Next Article in Journal
Environmental Factors as a Cause of Differences in the Feet of Ecuadorian Children and Its Relation to Their Footwear
Next Article in Special Issue
Changes in Body Composition and Anthropomorphic Measurements in Children Participating in Swimming and Non-Swimming Activities
Previous Article in Journal
The Effect of Short-Term Wingate-Based High Intensity Interval Training on Anaerobic Power and Isokinetic Muscle Function in Adolescent Badminton Players
Previous Article in Special Issue
Oral Diseases and Quality of Life between Obese and Normal Weight Adolescents: A Two-Year Observational Study
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Children
Volume 8
Issue 6
10.3390/children8060460
children-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Can Anthropometry and Body Composition Explain Physical Fitness Levels in School-Aged Children?

by
Chih-Yu Hsu
1,
Liang-Sien Chen
1,
I-Jen Chang
1,
Wei-Ching Fang
1,
Sun-Weng Huang
2,3,
Rong-Ho Lin
2,
Steve Wen-Neng Ueng
4,5 and
Hai-Hua Chuang
1,2,4,6,*
1
Department of Family Medicine, Linkou and Taipei Branches, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
2
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
3
Graduate Institute of Urban Planning, College of Public Affairs, National Taipei University, New Taipei City 23741, Taiwan
4
College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
5
Department of Orthopedic Surgery, Linkou Main Branch, Chang Gung Memorial Hospital, Taoyuan 33305, Taiwan
6
Obesity Institute, Geisinger, Danville, PA 17837, USA
*
Author to whom correspondence should be addressed.
Children 2021, 8(6), 460; https://doi.org/10.3390/children8060460
Submission received: 30 April 2021 / Revised: 23 May 2021 / Accepted: 26 May 2021 / Published: 31 May 2021
(This article belongs to the Special Issue Body Composition in Children)
Browse Figure
Versions Notes

Abstract

:
Physical fitness (PF) is closely related to various health outcomes and quality of life among children. However, the associations between anthropometry, body composition (BC), and PF are not fully elucidated. This cross-sectional study aimed to investigate the associations between demographic metrics (age, sex), anthropometric measures (body mass index z-score (BMI z-score) waist/height ratio (WHtR)), BC parameters (body-fat percentage (BF%), muscle weight), and PF levels (800-m run, sit-and-reach, 1-min sit-ups, standing long jump) in school-aged children. Continuous variables were dichotomized by median splits. The results of 180 girls and 180 boys (mean age: 10.0 ± 0.7 years; mean BMI z-score: 0.366 ± 1.216) were analyzed. Multivariable linear regressions revealed that BF% (regression coefficient (B) = 3.4, 95% confidence interval (CI) = 2.5–4.3) was independently correlated with the 800-m run. Sex (B = 4.6, 95% CI = 3.0–6.3), age (B = 3.1, 95% CI = 1.9–4.3), and BMI z-score (B = −0.7, 95% CI = −1.4–−0.1) were independently related to sit-and-reach. Age (B = 3.3, 95% CI = 2.0–4.7), BF% (B = −0.3, 95% CI = −0.4–−0.2), and muscle weight (B = 0.7, 95% CI = 0.2–1.2) were independently associated with 1-min sit-ups. In addition to demography, anthropometry and BC provided additional information concerning some PF levels in school-aged children. Weight management and PF promotion should be addressed simultaneously in terms of preventive medicine and health promotion for children.

Graphical Abstract

1. Introduction

Physical fitness (PF) is defined as one’s capability to work effectively, enjoy leisure time, be healthy, resist hypokinetic diseases, and meet emergencies [1,2]. PF also displays the body’s ability to function efficiently. A substantial volume of research has been conducted in adults on the relationships between PF and various health outcomes [3]. The literature suggests that a lack of physical activity (PA) or poor PF is associated with a wide range of chronic diseases [4]. PF is an important predictor for adiposity, bone health, premature mortality, diabetes, cardiovascular diseases, metabolic syndrome, and mental health [3,5,6,7,8].
Among all the PF metrics, cardiorespiratory and muscular fitness seem to be the two most influential parameters for disease and health [9]. Low cardiorespiratory fitness is associated with metabolic syndrome, diabetes, and an increased prevalence of cardiovascular disease [7,10]. A recent review elucidates that good cardiorespiratory fitness mitigates stroke hazard in both sexes, and the effect is comparable to well-established stroke risk factors such as blood pressure, obesity, and smoking [4,11]. Muscular strength is a commonly accepted parameter for cardiometabolic risk, sarcopenia, functional disabilities, and frailty connected to all-cause mortality [12,13,14].
PF is also related to mental health. Previous studies have observed an association between the PF component and cognitive development [8,9,15]. Evidence supports that reduced cardiorespiratory fitness and muscular strength are linked to lower levels of mental well-being, higher levels of depressive symptoms and stress-related exhaustion, and increased health-risk behavior such as suicidal attempts [9]. Silverman and Deuster have proposed two possible mechanisms to explain how PF can buffer against stress-related diseases. The first one is the “blunting effects” of PF on stress responses from the hypothalamic–pituitary–adrenal axis or the sympathetic nervous system. The other one is that improving PF may minimize excessive systemic inflammation. Moreover, PF enhances the neural plasticity and the expression of growth factor, and therefore improves an individual’s mood and cognition [16].
Similar observations have been reported among children. Children with suboptimal PF are at higher risk of pediatric obesity, type 2 diabetes, hypercholesterolemia, hypertension, metabolic syndrome, and cardiovascular diseases [17,18,19]. Poor childhood PF is linked to increased all-cause mortality in adulthood [5,20]. A low level of PF is associated with excessive inflammatory cytokine production [21], with promoting adipose tissue aggregate [22], disrupting platelet function [23], and increasing insulin resistance [24,25]. Moreover, one cross-sectional study suggests that PF plays a crucial role and may ameliorate the negative influence of obesity on children’s academic performance [15,26].
Notably, the effects of PF on health outcomes are independent of weight status. Individuals with poor PF, even normal-weighted, have higher mortality than individuals with optimal PF status [27]. Obesity (defined with anthropometric measures), suboptimal body composition (BC), and poor PF have various comorbidities in common, such as metabolic alterations, cardiovascular risks, and mental–cognitive impairment. However, most studies have focused on the medical impacts of each of the three, and the relationships between anthropometry, BC, and PF have not been studied as extensively. Mendoza-Muñoz et al. investigated the influence of BC on PF in adolescents and reported that overweight and obese adolescents had lower levels of PF performance [28]. Ortega, F.B., et al. and Hussey, J., et al. demonstrated that cardiorespiratory fitness was negatively correlated with body mass index (BMI), waist circumference and abdominal adiposity in children [5,29]. However, connections between anthropometric measures, BC, and PF in school-aged children had not yet been studied and elucidated thoroughly.
We hypothesized that not only anthropometry but also BC was associated with PF performance among children. The aim of this study was to investigate the associations between body mass index (BMI) z-score, waist/height ratio (WHtR), body-fat percentage (BF%), muscle weight (MW), 800-m run, sit-and-reach, 1-min sit-ups, and standing long jump in a sample of elementary students in Taiwan.

