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Running in a triathlon, a so-called brick run, is uniquely influenced by accumulated load from its preceding disciplines. Crucially, however, and irrespective of race type, the demands of a triathlon always exceed the sum of its parts. Triathletes of all levels commonly report subjectively perceived incoordination within the initial stages of the cycle run transition (T2). Although minimizing it, and its influence on running kinematics, can positively impact running and overall triathlon performance, the mechanisms behind the T2 effect remain unclear. In the present study, we assessed the influence of the pre-load exercise mode focusing on the biomechanical perspective. To analyze inertial sensor-based raw data from both legs, the so-called Attractor Method was applied. The latter represents a sensitive approach, allowing to quantify subtle changes of cyclic motions to uncover the transient effect, a potentially detrimental transient phase at the beginning of a run. The purpose was to analyze the impact of a pre-load on the biomechanics of a brick run during a simulated Olympic Distance triathlon (without the swimming section). Therefore, we assessed the influence of pre-load exercise mode on running pattern (δM) and precision (δD), and on the length of the transient effect (tT) within a 10 km field-based run in 22 well-trained triathletes. We found that δD, but not δM, differed significantly between an isolated run (IRun) and when it was preceded by a 40 km cycle (TRun) or an energetically matched run (RRun). The average distance ran until overcoming the transient phase (tT) was 679 m for TRun, 450 m for RRun, and 29 4 m for IRun. The results demonstrated that especially the first kilometer of a triathlon run is prone to an uncoordinated running sensation, which is also commonly reported by athletes. That is, i) the T2 effect appeared more linked to variability in running style than to running style per se ii) run tT distance was influenced by preceding exercise load mode, being greater for a TRun than for the RRun condition, and iii) the Attractor Method seemed to be a potentially promising method of sensitively monitoring T2 adaptation under ecologically valid conditions.

ObjectivesFatigue is a frequent and often disabling symptom in patients with post-COVID syndrome. To better understand and evaluate the symptom of motor fatigue in the context of the post-COVID syndrome, we conducted treadmill walking tests to detect the phenomenon of motor fatigability or to evaluate whether evidence of organic lesions of the motor system could be found, similar to patients with multiple sclerosis.MethodTwenty-nine non-hospitalized patients with post-COVID syndrome completed the Fatigue Scale for Motor and Cognitive Function (FSMC) questionnaire to determine the trait component of subjective fatigue before they were tested on a treadmill walking at a moderate speed for up to 60 min or until exhaustion. During the walking test oxygen uptake, ventilation and acceleration data of both feet were collected. To determine motor performance fatigability, the Fatigue Index Kliniken Schmieder (FKS) was calculated using the attractor method.ResultsThe average walking duration was 42.7 ± 18.6 min with 15 subjects stopping the walking test prematurely. The FSMC score revealed a severe cognitive (37.6 ± 8.2) and motor (37.1 ± 7.8) fatigue averaged over all subjects but only two subjects showed an FKS above the normal range (>4), representing performance fatigability. There was no significant correlation between subjective fatigue (FSMC) and FKS as well as walking time. Absolute values of oxygen uptake and ventilation were in the normal range reported in literature (r = 0.9, p < 0.05), although eight subjects did not produce a steady-state behavior.ConclusionAlmost all patients with post-COVID syndrome and subjectively severe motor fatigue, did not show motor fatigability nor severe metabolic anomalies. This is argued against organic, permanent damage to the motor system, as is often seen in MS. Many of the patients were - to our and their own surprise - motorically more exertable than expected.

