Introduction: Cardiorespiratory fitness (CRF) is used to assess the body's ability to perform daily activities and is a criterion of overall health besides endurance performance. Therefore, an estimation of CRF can provide valuable information for healthy people to better understand their risk of developing coronary diseases, as well as for athletes to design tailored training plans to optimize their performance. The gold standard for assessing CRF is a direct measurement of maximal oxygen uptake (VO2max) in laboratory settings, which quantifies oxygen consumption mostly during graded maximal exercise tests [1]. Although direct measurements provide high accuracy, they are time-consuming, costly, and require expertise. Several indirect methods have been developed to estimate VO2max, most of which are field tests. While these tests are more accessible, they often still require technical knowledge or specialized equipment, as most of them are designed primarily for athletes and administered by experts. Therefore, performing these tests and interpreting the results can be challenging for recreational individuals. Various studies have shown that deep learning models outperform linear regression models in predicting VO2max [2]. Previous studies also showed that, to accurately predict VO2max in a laboratory or through indirect tests, individuals need to reach their maximal effort rather than performing submaximal exercises [3]. In this study, we present an approach to predict VO2max from data collected during a maximal effort test on a 600-meter uphill course, using both linear regression and other machine learning models. Methods: The subjects consisted of 10 men and 8 women between the ages of 20 and 39 years. A maximal incremental treadmill test was completed by the 18 participants to assess VO2max values. The treadmill test began at an initial speed of 7 km.h-1 with a 7% incline and the speed was increased by 0.5 km.h-1 every 30 seconds until volitional exhaustion. After a rest period of 3 to 7 days, participants performed an all out test on a 600-meter uphill track with a mean gradient of 9.7%. Data were collected during this field test with participants wearing a Polar Vantage V2 watch and Polar H10 chest belt (Polar Electro Oy, Finland). In addition to the user’s anthropometric data (age, gender, body mass index) and time, we also used the speed to heart rate (HR) ratio as a predictor variable, since a previous study has shown its importance [4]. Multiple linear regression (MLR), XGboost, and a multilayer perceptron (MLP) were used to develop models to predict VO2max. The data set was randomly split into 80% and 20% as training and test set, respectively. For the purpose of overcoming multicollinearity among the predictor variables speed to HR ratio, time, and gender, principal component analysis with two components was applied before we fed the data into the multiple linear regression model. Results: The mean measured VO2max scores were 44.1 ± 4.7 mL.kg-1.min-1 and 55.8 ± 5.8 mL.kg-1.min-1 for women and men, respectively. The total distance was covered in an average time of 3:08 ± 0:41 minutes, with an average speed of 11.7 ± 2.5 km.h-1. Regarding the performance of VO2max estimation, the MLR model achieved an R2 of 0.77 with a standard error of the estimate (SEE) of 3.4 mL.kg-1.min-1, the XGBoost model an R2 of 0.82 with a SEE of 3.0 mL.kg-1.min-1, and the MLP model an R2 of 0.94 with a SEE of 1.6 mL.kg-1.min-1. Discussion/Conclusion: These results suggest that our short, high-intensity field test, when combined with a neural network model, can provide accurate predictions of VO2max. Within our data, we have uncovered a non-linear, complex relationship between VO2max and the predictor variables that a feed-forward neural network with one hidden layer can reliably approximate. This is consistent with the findings of the previous research that emphasizes the power of deep learning models to predict VO2max accurately. One potential limitation of this study is the size of the data, for having more data could unveil other relationships between VO2max and the predictor variables. Furthermore, the performance of the models may also suffer from overfitting due to the small and homogeneous sample population and potential discrepancies between the distributions of the training and the test data. Thus, future work should include increasing the sample size and exploring probabilistic models for VO2max prediction. References: [1] 10.1155/2016/3968393; [2] doi.org/10.1016/j.imu.2022.100863; [3] Buckley, D. J., & Rowe Jr, J. R. (2018). Actual Versus Predicted VO2max: A Comparison of 4 Different Methods. In International Journal of Exercise Science: Conference Proceedings (Vol. 2, No. 10, p. 41); [4] 10.1109/BHI.2017.7897252
This study investigated PAPE effects of two conditioning activities (CA) and recovery times on the peak jumping power (PP) of elite female volleyball athletes. Players performed CA separately: three sets of three repetitions of back squats with 85% of 1RM (BS) or one set of five depth drops (DD). PP was measured with countermovement (CMJ) and squat jumps (SJ) before (pre-test) and two minutes (post-test 1) and six hours (post-test 2) after each CA. BS significantly reduced PP at post-test 1 (CMJ and SJ: p < 0.04, d between −0.36 and −0.28). At post-test 2, following BS, PP for both jump forms was significantly greater than at post-test 1 (p < 0.001, d between 0.54 and 0.55) and at pre-test (p < 0.048, d between 0.21 and 0.30). DD increased PP significantly (CMJ and SJ p < 0.05, d between 0.40 and 0.41) relative to pre-test at post-test 2 (there was no significant difference between pre-test and post-test 1). Comparing BS with DD, there were no significant differences (p > 0.05). The greatest PAPE effects were observed six hours after BS. CA are recommended for female athletes to improve jumping performance, but individual responses should be determined prior to use.
