@article{SperlichDuekingLeppichetal.2023, author = {Sperlich, Billy and D{\"u}king, Peter and Leppich, Robert and Holmberg, Hans-Christer}, title = {Strengths, weaknesses, opportunities, and threats associated with the application of artificial intelligence in connection with sport research, coaching, and optimization of athletic performance: a brief SWOT analysis}, series = {Frontiers in Sports and Active Living}, volume = {5}, journal = {Frontiers in Sports and Active Living}, doi = {10.3389/fspor.2023.1258562}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357973}, year = {2023}, abstract = {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.}, language = {en} } @article{SperlichMatzkaHolmberg2023, author = {Sperlich, Billy and Matzka, Manuel and Holmberg, Hans-Christer}, title = {The proportional distribution of training by elite endurance athletes at different intensities during different phases of the season}, series = {Frontiers in Sports and Active Living}, volume = {5}, journal = {Frontiers in Sports and Active Living}, doi = {10.3389/fspor.2023.1258585}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-357988}, year = {2023}, abstract = {The present review examines retrospective analyses of training intensity distribution (TID), i.e., the proportion of training at moderate (Zone 1, Z1), heavy (Z2) and severe (Z3) intensity by elite-to-world-class endurance athletes during different phases of the season. In addition, we discuss potential implications of our findings for research in this field, as well as for training by these athletes. Altogether, we included 175 TIDs, of which 120 quantified exercise intensity on the basis of heart rate and measured time-in-zone or employed variations of the session goal approach, with demarcation of zones of exercise intensity based on physiological parameters. Notably, 49\% of the TIDs were single-case studies, predominantly concerning cross-country skiing and/or the biathlon. Eighty-nine TIDs were pyramidal (Z1 > Z2 > Z3), 65 polarized (Z1 > Z3 > Z2) and 8 "threshold" (Z2 > Z1 = Z3). However, these relative numbers varied between sports and the particular phases of the season. In 91\% (n = 160) of the TIDs >60\% of the endurance exercise was of low intensity. Regardless of the approach to quantification or phase of the season, cyclists and swimmers were found to perform a lower proportion of exercise in Z1 (<72\%) and higher proportion in Z2 (>16\%) than athletes involved in the triathlon, speed skating, rowing, running, cross-country skiing or biathlon (>80\% in Z1 and <12\% in Z2 in all these cases). For most of the athletes their proportion of heavy-to-severe exercise was higher during the period of competition than during the preparatory phase, although with considerable variability between sports. In conclusion, the existing literature in this area does not allow general conclusions to be drawn. The methods utilized for quantification vary widely and, moreover, contextual information concerning the mode of exercise, environmental conditions, and biomechanical aspects of the exercise is often lacking. Therefore, we recommend a more comprehensive approach in connection with future investigations on the TIDs of athletes involved in different endurance sports.}, language = {en} } @article{BauerSperlichHolmbergetal.2022, author = {Bauer, Nikolai and Sperlich, Billy and Holmberg, Hans-Christer and Engel, Florian A.}, title = {Effects of High-Intensity Interval Training in School on the Physical Performance and Health of Children and Adolescents: A Systematic Review with Meta-Analysis}, series = {Sports Medicine - Open}, volume = {8}, journal = {Sports Medicine - Open}, number = {1}, issn = {2199-1170}, doi = {10.1186/s40798-022-00437-8}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-301205}, year = {2022}, abstract = {Objectives To assess the impact of HIIT performed at school, i.e. both in connection with physical education (intra-PE) and extracurricular sports activities (extra-PE), on the physical fitness and health of children and adolescents. Methods PubMed and SPORTDiscus were searched systematically utilizing the following criteria for inclusion: (1) healthy children and adolescents (5-18 years old) of normal weight; (2) HIIT performed intra- and/or extra-PE for at least 5 days at an intensity ≥ 80\% of maximal heart rate (HR\(_{max}\)) or peak oxygen uptake (VO\(_{2peak}\)) or as Functional HIIT; (3) comparison with a control (HIIT versus alternative interventions); and (4) pre- and post-analysis of parameters related to physical fitness and health. The outcomes with HIIT and the control interventions were compared utilizing Hedges' g effect size (ES) and associated 95\% confidence intervals. Results Eleven studies involving 707 participants who performed intra-PE and 388 participants extra-PE HIIT were included. In comparison with the control interventions, intra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 5.89 ± 5.67 (range 1.88-9.90); ES number of push-ups: 6.22 (range n.a.); ES number of sit-ups: 2.66 ± 2.02 (range 1.24-4.09)), as well as ES fasting glucose levels (- 2.68 (range n.a.)) more effectively, with large effect sizes. Extra-PE HIIT improved mean ES for neuromuscular and anaerobic performance (ES jump performance: 1.81 (range n.a.); ES number of sit-ups: 2.60 (range n.a.)) to an even greater extent, again with large effect sizes. Neither form of HIIT was more beneficial for parameters related to cardiorespiratory fitness than the control interventions. Conclusion Compared to other forms of exercise (e.g. low-to-moderate-intensity running or walking), both intra- and extra-PE HIIT result in greater improvements in neuromuscular and anaerobic performance, as well as in fasting levels of glucose in school children.