In general, it is well recognized that both acute physical exercises and regular physical training influence brain plasticity and cognitive functions positively. However, growing evidence shows that the same physical exercises induce very heterogeneous outcomes across individuals. In an attempt to better understand this interindividual heterogeneity in response to acute and regular physical exercising, most research, so far, has focused on non-modifiable factors such as sex and different genotypes, while relatively little attention has been paid to exercise prescription as a modifiable factor. With an adapted exercise prescription, dosage can be made comparable across individuals, a procedure that is necessary to better understand the dose–response relationship in exercise–cognition research. This improved understanding of dose–response relationships could help to design more efficient physical training approaches against, for instance, cognitive decline.

The short-term scaling exponent alpha1 of detrended fluctuation analysis (DFA a1), a nonlinear index of heart rate variability (HRV) based on fractal correlation properties, has been shown to steadily change with increasing exercise intensity. To date, no study has specifically examined using the behavior of this index as a method for defining a low intensity exercise zone. The aim of this report is to compare both oxygen intake (VO2) and heart rate (HR) reached at the first ventilatory threshold (VT1), a well-established delimiter of low intensity exercise, to those derived from a predefined DFA a1 transitional value. Gas exchange and HRV data were obtained from 15 participants during an incremental treadmill run. Comparison of both VO2 and HR reached at VT1 defined by gas exchange (VT1 GAS) was made to those parameters derived from analysis of DFA a1 reaching a value of 0.75 (HRVT). Based on Bland Altman analysis, linear regression, intraclass correlation (ICC) and t testing, there was strong agreement between VT1 GAS and HRVT as measured by both HR and VO2. Mean VT1 GAS was reached at 39.8 ml/kg/min with a HR of 152 bpm compared to mean HRVT which was reached at 40.1 ml/kg/min with a HR of 154 bpm. Strong linear relationships were seen between test modalities, with Pearson’s r values of 0.99 (p < 0.001) and.97 (p < 0.001) for VO2 and HR comparisons, respectively. Intraclass correlation between VT1 GAS and HRVT was 0.99 for VO2 and 0.96 for HR. In addition, comparison of VT1 GAS and HRVT showed no differences by t testing, also supporting the method validity. In conclusion, it appears that reaching a DFA a1 value of 0.75 on an incremental treadmill test is closely associated with crossing the first ventilatory threshold. As training intensity below the first ventilatory threshold is felt to have great importance for endurance sport, utilization of DFA a1 activity may provide guidance for a valid low training zone.

Exercise and training prescription in endurance-type sports has a strong theoretical background with various practical applications based on threshold concepts. Given the challenges and pitfalls of determining individual training zones on the basis of subsystem indicators (e.g., blood lactate concentration, respiratory parameters), the question arises whether there are alternatives for intensity distribution demarcation. Considering that training in a low intensity zone substantially contributes to the performance outcome of endurance athletes and exceeding intensity targets based on a misleading aerobic threshold can lead to negative performance and recovery effects, it would be desirable to find a parameter that could be derived via non-invasive, low cost and commonly available wearable devices. In this regard, analytics conducted from non-linear dynamics of heart rate variability (HRV) have been adapted to gain further insights into the complex cardiovascular regulation during endurance-type exercise. Considering the reciprocal antagonistic behavior and the interaction of the sympathetic and parasympathetic branch of the autonomic nervous system from low to high exercise intensities, it may be promising to use an approach that utilizes information about the regulation quality of the organismic system to determine training-intensity distribution. Detrended fluctuation analysis of HRV and its short-term scaling exponent alpha1 (DFA-alpha1) seems suitable for applied sport-specific settings including exercise from low to high intensities. DFA-alpha1 may be taken as an indicator for exercise prescription and intensity distribution monitoring in endurance-type sports. The present perspective illustrates the potential of DFA-alpha1 for diagnostic and monitoring purposes as a "global" system parameter and proxy for organismic demands.