2. Materials and Methods

2.1. Study Design and Subjects

The study was quantitative and cross-sectional. Data were collected between 2013 and 2016 from a school-based health promotion project conducted by the Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan, Taiwan for several elementary schools in Northern Taiwan. The details of the project have been reported elsewhere [30]. Anonymous data was retrieved and analyzed in 2020. All sensitive pieces of information that could be linked to a specific person were deidentified. Demographic data (age, sex), anthropometric measures (BMI z-score, WHtR), BC measures (BF, MW), and PF outcomes (cardiorespiratory fitness (800-m run), flexibility (sit-and-reach), speed/agility (1-min sit-ups), lower body power (long-jump)) were analyzed. This study was approved by the Institutional Review Board of our hospital (101-4158A3). Written informed consent was obtained from all participants and their parents.

2.2. Anthropometric Measures

Anthropometric measures were obtained collaboratively by school teachers, nurses, and investigators in elementary schools. Each student was asked to take off his or her shoes before body height and weight were measured according to standard protocols [31]. Body height was assessed to the nearest 0.1 cm by using a wall-mounted stadiometer with head held in horizontal plane [32]. Body weight was measured to the nearest 0.1 kg by using a metric balance scale [33]. BMI was calculated by dividing the weight (kg) by the height squared (m2) [31]. To make weight status comparable across children at different ages, BMI z-scores were calculated based on sex and age in months according to the United States Centers for Disease Control and Prevention 2000 growth charts [34]. Waist circumference (cm) was measured as the circumference in the horizontal plane midway between the lowest ribs and the iliac crest, and it was further divided by the height (cm) to calculate WHtR [35,36].

2.3. Body Composition Parameters

The detailed BC measurement protocol has been described elsewhere [30]. Briefly, BF% and MW were automatically obtained using bioelectrical impedance analysis (X-scan model, Jawon Medical, Co., Ltd., Seoul, Korea). This segmental multi-frequency bioelectrical impedance analysis module can estimate not only fat-free mass/fat mass but also muscle weight, intracellular water weight and extracellular water weight across limbs and trunks [37]. We chose MW rather than fat-free mass as our parameter of interest since fat-free mass is more complex in its components, including not only muscles but also bone mineral, extracellular water, intracellular water, and visceral protein [37]. Jensen, B., et al. indicated that skeletal muscle mass accounted for 45% to 49% of fat-free mass across different sexes [38].

2.4. Physical Fitness Levels

PF levels were measured by four exercise tests, including 800-m run, sit-and-reach, 1-min sit-ups, and standing long jump. Cardiorespiratory fitness (endurance run) was assessed by 800-m run (sec), defined as the time required to sprint 800-m run [39,40]. Flexibility was evaluated using sit-and-reach (cm), defined by the maximum distance between fingertips and feet reached as the participants slide their hands forward as far as possible towards their feet without bending the hamstring, and maintain this maximum position for at least two seconds [41]. Speed/agility was measured by 1-min sit-ups (times), defined as the maximum number of correct sit-ups achieved in one minute [42]. Lower body power was evaluated using standing long jump, defined as the distance between the starting line and the heel of the closest foot [43]. All PF tests were performed following the methods reported by the Health Promotion Administration, Ministry of Health and Welfare in Taiwan. Standard and corrected stopwatches were used to measure time. Professional physical educators were enrolled to conduct the tests and document the results [44].

2.5. Statistical Analysis

The sample size was calculated using a priori calculation (correlation test, coefficient of determination ρ2 = 0.11, two-tailed α = 0.05, power = 0.85; sample size for each age–sex subgroup = 60), as reported in a previous study of BF% and standing long jump in 9-year-old boys [43]. For boys and girls aged 9–11 years old (six age–sex subgroups), the sample size was estimated to be 360.
Most of the distributions of the variables were non-normal, assessed by using the Kolmogorov–Smirnov test. Therefore, non-normally distributed continuous variables were transformed using a two-step approach: the fractional rank and inverse–normal transformation [45]. Thereafter, continuous variables were expressed as means and standard deviations, and categorical variables were expressed as numbers with percentages. Student t-tests were used to compare continuous variables, and chi-square tests were used to compare categorical variables in different subgroups, as appropriate. Pearson’s correlation test was used to investigate the relationships between continuous variables, whereas Spearman’s correlation test was used to analyze associations between continuous variables and categorical variables. The variables with a significance level of p < 0.05 in univariate analyses were further assessed using multivariable linear regression models with the forward selection for examining independent variables.
SPSS software (version 25; International Business Machines Corp., Armonk, NY, USA) and G*Power software (version 3.1.9.2; University of Kiel, Kiel, Germany) were used for the statistical analysis. A two-tailed p-value lower than 0.05 was considered statistically significant.

3. Results

3.1. Demographic and Clinical Characteristics of the Overall Cohort and Two Subgroups Stratified by Sex

Table 1 displayed demographic and clinical characteristics of the overall cohort and two subgroups stratifies by sex. A total of 360 Taiwanese children from Han ancestry (180 (50.0%) girls and 180 (50.0%) boys); mean age: 10.0 ± 0.7 years; mean BMI z-score: 0.366 ± 1.216) were analyzed.
In the overall cohort, the boys had a significantly higher BMI z-score, higher WHtR, lower BF%, higher MW, lower time in the 800-m run, lower distance of sit-and-reach, higher number of 1-min sit-ups, and higher distance of standing long jump compared to those of the girls. The boys and girls were comparable in age.

3.2. Associations of Physical Fitness Levels and Variables of Interest in the Overall Cohort

Table 2 displayed associations of PF and variables of interest in the overall cohort. The 800-m run was positively associated with female sex, BMI z-score, WHtR, BF%, and MW. Sit-and-reach was positively correlated with female sex and age, and inversely related to BMI z-score and WHtR. The 1-min sit-ups score was positively related to age and inversely associated with BMI z-score, WHtR, and BF%. Standing long jump was positively associated with age and MW, and inversely correlated with female sex, WHtR, and BF%.

