Specific Intensity for Peaking: Is Race Pace the Best Option?

Assessing the Validity of Two Non-Exercise Regression Equations for Predicting Maximal Oxygen Consumption

Purpose: This study aimed to develop two regression equations to predict maximal oxygen consumption (VO2max) using non-exercise data from a substantial cohort of healthy Iranian adult males. Additionally, this study sought to examine the predictive accuracy of these equations across four different levels of physical activity. Methods: A total of 126 participants (age: 34.9 ± 11.3 years, body mass index [BMI]: 24.9 ± 2.7 kg/m², and body fat percentage [BF%]: 18.3 ± 4.9) completed a maximal graded exercise test to measure VO2max, with a mean of 45.0 ± 3.4 ml.kg-1.min-1. Participants also provided information on age, current physical activity rating (PA-R), and either BMI or BF% to estimate VO2max using Jackson and colleagues' regression equations. The PA-R was assessed via a standardized questionnaire and categorized into four levels: sedentary, low, moderate, and high. Results: The key findings from this study indicate that both original models significantly underestimated actual VO2max in a large cohort of Iranian adults (both, p < .001 and mean differences exceeding 2.19 ml.kg-1.min-1). Nevertheless, these models provided accurate predictions for VO2max among individuals with moderate levels of physical activity (both, p > .08 and mean differences between 0.51 and 1.03 ml.kg-1.min-1). Furthermore, the models demonstrated moderate validity, as evidenced by an intraclass correlation coefficient (ICC) of 0.841 and a coefficient of variation averaging 10.9%, with a range from 8.5% to 13.6%. Conclusions: While Jackson's two non-exercise models showed limited accuracy in predicting VO2max among Iranian healthy male adults, they exhibited reasonable precision, particularly among moderately active men.

HIIT vs. SIT: What Is the Better to Improve V˙O2max? A Systematic Review and Meta-Analysis

Lack of time is seen as a barrier to maintaining a physically active lifestyle. In this sense, interval training has been suggested as a time-efficient strategy for improving health, mainly due to its potential to increase cardiorespiratory fitness. Currently, the most discussed interval training protocols in the literature are the high-intensity interval training (HIIT) and the sprint interval training (SIT). Objective: We investigated, through a systematic review and meta-analysis, which interval training protocol, HIIT or SIT, promotes greater gain in cardiorespiratory fitness (V˙O2max/peak). The studies were selected from the PubMed (MEDLINE), Scopus and Web of Science databases. From these searches, a screening was carried out, selecting studies that compared the effects of HIIT and SIT protocols on V˙O2max/peak. A total of 19 studies were included in the final analysis. Due to the homogeneity between studies (I2 = 0%), fixed-effects analyses were performed. There was no significant difference in the V˙O2max/peak gains between HIIT and SIT for the standardized mean difference (SMD = 0.150; 95% CI = -0.038 to 0.338; p = 0.119), including studies that presented both measurements in mL·kg-1·min-1 and l·min-1; and raw mean differences (RMD = 0.921 mL·kg-1·min-1; 95% CI = -0.185 to 2.028; p = 0.103) were calculated only with data presented in mL·kg-1·min-1. We conclude that the literature generates very consistent data to confirm that HIIT and SIT protocols promote similar gains in cardiorespiratory fitness. Thus, for this purpose, the choice of the protocol can be made for convenience.

Four Weeks of 16/8 Time Restrictive Feeding in Endurance Trained Male Runners Decreases Fat Mass, without Affecting Exercise Performance

Background: Time restricted Feeding (TRF) is a dietary pattern utilized by endurance athletes, but there is insufficient data regarding its effects on performance and metabolism in this population. The purpose of this investigation was to examine the effects of a 16/8 TRF dietary pattern on exercise performance in trained male endurance runners. Methods: A 4-week randomized crossover intervention was used to compare an 8-h TRF to a 12-h normal diet (ND) feeding window. Exercise training and dietary intake were similar across interventions. Runners completed a dual-energy X-ray absorptiometry (DXA) scan to assess body composition, a graded treadmill running test to assess substrate utilization, and ran a 10 km time trial to assess performance. Results: There was a significant decrease in fat mass in the TRF intervention (−0.8 ± 1.3 kg with TRF (p = 0.05), vs. +0.1 ± 4.3 kg with ND), with no significant change in fat-free mass. Exercise carbon dioxide production (VCO₂) and blood lactate concentration were significantly lower with the TRF intervention (p ≤ 0.02). No significant changes were seen in exercise respiratory exchange ratio or 10 km time trial performance (−00:20 ± 3:34 min:s TRF vs. −00:36 ± 2:57 min:s ND). Conclusion: This investigation demonstrated that adherence to a 4-week 16/8 TRF dietary intervention decreased fat mass and maintained fat-free mass, while not affecting running performance, in trained male endurance runners.

Anaerobic Speed/Power Reserve and Sport Performance: Scientific Basis, Current Applications and Future Directions

Many individual and team sport events require extended periods of exercise above the speed or power associated with maximal oxygen uptake (i.e., maximal aerobic speed/power, MAS/MAP). In the absence of valid and reliable measures of anaerobic metabolism, the anaerobic speed/power reserve (ASR/APR) concept, defined as the difference between an athlete's MAS/MAP and their maximal sprinting speed (MSS)/peak power (MPP), advances our understanding of athlete tolerance to high speed/power efforts in this range. When exercising at speeds above MAS/MAP, what likely matters most, irrespective of athlete profile or locomotor mode, is the proportion of the ASR/APR used, rather than the more commonly used reference to percent MAS/MAP. The locomotor construct of ASR/APR offers numerous underexplored opportunities. In particular, how differences in underlying athlete profiles (e.g., fiber typology) impact the training response for different 'speed', 'endurance' or 'hybrid' profiles is now emerging. Such an individualized approach to athlete training may be necessary to avoid 'maladaptive' or 'non-responses'. As a starting point for coaches and practitioners, we recommend upfront locomotor profiling to guide training content at both the macro (understanding athlete profile variability and training model selection, e.g., annual periodization) and micro levels (weekly daily planning of individual workouts, e.g., short vs long intervals vs repeated sprint training and recovery time between workouts). More specifically, we argue that high-intensity interval training formats should be tailored to the locomotor profile accordingly. New focus and appreciation for the ASR/APR is required to individualize training appropriately so as to maximize athlete preparation for elite competition.

