ECSS Paris 2023: OP-AP42
INTRODUCTION: Load–velocity (L–V) profiling is widely used to estimate the one-repetition maximum (1RM) on a session-by-session basis. However, it remains unclear whether a baseline profile can accurately predict strength changes following a resistance training intervention without recalibration. This study examined the longitudinal stability of L–V profiles in the free-weight back squat and whether training-induced changes in profile characteristics impair 1RM prediction. METHODS: Thirty-six strength-trained participants (15 females, 21 males) completed free-weight back squat 1RM testing before and after a six-week resistance training intervention. Barbell velocity was recorded using a linear position transducer. Individual L–V profiles were modelled using both multiple-point (all loads) and two-point models (30% and 70% 1RM) regression models. Terminal velocity was determined using individual and general values obtained during the 1RM and a repetition-to-failure test. To assess longitudinal validity, two prediction models were constructed: (A) pre-intervention slope and intercept combined with post-intervention terminal velocity, and (B) post-intervention slope and intercept combined with pre-intervention terminal velocity, both predicting post-training 1RM. Agreement was evaluated using Bland–Altman analyses, and mixed-effects models were used to examine changes in slope, intercept, and terminal velocity. RESULTS: When baseline L–V profiles were applied to post-intervention terminal velocities (Model A), 1RM was systematically underestimated (–3.4 to –7.9 kg), with wide limits of agreement (LoA span: 32.9–56.2 kg). This underestimation reflects a pivoting of the L–V profile following the intervention: slopes flattened in two-point models (p = 0.009), whereas intercepts increased in multiple-point models (p = 0.011). Conversely, combining post-intervention profiles with baseline terminal velocities (Model B) reduced prediction bias (–2.7 to +1.7 kg), but LoA remained larger than ±5 kg for all models (span: 19.3–42.4 kg). Two-point models consistently showed larger random error and lower concordance than multiple-point models. Mixed-effects models revealed a significant reduction in terminal velocity over time (p = 0.041), indicating a lower failure threshold post-intervention. These results indicate that changes in terminal velocity contributed to the prediction error, but to a lesser extent than longitudinal changes in slope and intercept. CONCLUSION: L–V parameters are not stable enough to predict 1RM changes over a training block. Resistance training notably alters the L-V profile configuration, leading to unacceptable prediction accuracy. Consequently, L–V profiling should not replace direct testing for tracking progress; regular recalibration is essential to maintain accuracy.
Read CV Erik HobeinECSS Paris 2023: OP-AP42
INTRODUCTION: Critical load (CL) represents the lower boundary required to recruit type II muscle fibers in the early phase of strength training. Traditional approaches for determining CL or critical power (CP) typically necessitate multiple testing trials, thereby limiting their applicability. The 3-min all-out test has been employed across various exercise modalities to estimate CP or its analogues within a single session. However, this methodology has not yet been adapted for resistance exercises. Therefore, this study aimed to evaluate the validity and reliability of a 3-min all-out squat test (3MST) for estimating CL and CP, utilizing data obtained from an inertial measurement unit (IMU) and a linear position transducer (LPT). METHODS: Following the determination of one repetition maximum (1RM), ten well-trained males (squat relative 1RM: 2.4 ± 0.3 times body mass) underwent three traditional repetition-to-failure tests at 45%, 55%, and 65% of 1RM. Critical load (CLTRAD) and critical power (CPTRAD) were derived from force/power–repetition, force/power–reciprocal repetition, and work–repetition relationships, where force was calculated using barbell load and power output was obtained through LPT. Participants then performed two 3MSTs at 20% 1RM. End-test metrics, defined as the average of the last five repetitions, included mean propulsive force/power for IMU (ELIMU/EPIMU) and peak force/mean power for LPT (ELLPT/EPLPT). RESULTS: ELIMU, ELLPT, EPIMU, and EPLPT demonstrated