ECSS Paris 2023: CP-AP18
INTRODUCTION: Most studies investigating the effects of different menstrual cycle phases on maximal strength have focused on maximal performance tests (e.g., 1RM tests). However, the influence of the menstrual cycle on performance in more practical, real-world settings, such as training sessions utilizing submaximal loads (%1RM), remains largely unexplored. This study aimed to investigate the effect of the menstrual cycle on strength performance variables during a velocity-based strength training session. METHODS: Twenty healthy, young, naturally menstruating women with strength training experience completed three identical velocity-based strength training sessions. These sessions were conducted during three distinct phases of the same menstrual cycle: (a) early follicular, (b) pre-ovulatory, and (c) mid-luteal. These menstrual cycle phases were individually determined using a combination of procedures, including a mobile application that tracked menstruation onset for four months prior, as well as continuous monitoring of body temperature and body weight throughout an entire menstrual cycle. During each strength training session, participants performed two sets of six repetitions at 80% 1RM in the semi-squat, bench press, deadlift, and barbell row exercises. They were instructed to execute each repetition at maximal velocity. Barbell velocity values for each repetition were recorded using a rotary encoder. The rating of perceived exertion (RPE) was measured at the end of the strength training session. A two-way analysis of variance (ANOVA; 2×12, menstrual cycle × repetition) was conducted to compare the effects of the menstrual cycle phase in each exercise (one-way ANOVA for RPE values). A Bonferroni post hoc analysis was performed when a significant F-value was detected. RESULTS: There was a main effect of the menstrual cycle on mean velocity during semi-squat (F= 4.91, P= 0.01), and bench press (F= 5.91, P< 0.01), with no effects on deadlift (F= 2.98, P= 0.06) and barbell row (F= 2.17, P= 0.13). The post hoc analysis revealed that for semi-squat exercise, mean velocity was lower during the early follicular than pre-ovulatory (0.36±0.10 vs 0.39±0.11 m/s, P=0.02) and luteal (0.40±0.10, P< 0.01). Likewise, mean velocity was lower during early follicular than pre-ovulatory (0.29±0.10 vs 0.31±0.10 m/s, P=0.01) and luteal (0.31±0.10 m/s, P< 0.01) during the bench press exercise. Finally, there was a main effect of the menstrual cycle on RPE (F = 15.56; P < 0.01) which was higher in the early follicular phase with respect to the preovulatory phase (P < 0.01;) and the mid-luteal phase (P < 0.01). CONCLUSION: These findings suggest that barbell mean velocity may decrease, while RPE values increase, during velocity-based strength training in the early follicular phase compared to the pre-ovulatory and mid-luteal phases. This information could be valuable for strength and conditioning professionals working with women to optimize strength training sessions across the menstrual cycle.
Read CV Juan Del Coso GarrigósECSS Paris 2023: CP-AP18
INTRODUCTION: Phase Angle (PhA), derived from bioelectrical impedance analysis as the arctangent value of the ratio of Xc (reactance) to R (resistance) (Martins et al., 2023). PhA, in fact, is considered an indicator of cellular integrity and muscle quality (Ballarin et al., 2022). Its association with physical fitness parameters such as muscular endurance, power, and agility in adolescent is unclear and, in particular, in girls samples is not well established (Akamatsu et al., 2022). This study aims to investigate the relationships between PhA and key physical fitness components, identifying its potential role as a predictor or affecting the performance in this population. METHODS: A total of adolescent girls 65 (mean age 12.52) were evaluated for core endurance (sit-ups), lower-limb power (Sargent vertical and standing broad jump [SBJ]), and agility (using T-agility test). PhA and anthropometric variables (i.e. weight, height and waist circumference) were also measured in different and dry location. The biological maturation was determinated as the menarchal status (age). Stepwise regression analysis was conducted to identify predictors of physical