3.3. Variables Independently Associated with Physical Fitness Levels in the Overall Cohort Using Logistic Regression Models

Table 3 displayed variables independently associated with PF in the overall cohort. Using univariate linear regression models, we determined that girls, BMI z-score, WHtR, BF%, and MW were significant variables of the 800-m run. However, only the BF% (regression coefficient (B) = 3.4, 95% confidence interval (CI) = 2.5–4.3, p < 0.001) was independently correlated with the 800-m run using multivariable linear regression models.
Female sex, age, BMI z-score, and WHtR were significant variables of sit-and-reach. Using multivariable analysis, girls (B = 4.6, 95% CI = 3.0–6.3, p < 0.001), age (B = 3.1, 95% CI = 1.9–4.3, p < 0.001), and BMI z-score (B = −0.7, 95% CI = −1.4–−0.1, p = 0.03) were still significantly and independently related to the sit-and-reach.
Female sex, age, BMI z-score, WHtR, and BF% were significant factors of 1-min sit-ups. Furthermore, age (B = 3.3, 95% CI = 2.0–4.7, p < 0.001) and BF% (B = −0.3, 95% CI = −0.4–−0.2, p < 0.001) were still significant variables of 1-min sit-and-stand-ups using multivariable analysis.
Female sex, age, WHtR, BF%, and MW were significant factors of the standing long jump. Their associations with the standing long jump remained significant using multivariable analysis: age (B = 14.1, 95% CI = 9.9–18.3, p < 0.001), BF% (B = −1.1, 95% CI = −1.4–1.2, p < 0.001), and MW (B = 0.7, 95% CI = 0.2–1.2, p = 0.01).

4. Discussion

The prevalence of pediatric obesity has been increasing alarmingly worldwide in the past 20 years [46,47,48]. It is generally believed that a decrease in PA and excessive calorie consumption are the two most important factors responsible for this obesity pandemic [49,50]. The detrimental effects of obesity can result in a wide range of negative impacts on health and quality of life [51]. The literature has elucidated various comorbidities of pediatric obesity, including early sexual maturation, polycystic ovary syndrome, nonalcoholic fatty liver disease, obstructive sleep apnea, asthma, left ventricular hypertrophy, hypertension, lower extremity malalignment and joint pain, acanthosis nigricans, higher risk of idiopathic intracranial hypertension, poor self-esteem, anxiety, and depression [51].
On the other hand, a decrease in PF performance has been observed in many developing and developed countries [50]. Young populations are becoming more obese and less fit at the same time, and some research has suggested a linkage between the changes in PF and weight status [50,52,53]. Overweight and obese adolescents had inferior PF compared to their normal-weight peers [28]. Children with higher cardiorespiratory fitness had less total and abdominal adiposity [5,28]. Tomkinson et al. demonstrated that the variability in fatness was accounted for about 20% of the variability in running performance [54]. A cross-sectional study also showed that fatness attributed to the decline of PF by 29–61%, more than other factors such as PA did [52].
In our results, and consistent with the literature, the PF performance of children was associated with their demographic variables [55]. Older children had better flexibility, speed/agility, and lower body power. Children grow up with their height, weight, lean mass, fat tissue, and organs increasing in size [56,57]. These changes are the results of cellular hyperplasia, hypertrophy, and intercellular accretion, enhancing the functional utility of skeletal muscles [58]. Moreover, children develop better motor performance with a better integration of their central nervous system and skeletal–muscular system [59]. Previous investigations report that older age is associated with higher running speed from increased stride length, frequency, and neuromuscular coordination [60,61].
Interesting sex differences were observed. Boys were higher in z-BMI and WHtR, consistent with the well-documented higher prevalence of overweight and obesity in boys in Taiwan [62,63]. Girls were higher in BF% and lower in MW, also consistent with the literature [64,65]. After adjusting for other confounders, girls were better with flexibility. Previous studies show that girls have more total and subcutaneous fat, more connective tissue, and less paraspinous musculature compared to boys [64,66]. This difference could be explained by the difference in sex hormone levels between prepubertal children [67]. Boys have a greater capacity of skeletal muscle because of a higher level of testosterone [57,58]. In comparison, girls have a higher BF% and lower percentage of muscle mass because of a higher amount of estrogen, which contributes to lower tissue density and better flexibility [66]. The divergence of sexual hormone level and BC causes distinct flexibility performance between different sexes [68,69].
As expected and consistent with some previous research [29,70,71], our data demonstrated connections between weight status, BC, and PF. In general, higher BMI-z score and BF% were linked with lower levels of PF.
First, higher z-BMI was associated with worse flexibility. Larger trunk mass increased mechanical work and moment of inertia. As a result, there was higher propulsion and extra load during dynamic activities [70]. Casonatto et al. indicated that abdominal obesity might affect the lower back and hamstring flexibility and hamper the trunk to the extreme reach position [72].
Second, excessive BF% was related to poorer muscle endurance, cardiorespiratory fitness, and lower-body power. Similar findings have been reported by other investigators [28,73,74,75]. One systematic review from the European Childhood Obesity Group suggests that insulin resistance causes mitochondrial dysfunction in the skeletal muscle, which leads to muscle fatigability and delayed post-effort muscle recovery [76]. Another systematic review shows the negative effects of childhood obesity on spirometric variables, namely reductions in forced expiratory volume in one second, forced vital capacity, and forced expiratory volume in one second/forced vital capacity ratio [77,78]. An increase in adipose tissue directly negatively impacts one’s pulmonary function by reducing functional residual capacity, expiratory reserve volume, and residual volume [79,80]. Previous studies have reported that children with obesity perform less well in weight-bearing activities such as jumping and running [53,75,81]. Children with higher BMI usually have higher muscle mass, which increases absolute muscle strength; however, relative muscle strength is the key component of muscle function for daily activities. Excessive BF% increases inert load, which impacts negatively on relative muscle strength and results in poorer lower-body power [75].
Third, children with higher muscle weight had better lower-body power. There are two types of skeletal muscle, type I (red fibers) and type II (white fibers). Type II fibers have higher force, power, and speed. Most one-burst moves, such as the long jump or badminton smash, are largely contributed by type II fibers due to their higher contractility and fast reactions. On the other hand, type I fibers are slow movers but have better ability in utilizing oxygen and higher endurance [82]. Gaining muscle weight is largely related to type II fiber rather than type I fibers [83]. Our results were consistent with the physiology, showing that muscle weight was significant in relation to lower-body explosive power, but not muscle endurance.
The main contribution of this study was to provide scientific evidence on how anthropometry and BC may affect the PF performance, in addition to age and sex. The study had some limitations. First, all the participants were Han and confined to age of 9–11 years, which might limit the generalizability of this study to other populations. Second, the information on pubertal status was not collected, which might have been an important confounder on weight status, BC, and PF. Third, BC was measured by BIA; BIA modules are less costly and more available, but less accurate than dual energy X-ray absorptiometry modules. Also, the study was cross-sectional and therefore unable to conduct causation in predicting the future performance. Further studies with a prospective design will be of interest.

5. Conclusions

Overall, younger age, higher BMI z-score, and higher BF% were associated with poorer PF performance among elementary school students; girls had better flexibility. Our study concluded that other than demographic variables, anthropometric measures and BC parameters provided additional information in concerning PF levels in school-aged children. PF testing should be considered in addition to clinical measures to understand children’s health more comprehensively. Health promotion for children should focus not only on anthropometry but also BC and PF.