excellent test-retest reliability, with intra-class correlation coefficients ranging from 0.96 to 0.98 (p < 0.05). CLTRAD significantly correlated with ELIMU and ELLPT (r = 0.86–0.98, p < 0.05). Bland–Altman analysis indicated that ELIMU (40 ± 8 kg, 22 ± 2%1RM, coefficient of variation [CV%]: 9–18%) and ELLPT (43 ± 10 kg, 24 ± 3%1RM, CV%: 18–27%) tended to overestimate CLTRAD (32 ± 12 to 36 ± 14 kg, 18 ± 5 to 20 ± 6%1RM). No significant difference existed between ELIMU and CLTRAD, while ELLPT was significantly higher than CLTRAD, which was based on force–repetition and work–repetition models (p < 0.05). Similarly, CPTRAD significantly correlated with EPIMU and EPLPT (r = 0.70–0.77, p < 0.05). However, Bland–Altman plots revealed that EPIMU (183 ± 76 W, CV%: 73–78%) and EPLPT (169 ± 67 W, CV%: 66–71%) substantially overestimated CPTRAD (81 ± 32 to 85 ± 37 W). EPIMU and EPLPT were significantly higher than CPTRAD (p < 0.05). CONCLUSION: The 3MST administered using portable devices constitutes a time-efficient and cost-effective approach with the potential to predict CLTRAD. The limited predictive accuracy of EPIMU and EPLPT for CP might be attributed to the heavier load and slower movement velocity characteristic of traditional methods, in contrast to 3MST. Future studies with larger sample sizes are required to determine the validity of 3MST in predicting CL. Supported by National Science and Technology Council, Taiwan (NSTC 113-2410-H-003-152-MY2 and 114-2424-H-003-007-DR). Contact: andescheng@ntnu.edu.tw
Read CV Yun-Rong YangECSS Paris 2023: OP-AP42
INTRODUCTION: Swimming is a highly streamlined sport requiring complex movement control, and performance enhancement relies on systematic strength training and effective fatigue monitoring strategies. These approaches need to be integrated within a periodized training framework to allow athletes to reach peak performance during the competition phase and to inform training planning and adjustments throughout the annual training cycle (1). Therefore, this study aimed to examine changes in in-water explosive performance across different training phases under varying resistance load conditions, to providing scientific support for periodized resistance training and performance monitoring in swimming. METHODS: This study recruited 18 male collegiate swimmers. Performed 25-m front crawl sprint tests at three time points of an annual training cycle: preparation phase (T1), competition phase (T2), and transition phase (T3). Trials were conducted under three resistance conditions corresponding to 1%, 3%, and 5% of the swimmers’ body mass using a semi-tethered system (1080 Sprint 2). Maximal velocity, peak force, peak power, and velocity decrement rate were analyzed from 5 to 20 m segments. Two-way repeated-measures ANOVA was conducted to determine the effects of training phase and resistance load on sprint performance under water. RESULTS: The results showed a significant interaction between training phase and resistance load for maximum velocity (p = .038). Simple main effects analysis indicated that maximum velocity under the 1% and 3% loads was significantly higher in the preparation phase than in the transition phase. For peak power, a significant main effect of training phase was found, with both the preparation and competition phases demonstrating higher values than the transition phase (p = .006). Similarly, peak force exhibited a significant main effect of training phase, with higher values observed during the competition phase compared to the transition phase (p = .045). CONCLUSION: Results indicate that swimming velocities at 1% and 3% of maximal velocity were higher in the preparatory phase than the transition phase, attributing high-speed performance maintenance to accumulated technical stability and neuromuscular capacity. Peak power output (preparation and competition phases) and peak force (competitive phase) were significantly higher than in the transition phase, suggesting that systematic training stimuli effectively sustain power and force. Additionally, both metrics increased with external load, demonstrating the utility of higher resistance in differentiating performance characteristics. Therefore, integrating underwater resisted swimming with periodized training enhances annual monitoring of in-water explosive performance.
Read CV Zi-Wei ZhouECSS Paris 2023: OP-AP42