It is heavily discussed whether larger variety or specialization benefit elite performance at peak age. Therefore, this study aimed to determine technical (number of different swimming strokes) and physiological (number of different race distances) variety required to become an international-class swimmer (> 750 swimming points) based on 1'522'803 race results.; Correlation analyses showed lower technical variety in higher ranked swimmers (P
To investigate performance progression from early-junior to peak performance age and compare variety in race distances and swimming strokes between swimmers of various performance levels.; Using a longitudinal data analysis and between-groups comparisons 306,165 annual best times of male swimmers (N = 3897) were used to establish a ranking based on annual best times at peak performance age. Individual performance trajectories were retrospectively analyzed to compare distance and stroke variety. Performances of world-class finalists and international- and national-class swimmers (swimming points: 886 [30], 793 [28], and 698 [28], respectively) were compared across 5 age groups-13-14, 15-16, 17-18, 19-20, and 21+ years-using a 2-way analysis of variance with repeated measures.; World-class finalists are not significantly faster than international-class swimmers up to the 17- to 18-year age group (F2|774 = 65, P < .001, ηp2=.14) but specialize in short- or long-distance races at a younger age. World-class breaststroke finalists show faster breaststroke times compared to their performance in other swimming strokes from an early age (P < .05), while world-class freestyle and individual medley finalists show less significant differences to their performance in other swimming strokes.; While federation officials should aim for late talent selection, that is, not before the 17- to 18-year age group, coaches should aim to identify swimmers' preferred race distances early on. However, the required stroke variety seems to be specific for each swimming stroke. Breaststroke swimmers could aim for early and strong specialization, while freestyle and individual medley swimmers could maintain large and very large stroke variety, respectively.
Mit der EInführung kompetenzorientierter Lehrpläne und zur Erreichung der diesbezüglichen Kompetenzerwartungen hat sich der Bedarf an geeigneten Aufgabenformaten für das Schulfach Bewegung und Sport verstärkt. Um sportunterrichtende Lehrpersonen bei der Formulierung kompetenzorientierter Aufgaben in der Eingangsstufe zu unterstützen, gibt dieser Beitrag eine Orientierungshilfe. Dazu werden im Beitrag ein sportdidaktisches Rahmenmodell wie auch zwei exemplarisch erpropte Lernaufgaben zur Förderung motorischer Basiskompetenzen in der Eingangsstufe vorgestellt.
Exzentrisches Krafttraining, auch als negativesTraining bezeichnet, stellt eine spezifische Formdes Widerstandstrainings dar, bei der der Fokusauf die exzentrische Phase der Muskelkontraktiongelegt wird. Diese Trainingsmethode wird in derSportwissenschaft und Rehabilitation zunehmendals effektives Mittel zur Verbesserung der Muskelkraft,der Sehnenfestigkeit und der neuromuskulärenKoordination anerkannt. Der folgendeArtikel erläutert Steuerungsgrößen, Wirkungsmechanismenund stellt die praktische Relevanzim Hochleistungssport und der Rehabilitation,anhand konkreter Beispiele, heraus.