performance while ANOVA was used to assess differences among PhA quartiles. RESULTS: Stepwise regression identified PhA as a significant predictor of sit-ups (adj R² = 0.274), Sargent jump (adj R² = 0.21), T-agility test (adj R² = 0.273), and SBJ (adj R² = 0.297). Waist circumference and fat mass (FM) also contributed to Abalakov, t-test, and SBJ performance. ANOVA revealed significant differences across PhA quartiles. Post-hoc analyses showed that girls in the highest quartile (Q4) outperformed those in Q1 in agility (p = 0.009), power (p = 0.003), and muscular endurance (p = 0.026), with notable Q1-Q4 differences. The stratification according to manarchal tempo did not show any significant differences (adj R2 =0.274). CONCLUSION: PhA is positively associated with strength, power, and agility in adolescent girls, suggesting its potential role as a functional marker of physical fitness. These findings highlight the importance of considering PhA in athletic performance assessments and during the plan of training periodization because we can included the biological condition of people within the training progression. 1. Akamatsu, Y., Kusakabe, T., Arai, H., Yamamoto, Y., Nakao, K., Ikeue, K., Ishihara, Y., Tagami, T., Yasoda, A., Ishii, K., & Satoh-Asahara, N. (2022). Phase angle from bioelectrical impedance analysis is a useful indicator of muscle quality. Journal of Cachexia, Sarcopenia and Muscle, 13(1), 180–189. 2. Ballarin, G., Valerio, G., Alicante, P., Di Vincenzo, O., & Scalfi, L. (2022). Bioelectrical Impedance Analysis (BIA)- Derived Phase Angle in Children and Adolescents: A Systematic Review. Journal of Pediatric Gastroenterology and Nutrition, 75(2), 120–130.
Read CV Nicola LovecchioECSS Paris 2023: CP-AP18
INTRODUCTION: Powerlifters have the lowest minimum velocity threshold (MVT) among strength trained individuals [1]. This may be related to either neural adaptations or the size of the lean body mass (LBM). The aim of the study was to investigate the relationship between MVT and LBM among experienced athletes who have similar lean body mass but train with either high or low external loads. METHODS: Nine powerlifters (height 1.77 ± 0.05 m, age 29.62 ± 7.54 years, body mass 92.52 ± 13.37 kg, resistance training experience 8.00 ± 2.39 years, competition experience 1.81 ± 1.80 years) and eight experienced gym trainees (height 1.85 ± 0.05 m, age 23.54 ± 4.64 years, body mass 89.31 ± 13.14 kg, resistance training experience 6.50 ± 4.11 years) participated in this study. Data collection occurred over three sessions. In the first session, maximum strength (1RM) was evaluated for the squat (SQ), bench press (BP), and deadlift (DL). In the second session, lean body mass (LBM) was assessed using dual-energy X-ray absorptiometry (DXA). In the third session, the force-velocity relationship was analyzed using external loads corresponding to 30%, 50%, 60%, 70%, 80%, 90%, and 100% of athletes’ 1RM in each exercise. Statistical analysis included descriptive statistics, independent sample t-tests, and Pearson’s r correlation coefficient (p < 0.05). RESULTS: No significant differences in LBM were observed between the groups (p > 0.05). Powerlifters had a 36% higher performance in the total lift sum than the other group (p < 0.05). The MVT was 37% lower in powerlifters’ DL (p = 0.033). No significant differences were observed in MVTs of SQ and BP between the groups (p > 0.05). In powerlifters, only MVT in SQ was positively correlated with total and arms’ lean mass (r = 0.67-0.69, p ≤ 0.05). In experienced gym trainees, only MVT in DL was positively correlated with total, arms’ and trunk’s lean mass (r = 0.77-0.82, p < 0.05). When all participants considered as one group, only MVT in SQ was positively correlated with trunk’s LBM (r = 0.495, p = 0.043). CONCLUSION: In conclusion, according to the results of the present study, it seems that LBM is not consistently related to MTV, and it seems that the correlations between MTV and LBM depends on the exercise and on athletes’ training background. Thus, it may conclude that LBM has only a minor impact on MTV, and other physiological factors, such as the function of neural system may determine MTV. 1. Helms et al. (2017)
Read CV KONSTANTINOS TROMARASECSS Paris 2023: CP-AP18