Relationships among anthropometric characteristics,muscular fitness,and sprint performance in adolescents

Abstract

BACKGROUND:

Assessment of physical fitness is a common and effective method for evaluating the health status of teenage students.

OBJECTIVES:

The present study was conducted to assess physical fitness in adolescents residing in Shanghai, and investigate the relationships among anthropometry, muscle fitness, and a 20-meter shuttle run test (20-m SRT).

METHODS:

A total of 449 middle school students (12–17 years old, 246 boys and 203 girls) from four different regions of Shanghai, China were included as study participants.

RESULTS:

Multiple linear regression analyses indicate that upper extremity muscle strength and standing vertical jump were positively related to age and weight, while negatively related to gender, scapular skinfold, and calf skinfold. Strong positive correlations were also demonstrated between the number of laps accomplished on the 20-m SRT and upper extremity muscle strength as well as standing vertical jump height.

CONCLUSION:

Muscle strength is an important anthropometric characteristic that may be used for the assessment of sprinting ability in adolescents.

Keywords

Anthropometry muscle strength 20-m SRT multiple linear regression analysis

1. Introduction

Good physical fitness performance in adolescents is strongly associated with more favorable health benefits, including skeletal muscle, psychological health, and lesser risk for obesity, cardiovascular diseases and other acute diseases [1]. It is generally accepted that an enhanced health status is often associated with a greater level of physical fitness and higher energy levels in students [2]. Social development has changed day-to-day lifestyle tendencies and in recent decades adolescents have spent less time participating in physical activity [3]. A previous study demonstrates that motor proficiency and physical fitness are correlated with the occurrence of autism spectrum disorders in adolescent males [4]. Additionally, in 2015, Haga et al. documented that motor competence has a strong association with physical fitness, a correlation that decreases with age [5]. Physical fitness evaluation can directly reflect the functional status of all body systems and actually predict human’s performance in daily life [6]. As such, assessment of physical fitness has been developed as a common and effective method to evaluate the health status of adolescent students.

Physical fitness is a prerequisite for completing daily activities without fatigue and consists of five components: body composition, cardiorespiratory endurance, muscular strength, muscular endurance, and flexibility [7]. Specifically, musculoskeletal fitness(MSF) is usually assessed by the standing vertical jump [8]. Cardiorespiratory endurance is important for both adequate delivery of oxygen and nutrients to the muscles, and remove of waste from the muscles [9]. As a pivotal parameter of aerobic performance, the maximal speed reached in a 20-m shuttle run test(20-m SRT) is measured to assess maximal oxygen uptake (one of the best indicators of cardiorespiratory endurance capacity) and blood-lactate accumulation [10]. Furthermore, strong evidences indicates that the 20-m SRT is a valid test for cardiorespiratory fitness (CRF), and the number of laps accomplished on a 20-m SRT is identified as an independent factor for cardio-metabolic risk [8].

Several physical fitness investigations have been conducted worldwide, however ethnic differences often lead to different results [11, 12]. Several studies have also been conducted to explore the relationships among muscular strength and power, and health outcomes in youth, especially in professional athletes [13, 14, 15]. The assessment of the relationship between anthropometry and muscular fitness, however, remains unclear in Chinese adolescents.

To explore the physical fitness of Chinese adolescents, a physical fitness assessment was conducted among 449 middle school students in Shanghai, China. The effects of anthropometric characteristics on muscular fitness and the relationship between muscle fitness and repeated sprint performance were analyzed to provide an evaluation of the physical fitness of adolescents in China.

2. Methods

2.1 Participants

Subjects from various schools in Shanghai, China, were selected by the Shanghai Institute of Physical Education using a cluster sampling method. Geography was considered during school selection, and all chosen schools were in different regions of Shanghai. A total of 449 middle school students aged 12 to 17 years (246 boys and 203 girls) were enrolled from four different middle schools. All protocols were prepared and conducted according to the local Ethical Committee of Shanghai Institute of Physical Education, and informed consents were obtained from all participants.