Author Contributions

Conceptualization, S.W.-N.U. and H.-H.C.; methodology, S.-W.H. and R.-H.L.; software, S.-W.H. and R.-H.L.; validation, L.-S.C., I.-J.C. and W.-C.F.; formal analysis, H.-H.C.; investigation, C.-Y.H. and H.-H.C.; resources, L.-S.C., I.-J.C., W.-C.F. and H.-H.C.; data curation, S.-W.H. and R.-H.L.; writing—original draft preparation, C.-Y.H., L.-S.C. and H.-H.C.; writing—review and editing, L.-S.C., I.-J.C. and H.-H.C.; visualization, S.-W.H. and R.-H.L.; supervision, S.W.-N.U.; project administration, S.W.-N.U. and H.-H.C.; funding acquisition, S.W.-N.U. and H.-H.C. All authors have read and agreed to the published version of the manuscript.

Funding

The study was funded by Chang Gung Medical Foundation, Grant number: CMRPG1H0061, CMRPG1H0062, CMRPG1H0063, CMRPG1J041, CMRPG1J042 (H.-H.C.).

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Board of Chang Gung Memorial Hospital, Linkou (101-4158A3).

Informed Consent Statement

Informed consent was obtained from all patients involved in the study.

Data Availability Statement

Data available on request due to privacy/ethical restrictions.