}, language = {en} } @article{GuederWilkesmannScholzetal.2022, author = {G{\"u}der, G{\"u}lmisal and Wilkesmann, Joana and Scholz, Nina and Leppich, Robert and D{\"u}king, Peter and Sperlich, Billy and Rost, Christian and Frantz, Stefan and Morbach, Caroline and Sahiti, Floran and Stefenelli, Ulrich and Breunig, Margret and St{\"o}rk, Stefan}, title = {Establishing a cardiac training group for patients with heart failure: the "HIP-in-W{\"u}rzburg" study}, series = {Clinical Research in Cardiology}, volume = {111}, journal = {Clinical Research in Cardiology}, issn = {1861-0692}, doi = {10.1007/s00392-021-01892-1}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-266678}, pages = {406-415}, year = {2022}, abstract = {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.}, language = {en} } @article{WallmannSperlichDuekingMuelleretal.2022, author = {Wallmann-Sperlich, Birgit and D{\"u}king, Peter and M{\"u}ller, Miriam and Frob{\"o}se, Ingo and Sperlich, Billy}, title = {Type and intensity distribution of structured and incidental lifestyle physical activity of students and office workers: a retrospective content analysis}, series = {BMC Public Health}, volume = {22}, journal = {BMC Public Health}, doi = {10.1186/s12889-022-12999-z}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-301217}, year = {2022}, abstract = {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.}, language = {en} } @article{McIlroyPassfieldHolmbergetal.2021, author = {McIlroy, Benjamin and Passfield, Louis and Holmberg, Hans-Christer and Sperlich, Billy}, title = {Virtual training of endurance cycling - A summary of strengths, weaknesses, opportunities and threats}, series = {Frontiers in Sports and Active Living}, volume = {3}, journal = {Frontiers in Sports and Active Living}, doi = {10.3389/fspor.2021.631101}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-258876}, year = {2021}, abstract = {Virtual online training has emerged as one of the top 20 worldwide fitness trends for 2021 and continues to develop rapidly. Although this allows the cycling community to engage in virtual training and competition, critical evaluation of virtual training platforms is limited. Here, we discuss the strengths, weaknesses, opportunities and threats associated with virtual training technology and cycling in an attempt to enhance awareness of such aspects. Strengths include immersive worlds, innovative drafting mechanics, and versatility. Weaknesses include questionable data accuracy, inadequate strength and reliability of power-speed algorithms. Opportunities exist for expanding strategic partnerships with major cycling races, brands, and sponsors and improving user experience with the addition of video capture and "e-coaching." Threats are present in the form of cheating during competition, and a lack of uptake and acceptance by a broader community.}, language = {en} } @article{DuekingHolmbergKunzetal.2020, author = {D{\"u}king, Peter and Holmberg, Hans‑Christer and Kunz, Philipp and Leppich, Robert and Sperlich, Billy}, title = {Intra-individual physiological response of recreational runners to different training mesocycles: a randomized cross-over study}, series = {European Journal of Applied Physiology}, volume = {120}, journal = {European Journal of Applied Physiology}, issn = {1439-6319}, doi = {10.1007/s00421-020-04477-4}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-235022}, pages = {2705-2713}, year = {2020}, abstract = {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.}, language = {en} } @article{DuekingGiessingFrenkeletal.2020, author = {D{\"u}king, Peter and Giessing, Laura and Frenkel, Marie Ottilie and Koehler, Karsten and Holmberg, Hans-Christer and Sperlich, Billy}, title = {Wrist-Worn Wearables for Monitoring Heart Rate and Energy Expenditure While Sitting or Performing Light-to-Vigorous Physical Activity: Validation Study}, series = {JMIR mhealth and uhealth}, volume = {8}, journal = {JMIR mhealth and uhealth}, number = {5}, doi = {10.2196/16716}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-229413}, year = {2020}, abstract = {Background: Physical activity reduces the incidences of noncommunicable diseases, obesity, and mortality, but an inactive lifestyle is becoming increasingly common. Innovative approaches to monitor and promote physical activity are warranted. While individual monitoring of physical activity aids in the design of effective interventions to enhance physical activity, a basic prerequisite is that the monitoring devices exhibit high validity. Objective: Our goal was to assess the validity of monitoring heart rate (HR) and energy expenditure (EE) while sitting or performing light-to-vigorous physical activity with 4 popular wrist-worn wearables (Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa). Methods: While wearing the 4 different wearables, 25 individuals performed 5 minutes each of sitting, walking, and running at different velocities (ie, 1.1 m/s, 1.9 m/s, 2.7 m/s, 3.6 m/s, and 4.1 m/s), as well as intermittent sprints. HR and EE were compared