2.2 Procedures

The testing team was organized by the Shanghai Institute of Physical Education, with 21 people, including 19 testers (at least six women), 1 doctor, and 1 instructor. All students followed a similar supervised training program and all tests were performed on the same day. Test sessions were undertaken between 07:00 and 09:00 AM. and repeated sprint performance was conducted between 16:00 and 18:00 PM. To assess cardiorespiratory endurance, repeated sprint performance testing was performed outside on a rubber track following 20 min standardized warm-up. Participants were fully informed of all test procedures, experimental design, and potential risks of the study. All tests were performed strictly according to the rules and requirements, and instrument calibration was conducted to minimize measurement and instrument error before testing. Basic participant information including school, name, age and gender was queried and recorded.

2.3 Anthropometry assessments

Primary dimensions including height, weight, body fat percentage, hip circumference, waist circumference, triceps skinfold, scapular skinfold, and calf skinfold were measured in each subject. Height (measured to the nearest 0.1 cm with a fixed audiometer) and weight (measured with a digital balance ( $\pm$ 0.1 kg)) were measured without shoes and in light clothing. Body mass index (BMI) was calculated using the following equation: BMI (kg/m ${}^{2})=$ weight (kg)/height squared (m ${}^{2}$ ). Hip circumference and waistline were measured using a soft tape measure ( $\pm$ 0.1 cm). All three skinfold thickness measurements (triceps skinfold, scapular skinfold and calf skinfold) were conducted using a Skinfold Caliper Gauge. Triplicate measurements were taken and the intermediate value or same value, if any, was recorded. Body fat percentage was measured using a body fat measuring device (HBF2306; OMRON, Dalian, China) and calculated according to bioelectrical impedance analysis (BIA) [16].

Table 1
Descriptive analysis for the participants

Variable	Mean $\pm$ SD	CI (90%)
Anthropometric data
Age (years)	12.98 $\pm$ 1.72	1.622–1.821
Height (cm)	155.92 $\pm$ 11.41	10.746–12.039
Weight (kg)	49.79 $\pm$ 12.22	11.457–12.946
Body mass index (kg/m ${}^{2})$	20.21 $\pm$ 3.27	3.025–3.499
Body fat percentage (%)	20.76 $\pm$ 5.44	5.156–5.686
Hips circumference (cm)	82.42 $\pm$ 9.39	8.552–10.391
Waist circumference (cm)	66.24 $\pm$ 9.12	8.384–9.797
Triceps skinfold (mm)	14.83 $\pm$ 6.67	6.094–7.239
Scapular skinfold (mm)	12.69 $\pm$ 8.33	7.400–9.220
Calf skinfold (mm)	15.92 $\pm$ 7.56	6.423–8.711
Muscular fitness parameters
Upper extremity muscle	37.29 $\pm$ 12.23	11.364–13.008
strength (kg)
Lower extremity muscle	35.04 $\pm$ 12.79	11.750–13.730
strength (kg)
Standing vertical jump (cm)	37.37 $\pm$ 10.15	9.567–10.721
20 m shuttle run (number	34.39 $\pm$ 15.13	14.185–16.103
of laps)

SD: Standard deviation; CI: Confidence intervals.

2.4 Physical fitness measurement

Physical fitness was quantified using muscular fitness (muscular strength, endurance and flexibility) and cardiorespiratory endurance. Upper and lower extremity muscle strength muscle strength in muscular fitness was examined using standardized operation of hand-held dynamometry (HHD; Chatillon MSC Series Medical Dynamomete) [17, 18]. Muscular endurance and leg flexibility were assessed via standing vertical jump performance [19], which was examined after an active warm-up period and measured with a soft tape measure ( $\pm$ 0.1 cm). Cardiorespiratory endurance was assessed using the 20-m SRT. Briefly, all participants underwent a multistage 20-m SRT accompanied with musi. The SRT lasted 21 minutes, with gradual increases in exercise intensity and gradual shortening of time for completing the run. During testing, all students were encouraged to try their best to complete as many courses as possible. Subjects immediately adjusted their running velocity according to the music when their movement rhythms were out of step, and their track record was terminated when movement rhythms were out of step twice. Final scores were recorded with the number of laps accomplished on the 20-m SRT before the second error occurring. After 3 $n$ ( $n=1,2,3,\ldots$ ) of pause, music rhythm quickened, and subjects had to be vigilant so not to delay and impact test results.