Acknowledgments

The authors would like to thank Pei-Chi Yang, Yu-Chan Liao, and Yi-Ting Lin, Chang Gung Memorial Hospital, Linkou and Taipei Branches, Taoyuan, Taiwan, for their help in the data collection, analysis, and manuscript editing. The authors would also like to express our respects and appreciations for Kuang-Hung Hsu from Chang Gung University, Taoyuan, Taiwan for his great help and support for the project.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Corbin, C.B.; Lindsey, R. Concepts of Physical Fitness; McGraw-Hill Education: New York, NY, USA, 1997. [Google Scholar]
  2. Pate, R.R. The Evolving Definition of Physical Fitness. Quest 1988, 40, 174–179. [Google Scholar] [CrossRef]
  3. Lin, Y.-T.; Lee, P.-F.; Lee, T.-S.; Ho, C.-C. Poor Physical Fitness Performance as a Predictor of General Adiposity in Taiwanese Adults. Int. J. Environ. Res. Public Health 2020, 17, 2686. [Google Scholar] [CrossRef] [Green Version]
  4. Booth, F.W.; Roberts, C.K.; Laye, M. Lack of Exercise Is a Major Cause of Chronic Diseases. Compr. Physiol. 2012, 2, 1143–1211. [Google Scholar] [CrossRef] [Green Version]
  5. Ortega, F.B.; Ruiz, J.; Castillo, M.J.; Sjöström, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes. 2007, 32, 1–11. [Google Scholar] [CrossRef] [Green Version]
  6. Henriques-Neto, D.; Magalhães, J.P.; Hetherington-Rauth, M.; Santos, D.A.; Baptista, F.; Sardinha, L. Physical Fitness and Bone Health in Young Athletes and Nonathletes. Sports Health 2020, 12, 441–448. [Google Scholar] [CrossRef] [PubMed]
  7. Mäestu, E.; Harro, J.; Veidebaum, T.; Kurrikoff, T.; Jürimäe, J.; Mäestu, J. Changes in cardiorespiratory fitness through adolescence predict metabolic syndrome in young adults. Nutr. Metab. Cardiovasc. Dis. 2020, 30, 701–708. [Google Scholar] [CrossRef]
  8. Åvitsland, A.; Leibinger, E.; Haugen, T.; Lerum, Ø.; Solberg, R.B.; Kolle, E.; Dyrstad, S.M. The association between physical fitness and mental health in Norwegian adolescents. BMC Public Health 2020, 20, 776. [Google Scholar] [CrossRef] [PubMed]
  9. Appelqvist-Schmidlechner, K.; Vaara, J.P.; Vasankari, T.; Häkkinen, A.; Mäntysaari, M.; Kyröläinen, H. Muscular and cardiorespiratory fitness are associated with health-related quality of life among young adult men. BMC Public Health 2020, 20, 842. [Google Scholar] [CrossRef] [PubMed]
  10. Kim, B.; Ku, M.; Kiyoji, T.; Isobe, T.; Sakae, T.; Oh, S. Cardiorespiratory fitness is strongly linked to metabolic syndrome among physical fitness components: A retrospective cross-sectional study. J. Physiol. Anthr. 2020, 39, 1–9. [Google Scholar] [CrossRef] [PubMed]
  11. Wang, Y.; Li, F.; Cheng, Y.; Gu, L.; Xie, Z. Cardiorespiratory fitness as a quantitative predictor of the risk of stroke: A dose–response meta-analysis. J. Neurol. 2020, 267, 491–501. [Google Scholar] [CrossRef]
  12. García-Hermoso, A.; Cavero-Redondo, I.; Ramírez-Vélez, R.; Ruiz, J.R.; Ortega, F.B.; Lee, D.-C.; Martínez-Vizcaíno, V. Muscular Strength as a Predictor of All-Cause Mortality in an Apparently Healthy Population: A Systematic Review and Meta-Analysis of Data from Approximately 2 Million Men and Women. Arch. Phys. Med. Rehabil. 2018, 99, 2100–2113. [Google Scholar] [CrossRef]
  13. Lee, S.H.; Gong, H.S. Measurement and Interpretation of Handgrip Strength for Research on Sarcopenia and Osteoporosis. J. Bone Metab. 2020, 27, 85–96. [Google Scholar] [CrossRef] [PubMed]
  14. Zaccagni, L.; Toselli, S.; Bramanti, B.; Gualdi-Russo, E.; Mongillo, J.; Rinaldo, N. Handgrip Strength in Young Adults: Association with Anthropometric Variables and Laterality. Int. J. Environ. Res. Public Health 2020, 17, 4273. [Google Scholar] [CrossRef]
  15. Moradi, A.; Damirchi, E.S.; Narimani, M.; Esmaeilzadeh, S.; Dziembowska, I.; Azevedo, L.B.; Prado, W.L.D. Association between Physical and Motor Fitness with Cognition in Children. Medicina 2019, 55, 7. [Google Scholar] [CrossRef] [Green Version]
  16. Silverman, M.N.; Deuster, P.A. Biological mechanisms underlying the role of physical fitness in health and resilience. Interface Focus 2014, 4, 20140040. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Dwyer, T.; Magnussen, C.G.; Schmidt, M.D.; Ukoumunne, O.C.; Ponsonby, A.-L.; Raitakari, O.T.; Zimmet, P.Z.; Blair, S.N.; Thomson, R.; Cleland, V.; et al. Decline in Physical Fitness from Childhood to Adulthood Associated with Increased Obesity and Insulin Resistance in Adults. Diabetes Care 2008, 32, 683–687. [Google Scholar] [CrossRef] [Green Version]
  18. García-Hermoso, A.; Alonso-Martinez, A.M.; Ramírez-Vélez, R.; Izquierdo, M. Effects of Exercise Intervention on Health-Related Physical Fitness and Blood Pressure in Preschool Children: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. Sports Med. 2020, 50, 187–203. [Google Scholar] [CrossRef] [PubMed]
  19. Pazzianotto-Forti, E.M.; Moreno, M.A.; Plater, E.; Baruki, S.B.S.; Junior, I.R.; Reid, W.D. Impact of Physical Training Programs on Physical Fitness in People with Class II and III Obesity: A Systematic Review and Meta-Analysis. Phys. Ther. 2020, 100, 963–978. [Google Scholar] [CrossRef] [PubMed]
  20. Sato, M.; Kodama, S.; Sugawara, A.; Saito, K.; Sone, H. Physical Fitness During Adolescence and Adult Mortality. Epidemiology 2009, 20, 463–464. [Google Scholar] [CrossRef]
  21. Sacheck, J. Pediatric Obesity: An Inflammatory Condition? J. Parenter. Enter. Nutr. 2008, 32, 633–637. [Google Scholar] [CrossRef] [PubMed]
  22. Dumuid, D.; Olds, T.; Lewis, L.K.; Martin-Fernández, J.A.; Barreira, T.; Broyles, S.; Chaput, J.-P.; Fogelholm, M.; Hu, G.; Kuriyan, R.; et al. The adiposity of children is associated with their lifestyle behaviours: A cluster analysis of school-aged children from 12 nations. Pediatr. Obes. 2018, 13, 111–119. [Google Scholar] [CrossRef] [PubMed]
  23. Buchan, D.S.; Thomas, N.E.; Baker, J.S. Novel risk factors of cardiovascular disease and their associations between obesity, physical activity and physical fitness. J. Public Health Res. 2012, 1, 11–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Schmitz, K.; Jacobs, D.R.; Hong, C.-P.; Steinberger, J.; Moran, A.; Sinaiko, A. Association of physical activity with insulin sensitivity in children. Int. J. Obes. 2002, 26, 1310–1316. [Google Scholar] [CrossRef] [Green Version]
