Anthropometric Profiles in Table Tennis Players: Analysis of Sex, Age, and Ranking

Fuente, Francisco Pradas de la; Torre, Ana de la; Páez, Luís Carrasco; Muñoz, Diego; Courel-Ibáñez, Javier; González-Jurado, José Antonio

doi:10.3390/app11020876

Cited by 14 publications

(15 citation statements)

References 40 publications

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“…In support of the slogan recently adopted by the World Health Organization, Every Move Counts [17], the fact that both physically active children and table tennis players presented similar low fat mass below 20% in boys and 30% in girls reinforces the positive impact of regular or recreational exercise during childhood. Indeed, young, high-level table tennis players presented an even lower fat mass, below 20% [46], suggesting that regular table tennis practice may benefit children in maintaining a healthier body composition. The current sex differences identified agreed with natural disparities at these ages [14,15], with girls reaching important maturational events earlier than boys [47].…”

Section: Discussionmentioning

confidence: 99%

Benefits of Regular Table Tennis Practice in Body Composition and Physical Fitness Compared to Physically Active Children Aged 10–11 Years

Fuente

Ara

Toro

et al. 2021

IJERPH

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The aim of this study was to identify the differences in body composition and physical fitness between children who played table tennis regularly during a two-year period compared to physically active children who were not engaged in a regular activity. Three hundred seventy-four children aged 10 to 11 years were divided into two groups: table tennis players (n = 109 boys and 73 girls) and physically active group (n = 88 boys and 104 girls). Anthropometric analysis included body mass index, skinfolds, perimeters and bone diameters. Somatotype and body composition were determined according to age-specific equations. Physical fitness assessment included hand grip dynamometry (strength), sit-and-reach test (range of movement) and maximal multistage 20 m shuttle run test (cardiovascular fitness). The result show that children who regularly played table tennis had greater bone development and superior physical fitness compared to those who were physically active but not engaged in a regular physical activity. This is the largest study to date presenting data about the potential of table tennis to benefit health in children. These results constitute an important first step in clarifying the effectiveness of table tennis as a health-promotion strategy to encourage children to undertake regular physical activity and limit sedentary behavior.

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Section: Discussionmentioning

confidence: 99%

Benefits of Regular Table Tennis Practice in Body Composition and Physical Fitness Compared to Physically Active Children Aged 10–11 Years

Fuente

Ara

Toro

et al. 2021

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“…The purposes of this study were to analyze the table tennis players' physical profile (i.e., handgrip isometric strength, vertical jump, horizontal jump, sprint, CODA, and flexibility) considering the age of the participants and to analyze the relationships among different physical capacities. Even though previous studies have analyzed the physical profile of table tennis players [16,17,28,37,38] the strength of this study lies in the assessment of a broader age range, because only a few of the aforementioned studies have considered a large age range. This novel approach allowed us to identify the differences on each of the capacities analyzed by this study in each of the age groups considered.…”

Section: Discussionmentioning

confidence: 99%

Physical Fitness Profiling of National Category Table Tennis Players: Implication for Health and Performance