Table 2
Multiple linear regression models with upper extremity muscle strength as the outcome and anthropometric data as the explanatory variables

Explanatory variables	Multiple linear regression ${}^{\rm a}$
	Coefficient	$t$	P-value
Age (years)	0.939	3.270	0.001
Weight (kg)	0.842	16.114	0.000
Gender (reference: male)	$-$ 6.911	$-$ 9.547	0.000
Scapular skinfold (mm)	$-$ 0.447	$-$ 6.461	0.000
Calf skinfold (mm)	$-$ 0.269	$-$ 3.890	0.000

${}^{\rm a}$ Only explanatory variables that have showed significant difference in the descriptive statistics were included. Key indicators of model: $F=$ 185.453, $P<$ 0.001; R-squared $=$ 0.677.

3. Statistical analysis

All data is presented as mean $\pm$ standard deviations (SD), and was analyzed using Statistical Product and Service Solutions (SPSS) version 19.0 (SPSS Inc., Chicago, IL, USA). Gender (male/female), age, height, weight, body fat percentage, hip circumference, waist circumference, triceps skinfold, scapular skinfold, calf skinfold were named as variable $x1$ , $x2$ , $x3,\ldots,x10$ . Upper extremity muscle strength, lower extremity muscle strength, and standing vertical jump were named as variable $y1$ , $y2$ , and $y3$ , respectively. Multiple linear regression analysis was performed to determine relationships between $x$ and $y$ , and models were built using the Stepwise method. Linear regression for muscle fitness (variable $y1$ , $y2$ , and $y3$ ) and the number of laps accomplished on the 20-m SRT (z) were conducted using the same method.

4. Results

4.1 Body composition, muscle strength and 20-m SRT

The body characteristics for all participants are presented in Table 1. The mean subject height and weight were 155.92 $\pm$ 11.41 cm and 49.79 $\pm$ 12.22 kg, respectively. Mean BMI was 20.21 $\pm$ 3.27 kg/m ${}^{2}$ and mean body fat percentage was 20.76 $\pm$ 5.44%. Hip and waist circumference were 82.42 $\pm$ 9.39 cm and 66.24 $\pm$ 9.12 cm, respectively. Mean triceps, scapular and calf were 14.83 $\pm$ 6.67 mm, 12.69 $\pm$ 8.33 mm, and 15.92 $\pm$ 7.56 mm, respectively. Mean muscle strength was 37.29 $\pm$ 12.23 kg for upper limb and 35.04 $\pm$ 12.79 kg for lower limb, and mean standing vertical jump was 37.37 $\pm$ 10.15 cm. The average number of laps accomplished on the 20-m SRT was 34.39 $\pm$ 15.13, with a 90% confidence interval (CI) of 14.185–16.103.

4.2 Multiple linear regression models for muscular fitness

Multiple linear regression models were built with muscular fitness indexes as outcomes and anthropometric data as explanatory variables. As shown in Table 2, upper extremity muscle strength was significantly associated with gender, age, weight, scapular skinfold, and calf skinfold ( $P<$ 0.01). Among these factors, gender, scapular skinfold, and calf skinfold were shown to have significant negative correlations with upper extremity muscle strength ( $P<$ 0.01), while age and weight were positively correlated with upper extremity muscle strength ( $P<$ 0.01). No regression model was obtained for lower extremity muscle strength. In addition, standing vertical jump was positively related to age and weight, and negatively correlated with gender, body fat percentage, scapular skinfold, and calf skinfold (Table 3).

