Effects of Work and Recovery Duration and Their Ratio on Cardiorespiratory and Metabolic Responses During Aerobic Interval Exercise

Customizing intense interval exercise training prescription using the "frequency, intensity, time, and type of exercise" (FITT) principle

Intense interval exercise training induces various physiological and metabolic adaptations related to performance and health. For designing a program, the F.I.T.T. principle, referring to frequency, intensity, time, and type of exercise, can be used to manipulate the level of physiological stress in the body, leading to various adaptations. Modifying these four parameters results in a wide range of interval protocols that are safe and effective for different populations including athletes and individuals with chronic diseases. In this review, we present how the manipulation of the F.I.T.T. components can alter the acute and chronic cardiorespiratory, metabolic, perceptual, and affective responses and adaptations to intense interval exercise training. From this evidence, it appears that the duration of the exercise bout and recovery interval are critical parameters for the manipulation of almost all acute responses, enabling periodization of intense interval exercise training, and promoting optimal adaptations and exercise adherence. In addition, a considerable level of adaptations may be achieved with training frequencies as low as once or twice per week and with lower than maximal intensities, adding to the feasibility of this exercise mode. Overall, by varying these parameters, the design of an intense interval exercise training program can be tailored according to the needs and abilities of each individual, and an optimized training prescription may be achieved.

The Method but Not the Protocol Affects Lactate-Threshold Determination in Competitive Swimmers

PURPOSE

The study validated variables corresponding to lactate threshold (LT) in swimming. Speed (sLT), blood lactate concentration (BLLT), oxygen uptake (VO2LT), and heart rate (HRLT) corresponding to LT were calculated by 2 different incremental protocols and validated in comparison with maximal lactate steady state (MLSS).

METHODS

Ten competitive swimmers performed a 7 × 200-m front-crawl incremental "step test" with 2 protocols: (1) with 30-second rests between repetitions (short-rest incremental protocols) and (2) on a 5-minute cycle (swim + rest time, long-rest incremental protocols). Five methods were used for the assessment of sLT and corresponding BLLT, VO2LT, and HRLT: intersection of 2 lines, Dmax, modified Dmax, visual inspection, and intersection of combined linear and exponential regression lines. Subsequently, swimmers performed two to three 30-minute continuous efforts to identify speed (sMLSS) and physiological parameters corresponding to MLSS.

RESULTS

Both protocols resulted in similar sLT and corresponding physiological variables (P > .05). Bland-Altman plots showed agreement between protocols (sLT, bias: -0.017 [0.002] m·s-1; BLLT, bias: 0.0 [0.5] mmol·L-1; VO2LT, bias: -0.1 [2.2] mL·kg-1·min-1; HRLT. bias: -2 [8] beats·min-1). However, sLT calculated by modified Dmax using short rest was higher compared with speed at MLSS (1.346 [0.076] vs 1.300 [0.101] m·s-1; P < .05).

CONCLUSIONS

Calculated sLT, BLLT, VO2LT, and HRLT using all other methods in short-rest and long-rest incremental protocols were no different compared with MLSS (P > .05). Both 7 × 200-m protocols are valid for determination of sLT and corresponding physiological parameters, but the modified Dmax method may overestimate sLT.

Influence of Recovery Mode on the Maximum Number of Intervals Until Exhaustion During an Aerobic Interval Training Session

ABSTRACT

Varela-Sanz, A, Sánchez-Otero, T, Tuimil, JL, Boullosa, D, and Iglesias-Soler, E. Influence of recovery mode on the maximum number of intervals until exhaustion during an aerobic interval training session. J Strength Cond Res 37(9): e510-e520, 2023-We analyzed work capacity, cardiometabolic, perceptual, and neuromuscular responses to an aerobic interval training (AIT) running session until exhaustion, with active (AR) vs. passive recovery (PR). Eight well-trained male endurance runners (36.88 ± 7.14 years, 58.22 ± 3.39 ml·kg -1 ·minute -1 ) randomly completed, after familiarizations and the University of Montreal Track Test (UMTT), 2 AIT track running sessions until exhaustion consisting in 2-minute bouts at 100% of maximum aerobic speed (MAS), with 2 minutes of recovery at 80% of the velocity associated to the second ventilatory threshold (vVT 2 ) (i.e., AR), or no exercise (i.e., PR). Oxygen consumption (V̇O 2 ), heart rate (HR), blood lactate [La], rating of perceived exertion (RPE), and countermovement jump (CMJ) were continuously monitored during sessions. The level of statistical significance was set at p ≤ 0.05. PR resulted in longer time to exhaustion during sessions (13.9 vs. 11.6 bouts, p = 0.045), but lower HR ( p < 0.01) when compared with AR. Time spent over 90% of maximum oxygen consumption (V̇O 2max ), blood lactate concentrations, neuromuscular performance, and RPE did not differ between AR and PR ( p > 0.05). Thus, PR allowed runners to perform more work intervals and, therefore, to accumulate a greater volume. On the other hand, when training goals are focused on reaching a higher chronotropic stress (i.e., higher HR) during the training session, athletes would obtain more benefits from AR. This study also demonstrates that the current volume recommendations for AIT are far below (54-64.5%) the maximum training capacity of well-trained runners.

Marathon Performance Depends on Pacing Oscillations between Non Symmetric Extreme Values

A marathon was recently run in less than 2 h by a man who ran the three fastest marathons ever recorded in a span of three years—Eliud Kipchoge—in the Tokyo Olympic games. Here, we demonstrate that the best marathons were run according to a pace distribution that is statistically not constant and with negative asymmetry. The concept of mirror race enables us to show that the sign of asymmetry is not due to sampling fluctuations. We show that marathon performance depends on pacing oscillations between extreme values, and that even the best marathons ever run differ and can be improved upon. The utilization of extreme values and oscillations allows for recovery and optimization of the complementary aerobic and anaerobic metabolisms. Our findings suggest new ways to approach the pacing for optimizing endurance performance.