This study aimed to identify Key Performance Indicators (KPIs) for men's swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men's individual 100 m races of the 2019 European Short-Course Swimming Championships.; Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs - 2.3% to + 2.3%), Breaststroke (0% bias, LOAs - 0.9% to + 0.9%), Butterfly (0% bias, LOAs - 1.2% to + 1.2%), and Freestyle (0% bias, LOAs - 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.
Scanning is an important perceptual skill that enables football players to gather information about opponents, teammates and the environment in real-time. This study investigated scanning before and during ball possession and its effect on the success of the subsequent action in U19 female footballers. Sixty-one elite and grassroots players (age: 16.7 ± 1.4) were recruited and analysed during 4v4 small-sided games. A total of 2010 game situations were video recorded for subsequent manual tagging. Multilevel logistic regression models revealed that elite players performed significantly more scans prior to first ball contact than their grassroots counterparts, but the number of scans performed during ball possession did not differ between competition levels. Furthermore, scans before and during ball possession positively influenced player's performance, whereas opponent pressure negatively influenced the success of subsequent actions, regardless of competition level. Differentiating between various subsequent actions revealed that scans before ball possession had a positive effect on the success of dribblings and passes, whereas scans during ball possession only had a positive effect on dribblings. Our results underline the importance of scanning in youth female football. These findings should be considered by coaches in the long-term development of players to increase the level of performance at elite age.
This study aimed to determine kinematic and kinetic key performance indicators (KPI) of swimming turn performance using principal component analysis (PCA) and multiple linear regression analysis and provide reference values using percentiles. Touch and tumble turn performances of male (; n; = 68) and female (; n; = 48) Swiss national team members from three age categories-adult (20.2 ± 2.7 yrs, 790 ± 57 points), junior (16.2 ± 0.8 yrs, 729 ± 53 points) and youth swimmers (14.4 ± 1.0 years of age, 667 ± 53 World Aquatics swimming points, respectively)-were assessed with a motion analysis system equipped with a force plate on the pool wall, one over- and four underwater cameras sampling forces at 500 Hz and footages at 100 Hz. The PCA reduced the 27 original variables by up to 15% depending on turn type and age category using Varimax component loading of >0.6 and explained up to 91% of the total variance. The highest Varimax component loadings for each principal component were used to determine KPI for each turn type and age category using multiple-regression analysis with total turn time as dependent variable. These KPI should be used to interpret turn performances and identify individual swimmers' strengths, weaknesses and future potentials with the help of the percentiles as reference values.
To compare performance progression and variety in race distances of comparable lengths (timewise) between pool swimming and track running. Quality of within-sport variety was determined as the performance differences between individual athletes' main and secondary race distances across (top-) elite and (highly-) trained swimmers and runners.; A total of 3,827,947 race times were used to calculate performance points (race times relative to the world record) for freestyle swimmers (; n; = 12,588 males and; n; = 7,561 females) and track runners (; n; = 9,230 males and; n; = 5,841 females). Athletes were ranked based on their personal best at peak performance age, then annual best times were retrospectively traced throughout adolescence.; Performance of world-class swimmers differentiates at an earlier age from their lower ranked peers (15-16 vs. 17-20 year age categories,; P;
This study aimed to optimise performance prediction in short-course swimming through Principal Component Analyses (PCA) and multiple regression. All women's freestyle races at the European Short-Course Swimming Championships were analysed. Established performance metrics were obtained including start, free-swimming, and turn performance metrics. PCA were conducted to reduce redundant variables, and a multiple linear regression was performed where the criterion was swimming time. A practical tool, the Potential Predictor, was developed from regression equations to facilitate performance prediction. Bland and Altman analyses with 95% limits of agreement (95% LOA) were used to assess agreement between predicted and actual swimming performance. There was a very strong agreement between predicted and actual swimming performance. The mean bias for all race distances was less than 0.1s with wider LOAs for the 800 m (95% LOA -7.6 to + 7.7s) but tighter LOAs for the other races (95% LOAs -0.6 to + 0.6s). Free-Swimming Speed (FSS) and turn performance were identified as Key Performance Indicators (KPIs) in the longer distance races (200 m, 400 m, 800 m). Start performance emerged as a KPI in sprint races (50 m and 100 m). The successful implementation of PCA and multiple regression provides coaches with a valuable tool to uncover individual potential and empowers data-driven decision-making in athlete training.