Table 3
Multiple linear regression models with standing vertical jump as the outcome and anthropometric data as the explanatory variables

Explanatory variables	Multiple linear regression ${}^{\rm a}$
	Coefficient	$t$	P-value
Age (years)	0.939	4.014	0.000
Gender (reference: male)	$-$ 6.717	$-$ 9.279	0.000
Weight (kg)	0.423	8.267	0.000
Body fat percentage	$-$ 0.096	$-$ 2.492	0.013
Scapular skinfold (mm)	$-$ 0.389	$-$ 5.683	0.000
Calf skinfold (mm)	$-$ 0.165	$-$ 2.426	0.016

${}^{\rm a}$ Only explanatory variables that have showed significant difference in the descriptive statistics were included. Key indicators of model: $F=$ 79.017, $P<$ 0.001; R-squared $=$ 0.514.

Table 4

Multiple linear regression models with the number of laps on the20-m SRT as the outcome and muscular fitness indexes as the explanatory variables

Explanatory variables	Multiple linear regression ${}^{\rm a}$
	Coefficient	$t$	P-value
Upper extremity muscle	0.476	7.352	0.000
strength (kg)
Standing vertical jump (cm)	0.490	6.312	0.000

${}^{\rm a}$ Only explanatory variables that have showed significant difference in the descriptive statistics were included. Key indicators of model: $F=$ 149.187, $P<$ 0.001; R-squared $=$ 0.422.

4.3 Multiple linear regression model for 20-m SRT

A Multiple linear regression model was built with the number of laps accomplished on the 20-m SRT as the outcome and muscular fitness indexes as explanatory variables. According to the result shown in Table 4, strong positive correlations existed between the number of laps accomplished on the 20-m SRT and upper extremity muscle strength as well as standing vertical jump, with the coefficients of 0.476 and 0.490, respectively.

5. Discussion

Physical fitness is a well-known indicator of health status. Physical fitness assessment, such as BMI and aerobic capacity, have also been correlated with academic achievement in preadolescents [20]. As such, multiple studies have been conducted to evaluate physical fitness in students. In the current study, age, weight, gender, scapular skinfold, and calf skinfold were significantly associated with upper muscle strength and standing vertical jumping in adolescents in Shanghai, China. Furthermore, upper extremity muscle strength and standing vertical jump were significantly correlated with the number of laps in the 20-m SRT, an important indicator for cardiorespiratory endurance in physical fitness.

Anthropometric characteristics, such as weight and stature, have been associated with both repeated-sprint ability and single-sprint performance. Our study found the BMI for middle school students (12.98 $\pm$ 1.72 years old) to be 20.21 $\pm$ 3.27 kg/m ${}^{2}$ in Shanghai, which is similar to BMI in European adolescents (21.0 $\pm$ 3.8 kg/m ${}^{2}$ for males (13.7 $\pm$ 0.8 years old) and 20.5 $\pm$ 2.9 kg/m ${}^{2}$ for females (13.6 $\pm$ 0.8 years old)) [21]. The average BMI value in this study was lower than the ‘overweight’ BMI cut-off of 21.93 kg/m ${}^{2}$ for boys and 23.08 kg/m ${}^{2}$ for girls at 13 years old, respectively [22], indicating that adolescents in Shanghai have a relative healthy body weight. Anthropometric characteristics such as weight and stature have been associated with both repeated-sprint ability and single-sprint performance [23, 24]. According to previous literature, physical fitness scores of obese participants are generally poorer than those of non-obese subjects according to the previous studies [25]. However, no statistical correlation was identified between BMI and physical fitness in this study, body fat percentage showed a significant negative correction with standing vertical jump ability, which was positively related with repeated sprint performance in our study. Mendez-Villanueva et al. have declared that fat-free mass is important for sprinting qualities in post-pubertal students [26]. Further, previous studies have documented that lean tissue mass is positively correlated with the anaerobic capacity [6, 27]. Collectively, these findings implicate body fat percentage as a supplemental anthropometric index for physical fitness examination.

