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The aim of this study was to evaluate the effect of a repeated sprint training with multi-directional change-of-direction (COD) movements (RSmulti) compared to repeated shuttle sprints (RSS) on variables related to COD speed and reactive agility. Nineteen highly-trained male U15 soccer players were assigned into two groups performing either RSmulti or RSS. For both groups, each training session involved 20 repeated 15 s sprints interspersed with 30 s recovery. With RSmulti the COD movements were randomized and performed in response to a visual stimulus, while the RSS involved predefined 180° COD movements. Before and following the six training sessions, performance in the Illinois agility test (IAT), COD speed in response to a visual stimulus, 20 m linear sprint time and vertical jumping height were assessed. Both groups improved their performance in the IAT (p < 0.01, ES = 1.13; p = 0.01, ES = 0.55). The COD speed in response to a visual stimulus improved with the RSmulti (p < 0.01, ES = 1.03), but not the RSS (p = 0.46, ES = 0.28). No differences were found for 20 m sprint time (P=0.73, ES = 0.07; p = 0.14, ES = 0.28) or vertical jumping height (p = 0.46, ES = 0.11; p = 0.29, ES = 0.12) for the RSmulti and RSS, respectively. In conclusion, performance in the IAT improved with the RSmulti as well as RSS. With the RSmulti however, the COD movements are performed in response to a visual stimulus, which may result in specific adaptations that improve COD speed and reactive agility in young highly trained soccer players.
Purpose
Pronounced differences in individual physiological adaptation may occur following various training mesocycles in runners. Here we aimed to assess the individual changes in performance and physiological adaptation of recreational runners performing mesocycles with different intensity, duration and frequency.
Methods
Employing a randomized cross-over design, the intra-individual physiological responses [i.e., peak (\(\dot{VO}_{2peak}\)) and submaximal (\(\dot{VO}_{2submax}\)) oxygen uptake, velocity at lactate thresholds (V\(_2\), V\(_4\))] and performance (time-to-exhaustion (TTE)) of 13 recreational runners who performed three 3-week sessions of high-intensity interval training (HIIT), high-volume low-intensity training (HVLIT) or more but shorter sessions of HVLIT (high-frequency training; HFT) were assessed.
Results
\(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) and TTE were not altered by HIIT, HVLIT or HFT (p > 0.05). \(\dot{VO}_{2peak}\) improved to the same extent following HVLIT (p = 0.045) and HFT (p = 0.02). The number of moderately negative responders was higher following HIIT (15.4%); and HFT (15.4%) than HVLIT (7.6%). The number of very positive responders was higher following HVLIT (38.5%) than HFT (23%) or HIIT (7.7%). 46% of the runners responded positively to two mesocycles, while 23% did not respond to any.
Conclusion
On a group level, none of the interventions altered \(\dot{VO}_{2submax}\), V\(_2\), V\(_4\) or TTE, while HVLIT and HFT improved \(\dot{VO}_{2peak}\). The mean adaptation index indicated similar numbers of positive, negative and non-responders to HIIT, HVLIT and HFT, but more very positive responders to HVLIT than HFT or HIIT. 46% responded positively to two mesocycles, while 23% did not respond to any. These findings indicate that the magnitude of responses to HIIT, HVLIT and HFT is highly individual and no pattern was apparent.
Monitoring variations in the functioning of the autonomic nervous system may help personalize training of runners and provide more pronounced physiological adaptations and performance improvements. We systematically reviewed the scientific literature comparing physiological adaptations and/or improvements in performance following training based on responses of the autonomic nervous system (ie, changes in heart rate variability) and predefined training. PubMed, SPORTDiscus, and Web of Science were searched systematically in July 2019. Keywords related to endurance, running, autonomic nervous system, and training. Studies were included if they (a) involved interventions consisting predominantly of running training; (b) lasted at least 3 weeks; (c) reported pre‐ and post‐intervention assessment of running performance and/or physiological parameters; (d) included an experimental group performing training adjusted continuously on the basis of alterations in HRV and a control group; and (e) involved healthy runners. Five studies involving six interventions and 166 participants fulfilled our inclusion criteria. Four HRV‐based interventions reduced the amount of moderate‐ and/or high‐intensity training significantly. In five interventions, improvements in performance parameters (3000 m, 5000 m, Loadmax, Tlim) were more pronounced following HRV‐based training. Peak oxygen uptake (VO\(_{2peak}\)) and submaximal running parameters (eg, LT1, LT2) improved following both HRV‐based and predefined training, with no clear difference in the extent of improvement in VO\(_{2peak}\). Submaximal running parameters tended to improve more following HRV‐based training. Research findings to date have been limited and inconsistent. Both HRV‐based and predefined training improve running performance and certain submaximal physiological adaptations, with effects of the former training tending to be greater.
Although it is becoming increasingly popular to monitor parameters related to training, recovery, and health with wearable sensor technology (wearables), scientific evaluation of the reliability, sensitivity, and validity of such data is limited and, where available, has involved a wide variety of approaches. To improve the trustworthiness of data collected by wearables and facilitate comparisons, we have outlined recommendations for standardized evaluation. We discuss the wearable devices themselves, as well as experimental and statistical considerations. Adherence to these recommendations should be beneficial not only for the individual, but also for regulatory organizations and insurance companies.