Background Non‐linear measures of heart rate variability (HRV) may provide new opportunities to monitor cardiac autonomic regulation during exercise. In healthy individuals, the HRV signal is mainly composed of quasi‐periodic oscillations, but it also possesses random fluctuations and so‐called fractal structures. One widely applied approach to investigate fractal correlation properties of heart rate (HR) time series is the detrended fluctuation analysis (DFA). DFA is a non‐linear method to quantify the fractal scale and the degree of correlation of a time series. Regarding the HRV analysis, it should be noted that the short‐term scaling exponent alpha1 of DFA has been used not only to assess cardiovascular risk but also to assess prognosis and predict mortality in clinical settings. It has also been proven to be useful for application in exercise settings including higher exercise intensities, non‐stationary data segments, and relatively short recording times. Method Therefore, the purpose of this systematic review was to analyze studies that investigated the effects of acute dynamic endurance exercise on DFA‐alpha1 as a proxy of correlation properties in the HR time series. Results The initial search identified 442 articles (351 in PubMed, 91 in Scopus), of which 11 met all inclusion criteria. Conclusions The included studies show that DFA‐alpha1 of HRV is suitable for distinguishing between different organismic demands during endurance exercise and may prove helpful to monitor responses to different exercise intensities, movement frequencies, and exercise durations. Additionally, non‐linear DFA of HRV is a suitable analytical approach, providing a differentiated and qualitative view of exercise physiology.

Past attempts to define an anaerobic threshold (AnT) have relied upon gas exchange kinetics, lactate testing and field-based evaluations. DFA a1, an index of heart rate (HR) variability (HRV) fractal correlation properties, has been shown to decrease with exercise intensity. The intent of this study is to investigate whether the AnT derived from gas exchange is associated with the transition from a correlated to uncorrelated random HRV pattern signified by a DFA a1 value of 0.5. HRV and gas exchange data were obtained from 15 participants during an incremental treadmill run. Comparison of the HR reached at the second ventilatory threshold (VT2) was made to the HR reached at a DFA a1 value of 0.5 (HRVT2). Based on Bland–Altman analysis and linear regression, there was strong agreement between VT2 and HRVT2 measured by HR (r = 0.78, p < 0.001). Mean VT2 was reached at a HR of 174 (±12) bpm compared to mean HRVT2 at a HR of 171 (±16) bpm. In summary, the HR associated with a DFA a1 value of 0.5 on an incremental treadmill ramp was closely related to that of the HR at the VT2 derived from gas exchange analysis. A distinct numerical value of DFA a1 representing an uncorrelated, random interbeat pattern appears to be associated with the VT2 and shows potential as a noninvasive marker for training intensity distribution and performance status.

ObjectiveTo closely describe the injury inciting events of acute hamstring injuries in professional male football (soccer) using systematic video analysis.MethodsVideo footage from four seasons (2014–2019) of the two highest divisions in German male football was searched for moderate and severe (ie, time loss of >7 days) acute non-contact and indirect contact match hamstring injuries. Two raters independently categorised inciting events using a standardised procedure to determine specific injury patterns and kinematics.Results52 cases of hamstring injuries were included for specific pattern analysis. The pattern analysis revealed 25 sprint-related (48%) and 27 stretch-related hamstring injuries (52%). All sprint-related hamstring injuries occured during linear acceleration or high-speed running. Stretch-related hamstring injuries were connected with closed chain movements like braking or stopping with a lunging or landing action and open chain movements like kicking. The kinematic analysis of stretch-related injuries revealed a change of movement involving knee flexion to knee extension and a knee angle of <45° at the assumed injury frame in all open and closed chain movements. Biceps femoris was the most affected muscle (79%) of all included cases.ConclusionDespite the variety of inciting events, rapid movements with high eccentric demands of the posterior thigh are likely the main hamstring injury mechanism. This study provides important data about how hamstring injuries occur in professional male football and supports the need for demand-specific multicomponent risk reduction programmes.

Physical activity and physical exercise are key factors to help the population to mitigate the effects that the current pandemic is causing on the mental and physical health of citizens worldwide. In such an extreme situation, it is highly recommended to be physically active.

Within the last years complex models of cardiovascular regulation and exercise fatigue have implemented heart rate variability (HRV) as a measure of autonomic nervous system. Using detrended fluctuation analysis (DFA) to assess heart rate correlation properties, the present study examines the influence of exercise intensity on total variability and complexity in non-linear dynamics of HRV. Sixteen cyclists performed a graded exercise test on a bicycle ergometer. HRV time domain measures and fractal correlation properties were analyzed using short-term scaling exponent alpha1 of DFA. Amplitude and complexity of HRV parameters decreased significantly. DFA-alpha1 increased from rest to low exercise intensity and showed an almost linear decrease from higher intensities until exhaustion. These findings support a qualitative change in self-organized heart rate regulation from a complex autonomic control at rest and low intensities towards a breakdown of the interaction in control mechanisms with non-autonomic heart rate control dominating at high intensities.