Body composition plays an important role in determining an elite athlete and is closely related to athletic performance. A strong positive correlation has also reported between maximum power and maximum strength [28], and explosive leg strength has been correlated with peak and mean power by Arslan [29] and Mayhew et al. [30]. In the current study, extremity muscle strength was 37.29 $\pm$ 12.23 kg in the upper limb and 35.04 $\pm$ 12.79 kg in the lower limb in adolescents averaging 12.9 years old in Shanghai. This is comparable to the results collected from 12 to 13 years old in American students [31]. and Canadian adolescents [32]. Furthermore, the number of laps accomplished on the 20-m SRT was 34.39 $\pm$ 15.13, which was between the number of laps for 10.5 years old (30.5) and 15.4 years old (41.1) according to an another study on physical fitness in 6–19 years old adolescents in Argentina [33]. This result, however, remained under the Fitnessgram criterion recommended by the USA, indicating that adolescents in Shanghai presented with lower physical fitness, indicating that more exercise might be required in this population. Previous investigations have shown that the ability to repeat short-term maximal efforts declined with age, which might led to a reduced ability to resist fatigue in adults [26, 27]. Previous document have also proved that a repeated-sprint ability test was not negatively affected by age in a well-trained soccer team with members ranging from 11 to 18 years of age [23]. In our study, all participants were middle school students aged 12 to 17 years. According to the multiple linear regression analysis, age was a significant positive factor for upper extremity muscle strength and standing vertical jump which were positively associated with the number of laps accomplished on the 20-m SRT. Taken together, age is identified as a positive factor for repeated-sprint ability of middle school students in our study, and increasing exercise frequency might be beneficial to improve the physical fitness of adolescents in Shanghai.

One unexpected finding of the present study was a lack of significant regression equation between lower extremity muscle strength and anthropometric characteristics. Other researches also demonstrated that no relation was found between leg or keen strength and 10 or 40 m sprint performance [28, 34]. Meanwhile, running, jumping, and training are beneficial for quadriceps development, whereas they have no effects on the hamstring improvement, in which flexor muscles play an important role [35, 36]. Another possible explanation for lack of association might be in characteristics variability between different participants.

It is common for specialized athletes to have improved repeated-sprint abilities with better acid-base regulation, a greater oxidative capacity, and faster pho- sphocreatine resynthesis than non-specifically trained students [27]. Training can significantly decrease body fat percentage and body fat content, as well as maximize lean tissue content [35]. A short period of training can also increase muscle strength, as a result of neural adaptation [35]. Thus, exercise and training might improve muscle fitness and repeated-sprint ability by changing body composition.

Although many positive results have been obtained by the current investigation, several limitations still existed in our study. This study was only conducted in a single city in China, which might not adequately represent the student sample of the entire country. Additionally, maximal oxygen uptake was not detected to reflect cardiorespiratory endurance capability of the participants. Furthermore, these multiple regression equations should be verified via further research.

In conclusion, anthropometric characteristic examination revealed that Shanghai middle school students have a relatively lower physical fitness level, and anthropometric characteristics could significantly influence physical fitness which might be used as a predictor for repeated sprint performance. Considering the effects of standing vertical jump and 20-m SRT on MSF and CRF respectively, the preoccupation of training these variables as separate qualities could thus be overemphasized.

Footnotes

Acknowledgments

This work was supported by the Key Projects in the National Science and Technology Pillar Program during the Twelfth Five-Year Plan Period (2012BAK21B 03).

Conflict of interest

All authors declare no conflict of interest.

References

Ortega

Artero

Ruiz

Españaromero

Jiménezpavón

Vicenterodríguez

, et al. Physical fitness levels among European adolescents: The HELENA study. British Journal of Sports Medicine 2011; 45: 20.

Ortega

Cadenas-Sánchez

Sánchez-Delgado

Mora-González

Martínez-Téllez

Artero

, et al. Systematic review and proposal of a field-based physical fitness-test battery in preschool children: The PREFIT battery. Sports Medicine 2015; 45: 533-555.