Here, we performed a non-systematic analysis of the strength, weaknesses, opportunities, and threats (SWOT) associated with the application of artificial intelligence to sports research, coaching and optimization of athletic performance. The strength of AI with regards to applied sports research, coaching and athletic performance involve the automation of time-consuming tasks, processing and analysis of large amounts of data, and recognition of complex patterns and relationships. However, it is also essential to be aware of the weaknesses associated with the integration of AI into this field. For instance, it is imperative that the data employed to train the AI system be both diverse and complete, in addition to as unbiased as possible with respect to factors such as the gender, level of performance, and experience of an athlete. Other challenges include e.g., limited adaptability to novel situations and the cost and other resources required. Opportunities include the possibility to monitor athletes both long-term and in real-time, the potential discovery of novel indicators of performance, and prediction of risk for future injury. Leveraging these opportunities can transform athletic development and the practice of sports science in general. Threats include over-dependence on technology, less involvement of human expertise, risks with respect to data privacy, breaching of the integrity and manipulation of data, and resistance to adopting such new technology. Understanding and addressing these SWOT factors is essential for maximizing the benefits of AI while mitigating its risks, thereby paving the way for its successful integration into sport science research, coaching, and optimization of athletic performance.
Establishing a cardiac training group for patients with heart failure: the "HIP-in-Würzburg" study
(2022)
Background
Exercise training in heart failure (HF) is recommended but not routinely offered, because of logistic and safety-related reasons. In 2020, the German Society for Prevention&Rehabilitation and the German Society for Cardiology requested establishing dedicated ""HF training groups."" Here, we aimed to implement and evaluate the feasibility and safety of one of the first HF training groups in Germany.
Methods
Twelve patients (three women) with symptomatic HF (NYHA class II/III) and an ejection fraction ≤ 45% participated and were offered weekly, physician-supervised exercise training for 1 year. Patients received a wrist-worn pedometer (M430 Polar) and underwent the following assessments at baseline and after 4, 8 and 12 months: cardiopulmonary exercise test, 6-min walk test, echocardiography (blinded reading), and quality of life assessment (Kansas City Cardiomyopathy Questionnaire, KCCQ).
Results
All patients (median age [quartiles] 64 [49; 64] years) completed the study and participated in 76% of the offered 36 training sessions. The pedometer was worn ≥ 1000 min per day over 86% of the time. No cardiovascular events occurred during training. Across 12 months, NT-proBNP dropped from 986 pg/ml [455; 1937] to 483 pg/ml [247; 2322], and LVEF increased from 36% [29;41] to 41% [32;46]%, (p for trend = 0.01). We observed no changes in exercise capacity except for a subtle increase in peak VO2% predicted, from 66.5 [49; 77] to 67 [52; 78]; p for trend = 0.03. The physical function and social limitation domains of the KCCQ improved from 60 [54; 82] to 71 [58; 95, and from 63 [39; 83] to 78 [64; 92]; p for trend = 0.04 and = 0.01, respectively. Positive trends were further seen for the clinical and overall summary scores.
Conclusion
This pilot study showed that the implementation of a supervised HF-exercise program is feasible, safe, and has the potential to improve both quality of life and surrogate markers of HF severity. This first exercise experiment should facilitate the design of risk-adopted training programs for patients with HF.
Background
Physical activity (PA) guidelines acknowledge the health benefits of regular moderate-to-vigorous physical activity (MVPA) regardless of bout duration. However, little knowledge exists concerning the type and intensity distribution of structured and incidental lifestyle PA of students and office workers. The present study aimed to i) assess the duration and distribution of intensity of MVPAs during waking hours ≥50% of heart rate reserve (HRR), ii) to identify the type of PA through diary assessment, iii) to assign these activities into structured and lifestyle incidental PA, and iv) to compare this information between students and office workers.
Methods
Twenty-three healthy participants (11 students, 12 office workers) recorded heart rate (HR) with a wrist-worn HR monitor (Polar M600) and filled out a PA diary throughout seven consecutive days (i.e. ≥ 8 waking h/day). Relative HR zones were calculated, and PA diary information was coded using the Compendium of PA. We matched HR data with the reported PA and identified PA bouts during waking time ≥ 50% HRR concerning duration, HRR zone, type of PA, and assigned each activity to incidental and structured PA. Descriptive measures for time spend in different HRR zones and differences between students and office workers were calculated.
Results
In total, we analyzed 276.894 s (76 h 54 min 54 s) of waking time in HRR zones ≥50% and identified 169 different types of PA. The participants spend 31.9 ± 27.1 min/day or 3.9 ± 3.2% of their waking time in zones of ≥50% HRR with no difference between students and office workers (p > 0.01). The proportion of assigned incidental lifestyle PA was 76.9 ± 22.5%.
Conclusions
The present study provides initial insights regarding the type, amount, and distribution of intensity of structured and incidental lifestyle PA ≥ 50% HRR. Findings show a substantial amount of incidental lifestyle PA during waking hours and display the importance of promoting a physically active lifestyle. Future research could employ ambulatory assessments with integrated electronic diaries to detect information on the type and context of MVPA during the day.
This paper provides the evidence of a sweet spot on the boot/foot as well as the method for detecting it with a wearable pressure sensitive device. This study confirmed the hypothesized existence of sweet and dead spots on a soccer boot or foot when kicking a ball. For a stationary curved kick, kicking the ball at the sweet spot maximized the probability of scoring a goal (58–86%), whereas having the impact point at the dead zone minimized the probability (11–22%). The sweet spot was found based on hypothesized favorable parameter ranges (center of pressure in x/y-directions and/or peak impact force) and the dead zone based on hypothesized unfavorable parameter ranges. The sweet spot was rather concentrated, independent of which parameter combination was used (two- or three-parameter combination), whereas the dead zone, located 21 mm from the sweet spot, was more widespread.