  25. Chae, H.-W.; Kwon, Y.-N.; Rhie, Y.-J.; Kim, H.-S.; Kim, Y.-S.; Paik, I.-Y.; Suh, S.-H.; Kim, D.-H. Effects of a Structured Exercise Program on Insulin Resistance, Inflammatory Markers and Physical Fitness in Obese Korean Children. J. Pediatr. Endocrinol. Metab. 2010, 23, 1065–1072. [Google Scholar] [CrossRef]
  26. Muntaner-Mas, A.; Palou, P.; Vidal-Conti, J.; Esteban-Cornejo, I. A Mediation Analysis on the Relationship of Physical Fitness Components, Obesity, and Academic Performance in Children. J. Pediatr. 2018, 198, 90–97. [Google Scholar] [CrossRef]
  27. Barry, V.W.; Baruth, M.; Beets, M.W.; Durstine, J.L.; Liu, J.; Blair, S.N. Fitness vs. Fatness on All-Cause Mortality: A Meta-Analysis. Prog. Cardiovasc. Dis. 2014, 56, 382–390. [Google Scholar] [CrossRef] [PubMed]
  28. Mendoza-Muñoz, M.; Adsuar, J.; Pérez-Gómez, J.; Muñoz-Bermejo, L.; Garcia-Gordillo, M.; Carlos-Vivas, J. Influence of Body Composition on Physical Fitness in Adolescents. Medicina 2020, 56, 328. [Google Scholar] [CrossRef] [PubMed]
  29. Hussey, J.; Bell, C.; Bennett, K.; O’Dwyer, J.; Gormley, J. Relationship between the intensity of physical activity, inactivity, cardiorespiratory fitness and body composition in 7-10-year-old Dublin children. Br. J. Sports Med. 2007, 41, 311–316. [Google Scholar] [CrossRef]
  30. Chuang, H.-H.; Lin, R.-H.; Chen, J.-Y.; Yeh, W.-C.; Lin, H.-F.; Ueng, S.W.-N.; Hsu, K.-H. Effectiveness of a multi-faceted intervention among elementary school children. Medicina 2019, 98, e15079. [Google Scholar] [CrossRef] [PubMed]
  31. Sekgala, M.D.; Monyeki, K.D.; Mogale, M.A.; Ramoshaba, N.E. Performance of blood pressure to height ratio as a screening tool for elevated blood pressure in rural children: Ellisras Longitudinal Study. J. Hum. Hypertens. 2017, 31, 591–595. [Google Scholar] [CrossRef]
  32. Nafiu, O.O.; Burke, C.; Lee, J.; Voepel-Lewis, T.; Malviya, S.; Tremper, K.K. Neck Circumference as a Screening Measure for Identifying Children with High Body Mass Index. Pediatrics 2010, 126, e306–e310. [Google Scholar] [CrossRef]
  33. Chung, K.; Chiou, H.; Chang, J.; Chen, Y. Associations of nitric oxide with obesity and psychological traits among children and adolescents in Taiwan. Pediatr. Obes. 2019, 15, e12593. [Google Scholar] [CrossRef]
  34. Flegal, K.M.; Cole, T.J. Construction of LMS parameters for the Centers for Disease Control and Prevention 2000 growth charts. Natl. Health Stat. Rep. 2013, 2013, 1–3. [Google Scholar]
  35. Ma, W.-Y.; Yang, C.-Y.; Shih, S.-R.; Hsieh, H.-J.; Hung, C.S.; Chiu, F.-C.; Lin, M.-S.; Liu, P.-H.; Hua, C.-H.; Hsein, Y.-C.; et al. Measurement of Waist Circumference: Midabdominal or iliac crest? Diabetes Care 2013, 36, 1660–1666. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Lohman, T.G.; Roche, A.F.; Martorell, R. Anthropometric Standardization Reference Manual; Human Kinetics Books: Champaign, IL, USA, 1998. [Google Scholar]
  37. Grossi, M.; Riccò, B. Electrical impedance spectroscopy (EIS) for biological analysis and food characterization: A review. J. Sens. Sens. Syst. 2017, 6, 303–325. [Google Scholar] [CrossRef] [Green Version]
  38. Jensen, B.; Braun, W.; Geisler, C.; Both, M.; Klückmann, K.; Müller, M.J.; Bosy-Westphal, A. Limitations of Fat-Free Mass for the Assessment of Muscle Mass in Obesity. Obes. Facts 2019, 12, 307–315. [Google Scholar] [CrossRef]
  39. Hu, J.; Li, Z.; Li, S.; Li, H.; Wang, S.; Wang, S.; Han, L. Skipping breakfast and physical fitness among school-aged adolescents. Clinics 2020, 75, e1599. [Google Scholar] [CrossRef]
  40. Hsieh, P.-L.; Chen, M.-L.; Huang, C.-M.; Chen, W.-C.; Li, C.-H.; Chang, L.-C. Physical Activity, Body Mass Index, and Cardiorespiratory Fitness among School Children in Taiwan: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2014, 11, 7275–7285. [Google Scholar] [CrossRef] [Green Version]
  41. Hartman, J.G.; Looney, M. Norm-Referenced and Criterion-Referenced Reliability and Validity of the Back-Saver Sit-and-Reach. Meas. Phys. Educ. Exerc. Sci. 2003, 7, 71–87. [Google Scholar] [CrossRef]
  42. Lovecchio, N.; Novak, D.; Sedlacek, J.; Hamar, P.; Milanovic, I.; Radisavljevic-Janic, S.; Emeljanovas, A.; Eid, L.; Zago, M. Physical fitness for sedentary students: A common trend from six European countries. J. Sports Med. Phys. Fit. 2019, 59, 1389–1396. Available online: https://www.minervamedica.it/index2.php?show=R40Y2019N08A1389 (accessed on 4 May 2020). [CrossRef] [PubMed]
  43. Reid, R.E.; Fillon, A.; Thivel, D.; Henderson, M.; Barnett, T.A.; Bigras, J.-L.; Mathieu, M.-E. Can anthropometry and physical fitness testing explain physical activity levels in children and adolescents with obesity? J. Sci. Med. Sport 2020, 23, 580–585. [Google Scholar] [CrossRef] [PubMed]
  44. Ministry of Education. Measures for the Implementation of Physical Fitness Testing in Taiwan. 2018. Available online: https://edu.law.moe.gov.tw/LawContent.aspx?id=GL000769 (accessed on 28 March 2021).
  45. Templeton, G.F. A Two-Step Approach for Transforming Continuous Variables to Normal: Implications and Recommendations for IS Research. Commun. Assoc. Inf. Syst. 2011, 28, 41–58. [Google Scholar] [CrossRef] [Green Version]
  46. Wang, Y.; Lim, H. The global childhood obesity epidemic and the association between socio-economic status and childhood obesity. Int. Rev. Psychiatry 2012, 24, 176–188. [Google Scholar] [CrossRef] [Green Version]
  47. NCD Risk Factor Collaboration (NCD-RisC). Heterogeneous contributions of change in population distribution of body mass index to change in obesity and underweight. Elife 2021, 10, e60060. [Google Scholar] [CrossRef] [PubMed]
  48. Rodriguez-Martinez, A.; Zhou, B.; Sophiea, M.K.; Bentham, J.; Paciorek, C.J.; Iurilli, M.L.; Carrillo-Larco, R.M.; Bennett, J.E.; Di Cesare, M.; Taddei, C.; et al. Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: A pooled analysis of 2181 population-based studies with 65 million participants. Lancet 2020, 396, 1511–1524. [Google Scholar] [CrossRef]
  49. Ostojic, S.M.; Stojanovic, M.D.; Stojanovic, V.; Maric, J.; Njaradi, N. Correlation between Fitness and Fatness in 6-14-year Old Serbian School Children. J. Health Popul. Nutr. 2011, 29, 53–60. [Google Scholar] [CrossRef] [Green Version]
  50. Tomkinson, G.R.; Léger, L.A.; Olds, T.S.; Cazorla, G. Secular Trends in the Performance of Children and Adolescents (1980–2000). Sports Med. 2003, 33, 285–300. [Google Scholar] [CrossRef]