Fedewa

Ahn

. The effects of physical activity and physical fitness on children’s achievement and cognitive outcomes: A meta-analysis. Research Quarterly for Exercise & Sport 2011; 82: 521-535.

Pan

. Motor proficiency and physical fitness in adolescent males with and without autism spectrum disorders. Autism the International Journal of Research & Practice 2014; 18: 156-165.

Haga

Gísladóttír

Sigmundsson

. The relationship between motor competence and physical fitness is weaker in the 15-16 yr. adolescent age group than in younger age groups (4-5 YR. AND 11-12 YR.). Perceptual & Motor Skills 2015; 121: 900.

Argus

Gill

Keogh

Hopkins

Beaven

. Effects of a short-term pre-season training programme on the body composition and anaerobic performance of professional rugby union players. J Sports Sci 2010; 28: 679-686.

Thorpe

. The role of fitness in health and disease: Status of adults with cerebral palsy. Dev Med Child Neurol 2009; 51(Suppl 4): 52-58.

Ortega

Ruiz

Castillo

Sjostrom

. Physical fitness in childhood and adolescence: A powerful marker of health. Int J Obes (Lond) 2008; 32: 1-11.

Hovey

. Six steps for planning a fitness circuit for individuals with autism. Strategies 2011; 24: 12-15.

10.

Sandbakk

Welde

Holmberg

. Endurance training and sprint performance in elite junior cross-country skiers. J Strength Cond Res 2011; 25: 1299-1305.

11.

Castro-Piñero

Ortega

Artero

Girela-Rejón

Mora

Sjöström

, et al. Assessing muscular strength in youth: Usefulness of standing long jump as a general index of muscular fitness. The Journal of Strength & Conditioning Research 2010; 24: 1810-1817.

12.

Castelli

Hillman

Buck

Erwin

. Physical fitness and academic achievement in third-and fifth-grade students. Journal of Sport and Exercise Psychology 2007; 29: 239-252.

13.

López-Segovia

Dellal

Chamari

González-Badillo

. Importance of muscle power variables in repeated and single sprint performance in soccer players. Journal of Human Kinetics 2014; 40: 201-211.

14.

Brocherie

Girard

Forchino

Al Haddad

Dos Santos

Millet

. Relationships between anthropometric measures and athletic performance, with special reference to repeated-sprint ability, in the Qatar national soccer team. Journal of Sports Sciences 2014; 32: 1243-1254.

15.

Alemdaroğlu

. The relationship between muscle strength, anaerobic performance, agility, sprint ability and vertical jump performance in professional basketball players. Journal of Human Kinetics 2012; 31: 149-158.

16.

Kushner

. Bioelectrical impedance analysis: A review of principles and applications. Journal of the American College of Nutrition 1992; 11: 199-209.

17.

Abizanda

Navarro

García-Tomás

López-Jiménez

Martínez-Sánchez

Paterna

. Validity and usefulness of hand-held dynamometry for measuring muscle strength in community-dwelling older persons. Archives of Gerontology and Geriatrics 2012; 54: 21-27.

18.

Thorborg

Bandholm

Hölmich

. Hip-and knee-strength assessments using a hand-held dynamometer with external belt-fixation are inter-tester reliable. Knee Surgery, Sports Traumatology, Arthroscopy 2013; 21: 550-555.

19.

Ostergaard

Kolle

Steene-Johannessen

Anderssen

Andersen

. Cross sectional analysis of the association between mode of school transportation and physical fitness in children and adolescents. Int J Behav Nutr Phys Act 2013; 10: 91-97.

20.

Castelli

Hillman

Buck

Erwin

. Physical fitness and academic achievement in third- and fifth-grade students. J Sport Exerc Psychol 2007; 29: 239-252.

21.

Ortega

Artero

Ruiz

Vicente-Rodriguez

Bergman

Hagstromer

, et al. Reliability of health-related physical fitness tests in European adolescents. The HELENA Study. Int J Obes (Lond) 2008; 32(Suppl 5): S49-57.