Is the Functional Threshold Power a Valid Metric to Estimate the Maximal Lactate Steady State in Cyclists?

Lillo-Beviá, JR, Courel-Ibáñez, J, Cerezuela-Espejo, V, Morán-Navarro, R, Martínez-Cava, A, and Pallarés, JG. Is the functional threshold power a valid metric to estimate the maximal lactate steady state in cyclists? J Strength Cond Res XX(X): 000-000, 2019-The aims of this study were to determine (a) the repeatability of a 20-minute time-trial (TT20), (b) the location of the TT20 in relation to the main physiological events of the aerobic-anaerobic transition, and (c) the predictive power of a list of correction factors and linear/multiple regression analysis applied to the TT20 result to estimate the individual maximal lactate steady state (MLSS). Under laboratory conditions, 11 trained male cyclists and triathletes (V[Combining Dot Above]O2max 59.7 ± 3.0 ml·kg·min) completed a maximal graded exercise test to record the power output associated with the first and second ventilatory thresholds and V[Combining Dot Above]O2max measured by indirect calorimetry, several 30 minutes constant tests to determine the MLSS, and 2 TT20 tests with a short warm-up. Very high repeatability of TT20 tests was confirmed (standard error of measurement of ±3 W and smallest detectable change of ±9 W). Validity results revealed that MLSS differed substantially from TT20 (bias = 26 ± 7 W). The maximal lactate steady state was then estimated from the traditional 95% factor (bias = 12 ± 7 W) and a novel individual correction factor (ICF% = MLSS/TT20), resulting in 91% (bias = 1 ± 6 W). Complementary linear (MLSS = 0.7488 × TT20 + 43.24; bias = 0 ± 5 W) and multiple regression analysis (bias = 0 ± 4 W) substantially improved the individual MLSS workload estimation. These findings suggest reconsidering the TT20 procedures and calculations to increase the effectiveness of the MLSS prediction.

Effects of an increase in intensity during tapering on 1500-m running performance

We examined the effect of completing the final interval training session during a taper at either (i) race pace (RP) or (ii) faster than RP on 1500-m running performance and neuromuscular performance. Ten trained runners (age, 21.7 ± 3.0 years; height, 182.9 ± 7.0 cm; body mass, 73.4 ± 6.8 kg; and personal best 1500-m time, 4:17.5 ± 0:26.9 min) completed 2 conditions consisting of 7 days of regular training and a 7-day taper, separated by 3 weeks of training. In 1 condition, the taper was prescribed using prediction models based on the practices of elite British middle-distance runners, with the intensity of the final interval session being equal to 1500-m RP. The taper was repeated in the high-intensity (HI) condition, with the exception that the final interval session was completed at 115% of 1500-m RP. A 1500-m treadmill time trial and measures of maximal voluntary contraction (MVC) and rate of force development (RFD) were completed before and after regular training and tapering. Performance was most likely improved after RP (mean ± 90% confidence limits, 10.1 ± 1.6 s), and possibly beneficial after HI (4.2 ± 12.0 s). Both MVC force (p = 0.002) and RFD (p = 0.02) were improved after tapering, without differences between conditions. An RP taper based on the practices of elite middle-distance runners is recommended to improve performance in young, subelite runners. The effect of this strategy with an increase in interval intensity is highly variable and should be implemented with caution.

Interval running with self-selected recovery: Physiology, performance, and perception

This study (1) compared the physiological responses and performance during a high-intensity interval training (HIIT) session incorporating externally regulated (ER) and self-selected (SS) recovery periods and (2) examined the psychophysiological cues underpinning SS recovery durations. Following an incremental maximal exercise test to determine maximal aerobic speed (MAS), 14 recreationally active males completed 2 HIIT sessions on a non-motorised treadmill. Participants performed 12 × 30 s running intervals at a target intensity of 105% MAS interspersed with 30 s (ER) or SS recovery periods. During SS, participants were instructed to provide themselves with sufficient recovery to complete all 12 efforts at the required intensity. A semi-structured interview was undertaken following the completion of SS. Mean recovery duration was longer during SS (51 ± 15 s) compared to ER (30 ± 0 s; p < .001; d = 1.46 ± 0.46). Between-interval heart rate recovery was higher (SS: 19 ± 9 b min^-1; ER: 8 ± 5 b min^-1; p < .001; d = 1.43 ± 0.43) and absolute time ≥90% maximal heart rate (HR_max) was lower (SS: 335 ± 193 s; ER: 433 ± 147 s; p = .075; d = 0.52 ± 0.39) during SS compared to ER. Relative time ≥105% MAS was greater during SS (90 ± 6%) compared to ER (74 ± 20%; p < .01; d = 0.87 ± 0.40). Different sources of afferent information underpinned decision-making during SS. The extended durations of recovery during SS resulted in a reduced time ≥90% HR_max but enhanced time ≥105% MAS, compared with ER exercise. Differences in the afferent cue utilisation of participants likely explain the large levels of inter-individual variability observed.