Sport and exercise can be boring. In the general population, thinking of sports as boring has been linked to exercising less. However, less is known about the role of boredom in people who participate in ultra-endurance competitions: Do these athletes also associate their sports with boredom, and does boredom pose a self-regulatory challenge that predicts if they encounter a crisis during an ultra-endurance competition? Here, we investigate these questions with a sample of N = 113 (n = 34 female) competitors of a 24 h hour running competition, aged M = 37.6 ± 13.8 years. In this study, n = 23 very extreme athletes competed as single starters or in a relay team of 2, and n = 84 less extreme athletes competed in relay teams of 4 or 6. Before the run, athletes completed self-report measures on sport-specific trait boredom, as well as the degree to which they expected boredom, pain, effort, and willpower to constitute self-regulatory challenges they would have to cope with. After the run, athletes reported the degree to which they actually had to deal with these self-regulatory challenges and if they had faced an action crisis during the competition. Analyses revealed that very extreme athletes displayed a significantly lower sport-specific trait boredom than less extreme athletes (p = 0.024, d=−0.48 d=−0.48 ). With respect to self-regulatory challenges, willpower, pain, and effort were expected and reported at a much higher rate than boredom. However, only boredom was as a significant predictor of experiencing a crisis during the competition (odds ratio = 12.5, p = 0.02). Our results show that boredom also matters for highly active athletes. The fact that the experience of boredom—and not more prototypical competition-induced challenges, such as pain or effort—were linked to having an action crisis highlights the relevance of incorporating boredom into the preparation for a race and to the performance management during competition. published

Physical activity plays a major role in daily life of a human being. This is especially true when considering bipedal locomotion but can also encompass swimming or riding a bicycle for example. The analysis of human motion becomes particularly interesting when it has to be learned (during growth), when it is limited (in cases of disease and rehabilitation), or for the purpose of improving physical performance (as for sport). In this cumulative dissertation a modern approach, the so-called attractor method, is applied to kinematically differentiate human cyclic motion, especially running. Two theoretical papers have been published in international journals. Two additional works provide insight into the fields of application of the method and its significance for sports practice. The first theoretical work describes the kinematic components of cyclic human running motion using the attractor method. The model presented can be described by six superimposed components: first, the individual attractor, i.e., the running movement itself, and morphing, which represents continuous deviations from it during the course of the run. In addition, the transient effect, which explains fluctuations in running behavior that occur especially in the first minutes after the onset of a session. In addition, there are less strong factors such as short-term fluctuations, a control mechanism that directs the movement back to the attractor should it deviate too much from it, and finally technical noise, which is derived from the measurement technology. The corresponding analyses and validation were performed with both, measured and simulated data. In a second, continued work concerning the above-mentioned factors, the transient effect was investigated in more details. This aspect is particularly interesting from a practical point of view, since many athletes report an arrhythmic feeling at the beginning of a running session. However, this sensation should diminish after a few minutes, once the athletes have found their rhythm. With this study it was possible to objectively quantify exactly this initial feeling. In addition, the methodology also made it possible to estimate the duration of this transient phase and to check whether it is related to athletic performance and training experience. These findings provide novel information for a proper estimation of human running and thus a possible tool to steer athletic running performance. Two further application studies support the theoretical works described above in terms of its practical relevance. It is known that familiar people can often be recognized by their posture or gait pattern even from a distance. With the help of the attractor method, we created a gaitprint for each recorded runner in the first of the two application-oriented papers. The gaitprint refers to an individual attractor that is so typical and inherent that the respective person can be identified with a very high probability solely on the basis of it. Despite the strong individuality of the gaitprints, it is still possible to create a global attractor for the running movement itself. Another practical project was conducted at Northern Michigan University in Marquette (MI, USA) with the local cross-country skiing college team. The skating technique of 16 athletes was examined and the attractor method was used to identify the respective techniques, the so-called V1 or V2 style. In addition, individual technique assessments were carried out for case studies, which made it possible to obtain a differentiated impression of leg, arm and trunk movements. Overall, the findings of both studies open up new possibilities to analyze movements of cyclic sports in a cost- and effort-efficient way and to upgrade identification algorithms to the detection of movements. published

Recognizing the characteristics of a well-developed running style is a central issue in athletic sub-disciplines. The development of portable micro-electro-mechanical-system (MEMS) sensors within the last decades has made it possible to accurately quantify movements. This paper introduces an analysis method, based on limit-cycle attractors, to identify subjects by their specific running style. The movement data of 30 athletes were collected over 20 min. in three running sessions to create an individual gaitprint. A recognition algorithm was applied to identify each single individual as compared to other participants. The analyses resulted in a detection rate of 99% with a false identification probability of 0.28%, which demonstrates a very sensitive method for the recognition of athletes based solely on their running style. Further, it can be seen that these differentiations can be described as individual modifications of a general running pattern inherent in all participants. These findings open new perspectives for the assessment of running style, motion in general, and a person&rsquo, s identification, in, for example, the growing e-sports movement.