During puberty, the biological maturity of children of the same chronological age differs. To generate equal opportunities for talent selection in youth sports, the athlete's biological maturity should be considered. This is often assessed with a left hand and wrist radiography. Alternatively, ultrasound (US) could be advantageous, especially by avoiding ionizing radiation. This pilot study aimed to assess intrarater and interrater reliability of an experienced and a non-experienced examiner in an US-based examination of the knee in 20 healthy females (10-17 years). Epiphyseal closure at five anatomical landmarks was staged (stages 1-3) and its interrater and intrarater reliabilities were analyzed using Cohen's kappa (; k; ). Interrater reliability of the calculation of the ossification ratio (OssR) was analyzed using the Bland-Altman method and intraclass correlation coefficients (ICCs). Interrater reliability for the stages was almost perfect for four landmarks. Interrater reliability ranged from; k; = 0.69 to; k; = 0.90. Intrarater reliability for the stages was almost perfect for four landmarks. Intrarater reliability ranged from; k; = 0.70 to; k; = 1.0. For the OssR, ICC was 0.930 and a minimal detectable change of 0.030 was determined. To conclude, experienced and non-experienced examiners can reliably assign individuals to different ossification stages and calculate an OssR using US-based imaging of the knee.
This study aimed to identify Key Performance Indicators (KPIs) for men's swimming strokes using Principal Component Analysis (PCA) and Multiple Regression Analysis to enhance training strategies and performance optimization. The analyses included all men's individual 100 m races of the 2019 European Short-Course Swimming Championships.; Duration from 5 m prior to wall contact (In5) emerged as a consistent KPI for all strokes. Free Swimming Speed (FSS) was identified as a KPI for 'continuous' strokes (Breaststroke and Butterfly), while duration from wall contact to 10 m after (Out10) was a crucial KPI for strokes with touch turns (Breaststroke and Butterfly). The regression model accurately predicted swim times, demonstrating strong agreement with actual performance. Bland and Altman analyses revealed negligible mean biases: Backstroke (0% bias, LOAs - 2.3% to + 2.3%), Breaststroke (0% bias, LOAs - 0.9% to + 0.9%), Butterfly (0% bias, LOAs - 1.2% to + 1.2%), and Freestyle (0% bias, LOAs - 3.1% to + 3.1%). This study emphasizes the importance of swift turning and maintaining consistent speed, offering valuable insights for coaches and athletes to optimize training and set performance goals. The regression model and predictor tool provide a data-driven approach to enhance swim training and competition across different strokes.
Background: Maintaining physical activity (PA) throughout the lifespan is crucial for overall health. Purpose: This study aimed to identify if organised sports (OS) can mitigate the age-related decline in PA among children and adolescents during five years of follow-up. Methods: The Swiss population-based SOPHYA cohort included participants aged 6-16 years at SOPHYA1 (2014) with complete accelerometer data from baseline and follow-up (SOPHYA2, 2019). Information on sex, age, BMI, and sociodemographic factors were collected by self-report. Participation in OS was calculated by linkage with the Youth and Sports (Y+S) database as the number of days participating in OS during the follow-up period. Participants were categorised as improvers or worseners based on counts per minute (CPM) and minutes in moderate-to-vigorous activity (MVPA). Participants who maintained or increased their PA in the respective domain were considered improvers. A generalised linear model examined the relationship between OS participation, baseline characteristics, and the probability of becoming a PA improver. Results: The analysis included 432 participants. There was a strong decline in CPM and MVPA from 2014 to 2019. The prevalence of improvers was 22.5% for CPM and 9.5% for MVPA. Participation in OS between 2014 and 2019 was positively associated with CPM and MVPA improvement. For 30 additional days of participation in OS, the log odds of being an improver vs. being a worsener increased by about 3.9% for CPM (p=0.04) and by about 6.1% for MVPA (p=0.03). Conclusions: As organised in the Swiss national Y+S program, OS partially counteracts age-related PA decline from childhood to young adulthood. Practical implications: This finding underscores the relevance of population-level OS promotion with specific attention to girls and children from lower socio-economic backgrounds who are at a higher risk of PA decline. Funding: Swiss Federal Office of Sport, Swiss Federal Office of Public Health, Health Promotion Switzerland.
