were neither observed on EEG nor physiological outcomes. CONCLUSION: In the present study, exercise duration but not setting affected RSNs. Systematic increases in alpha-1 network efficiency were observed following both indoors and outdoors running and may indicate modulations of alertness following prolonged exercise. The analysis of RSNs after exercise may therefore provide valuable insights into brain-exercise-interactions in variable exercise settings, e.g. indoors and outdoors exercise. 1 Voelcker-Rehage & Niemann (2013). Structural and functional brain changes related to different types of physical activity across the life span. Neurosci Biobehav Rev. 37:2268–95. 2 Büchel et al (2023). The Mode of Endurance Exercise Influences Changes in EEG Resting-State Graphs among High-Level Cross-Country Skiers. Medicine & Science in Sports & Exercise. 55(6). 3 Boere et al (2023). Exercising is good for the brain but exercising outside is potentially better. Sci Rep. 13, 1140.
Read CV Daniel BüchelECSS Paris 2023: OP-AP25
INTRODUCTION: Achieving a scientific consensus on defining training zones for exercise practice has proven challenging, both in athletes and sedentary individuals. Currently, methodologies such as lactate threshold and respiratory cycle analysis during maximal or sub-maximal tests serve as standard practices. These techniques come with drawbacks: the need for specific and costly equipment, as well as variations in analysis methods worldwide (Sandercock & Brodie, 2006). It is being recognized the importance of heart rate variability (HRV), as it reflects sympathetic and parasympathetic activity, among other related factors. Certain non-linear models, notably DFA alpha 1 and alpha 2 (DFAa1, DFAa2), have shown promising correlations with the aerobic threshold, making them potential markers for demarcating transitions in exercise intensity (Rogers, Giles, Draper, Hoos, & Gronwald, 2020). This study aims to analyze how HRV, specifically alpha 1, varies in sedentary individuals during and after a 3-month exercise protocol and compare it with athletes. METHODS: For this study, 30 sedentary male volunteers underwent an exercise protocol, exercising twice per week (30 minutes/session on a treadmill at 50-60% of maximal heart rate) for 12 weeks. During each session, the heart rate of each individual was tracked using a POLAR V600 (POLAR ELECTRO, Finland). Before the start (T0), in the middle (T1), and after the completion of the program (T2), all participants underwent a maximal oxygen consumption test (VO2max) (starting at 6 km/h with a 1 km/h increment every 2 minutes) using a Quark CPET system (COSMED, Italy) and tetrapolar bioimpedance analysis (InBody 770, Inbody, California). During the first month and a half (M1) and the second month and a half (M2), the HRV data were analyzed using KUBIOS Software (KUBIOS Premium OY, Finland). RESULTS: The sample was 28 ± 9 years old (age); 175.8 ± 5.5 cm (height), and 81.7 kg (total weight). Before the beginning (T0), the sample had 24 ± 9.4% of body fat and finished the exercise protocol with 23.6 ± 9.6%. The participants lost fat during these three months, as expected. Yet, the average DFAa1 in M1 was 1.13, and the DFAa2 in M2 was 1.14. On the other hand, the DFAa2 in M1 was 0.91, and the DFAa2 in M2 was 0.89. CONCLUSION: These values do not accord with the values established by the sports community for athletes exercise practice (Rogers, Giles, Draper, Mourot, & Gronwald, 2021). As far as we know, it is recommended that athletes who practice exercise must have the DFAa1 around 0.75 and the DFAa2 around 0.5. Yet, these established values do not consider newcomers or sedentary individuals who practice exercise recreationally. In conclusion, this study suggests rephrasing the pattern values to use DFAa1 and DFAa2 for recreational exercise. These values must be confirmed with a greater sample to be standardized for this type of population.
Read CV Pedro Afonso ValenteECSS Paris 2023: OP-AP25