22.

. Report on childhood obesity in China (1)-body mass index reference for screening overweight and obesity in Chinese school-age children. Biomedical and Environmental Sciences 2005; 18: 390.

23.

Mujika

Spencer

Santisteban

Goiriena

Bishop

. Age-related differences in repeated-sprint ability in highly trained youth football players. J Sports Sci 2009; 27: 1581-1590.

24.

Martin

Dore

Twisk

van Praagh

Hautier

Bedu

. Longitudinal changes of maximal short-term peak power in girls and boys during growth. Med Sci Sports Exerc 2004; 36: 498-503.

25.

Ara

Sanchez-Villegas

Vicente-Rodriguez

Moreno

Leiva

Martinez-Gonzalez

, et al. Physical fitness and obesity are associated in a dose-dependent manner in children. Ann Nutr Metab 2010; 57: 251-259.

26.

Mendez-Villanueva

Buchheit

Kuitunen

Douglas

Peltola

Bourdon

. Age-related differences in acceleration, maximum running speed, and repeated-sprint performance in young soccer players. J Sports Sci 2011; 29: 477-484.

27.

Ratel

Duche

Williams

. Muscle fatigue during high-intensity exercise in children. Sports Med 2006; 36: 1031-1065.

28.

Alemdaroglu

. The relationship between muscle strength, anaerobic performance, agility, sprint ability and vertical jump performance in professional basketball players. J Hum Kinet 2012; 31: 149-158.

29.

Arslan

. Relationship between the 30-second wingate test and characteristics of isometric and explosive leg strength in young subjects. J Strength Cond Res 2005; 19: 658-666.

30.

Mayhew

Hancock

Rollison

Ball

Bowen

. Contributions of strength and body composition to the gender difference in anaerobic power. J Sports Med Phys Fitness 2001; 41: 33-38.

31.

. The Exploratory Research on young students physical health test effectiveness. In 2013.

32.

Héroux

Onywera

Tremblay

Adamo

Lopez Taylor

Jáuregui Ulloa

, et al. The relation between aerobic fitness, muscular fitness, and obesity in children from three countries at different stages of the physical activity transition. ISRN Obesity 2013; 2013: 134835.

33.

Janssens

Oldehinkel

Bonvanie

Rosmalen

. An inactive lifestyle and low physical fitness are associated with functional somatic symptoms in adolescents. The TRAILS Study. Journal of Psychosomatic Research 2014; 76: 454-457.

34.

Cronin

Hansen

. Strength and power predictors of sports speed. J Strength Cond Res 2005; 19: 349-357.

35.

Seo

Jung

Song

Kim

. Effect of 8 weeks of pre-season training on body composition, physical fitness, anaerobic capacity, and isokinetic muscle strength in male and female collegiate taekwondo athletes. J Exerc Rehabil 2015; 11: 101-107.

36.

Tornello

Capranica

Chiodo

Minganti

Tessitore

. Time-motion analysis of youth Olympic Taekwondo combats. J Strength Cond Res 2013; 27: 223-228.

Relationships among anthropometric characteristics,muscular fitness,and sprint performance in adolescents

Abstract

BACKGROUND:

OBJECTIVES:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Methods

2.1 Participants

2.2 Procedures

2.3 Anthropometry assessments

Table 1 Descriptive analysis for the participants

Table 2 Multiple linear regression models with upper extremity muscle strength as the outcome and anthropometric data as the explanatory variables

4. Results

4.1 Body composition, muscle strength and 20-m SRT

4.2 Multiple linear regression models for muscular fitness

Table 3 Multiple linear regression models with standing vertical jump as the outcome and anthropometric data as the explanatory variables

5. Discussion

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Descriptive analysis for the participants

Table 2
Multiple linear regression models with upper extremity muscle strength as the outcome and anthropometric data as the explanatory variables

Table 3
Multiple linear regression models with standing vertical jump as the outcome and anthropometric data as the explanatory variables