Sexual dimorphism in heart rate recovery from peak exercise

Considerations for Sex-Cognizant Research in Exercise Biology and Medicine

As the fields of kinesiology, exercise science, and human movement developed, the majority of the research focused on male physiology and extrapolated findings to females. In the medical sphere, basing practice on data developed in only males resulted in the removal of drugs from the market in the late 1990s due to severe side effects (some life-threatening) in females that were not observed in males. In response to substantial evidence demonstrating exercise-induced health benefits, exercise is often promoted as a key modality in disease prevention, management, and rehabilitation. However, much like the early days of drug development, a historical literature knowledge base of predominantly male studies may leave the exercise field vulnerable to overlooking potentially key biological differences in males and females that may be important to consider in prescribing exercise (e.g., how exercise responses may differ between sexes and whether there are optimal approaches to consider for females that differ from conventional approaches that are based on male physiology). Thus, this review will discuss anatomical, physiological, and skeletal muscle molecular differences that may contribute to sex differences in exercise responses, as well as clinical considerations based on this knowledge in athletic and general populations over the continuum of age. Finally, this review summarizes the current gaps in knowledge, highlights the areas ripe for future research, and considerations for sex-cognizant research in exercise fields.

Age- and Sex-Related Differences in Recovery From High-Intensity and Endurance Exercise: A Brief Review

Postexercise recovery is a fundamental component for continuous performance enhancement. Due to physiological and morphological changes in aging and alterations in performance capacity, athletes of different ages may recover at different rates from physical exercise. Differences in body composition, physiological function, and exercise performance between men and women may also have a direct influence on restoration processes.

PURPOSE

This brief review examines current research to indicate possible differences in recovery processes between male and female athletes of different age groups. The paper focuses on postexercise recovery following sprint and endurance tests and tries to identify determinants that modulate possible differences in recovery between male and female subjects of different age groups.

RESULTS

The literature analysis indicates age- and sex-dependent differences in short- and long-term recovery. Short-term recovery differs among children, adults, and masters. Children have shorter lactate half-life and a faster cardiac and respiratory recovery compared to adults. Additionally, children and masters require shorter recovery periods during interval bouts than trained adults. Trained women show a slower cardiac and respiratory recovery compared to trained men. Long-term recovery is strongly determined by the extent of muscle damage. Trained adults tend to have more extensive muscle damage compared to masters and children.

CONCLUSION

The influence of age and sex on the recovery process varies among the different functional systems and depends on the time of the recovery processes. Irrespective of age and sex, the performance capacity of the individual determines the recovery process after high-intensity and endurance exercise.

Sex differences in cardiac vagal reactivation from the end of isometric handgrip exercise and at the onset of muscle metaboreflex isolation

A parasympathetic reactivation is an underlying mechanism mediating the rapid fall in heart rate (HR) at the onset of post-exercise ischemia (PEI) in humans. Herein, we tested the hypothesis that, compared to men, women present a slower HR recovery at the cessation of isometric handgrip exercise (i.e., onset of PEI) due to an attenuated cardiac vagal reactivation. Forty-seven (23 women) young and healthy volunteers were recruited. Subjects performed 90s of isometric handgrip exercise at 40% of maximal voluntary contraction followed by 3-min of PEI. The onset of PEI was analyzed over the first 30s in 10s windows. Cardiac vagal reactivation was indexed using the HR fall and by HR variability metrics (e.g., RMSSD and SDNN) immediately after the cessation of the exercise. HR was significantly increased from rest during exercise in men and women and increases were similar between sexes. However, following the cessation of exercise, the HR recovery was significantly slower in women compared to men regardless of the time point (women vs. men: ∆-14 ± 8 vs. ∆-18 ± 6 beats.min^-1 at 10s; ∆-20 ± 9 vs. ∆-25 ± 8 beats.min^-1 at 20s; ∆-22 ± 10 vs. ∆-27 ± 9 beats.min^-1 at 30s; P = .027). RMSSD and SDNN increased at the cessation of exercise in greater magnitude in men compared to women. These findings demonstrate that women had a slower HR recovery at the cessation of isometric handgrip exercise and onset of PEI compared to men, suggesting a sex-related difference in cardiac vagal reactivation in healthy young humans.

Fatigue Evaluation through Machine Learning and a Global Fatigue Descriptor

Research in physiology and sports science has shown that fatigue, a complex psychophysiological phenomenon, has a relevant impact in performance and in the correct functioning of our motricity system, potentially being a cause of damage to the human organism. Fatigue can be seen as a subjective or objective phenomenon. Subjective fatigue corresponds to a mental and cognitive event, while fatigue referred as objective is a physical phenomenon. Despite the fact that subjective fatigue is often undervalued, only a physically and mentally healthy athlete is able to achieve top performance in a discipline. Therefore, we argue that physical training programs should address the preventive assessment of both subjective and objective fatigue mechanisms in order to minimize the risk of injuries. In this context, our paper presents a machine-learning system capable of extracting individual fatigue descriptors (IFDs) from electromyographic (EMG) and heart rate variability (HRV) measurements. Our novel approach, using two types of biosignals so that a global (mental and physical) fatigue assessment is taken into account, reflects the onset of fatigue by implementing a combination of a dimensionless (0-1) global fatigue descriptor (GFD) and a support vector machine (SVM) classifier. The system, based on 9 main combined features, achieves fatigue regime classification performances of 0.82 ± 0.24, ensuring a successful preventive assessment when dangerous fatigue levels are reached. Training data were acquired in a constant work rate test (executed by 14 subjects using a cycloergometry device), where the variable under study (fatigue) gradually increased until the volunteer reached an objective exhaustion state.

A "Wearable" Test for Maximum Aerobic Power: Real-Time Analysis of a 60-m Sprint Performance and Heart Rate Off-Kinetics

Maximum aerobic power (V˙O2peak) as an indicator of body fitness is today a very well-known concept not just for athletes but also for the layman. Unfortunately, the accurate measurement of that variable has remained a complex and exhaustive laboratory procedure, which makes it inaccessible to many active people. In this paper we propose a quick estimate of it, mainly based on the heart rate off-kinetics immediately after an all-out 60-m sprint run. The design of this test took into account the recent availability of wrist wearable, heart band free, multi-sensor smart devices, which could also inertially detect the different phases of the sprint and check the distance run. 25 subjects undertook the 60-m test outdoor and a V˙O2peak test on the laboratory treadmill. Running average speed, HR excursion during the sprint and the time constant (τ) of HR exponential decay in the off-kinetics were fed into a multiple regression, with measured V˙O2peak as the dependent variable. Statistics revealed that within the investigated range (25–55 ml O₂/(kg min)), despite a tendency to overestimate low values and underestimate high values, the three predictors confidently estimate individual V˙O2peak (R² = 0.65, p < 0.001). The same analysis has been performed on a 5-s averaged time course of the same measured HR off-kinetics, as these are the most time resolved data for HR provided by many modern smart watches. Results indicate that despite of the substantial reduction in sample size, predicted V˙O2peak still explain 59% of the variability of the measured V˙O2peak.