  51. Kumar, S.; Kelly, A.S. Review of Childhood Obesity: From Epidemiology, Etiology, and Comorbidities to Clinical Assessment and Treatment. Mayo Clin. Proc. 2017, 92, 251–265. [Google Scholar] [CrossRef] [Green Version]
  52. Olds, T.S.; Ridley, K.; Tomkinson, G.R. Declines in aerobic fitness: Are they only due to increasing fatness? Med. Sport Sci. 2007, 50, 226–240. [Google Scholar] [PubMed]
  53. Sacchetti, R.; Ceciliani, A.; Garulli, A.; Masotti, A.; Poletti, G.; Beltrami, P.; Leoni, E. Physical fitness of primary school children in relation to overweight prevalence and physical activity habits. J. Sports Sci. 2012, 30, 633–640. [Google Scholar] [CrossRef] [PubMed]
  54. Tomkinson, G.R.; Olds, T.S. Secular Changes in Pediatric Aerobic Fitness Test Performance: The Global Picture. Med. Sport Sci. 2007, 50, 46–66. [Google Scholar]
  55. Zaqout, M.; Vyncke, K.; Moreno, L.A.; De Miguel-Etayo, P.; Lauria, F.; Molnar, D.; Lissner, L.; Hunsberger, M.; Veidebaum, T.; Tornaritis, M.; et al. Determinant factors of physical fitness in European children. Int. J. Public Health 2016, 61, 573–582. [Google Scholar] [CrossRef] [Green Version]
  56. Lämmle, L.; Worth, A.; Bös, K. Socio-demographic correlates of physical activity and physical fitness in German children and adolescents. Eur. J. Public Health 2012, 22, 880–884. [Google Scholar] [CrossRef] [Green Version]
  57. Rosa-Guillamón, A.; Carrillo-López, P.J.; García-Cantó, E. Analysis of physical fitness according to sex, age, body mass index and level of physical activity in Spanish elementary school students. Rev. Fac. Med. 2020, 68, 92–99. [Google Scholar]
  58. Malina, R.M.; Bouchard, C. Growth, Maturation, and Physical Activity. Med. Sci. Sports Exerc. 1992, 24, 841. [Google Scholar] [CrossRef]
  59. Haywood, K.M.; Getchell, N. Life Span Motor Development; Human Kinetics: Champaign, IL, USA, 2019. [Google Scholar]
  60. Milanese, C.; Sandri, M.; Cavedon, V.; Zancanaro, C. The role of age, sex, anthropometry, and body composition as determinants of physical fitness in nonobese children aged 6-12. PeerJ 2020, 8, e8657. [Google Scholar] [CrossRef]
  61. Praagh, E.V. Anaerobic fitness tests: What are we measuring? Med. Sport Sci. 2007, 50, 26–45. [Google Scholar]
  62. Chu, N.-F. Prevalence and trends of obesity among school children in Taiwan—The Taipei Children Heart Study. Int. J. Obes. 2001, 25, 170–176. [Google Scholar] [CrossRef] [Green Version]
  63. Chu, N.-F.; Pan, W.-H. Prevalence of obesity and its comorbidities among schoolchildren in Taiwan. Asia Pac. J. Clin. Nutr. 2007, 16, 601. [Google Scholar]
  64. Arfai, K.; Pitukcheewanont, P.D.; Goran, M.I.; Tavaré, C.J.; Heller, L.; Gilsanz, V. Bone, Muscle, and Fat: Sex-related Differences in Prepubertal Children. Radiology 2002, 224, 338–344. [Google Scholar] [CrossRef]
  65. Leonard, M.B.; Elmi, A.; Mostoufi-Moab, S.; Shults, J.; Burnham, J.M.; Thayu, M.; Kibe, L.; Wetzsteon, R.J.; Zemel, B.S. Effects of Sex, Race, and Puberty on Cortical Bone and the Functional Muscle Bone Unit in Children, Adolescents, and Young Adults. J. Clin. Endocrinol. Metab. 2010, 95, 1681–1689. [Google Scholar] [CrossRef] [PubMed]
  66. Graf, C.; Schierz, O.; Steinke, H.; Körner, A.; Kiess, W.; Kratzsch, J.; Hirsch, C. The LIFE Child study team; LIFE Child study team Sex hormones in association with general joint laxity and hypermobility in the temporomandibular joint in adolescents—results of the epidemiologic LIFE child study. J. Oral Rehabil. 2019, 46, 1023–1030. [Google Scholar] [CrossRef] [PubMed]
  67. Garnett, S.P.; Högler, W.; Blades, B.; Baur, L.A.; Peat, J.; Lee, J.; Cowell, C.T. Relation between hormones and body composition, including bone, in prepubertal children. Am. J. Clin. Nutr. 2004, 80, 966–972. [Google Scholar] [CrossRef] [Green Version]
  68. Valdivia, O.D.; Ortega, F.Z.; Rodríguez, J.J.A.; Sánchez, M. Changes in flexibility according to gender and educational stage. Apunt. Med. l’esport 2009, 161, 10–17. [Google Scholar] [CrossRef]
  69. Golle, K.; Muehlbauer, T.; Wick, D.; Granacher, U. Physical Fitness Percentiles of German Children Aged 9–12 Years: Findings from a Longitudinal Study. PLoS ONE 2015, 10, e0142393. [Google Scholar] [CrossRef] [PubMed]
  70. Artero, E.G.; España-Romero, V.; Ortega, F.B.; Jimenez-Pavon, D.; Ruiz, J.R.; Vicente-Rodriguez, G.; Bueno, M.; Marcos, A.; Gómez-Martinez, S.; Urzanqui, A.; et al. Health-related fitness in adolescents: Underweight, and not only overweight, as an influencing factor. The AVENA study. Scand. J. Med. Sci. Sports 2010, 20, 418–427. [Google Scholar] [CrossRef]
  71. Xu, Y.; Mei, M.; Wang, H.; Yan, Q.; He, G. Association between Weight Status and Physical Fitness in Chinese Mainland Children and Adolescents: A Cross-Sectional Study. Int. J. Environ. Res. Public Health 2020, 17, 2468. [Google Scholar] [CrossRef] [Green Version]
  72. Casonatto, J.; Fernandes, R.A.; Batista, M.B.; Cyrino, E.S.; Coelho-E-Silva, M.J.; De Arruda, M.; Ronque, E.R.V. Association between health-related physical fitness and body mass index status in children. J. Child. Health Care 2016, 20, 294–303. [Google Scholar] [CrossRef] [PubMed]
  73. Silva, D.A.S.; Nunes, H.E.G. Prevalence and Factors Associated with Low Aerobic Performance Levels in Adolescents: A Systematic Review. Curr. Pediatr. Rev. 2015, 11, 56–70. [Google Scholar] [CrossRef]
  74. Zanini, D.; Kuipers, A.; Somensi, I.V.; Pasqualotto, J.F.; Quevedo, J.D.G.; Teo, J.C.; Antes, D.L. Relationship between body composition and physical capacities in junior soccer players. Rev. Bras. Cineantropometria Desempenho Humano 2020, 22, 22. [Google Scholar] [CrossRef]
  75. Ceschia, A.; Giacomini, S.; Santarossa, S.; Rugo, M.; Salvadego, D.; Da Ponte, A.; Driussi, C.; Mihaleje, M.; Poser, S.; Lazzer, S. Deleterious effects of obesity on physical fitness in pre-pubertal children. Eur. J. Sport Sci. 2016, 16, 271–278. [Google Scholar] [CrossRef] [PubMed]
  76. Thivel, D.; Ring-Dimitriou, S.; Weghuber, D.; Frelut, M.-L.; O’Malley, G. Muscle Strength and Fitness in Pediatric Obesity: A Systematic Review from the European Childhood Obesity Group. Obes. Facts 2016, 9, 52–63. [Google Scholar] [CrossRef] [PubMed]
  77. Winck, A.D.; Heinzmann-Filho, J.P.; Soares, R.B.; da Silva, J.S.; Woszezenki, C.T.; Zanatta, L.B. Effects of obesity on lung volume and capacity in children and adolescents: A systematic review. Rev. Paul Pediatr. 2016, 34, 510–517. [Google Scholar] [CrossRef] [PubMed]