Picabea

Cámara

Yanci

2021

IJERPH

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The aims of this study were to: (1) analyze table tennis players’ physical profiles considering and comparing players age categories (i.e., under U12, U14, U16, U20, Senior and Older); and (2) to quantify the correlations among the variables measured by each test. Seventy-one table tennis players (61 men and 10 women, 19.7 ± 11.23 years, 1.65 ± 0.13 m, 59.71 ± 17.72 kg and 21.60 ± 4.22 kg/m2) divided into six age groups, performed a sprint test, forearm isometric strength test, countermovement vertical test, countermovement horizontal test, change of direction ability (CODA) test and flexibility test. U14 players performed better than U12 in all tests (ES = −0.70 to 1.98, moderate to large) except in Sit and Reach (SAR) test (ES = 0.19, trivial). The U16 group also obtained better results than U14 in all tests (ES = 0.77 to −2.31, moderate to large) except for the SAR test (ES = 0.19, trivial). The U20 group performed better than U16 in all the tests (ES = 0.73 to −1.53, moderate to large) except for the 5 m sprint test (ES = −0.02, trivial), 10 m sprint test (ES = −0.51, moderate) and SAR (ES = 0.11, trivial). Differences between Senior and U20 were only found in the arm swing counter movement jump (CMJAS) (ES = −0.82, large) and modified agility test (MAT) (ES = 1.19, large), with the U20 group being better in both variables. The senior group performed better in the MAT test than the older group (ES = 0.94, large). The relation found between forearm isometric strength, vertical jump, horizontal jump, sprint and CODA ability (r = −0.53; ±0.14, 0/0/100, most likely to r = 0.83; ±0.06, 100/0/0, most likely) indicates that these capacities are related in table tennis players. Nevertheless, the lack of association between the sit and reach test with the other capacities may indicate that flexibility is an independent capacity.

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“…The fifth layer: Output layer Computational Intelligence and Neuroscience e nodes in the compensation operation layer of the fourth layer are compensation nodes, which mainly carry out the operation of each node's compensation operation, so as to achieve the purpose of dynamically adjusting the fuzzy rules. As shown in formulas ( 9) and ( 10), it is the input and output of the fourth layer: f (4) j � α j 1− r j +r j /n � z j , (…”

Section: Feedback Layermentioning

confidence: 99%

“…As shown in formulas ( 11) and ( 12), it is the formula of the fifth antifuzzifying layer: (4) l m l�1 d l g (4) l 5) � g (5) � f (5) . (…”

Section: Feedback Layermentioning

confidence: 99%

“…In recent years, with the continuous development and application of big data analysis technology, video technology, and tactics data extraction technology, the pregame auxiliary system has been widely used in the sports industry, which makes the sports industry show significant changes in all aspects [1][2][3][4]. e application of these technologies in sports can provide detailed basic data and classified advanced data for athletes and coaches and comprehensively show the advantages and disadvantages of athletes in the competition [5,6] so that athletes find their own shortcomings and problems and further targeted training.…”

Section: Introductionmentioning

confidence: 99%

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[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

Zhang

2021

Computational Intelligence and Neuroscience

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In the system design of table tennis robot, the important influencing factors of automatic detection of technical and tactical indicators for table tennis are table tennis rotation state, trajectory, and rebound force. But the general prediction algorithm cannot process the time series data and give the corresponding rotation state. Therefore, this paper studies the automatic detection method of technical and tactical indicators for table tennis based on the trajectory prediction using the compensation fuzzy neural network. In this paper, the compensation fuzzy neural network algorithm which combines the compensation fuzzy algorithm and recurrent neural network is selected to construct the automatic detection of technical and tactical indicators for table tennis. The experimental results show that the convergence time of the compensation fuzzy neural network is shorter, the training time is shortened, and the prediction accuracy is improved. At the same time, in terms of performance testing, the model can accurately distinguish the influence of table tennis rotation state and rebound on table tennis motion estimation, so as to improve the accuracy of motion trajectory prediction. In addition, the accuracy of trajectory prediction will be improved with the increase of input data. When the number of data reaches 30, the trajectory prediction error is within the actual acceptable error range.

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Anthropometric Profiles in Table Tennis Players: Analysis of Sex, Age, and Ranking

Cited by 14 publications

References 40 publications

Benefits of Regular Table Tennis Practice in Body Composition and Physical Fitness Compared to Physically Active Children Aged 10–11 Years

Benefits of Regular Table Tennis Practice in Body Composition and Physical Fitness Compared to Physically Active Children Aged 10–11 Years

Physical Fitness Profiling of National Category Table Tennis Players: Implication for Health and Performance

[Retracted] Automatic Detection Method of Technical and Tactical Indicators for Table Tennis Based on Trajectory Prediction Using Compensation Fuzzy Neural Network

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