to common criterion measures: Polar-H7 chest belt for HR and indirect calorimetry for EE. Results: While monitoring HR at different exercise intensities, the standardized typical errors of the estimates were 0.09-0.62, 0.13-0.88, 0.62-1.24, and 0.47-1.94 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 0.9\%-4.3\%, 2.2\%-6.7\%, 2.9\%-9.2\%, and 4.1\%-19.1\%, respectively, for the 4 wearables. While monitoring EE at different exercise intensities, the standardized typical errors of the estimates were 0.34-1.84, 0.32-1.33, 0.46-4.86, and 0.41-1.65 for the Apple Watch Series 4, Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, respectively. Depending on exercise intensity, the corresponding coefficients of variation were 13.5\%-27.1\%, 16.3\%-28.0\%, 15.9\%-34.5\%, and 8.0\%-32.3\%, respectively. Conclusions: The Apple Watch Series 4 provides the highest validity (ie, smallest error rates) when measuring HR while sitting or performing light-to-vigorous physical activity, followed by the Polar Vantage V, Garmin Fenix 5, and Fitbit Versa, in that order. The Apple Watch Series 4 and Polar Vantage V are suitable for valid HR measurements at the intensities tested, but HR data provided by the Garmin Fenix 5 and Fitbit Versa should be interpreted with caution due to higher error rates at certain intensities. None of the 4 wrist-worn wearables should be employed to monitor EE at the intensities and durations tested."}, language = {en} } @article{BeckerSperlichZinneretal.2020, author = {Becker, Manuel and Sperlich, Billy and Zinner, Christoph and Achtzehn, Silvia}, title = {Intra-Individual and Seasonal Variation of Selected Biomarkers for Internal Load Monitoring in U-19 Soccer Players}, series = {Frontiers in Physiology}, volume = {11}, journal = {Frontiers in Physiology}, number = {838}, issn = {1664-042X}, doi = {10.3389/fphys.2020.00838}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-209616}, year = {2020}, abstract = {The aim of this study was to investigate inter-day and -week as well as intra- and inter-individual variation of selected biomarkers in high-performance youth soccer players to assist practitioners interpreting player's internal load to counteract underperformance and unwanted health risks. Eleven male youth soccer players were tested multiple times during two 3-week periods at midpoint (3-wkmid) and at the end (3-wkend) of the first half of a German under-19 1. Bundesliga season. The levels of creatine kinase (CK), urea, and C-reactive protein (CRP) were measured during 3-wkmid and 3-wkend each Monday, Wednesday, and Friday. In 3-wkmid the CK median was 14\% higher (241 vs. 212 U/L) compared to 3-wkend (P = 0.26, ES = 0.16). Overall, the medians of CK, urea (P = 0.59, ES = 0.08), and CRP (P = 0.56, ES = 0.10) during 3-wkmid did not differ to the values of 3-wkend. Daily coefficient of variations (CVs) ranged from 22 to 71\% (CK), 17 to 37\% (urea), and 9 to 164\% (CRP). Individual medians ranged from 101 to 350 U/L (CK), 23 to 50 mg/dL (urea), and 0.6 to 1.1 mg/L (CRP). High intra-individual variability was demonstrated by large intra-individual CVs (medians: CK 50\%, urea 18\%, and CRP 45\%). Our data show (i) large inter-day and inter-week variability of all biomarkers, depending on the external load and (ii) considerable inter- and intra-individual parameter variations. Creatine kinase concentrations could sensitively reflect soccer-specific loads during the season.}, language = {en} } @article{DavidsonDuekingZinneretal.2020, author = {Davidson, Padraig and D{\"u}king, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas}, title = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study}, series = {Sensors}, volume = {20}, journal = {Sensors}, number = {9}, issn = {1424-8220}, doi = {10.3390/s20092637}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-205686}, year = {2020}, abstract = {The rating of perceived exertion (RPE) is a subjective load marker and may assist in individualizing training prescription, particularly by adjusting running intensity. Unfortunately, RPE has shortcomings (e.g., underreporting) and cannot be monitored continuously and automatically throughout a training sessions. In this pilot study, we aimed to predict two classes of RPE (≤15 "Somewhat hard to hard" on Borg's 6-20 scale vs. RPE >15 in runners by analyzing data recorded by a commercially-available smartwatch with machine learning algorithms. Twelve trained and untrained runners performed long-continuous runs at a constant self-selected pace to volitional exhaustion. Untrained runners reported their RPE each kilometer, whereas trained runners reported every five kilometers. The kinetics of heart rate, step cadence, and running velocity were recorded continuously ( 1 Hz ) with a commercially-available smartwatch (Polar V800). We trained different machine learning algorithms to estimate the two classes of RPE based on the time series sensor data derived from the smartwatch. Predictions were analyzed in different settings: accuracy overall and per runner type; i.e., accuracy for trained and untrained runners independently. We achieved top accuracies of 84.8 \% for the whole dataset, 81.8 \% for the trained runners, and 86.1 \% for the untrained runners. We predict two classes of RPE with high accuracy using machine learning and smartwatch data. This approach might aid in individualizing training prescriptions.}, language = {en} }