  78. Jones, R.L.; Nzekwu, M.-M.U. The Effects of Body Mass Index on Lung Volumes. Chest 2006, 130, 827–833. [Google Scholar] [CrossRef]
  79. Davidson, W.J.; Mackenzie-Rife, K.A.; Witmans, M.B.; Montgomery, M.D.; Ball, G.D.; Egbogah, S.; Eves, N.D. Obesity negatively impacts lung function in children and adolescents. Pediatr. Pulmonol. 2014, 49, 1003–1010. [Google Scholar] [CrossRef]
  80. Dubern, B.; Tounian, P.; Medjadhi, N.; Maingot, L.; Girardet, J.-P.; Boulé, M. Pulmonary function and sleep-related breathing disorders in severely obese children. Clin. Nutr. 2006, 25, 803–809. [Google Scholar] [CrossRef]
  81. Brunet, M.; Chaput, J.-P.; Tremblay, A. The association between low physical fitness and high body mass index or waist circumference is increasing with age in children: The ‘Québec en Forme’ Project. Int. J. Obes. 2006, 31, 637–643. [Google Scholar] [CrossRef] [Green Version]
  82. Hopkins, P.M. Skeletal muscle physiology. Contin. Educ. Anaesth. Crit. Care Pain 2006, 6, 1–6. [Google Scholar] [CrossRef] [Green Version]
  83. Ogborn, D.; Schoenfeld, B.J. The role of fiber types in muscle hypertrophy: Implications for loading strategies. Strength Cond. J. 2014, 36, 20–25. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Participant characteristics of the overall cohort as well as two subgroups stratified by sex.
Table 1. Participant characteristics of the overall cohort as well as two subgroups stratified by sex.
VariablesOverallBoysGirlsp-Value a
Participantsn = 360n = 180n = 180
Demographic measures
Age (years)10.0 ± 0.610.0 ± 0.710.0 ± 0.60.72
Anthropometric measures
Body mass index z-score0.366 ± 1.2160.543 ± 1.2690.190 ± 1.1370.01
Waist/height ratio0.46 ± 0.060.47 ± 0.060.45 ± 0.06<0.001
Body composition measures
Body-fat percentage16.9 ± 7.614.1 ± 8.019.5 ± 6.1<0.001
Muscle weight (kg)29.0 ± 6.130.7 ± 5.627.5 ± 6.2<0.001
Physical fitness levels
800-m run (sec)306.9 ± 69.4297.1 ± 74.2316.7 ± 63.00.01
Sit-and-reach (cm)25.6 ± 8.423.1 ± 8.128.1 ± 7.9<0.001
1-min sit-ups (time)27.8 ± 8.828.7 ± 9.626.9 ± 7.90.049
Standing long jump (cm)137.1 ± 25.6141.1 ± 27.0133.1 ± 23.50.003
Data are summarized as means ± standard deviations. a Data were compared using the Student t-test.
Table
Table 2. Associations a of dichotomized variables of interest.
Table 2. Associations a of dichotomized variables of interest.
PredictorsFemale Sex aAge bBody Mass Index z-Score bWaist/Height Ratio bBody-Fat
Percentage b		Muscle Weight b800-m Run bSit-and-Reach b1-min Sit-Ups bStanding Long Jump b
Female sex–
Age0.02 (0.72)–
Body mass index z-score−0.14 (0.01 *)−0.03 (0.63)–
Waist/height ratio−0.19 (<0.001 *)−0.05 (0.37)0.80 (<0.001 *)–
Body-fat percentage0.36 (<0.001 *)0.03 (0.57)0.69 (<0.001 *)0.62 (<0.001 *)–
Muscle weight−0.24 (<0.001 *)0.44 (<0.001 *)0.68 (<0.001 *)0.43 (<0.001 *)0.43 (<0.001 *)–
800-m run0.15 (0.004 *)−0.04 (0.42)0.28 (<0.001 *)0.27 (<0.001 *)0.37 (<0.001 *)0.11 (0.04 *)–
Sit-and-reach0.32 (<0.001 *)0.26 (<0.001 *)−0.15 (0.004 *)−0.15 (0.01 *)0.02 (0.69)−0.07 (0.22)−0.04 (0.51)–
1-min sit-ups−0.10 (0.06)0.22 (<0.001 *)−0.14 (0.01 *)−0.18 (<0.001 *)−0.28 (<0.001 *)0.01 (0.92)−0.48 (<0.001 *)0.08 (0.11)–
Standing long jump−0.15 (0.004 *)0.42 (<0.001 *)−0.10 (0.06)−0.11 (0.04 *)−0.24 (<0.001 *)0.18 (0.001 *)−0.39 (<0.001 *)0.27 (<0.001 *)0.44 (<0.001 *)–
Data are summarized as Spearman correlation coefficients (p-values). a Data were analyzed using the Spearman correlation test. b Data were analyzed using the Pearson correlation test. * p-value < 0.05.
Table
Table 3. Univariate and multivariable regression models of physical fitness levels in the overall cohort.
Table 3. Univariate and multivariable regression models of physical fitness levels in the overall cohort.
PredictorsB (95%CI)p-Value aB (95%CI)p-Value a
Univariate ModelMultivariate Model
800-m run
Female sex19.6 (5.3–33.9)0.01 NS
Age−4.5 (−15.5–6.5)0.42 NI
Body mass index z-score15.9 (10.2–21.6)<0.001 NS
Waist/height ratio290.5 (180.7–400.2)<0.001 NS
Body-fat percentage3.3 (2.4–4.2)<0.0013.4 (2.5–4.3)<0.001
Muscle weight1.3 (0.1–2.5)0.04 NS
Sit-and-reach
Female sex5.0 (3.4–6.7)<0.0014.6 (3.0–6.3)<0.001
Age3.3 (2.0–4.6)<0.0013.1 (1.9–4.3)<0.001
Body mass index z-score−1.1 (−1.8–−0.3)0.004−0.7 (−1.4–−0.1)0.03
Waist/height ratio−19.0 (−32.5–−5.5)0.01 NS
Body-fat percentage0.02 (−0.10–0.14)0.69 NI
Muscle weight−0.1 (−0.2–0.1)0.22 NI
1-min sit-ups
Female sex−1.8 (−3.7–−0.1)0.049 NS
Age2.9 (1.6–4.3)<0.0013.3 (2.0–4.7)<0.001
Body mass index z-score−1.0 (−1.8–−0.3)0.01 NS
Waist/height ratio−24.8 (−38.9–−10.7)0.001 NS
Body-fat percentage−0.3 (−0.4–−0.2)<0.001−0.3 (−0.4–−0.2)<0.001
Muscle weight0.1 (−0.1–0.2)0.92 NI
Standing long jump
Female sex−8.0 (−13.2–−2.7)0.003 NS
Age16.3 (12.6–20.0)<0.00114.1 (9.9–18.3)<0.001
Body mass index z-score−2.1 (−4.3–0.1)0.06 NS
Waist/height ratio−42.5 (−83.8–−1.1)0.04 NS
Body-fat percentage−0.8 (−1.2–−0.5)<0.001−1.1 (−1.4–−0.7)<0.001
Muscle weight0.8 (0.3–1.2)0.0010.7 (0.2–1.2)0.01
Abbreviations: B: regression coefficient; CI: confidence interval; NI: not included; NS: not significant. a Data were compared using linear regression models.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Hsu, C.-Y.; Chen, L.-S.; Chang, I.-J.; Fang, W.-C.; Huang, S.-W.; Lin, R.-H.; Ueng, S.W.-N.; Chuang, H.-H. Can Anthropometry and Body Composition Explain Physical Fitness Levels in School-Aged Children? Children 2021, 8, 460. https://doi.org/10.3390/children8060460

AMA Style

Hsu C-Y, Chen L-S, Chang I-J, Fang W-C, Huang S-W, Lin R-H, Ueng SW-N, Chuang H-H. Can Anthropometry and Body Composition Explain Physical Fitness Levels in School-Aged Children? Children. 2021; 8(6):460. https://doi.org/10.3390/children8060460

Chicago/Turabian Style

Hsu, Chih-Yu, Liang-Sien Chen, I-Jen Chang, Wei-Ching Fang, Sun-Weng Huang, Rong-Ho Lin, Steve Wen-Neng Ueng, and Hai-Hua Chuang. 2021. "Can Anthropometry and Body Composition Explain Physical Fitness Levels in School-Aged Children?" Children 8, no. 6: 460. https://doi.org/10.3390/children8060460

APA Style

Hsu, C.-Y., Chen, L.-S., Chang, I.-J., Fang, W.-C., Huang, S.-W., Lin, R.-H., Ueng, S. W.-N., & Chuang, H.-H. (2021). Can Anthropometry and Body Composition Explain Physical Fitness Levels in School-Aged Children? Children, 8(6), 460. https://doi.org/10.3390/children8060460

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop