1. Introduction
The lifelong health benefits of adequate physical activity in childhood are well established [1,2,3,4,5,6,7]. Interactions exist between physical activity, physical fitness, motor competence, the human psyche, and body weight, and their interplay has important effects on health [8,9,10,11,12,13,14].
In older studies, the level of physical fitness was defined as the sum of the performance of cardiorespiratory endurance, muscular endurance, muscular strength, speed, flexibility, full body coordination, balance, and body composition and was divided into health-related and motor fitness [15,16].
Health-related fitness (HRF) includes cardiorespiratory endurance, muscular strength, flexibility, and body composition [15,16]. Recent research views body composition as the result of relationships among HRF, physical activity, motor competence, and perceived motor competence, and thus excludes body composition as a component of HRF [11]. In addition, studies reported a weak relationship between health and flexibility [17,18] and showed that in youth, HRF is well represented by a three-component model that includes cardiorespiratory endurance, muscular endurance, and muscular strength [19].
Motor fitness includes all components described in the context of physical fitness except body composition [15,16,20]. However, balance is not a component of motor fitness, but is also considered a strong predictor of children’s spatial and proportional reasoning skills, with implications for many other milestones in childhood development [21].
There are several single motor tests available that can be used to assess components of physical fitness [22]. Likewise, there are some test batteries that allow us to assess health-related or motor fitness on the construct level. FitnessGram from the USA [23,24] and the Indares project from the International Database for Research and Educational Support [25] are examples of test batteries that allow us to assess parameters of HRF. The ALPHA-FIT test battery (an adapted version of the Eurofit test battery introduced in 1983) [26], the German motor test [27], the Czech UNIFITTEST (6–60) test battery [28], the Düsseldorf model [29], and the Identification and Prevention of Dietary and Lifestyle-Induced Health Effects in Children and Infants (IDEFICS) study [30,31] are some of the test batteries established in Europe that allow assessment of motor fitness.
Most of these test batteries are often associated with relatively high cost and require special test materials and a lot of space as well as intensive instruction of the test personnel [32]. This makes it difficult to implement them in day-to-day school life.
Newly designed monitoring tools should achieve two main goals. First, they should be able to detect deficiencies or problematic development in body composition and HRF at an early stage in order to counteract them with targeted measures. Second, the assessment of motor fitness should allow scouting of talented children for specialized sports schools and sports clubs. The aim of this pilot study was to design monitoring tools for anthropometrics and physical fitness that can be organized without high additional cost, require limited space, and can be carried out with simple instructions for the test personnel.
2. Materials and Methods
2.1. Design
This is a cross-sectional study based on the baseline measurements from a randomized controlled trial to evaluate the effects of an intervention in primary schools on the fitness and health status of children. The baseline measurements were conducted in September 2019. In early 2020, the intervention program had to be discontinued due to the COVID-19 pandemic. As part of this study, an Austrian monitoring system for anthropometrics and physical fitness was developed, the Austrian fitness monitoring tools for schoolkids (AUT FIT). The study was approved by the Research Ethics Committee of the University of Graz, Styria, Austria (GZ. 39/23/63 ex 2018/1/9) and has been registered in the German Clinical Trials Registry (ID DRKS00023824).
2.2. Selection of Schools and Participants
Using a random number generator, 12 of 39 primary schools in the urban and rural districts of Klagenfurt, Austria, were selected. All schools agreed to participate in the study. The following inclusion criteria were defined: children had to be between 7 and 10 years old at the beginning of the study and had to be able to perform all physical activity motor tests without limitations. In spring 2019, we invited all 1013 children attending the 12 schools to participate. Before the baseline measurements, 860 (85%) legal guardians gave written consent for their children to participate.
2.3. Procedures
Measurement of anthropometrics and fitness status was performed by trained members of the research team and took place in the schools during physical education (PE) lessons (the time sequence is explained in Appendix A.1 and Table A1). Information on the age and sex of the children was collected by the school teachers.
2.4. AUT FIT Monitoring Tools
AUT FIT was constructed to assess, in the simplest way possible, the anthropometrics and physical fitness status of school children based on three monitoring tools (Table 1).
Monitoring tools 1 (Mt1) and 2 (Mt2) assess body shape. Height (cm) was measured to the nearest 0.1 cm using a SECA 213 stadiometer. Weight (kg) was measured to the nearest 0.1 kg using a Bosch PPW4202/01 body scale, and waist circumference (cm) was measured to the nearest 0.1 cm using a GIMA 27,343 body tape measure.
2.4.1. Weight Status (Mt1)
Standardized body mass index (BMI) was used for classification into weight classes (see Section 2.5). For calculation of crude BMI, body weight in kilograms was divided by height squared in meters.
2.4.2. Estimate of Visceral Adiposity (Mt2)
Waist circumference was measured at the end of the breathing-out phase with a body tape measure at the level of the navel. The measurement was performed twice and the mean value from both measurements was recorded in the overall assessment. For the assessment of visceral adiposity, the waist-to-height ratio (WHtR) was used, calculated by dividing waist circumference in centimeters by height in centimeters.
2.4.3. Physical Fitness (Mt3)
Monitoring tool 3 (Mt3) assesses physical fitness, which is categorized into health-related fitness (Mt3-A) and motor fitness (Mt3-B). Cardiorespiratory endurance, muscular endurance, and muscle strength were measured to assess health-related fitness [19]. Cardiorespiratory endurance, muscular endurance, muscle strength, flexibility, speed, and balance data were used to assess motor fitness [15,16].
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Cardiorespiratory endurance
The 6-min run (6 MR) was used to analyze cardiorespiratory endurance [22,29]. The children were instructed to run as far as possible within 6 min. The test was performed on the playgrounds and sports fields of the schools. A square (6 × 18 m) was marked out with sports poles, then the four corner poles were moved 0.5 m inward. The children had to run around the marked square. A group of 6 to 7 children performed the test simultaneously and their running distance was measured in meters.
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Muscular endurance and full body coordination
Jumping sideway (JS) was tested to assess muscular endurance [33] and full body coordination [22]. The test instructor marked an area on the ground (100 × 50 cm), which was divided into two squares (50 × 50 cm) with marking tape. The children stood with both legs in the middle of one square and jumped for 15 s with both legs between the squares after the start command. The aim was to complete as many jumps as possible without touching the marker. If the child touched the marker, this jump was not counted. The test instructor counted the number of valid jumps, and each jump over the center line was counted as one jump attempt. Each child had two scoring attempts, and the average of the number of valid jumps from both attempts was used.
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Lower body strength
The standing long jump (SLJ) provides an assessment of lower body muscle strength [22] and is considered as an index for the general assessment of muscular fitness in children [34]. The children had to jump as far as possible with both legs from a starting line, and the shortest distance between the start line and the child’s heel contact with the ground was measured to the nearest cm using a tape measure. Three scoring attempts were performed, and the longest of the three jumps was used.
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Upper body strength
The 1 kg medicine ball throw (MB1kg) was used to measure upper body muscle strength [22]. Each child stood on a starting line holding a 1 kg medicine ball with both hands, the ball touching their chest, then threw the ball with both hands as far forward as possible. The shortest distance between the starting line and the ball’s contact with the ground was measured to the nearest cm using a tape measure. Each child had two attempts to throw the ball, and the longest throw was considered.
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Flexibility
Flexibility was measured using the sit-and-reach test. To perform the classical sit-and-reach test, an expensive test box is needed. Therefore, the V sit-and-reach test (VSR) was chosen, which can be performed using a tape measure and marking tape. The tape measure was fixed to the ground and a heel line was marked with tape. The children sat down on ground, with feet spread 30 cm apart and heels placed at the heel line, then placed one hand on top of the other and slowly reached forward as far as they could. The distance between the heel line and the maximum position reached with the fingertips that could be held for two seconds was noted. Each child had two scoring attempts, and the longest reach was used in the overall assessment. To use reference values of the classical sit-and-reach test, 15 cm was added to the scoring attempt [35,36,37].
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Action speed
To assess the children’s action speed, a shuttle running test (4 × 10 SHR) was performed [2]. Two lines (start line and turning line) at a distance of 10 m were marked on the ground. Two objects (O1 and O3) were placed behind the turning line and one easily graspable object (O2) was placed in front of the start line. The children had to run from the start line across the turning line, pick up O1, run back across the start line, and put down O1. They then picked up O2, completed the run, ran across the turning line, put down O2, picked up O3, and ran across the start line with it. The children were instructed to complete this test as quickly as possible. Two scoring attempts were made and the time was measured to the nearest 0.01 s using a stopwatch. Each child had two attempts, and the fastest run was considered.
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Reaction speed
In order to test reaction speed, a ruler drop test (RD) was performed [33]; for this, a ruler drop stick was constructed (Methods S1) and held by the test instructor. Each child formed an angle of 45° between the thumb and the outstretched fingers, and the test instructor held the ruler drop stick centrally in this area. The zero point was held at the level of the bottom of the thumb, and the test instructor dropped the stick within three seconds after the command “Ready”. The distance in cm that the stick fell was recorded. Each child had one test attempt and five scoring attempts. The best and worst attempts were eliminated from the evaluation, and the average value was calculated from the remaining three attempts and recorded in the overall evaluation. For simplicity, a straight ruler can be used instead of the ruler drop stick construction described in Methods S1.
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Balance
Existing balance tests are very time intensive; therefore, the standard single-leg stand (SLS) test [22], where each leg is assessed for 1 min, was adopted. The children were instructed to stand with one leg on a thin wooden plank, keeping their hands on their hips, and hold this position for as long as possible, for a maximum of 45 s. The test was performed twice with each leg (left, SLS-L, and right, SLS-R), and the best score (in seconds) for each leg was considered. If the child reached the maximum value (45 s) on the first attempt, a second attempt was not performed with the same leg. Exact details about the construction of the test device are described in the Supplementary Materials (Methods S2).
2.4.4. Procedure
Except the 6MR, all tests were performed in the gym or physical education room of the primary schools. The children completed all tests barefoot in sportswear, except the 6MR, which was done in sneakers. Each test was explained verbally to the children before beginning and visually demonstrated by the test instructor.
The tests were completed over four PE lessons in September 2019 and were carried out by trained test instructors. Anthropometric values and balance were measured in the first PE lesson. Flexibility, action speed, and full body coordination were measured in the second PE lesson. Reaction speed, muscular endurance, and muscle strength were measured in the third PE lesson. Cardiorespiratory endurance was assessed in the fourth PE lesson. Children who were absent at the time of testing had the opportunity to make up the missing tests during additional PE lessons.
To perform AUT FIT, a body scale (Mt1-weight), a body tape measure (Mt2), marking cones (6MR), a tape measure (6MR, SLJ, MB1kg, 4 × 10 SHR), a meter stick (Mt1-height, VSR, RD) marking tape (VSR, JS, SLJ, MB1kg, 4 × 10 SHR), three easy-to-grab items (4 × 10 SHR), a measuring construction made of wood for the balance test (Methods S2), and a stopwatch are needed. All of these items are in the typical inventory of an elementary school or can be obtained easily and inexpensively.
2.5. Standardization and Classification
2.5.1. Weight Classification
For the standardization of BMI and classification of weight, national reference values were used [38]. National reference data were expressed in BMI centile curves (i.e., equicurves, in this report named EQUI BMI) [38]. The absolute BMI values were converted to EQUI BMI values using the procedure described in Mayer et al. [38] (based on Cole et al. [39]). EQUI BMI curves were used to project actual BMI to cut-off values at age 18 years in order to classify the children’s weight in five categories (underweight < 18.5 kg/m2, normal weight = 18.5 to 25.0 kg/m2, overweight ≥ 25.0 kg/m2, obese ≥ 30.0 kg/m2, morbidly obese ≥ 35.0 kg/m2) (Table S1).
2.5.2. Waist-to-Height Ratio
The WHtR is a quick and an easy screening tool that can provide a simple overview of obesity and the associated cardiovascular risk. A WHtR value of 0.5 can be used for a simple general assessment of potentially increased health risk due to abdominal obesity [40]. Ashwell and Gibson proposed cut-off values of 0.5 and 0.6 to classify WHtR into no (≤0.5), increased (0.5 to 0.6), and high (≥0.6) health risk (Table S1) [41].
2.5.3. Fitness Tests
Health-related fitness (Mt3-A) and MF (Mt3-B) were assessed using a nine-point rating, and the calculation process is based on seven steps, which are described in detail in the appendix (Appendix A.2, Table A2).
2.5.4. Test–Retest Reliability
To assess the test–retest reliability of the four monitoring tools, 17 children in one class were tested twice by the same test instructor, with a one-week period between the test days.
2.5.5. Interrater Reliability
To assess the interrater reliability of the four monitoring tools, 18 children in one class were tested and assessed independently by two test instructors at the same time.
2.5.6. Criterion Validity
To estimate the usefulness of the Mt3-B monitoring tool and to provide criterion validity, the physical education teacher in one of the tested classes was asked to complete an assessment according to his perception. He ranked the children’s motor fitness (for boys and girls separately), and the number of boys (n = 10) and girls (n = 10) who were assessed determined the highest score to be achieved, with the most points given for the best performance and one point for the worst performance. The teacher was trained in physical education, had extensive experience in grading the physical performance of students, and was not informed about the AUT FIT results from Mt3-B.
For this class, a parallel ranking was created (using the same method as for the physical education teacher assessment) based on the results from Mt3-B.
2.6. Statistical Analysis
Continuous variables are reported as mean (M) and standard deviation (SD), and categorical variables as absolute value (n) and percentage (%) for descriptive statistics. No imputation of the data was performed. All statistical analyses were performed in SPSS 27.0 (IBM SPSS Statistics 27, IBM, New York, USA) with a significance level of p < 0.05.
For both test–retest and interrater reliability, a two-way mixed intraclass correlation coefficient (ICC) based on single measures and absolute agreement was calculated for the raw scores of each physical fitness test. The strength of ICC was classified according to Koo and Li [42] as poor (ICC < 0.50), moderate (0.50 ≤ ICC ≤ 0.75), good (0.75 < ICC ≤ 0.90), and excellent (ICC > 0.90) reliability.
Spearman correlation coefficient (rho) was calculated between the rankings based on Mt3-B and the physical education teacher, as well as between the monitoring tools and the individual fitness tests. The strength of the correlations was classified according to Cohen [43], with a weak correlation classified as rho ≥0.1, a medium strong correlation as rho ≥0.3, and a strong correlation as rho ≥0.5.
3. Results
3.1. Results of AUT FIT
In September 2019, 824 children participated in anthropometric and fitness measurements. Three children did not participate in all measurements and were excluded from the analysis; thus, data from 821 children were used for analysis. The mean age of the study population was 8.3 years (0.7 SD), and 419 (51.0%) were girls (Table 2). The results from Mt1 show that 124 children (15.1%) were overweight or obese. In Mt2, 119 children (14.5%) showed increased health risk and 21 (2.6%) high health risk based on WHtR (Table 2).
The mean values of SDS and z-score calculations of JS are very high when using the German reference values (M = 2.16) [27]. Similar results are observed when using the Indian (M = 2.02) [44] or German (M = 1.18) [45] reference values for RD (Table S2).
VSR and RD showed no correlation, and the other fitness measurements showed weak or moderate correlation with both anthropometric monitoring tools (Mt1 and Mt2) (Table S3).
Comparing the results of the individual fitness tests between children in different weight categories, for MB1kg, there was no significant difference between children with normal weight and those with overweight or obesity (normal weight vs. overweight: p = 0.11; normal weight vs. obesity: p = 0.76; normal weight vs. extreme obesity: p > 0.99; Table S4, Figure S1). All other test results were poorer for children with overweight or obesity compared to those with normal weight (Table S4, Figure S1). Similar results were found when comparing Mt2 and the different motor fitness tests; children with lower health risk had better results on the fitness tests, except for MB1kg, on which they had poorer results compared to children with high health risk (Table S5, Figure S2).
3.2. Reliability of AUT FIT
3.2.1. Test–Retest Reliability
The raw scores showed good or excellent test–retest reliability for eight items (weight, height, waist circumference, 6MR, SLJ, VSR, 4 × 10 SHR, and SLS-L). Moderate test–retest reliability was observed for MB1kg and SLS-R, and poor test–retest reliability was observed for JS. The test–retest reliability for the RD was poor (Table 3).
3.2.2. Interrater Reliability
The results showed excellent (ICC ≥ 0.90) interrater reliability for all items except VSR (ICC = 0.88) and RD (ICC = 0.18) (Table 4).
3.3. Validity of AUT FIT
Criterion validity was evident by a high Spearman’s correlation coefficient (rho = 0.95, p < 0.001) between the two rank scores (teacher ranking and ranking from Mt3-B), which was found to be somewhat lower for girls (rho = 0.92, p < 0.001) than boys (rho = 0.98, p < 0.001) (Figure 1).
4. Discussion
The monitoring tools of AUT FIT offer the possibility to conduct a multifaceted assessment of child development in terms of anthropometrics and fitness. With the data collected by AUT FIT, it may be possible to identify undesirable development at an early stage. Since the tests that make up this battery may be valid for older age groups, it might be possible to initiate effective countermeasures and monitor their effects in the long term. This approach already exists, at least in early childhood, in Austria and many other countries. Parameters of human development are routinely observed, starting before birth, and regulated by law up to the age of 60 months through mandatory examinations described in the “mother–child passport” [46,47,48,49]. After children enter the school system, monitoring is carried out by school physicians as part of the annual school examinations [50,51,52]. Although monitoring of cognitive competency is obligatory in the Austrian school system [53], a systematic examination of health-related fitness or motor fitness is currently not integrated into the system [54].
Some countries have routinely collected children’s anthropometric and fitness data nationwide for years [23,55,56,57,58], or implemented nationwide monitoring systems in primary schools, such as the German Health Interview and Examination Survey for Children and Adolescents (KiGGS) [59] and the Slovenian National Surveillance System for physical and motor development (SLOFit) [60]. SLOFit is a positive example of how a monitoring system in schools can reduce the prevalence of obesity and at the same time increase the physical fitness level of children through interventions based on its results [61].
Based on our findings in this pilot study, some of the fitness tests need to be replaced. In general, the RD test showed very poor reliability and a low correlation with physical fitness. In addition, only old reference values were available for this test, and it is not clear whether these data continue to reflect current trends [44,45]. These results render the test inadequate for further use.
The JS test resulted in very high z-scores, with a mean value of more than 2. This indicates that the children in our sample performed on average two standard deviations better than the reference sample. Various manuals of the JS test can be found in the literature, and we used the protocol described by Bös [27]. In another study using this test procedure, similarly high mean z-scores were found, as in our sample [62]. Therefore, it is suggested to use a slightly different protocol for the JS test, which uses a wooden stick [63] or a jump rope [64] to define the midline.
The new monitoring tools (Mt3-A and Mt3B) showed good results on the main quality criteria. Reliability was very satisfactory based on excellent interrater and test–retest reliability. The present study is a pilot study for the development of such a tool, and components of Mt3-A and Mt3-B are still being adapted until the final development stage of AUT FIT is completed. Criterion-related validity was demonstrated by the strong correlation between physical education teacher rankings and Mt3-B scores.
AUT FIT has several strengths and limitations. One strength is that almost all of the data collected in AUT FIT come from established and widely used motor tests that are easy to perform without requiring much additional cost, space, or time. Seven of the eight items are existing standardized tests that have been used for decades in a plethora of test batteries (FitnessGram [24], CNSPFS [56], AFEA [55], GTO [58], PFAAT [57], SLOFit [60], ALPHA [26], GMT [27], DÜMO [29]) and extensively tested for their validity. Over four physical education lessons, it was possible to collect a broad panel of anthropometric and fitness-related parameters. A major strength of AUT FIT is that no material or aspect of the test battery is culturally specific to Austrian children. AUT FIT has the potential to be used in the economically weakest regions of the world, since a good proportion of the test material could be made available in schools even in developing countries, and a vast majority of schools worldwide offer physical education lessons for this age group.
A limitation is that no national norms are available for the fitness tests so far, and some of the available international norms may be outdated. Another limitation is that AUT FIT is still in the development phase and the adaptations described above remain to be done, and for this reason confirmatory factor analysis of the proposed models is still lacking. Specific test limitations are described in the Methods and Discussion sections.
5. Conclusions
Our study shows that the AUT FIT monitoring tool can be easily implemented in primary schools in Austria. The tool can be easily further improved by adapting some fitness tests. The authors urgently recommend implementation of the adapted monitoring tools in schools in Austria and evaluation of the impact of the system on the anthropometrics and fitness of primary school children in the long term. This is also in view of the striking changes seen as a consequence of the COVID-19 pandemic and associated measures such as school closures and sport restrictions [65].
Conceptualization, G.J., J.J. and M.N.M.v.P.; methodology, G.J.; formal analysis, G.J., J.J. and M.N.M.v.P.; investigation, G.J.; resources, G.J.; data curation, G.J.; writing—original draft preparation, G.J.; writing—review and editing, G.J., J.J., R.K., R.A.L. and M.N.M.v.P.; visualization, G.J. and M.N.M.v.P.; supervision, M.N.M.v.P.; project administration, G.J.; funding acquisition, G.J. All authors have read and agreed to the published version of the manuscript.
This research was funded by the Austrian Federal Ministry for Arts, Culture, Civil Service and Sport, grant number GZ205.410/0014-II/B/5/2018. Open Access Funding by the University of Graz.
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Research Ethics Committee of the University of Graz, Styria, Austria (GZ. 39/23/63 ex 2018/19).
Informed consent was obtained from all subjects involved in the study.
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy/ethical restrictions.
This study was organized by the non-profit association NAMOA, Nachwuchsmodell Austria. We would like to thank all participants and their guardians and the trainers and staff of this study. We also thank Wolfgang Modritz for initiating the study and Peter Hofmann (University of Graz) for support in the conception phase. We would like to express our thanks to the Austrian Working Group on Pediatric Endocrinology and Diabetics (
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Enlarge this image.Figure 1. Criterion validity of motor fitness monitoring tool. Correspondence between rankings of 10 girls and 10 boys from one primary school class on a scale from worst (1) to best (10) motor fitness by assessment of PE teacher and monitoring tool 3B (Mt3-B).
Detailed overview of monitoring tools in AUT FIT.
| AUT FIT | Monitoring | ||||||
|---|---|---|---|---|---|---|---|
| Tool 1 (Mt1) | Tool 2 (Mt2) | Tool 3 (Mt3) | |||||
| Mt3-A | Mt3-B | ||||||
| Anthropometrics | Weight (kg) | BMI (kg/m2) | Weight classification by BMI | ||||
| Height (cm) | |||||||
| WHtR | Estimate of visceral adiposity | ||||||
| Waist circumference (cm) | |||||||
| Physical fitness | Cardiorespiratory fitness | 6 MR (m) | Health-related fitness | Motor |
|||
| Muscular endurance/full-body coordination | JS (N) | ||||||
| Muscular strength | Lower body strength | SLJ (cm) | |||||
| Upper body strength | MB1kg (cm) | ||||||
| Flexibility | VSR (cm) | ||||||
| Speed | Action speed | 4 × 10 SHR (s) | |||||
| Reaction speed | RD (cm) | ||||||
| Balance | SLS-L (s) | ||||||
| SLS-R (s) | |||||||
Mt3-A, monitoring tool for health-related fitness; Mt3-B, monitoring tool for motor fitness; BMI, body mass index, WHtR, waist-to-height ratio; 6 MR, 6 min run; JS, jumping sideways; SLJ, standing long jump; MB1kg, medicine ball throw (1 kg); VSR, V sit-and-reach test; 4 × 10 SHR, 4 × 10 m shuttle run; RD, ruler drop test; SLS-L, single leg stand test, left; SLS-R, single leg stand test, right. The four monitoring tools are indicated with different colors.
Overall results for AUT FIT monitoring tools.
| Variable | Classification | All (n = 821) |
|---|---|---|
| Age (years) | 8.3 (0.7) | |
| Weight (kg) | 29.8 (7.2) | |
| Height (cm) | 132.1 (6.7) | |
| Waist circumference (cm) | 60.9 (8.1) | |
| BMI (kg/m2) | 16.9 (2.9) | |
| EQUI BMI | 22.2 (3.5) | |
| WHtR | 0.46 (0.05) | |
| 6MR (m) | 913 (140) | |
| JS (N) | 31.7 (7.1) | |
| SLJ (cm) | 124 (20) | |
| MB1kg (kg) | 343 (73) | |
| VSR (cm) | 17.4 (8.5) | |
| 4 × 10 SHR (s) | 15.0 (1.5) | |
| RD (cm) | 17.6 (8.1) | |
| SLS-L (s) | 22.7 (16.4) | |
| SLS-R (s) | 26.2 (15.9) | |
| Mt1: Weight classification (N (%)) | Underweight | 52 (6.4%) |
| Normal weight | 645 (78.6%) | |
| Overweight | 89 (10.8%) | |
| Obese | 27 (3.3%) | |
| Morbidly obese | 8 (1.0%) | |
| Mt2: Health risk (N (%)) | No health risk | 681 (82.9%) |
| Increased health risk | 119 (14.5%) | |
| High health risk | 21 (2.6%) | |
| Mt3-A: Health-related fitness level (N (%)) | Low performance | 92 (11.2%) |
| Average performance | 516 (62.9%) | |
| Good performance | 213 (25.9%) | |
| Mt3-B: Motor fitness level (N (%)) | Low performance | 48 (5.8%) |
| Average performance | 639 (77.8%) | |
| Good performance | 134 (16.3%) |
Data are mean (SD) or N (%). AUT FIT, Austrian fitness monitoring tools for primary schoolkids; BMI, body mass index; EQUI BMI, equivalent BMI based on Austrian reference centile curves passing through adult BMI values (Mayer et al., 2015); WHtR, waist-to-height ratio; 6MR, 6 min run; JS, jumping sideways; SLJ, standing long jump; MB1kg, medicine ball throw (1 kg); VSR, V sit-and-reach test; 4 × 10 SHR, 4 × 10 m shuttle run; RD, ruler drop test; SLS-L, single leg stand test, left; SLS-R, single leg stand test, right; Mt1, monitoring tool for weight classification; Mt2, monitoring tool for estimating visceral adipose tissue; Mt3-A, monitoring tool for health-related fitness; Mt3-B, monitoring tool for motor fitness. Low performance includes Mt3 classification groups poor, very weak, and weak; average performance includes below average, average, and above average; good performance includes good, excellent, and outstanding.
Test–retest reliability.
| Antropometrics and Fitness Tests | Test Time 1 | Test Time 2 | ICC* (2.1) | 95% CI |
|---|---|---|---|---|
| Weight (kg) | 39.2 (10.1) | 39.1 (10.3) | 0.99 | 0.99 to >0.99 |
| Height (cm) | 142.2 (6.4) | 142.1 (6.4) | 0.99 | 0.99 to 0.99 |
| Waist circumference (cm) | 65.3 (9.4) | 64.9 (9.6) | 0.97 | 0.92 to 0.99 |
| 6MR (m) | 1004 (77) | 986 (72) | 0.86 | 0.64 to 0.95 |
| JS (N) | 30.6 (5.5) | 36.1 (5.3) | 0.41 | −0.10 to 0.76 |
| SLJ (cm) | 142.8 (15.8) | 141.5 (16.2) | 0.79 | 0.51 to 0.92 |
| MB1kg (s) | 411 (84) | 436 (76) | 0.70 | 0.35 to 0.88 |
| VSR (cm) | 11.2 (7.5) | 14.5 (6.8) | 0.85 | 0.20 to 0.96 |
| 4 × 10 SHR (s) | 13.33 (0.96) | 13.38 (0.69) | 0.80 | 0.53 to 0.92 |
| RD (cm) | 24.1 (6.1) | 19.4 (5.2) | −0.07 | −0.40 to 0.35 |
| SLS-L (s) | 29.0 (15.9) | 29.7 (14.5) | 0.81 | 0.55 to 0.93 |
| SLS-R (s) | 31.2 (13.8) | 31.7 (15.6) | 0.57 | 0.12 to 0.82 |
Data are mean (SD). *ICC model is based on single measures and absolute agreement. SD, standard deviation; ICC, intraclass correlation; CI, confidence interval; 6MR, 6 min run; JS, jumping sideways; SLJ, standing long jump; MB1kg, medicine ball throw (1 kg); VSR, V sit-and-reach test; 4 × 10 SHR, 4 × 10 m shuttle run; RD, ruler drop test; SLS, single leg stand.
Interrater reliability.
| Antropometrics and Fitness Tests | Rater 1 | Rater 2 | ICC* (2.1) | 95% CI |
|---|---|---|---|---|
| Weight (kg) | 38.5 (10.2) | 38.5 (10.2) | >0.99 | >0.99 to >0.99 |
| Height (cm) | 141.6 (6.9) | 141.5 (6.9) | 0.99 | 0.99 to >0.99 |
| Waist circumference (cm) | 65.8 (9.2) | 65.1 (9.2) | 0.96 | 0.90 to 0.98 |
| 6MR (m) | 983 (118) | 982 (118) | >0.99 | >0.99 to >0.99 |
| JS (N) | 30.5 (5.3) | 30.7 (5.2) | 0.98 | 0.95 to 0.99 |
| SLJ (cm) | 140.9 (18.0) | 140.6 (17.8) | 0.99 | 0.99 to 0.99 |
| MB1kg (s) | 412 (86) | 408 (83) | 0.99 | 0.99 to 0.99 |
| VSR (cm) | 11.2 (7.5) | 10.6 (7.8) | 0.88 | 0.70 to 0.95 |
| 4 × 10 SHR (s) | 13.22 (0.90) | 13.37 (0.95) | 0.97 | 0.84 to 0.99 |
| RD (cm) | 23.6 (6.3) | 18.0 (5.0) | 0.18 | −0.15 to 0.53 |
| SLS-L (s) | 27.6 (16.6) | 27.8 (16.5) | 0.99 | 0.99 to 0.99 |
| SLS-R (s) | 29.8 (14.7) | 29.7 (14.7) | >0.99 | 0.99 to >0.99 |
Data are number or mean (SD). *ICC model is based on single measures and absolute agreement. SD, standard deviation; ICC, intraclass correlation; CI, confidence interval; 6MR, 6 min run; JS, jumping sideways; SLJ, standing long jump; MB1kg, medicine ball throw (1 kg); VSR, V sit-and-reach test; 4 × 10 SHR, 4 × 10 m shuttle run; RD, ruler drop test; SLS, single leg stand.
Supplementary Materials
The following are available online at
References
1. Myers, J.; McAuley, P.; Lavie, C.J.; Despres, J.-P.; Arena, R.; Kokkinos, P. Physical activity and cardiorespiratory fitness as major markers of cardiovascular risk: Their independent and interwoven importance to health status. Prog. Cardiovasc. Dis.; 2015; 57, pp. 306-314. [DOI: https://dx.doi.org/10.1016/j.pcad.2014.09.011]
2. Ortega, F.B.; Ruiz, J.R.; Castillo, M.J.; Sjöström, M. Physical fitness in childhood and adolescence: A powerful marker of health. Int. J. Obes.; 2008; 32, pp. 1-11. [DOI: https://dx.doi.org/10.1038/sj.ijo.0803774]
3. Mintjens, S.; Menting, M.D.; Daams, J.G.; van Poppel, M.N.M.; Roseboom, T.J.; Gemke, R.J.B.J. Cardiorespiratory Fitness in Childhood and Adolescence Affects Future Cardiovascular Risk Factors: A Systematic Review of Longitudinal Studies. Sports Med.; 2018; 48, pp. 2577-2605. [DOI: https://dx.doi.org/10.1007/s40279-018-0974-5]
4. Umer, A.; Kelley, G.A.; Cottrell, L.E.; Giacobbi, P.; Innes, K.E.; Lilly, C.L. Childhood obesity and adult cardiovascular disease risk factors: A systematic review with meta-analysis. BMC Public Health; 2017; 17, 683. [DOI: https://dx.doi.org/10.1186/s12889-017-4691-z] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28851330]
5. Freedman, D.S.; Khan, L.K.; Dietz, W.H.; Srinivasan, S.R.; Berenson, G.S. Relationship of childhood obesity to coronary heart disease risk factors in adulthood: The Bogalusa Heart Study. Pediatrics; 2001; 108, pp. 712-718. [DOI: https://dx.doi.org/10.1542/peds.108.3.712]
6. Cleland, V.J.; Ball, K.; Magnussen, C.; Dwyer, T.; Venn, A. Socioeconomic position and the tracking of physical activity and cardiorespiratory fitness from childhood to adulthood. Am. J. Epidemiol.; 2009; 170, pp. 1069-1077. [DOI: https://dx.doi.org/10.1093/aje/kwp271] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19767351]
7. Palermi, S.; Sacco, A.M.; Belviso, I.; Romano, V.; Montesano, P.; Corrado, B.; Sirico, F. Guidelines for Physical Activity-A Cross-Sectional Study to Assess Their Application in the General Population. Have We Achieved Our Goal?. Int. J. Environ. Res. Public Health; 2020; 17, 3980. [DOI: https://dx.doi.org/10.3390/ijerph17113980] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32512767]
8. Libertus, K.; Hauf, P. Editorial: Motor Skills and Their Foundational Role for Perceptual, Social, and Cognitive Development. Front. Psychol.; 2017; 8, 301. [DOI: https://dx.doi.org/10.3389/fpsyg.2017.00301] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/28321199]
9. García-Hermoso, A.; Ramírez-Vélez, R.; García-Alonso, Y.; Alonso-Martínez, A.M.; Izquierdo, M. Association of Cardiorespiratory Fitness Levels During Youth With Health Risk Later in Life: A Systematic Review and Meta-analysis. JAMA Pediatr.; 2020; 174, pp. 952-960. [DOI: https://dx.doi.org/10.1001/jamapediatrics.2020.2400]
10. Barnett, L.M.; Webster, E.K.; Hulteen, R.M.; De Meester, A.; Valentini, N.C.; Lenoir, M.; Pesce, C.; Getchell, N.; Lopes, V.P.; Robinson, L.E. et al. Through the Looking Glass: A Systematic Review of Longitudinal Evidence, Providing New Insight for Motor Competence and Health. Sports Med.; 2021; [DOI: https://dx.doi.org/10.1007/s40279-021-01516-8]
11. Stodden, D.F.; Goodway, J.D.; Langendorfer, S.J.; Roberton, M.A.; Rudisill, M.E.; Garcia, C.; Garcia, L.E. A Developmental Perspective on the Role of Motor Skill Competence in Physical Activity: An Emergent Relationship. Quest; 2008; 60, pp. 290-306. [DOI: https://dx.doi.org/10.1080/00336297.2008.10483582]
12. Robinson, L.E.; Stodden, D.F.; Barnett, L.M.; Lopes, V.P.; Logan, S.W.; Rodrigues, L.P.; D’Hondt, E. Motor Competence and its Effect on Positive Developmental Trajectories of Health. Sports Med.; 2015; 45, pp. 1273-1284. [DOI: https://dx.doi.org/10.1007/s40279-015-0351-6]
13. Haga, M. The relationship between physical fitness and motor competence in children. Child Care Health Dev.; 2008; 34, pp. 329-334. [DOI: https://dx.doi.org/10.1111/j.1365-2214.2008.00814.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/18410639]
14. Jaakkola, T.; Yli-Piipari, S.; Huhtiniemi, M.; Salin, K.; Seppälä, S.; Hakonen, H.; Gråstén, A. Longitudinal associations among cardiorespiratory and muscular fitness, motor competence and objectively measured physical activity. J. Sci. Med. Sport; 2019; 22, pp. 1243-1248. [DOI: https://dx.doi.org/10.1016/j.jsams.2019.06.018]
15. Pate, R.R. A New Definition of Youth Fitness. Physician Sportsmed.; 1983; 11, pp. 77-83. [DOI: https://dx.doi.org/10.1080/00913847.1983.11708509]
16. Caspersen, C.J.; Powell, K.E.; Christenson, G.M. Physical Activity, Exercise, and Physical Fitness: Definitions and Distinctions for Health-Related Research. Public Health Rep.; 1985; 100, pp. 126-131. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/3920711]
17. Stodden, D.; Sacko, R.; Nesbitt, D. A Review of the Promotion of Fitness Measures and Health Outcomes in Youth. Am. J. Lifestyle Med.; 2017; 11, pp. 232-242. [DOI: https://dx.doi.org/10.1177/1559827615619577] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/30202338]
18. Casonatto, J.; Fernandes, R.A.; Batista, M.B.; Cyrino, E.S.; Coelho-e-Silva, M.J.; de Arruda, M.; Vaz Ronque, E.R. Association between health-related physical fitness and body mass index status in children. J. Child Health Care; 2016; 20, pp. 294-303. [DOI: https://dx.doi.org/10.1177/1367493515598645] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26396021]
19. Britton, Ú.; Issartel, J.; Fahey, G.; Conyngham, G.; Belton, S. What is health-related fitness? Investigating the underlying factor structure of fitness in youth. Eur. Phys. Educ. Rev.; 2020; 26, pp. 782-796. [DOI: https://dx.doi.org/10.1177/1356336X19882060]
20. Corbin, C.B.; Pangrazi, R.P.; Franks, B.D. Definitions: Health, Fitness, and Physical Activity: President’s Council on Physical Fitness and Sports Research Digest; Series 3 n9 Mar 2000 President’s Council on Physical Fitness and Sports: Washington, DC, USA, 2000.
21. Frick, A.; Möhring, W. A Matter of Balance: Motor Control is Related to Children’s Spatial and Proportional Reasoning Skills. Front. Psychol.; 2015; 6, 2049. [DOI: https://dx.doi.org/10.3389/fpsyg.2015.02049]
22. Bös, K. Handbuch Motorische Tests: Sportmotorische Tests, Motorische Funktionstests, Fragebögen zur körperlich-Sportlichen Aktivität und Sportpsychologische Diagnoseverfahren, 3., überarbeitete und erweiterte Auflage; Hogrefe: Göttingen, Germany, 2017; ISBN 9783801723699
23. Carrel, A.L.; Bowser, J.; White, D.; Moberg, D.P.; Weaver, B.; Hisgen, J.; Eickhoff, J.; Allen, D.B. Standardized childhood fitness percentiles derived from school-based testing. J. Pediatr.; 2012; 161, pp. 120-124. [DOI: https://dx.doi.org/10.1016/j.jpeds.2012.01.036]
24. The Cooper Institute. FitnessGram Administration Manual: The Journey to MyHealthyZone; 5th ed. Human Kinetics: Champaign, IL, USA, 2017; ISBN 9781492579670
25. Król-Zielińska, M.; Groffik, D.; Bronikowski, M.; Kantanista, A.; Laudańska-Krzemińska, I.; Bronikowska, M.; Korcz, A.; Borowiec, J.; Frömel, K. Understanding the Motives of Undertaking Physical Activity with Different Levels of Intensity among Adolescents: Results of the INDARES Study. BioMed Res. Int.; 2018; 2018, 1849715. [DOI: https://dx.doi.org/10.1155/2018/1849715]
26. Ruiz, J.R.; Castro-Piñero, J.; España-Romero, V.; Artero, E.G.; Ortega, F.B.; Cuenca, M.M.; Jimenez-Pavón, D.; Chillón, P.; Girela-Rejón, M.J.; Mora, J. et al. Field-based fitness assessment in young people: The ALPHA health-related fitness test battery for children and adolescents. Br. J. Sports Med.; 2011; 45, pp. 518-524. [DOI: https://dx.doi.org/10.1136/bjsm.2010.075341]
27. Bös, K. Deutscher Motorik-Test 6-18: (DMT 6-18): Manual und internetbasierte Auswertungssoftware, 2. Auflage; Feldhaus, Edition Czwalina: Hamburg, Germany, 2016; ISBN 9783880206434
28. Měkota, K.; Kovář, R. UNIFITTEST (6–60): Tests and Norms of Motor Performance and Physical Fitness in Youth and in Adult Age; Vydavatelstvi Univerzity Palackeho: Olomouc, Czech Republic, 1995; ISBN 8070675810
29. Stemper, T.; Bachmann, C.; Diehlmann, K.; Kemper, B. DüMo Düsseldorfer Modell der Bewegungs-, Sport- und Talentförderung: 2003–2018: Konzept, Normwerte, Untersuchungsergebnisse; LIT: Berlin, Germany, 2020; ISBN 9783643147783
30. Ahrens, W.; Bammann, K.; Siani, A.; Buchecker, K.; de Henauw, S.; Iacoviello, L.; Hebestreit, A.; Krogh, V.; Lissner, L.; Mårild, S. et al. The IDEFICS cohort: Design, characteristics and participation in the baseline survey. Int. J. Obes.; 2011; 35, (Suppl. S1), pp. S3-S15. [DOI: https://dx.doi.org/10.1038/ijo.2011.30]
31. De Miguel-Etayo, P.; Gracia-Marco, L.; Ortega, F.B.; Intemann, T.; Foraita, R.; Lissner, L.; Oja, L.; Barba, G.; Michels, N.; Tornaritis, M. et al. Physical fitness reference standards in European children: The IDEFICS study. Int. J. Obes.; 2014; 38, (Suppl. S2), pp. S57-S66. [DOI: https://dx.doi.org/10.1038/ijo.2014.136]
32. Keating, X.D.; Smolianov, P.; Liu, X.; Castro-Piñero, J.; Smith, J. Youth Fitness Testing Practices: Global Trends and New Development. Available online: https://thesportjournal.org/article/youth-fitness-testing-practices-global-trends-and-new-development/ (accessed on 15 October 2021).
33. Graf, C.; Jouck, S.; Koch, B.; Staudenmaier, K.; von Schlenk, D.; Predel, H.-G.; Tokarski, W.; Dordel, S. Motorische Defizite–wie schwer wiegen sie?. Monatsschr Kinderheilkd; 2007; 155, pp. 631-637. [DOI: https://dx.doi.org/10.1007/s00112-007-1502-0]
34. Castro-Piñero, J.; Ortega, F.B.; Artero, E.G.; Girela-Rejón, M.J.; Mora, J.; Sjöström, M.; Ruiz, J.R. Assessing muscular strength in youth: Usefulness of standing long jump as a general index of muscular fitness. J. Strength Cond. Res.; 2010; 24, pp. 1810-1817. [DOI: https://dx.doi.org/10.1519/JSC.0b013e3181ddb03d] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20555277]
35. Hui, S.C.; Yuen, P.Y.; Morrow, J.R.; Jackson, A.W. Comparison of the Criterion-Related Validity of Sit-and-Reach Tests with and without Limb Length Adjustment in Asian Adults. Res. Q. Exerc. Sport; 1999; 70, pp. 401-406. [DOI: https://dx.doi.org/10.1080/02701367.1999.10608061]
36. Hui, S.S.; Yuen, P.Y. Validity of the modified back-saver sit-and-reach test: A comparison with other protocols. Med. Sci. Sports Exerc.; 2000; 32, pp. 1655-1659. [DOI: https://dx.doi.org/10.1097/00005768-200009000-00021] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/10994920]
37. Castro-Piñero, J.; Chillón, P.; Ortega, F.B.; Montesinos, J.L.; Sjöström, M.; Ruiz, J.R. Criterion-related validity of sit-and-reach and modified sit-and-reach test for estimating hamstring flexibility in children and adolescents aged 6-17 years. Int. J. Sports Med.; 2009; 30, pp. 658-662. [DOI: https://dx.doi.org/10.1055/s-0029-1224175] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/19585399]
38. Mayer, M.; Gleiss, A.; Häusler, G.; Borkenstein, M.; Kapelari, K.; Köstl, G.; Lassi, M.; Schemper, M.; Schmitt, K.; Blümel, P. Weight and body mass index (BMI): Current data for Austrian boys and girls aged 4 to under 19 years. Ann. Hum. Biol.; 2015; 42, pp. 45-55. [DOI: https://dx.doi.org/10.3109/03014460.2014.907444]
39. Cole, T.J.; Bellizzi, M.C.; Flegal, K.M.; Dietz, W.H. Establishing a standard definition for child overweight and obesity worldwide: International survey. BMJ; 2000; 320, pp. 1240-1243. [DOI: https://dx.doi.org/10.1136/bmj.320.7244.1240]
40. Browning, L.M.; Hsieh, S.D.; Ashwell, M. A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0·5 could be a suitable global boundary value. Nutr. Res. Rev.; 2010; 23, pp. 247-269. [DOI: https://dx.doi.org/10.1017/S0954422410000144]
41. Ashwell, M.; Gibson, S. Waist-to-height ratio as an indicator of ‘early health risk’: Simpler and more predictive than using a ‘matrix’ based on BMI and waist circumference. BMJ Open; 2016; 6, e010159. [DOI: https://dx.doi.org/10.1136/bmjopen-2015-010159] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26975935]
42. Koo, T.K.; Li, M.Y. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. J. Chiropr. Med.; 2016; 15, pp. 155-163. [DOI: https://dx.doi.org/10.1016/j.jcm.2016.02.012]
43. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; 2nd ed. Routledge: New York, USA, 1988; ISBN 0805802835
44. Aranha, V.P.; Moitra, M.; Saxena, S.; Narkeesh, K.; Arumugam, N.; Samuel, A.J. Motor cognitive processing speed estimation among the primary schoolchildren by deriving prediction formula: A cross-sectional study. J. Neurosci. Rural Pract.; 2017; 8, pp. 79-83. [DOI: https://dx.doi.org/10.4103/0976-3147.193544]
45. Fetz, F.; Kornexl, E. Sportmotorische Tests: Praktische Anleitung zu sportmotorischen Tests in Schule und Verein, 2., überarb. u. erw. Aufl.; Österr. Bundesverl.: Wien, Austria, 1978; ISBN 387039028X
46. Leodolter, I. Short report: The mother-child health passport: Austria’s successful weapon against infant mortality. Prev. Med.; 1978; 7, pp. 561-563. [DOI: https://dx.doi.org/10.1016/0091-7435(78)90268-2]
47. Republik Österreich. Stenographisches Protokoll-327. Sitzung des Bundesrates der Republik Österreich. S 9909–9914: Beschluss-Einführung Mutter-Kind-Pass. 1973; Available online: https://www.parlament.gv.at/PAKT/VHG/BR/BRSITZ/BRSITZ_00327/imfname_179113.pdf (accessed on 3 September 2021).
48. Republik Österreich. Stenographisches Protokoll-96. Sitzung des Nationalrates der Republik Österreich. XV. Gesetzgebungsperiode. S 9637: Erhöhung der Zeitspanne-der Mutter-Kind-Pass Untersuchungen, Donnerstag. 1981; Available online: https://www.parlament.gv.at/PAKT/VHG/XV/NRSITZ/NRSITZ_00096/imfname_146978.pdf (accessed on 3 September 2021).
49. Kerbl, R. 37 Jahre Österreichischer Mutter-Kind-Pass. Paediatr. Paedolog.; 2011; 46, pp. 14-17. [DOI: https://dx.doi.org/10.1007/s00608-011-0317-3]
50. Langmann, A.; Lindner, S.; Koch, M.; Wackernagel, W.; Gschiel, G. Ophthalmological care of children’s eyes in Austria. Spektrum Der Augenheilkunde-Spektrum Augenheilkd; 2005; 19, pp. 294-295. [DOI: https://dx.doi.org/10.1007/BF03163463]
51. Wendt, C. Health Services for Children in Denmark, Germany, Austria and Great Britain; MZES: Mannheim, Germany, 1999.
52. Kerbl, R.; Ziniel, G.; Winkler, P.; Habl, C.; Püspök, R.; Waldhauser, F. Child Health Care Services in Austria. J. Pediatr.; 2016; 177, pp. S35-S47. [DOI: https://dx.doi.org/10.1016/j.jpeds.2016.04.039]
53. Republik Österreich. RIS-Rechtsinformationssytem des Bundes-Österreich-Bundesgesetzblatt und Verordnungen: BGBl. II Nr. 1/2009-letzte Änderung BGBl. II Nr. 548/2020. Bundesgesetzblatt. Available online: https://www.ris.bka.gv.at/Bgbl-Auth/ (accessed on 3 September 2021).
54. Gamper, M. Die Entwicklung des Schularztwesens in Österreich: Eine Grundlage zur Leitbildentwicklung der Schulärztinnen und Schulärzte und Ausgangspunkt für Überlegungen zum “SCHULARZT NEU”., Wien, ISBN 3-85031-001-9. 2002; Available online: https://schularzt.at/fileadmin/user_upload/bilder/SAWesen_nurtext.pdf (accessed on 3 September 2021).
55. Catley, M.J.; Tomkinson, G.R. Normative health-related fitness values for children: Analysis of 85347 test results on 9-17-year-old Australians since 1985. Br. J. Sports Med.; 2013; 47, pp. 98-108. [DOI: https://dx.doi.org/10.1136/bjsports-2011-090218]
56. Liu, X.; Keating, X.D.; Shangguan, R. Historical Analyses of Fitness Testing of College Students in China. ICHPER-SD J. Res.; 2017; 9, pp. 24-32.
57. Shingo, N.; Takeo, M. The educational experiments of school health promotion for the youth in Japan: Analysis of the ‘sport test’ over the past 34 years. Health Promot. Int.; 2002; 17, pp. 147-160. [DOI: https://dx.doi.org/10.1093/heapro/17.2.147] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11986296]
58. Tantsekuzhin, N.O.; Astafev, N.V.; Nayn, A.A. The Role of All-Russian Physical Culture-Sports “Ready For Labor and Defense” (GTO) Complex in the System of Junior School Children Education. First International Volga Region Conference on Economics, Humanities and Sports; Atlantis Press: Amsterdam, The Netherlands, 2019.
59. Mauz, E.; Lange, M.; Houben, R.; Hoffmann, R.; Allen, J.; Gößwald, A.; Hölling, H.; Lampert, T.; Lange, C.; Poethko-Müller, C. et al. Cohort profile: KiGGS cohort longitudinal study on the health of children, adolescents and young adults in Germany. Int. J. Epidemiol.; 2020; 49, pp. 375-375k. [DOI: https://dx.doi.org/10.1093/ije/dyz231]
60. Jurak, G.; Leskošek, B.; Kovač, M.; Sorić, M.; Kramaršič, J.; Sember, V.; Đurić, S.; Meh, K.; Morrison, S.A.; Strel, J. et al. SLOfit surveillance system of somatic and motor development of children and adolescents: Upgrading the Slovenian Sports Educational Chart. AUC KINANTHROPOLOGICA; 2020; 56, pp. 28-40. [DOI: https://dx.doi.org/10.14712/23366052.2020.4]
61. Jurak, G.; Kovac, M.; Sember, V.; Starc, G. 30 Years of SLOfit: Its Legacy and Perspective. Turk. J. Sports Med.; 2019; 54, pp. 23-27. [DOI: https://dx.doi.org/10.5152/tjsm.2019.148]
62. Becker, J. Der Deutsche Motorik Test 6-18 (DMT6-18) in der Praxis, 1. Auflage; GRIN Verlag: München, Germany, 2020; ISBN 9783346149244
63. Vandorpe, B.; Vandendriessche, J.; Lefevre, J.; Pion, J.; Vaeyens, R.; Matthys, S.; Philippaerts, R.; Lenoir, M. The KörperkoordinationsTest für Kinder: Reference values and suitability for 6-12-year-old children in Flanders. Scand. J. Med. Sci. Sports; 2011; 21, pp. 378-388. [DOI: https://dx.doi.org/10.1111/j.1600-0838.2009.01067.x]
64. Jouck, S.; Koch, B.; Graf, C.; Predel, H.G.; Dordel, S. Dordel-Koch-Test (DKT)–zur Erfassung der motorischen Basisfunktionen im Kindes- und Jugendalter; erste Ergebnisse der Normierung. Aktuelle Ernahr.; 2006; 31, 27. [DOI: https://dx.doi.org/10.1055/s-2006-954528]
65. Jarnig, G.; Jaunig, J.; van Poppel, M.N.M. Association of COVID-19 Mitigation Measures with Changes in Cardiorespiratory Fitness and Body Mass Index Among Children Aged 7 to 10 Years in Austria. JAMA Netw. Open; 2021; 4, e2121675. [DOI: https://dx.doi.org/10.1001/jamanetworkopen.2021.21675]
66. Miller, L.J. Developing Norm-referenced Standardized Tests; Psychology Press: Hove, East Sussex, UK, 1989; ISBN 9780866568838
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Details
; Jaunig, Johannes 1
; Kerbl, Reinhold 2 ; Rodrigo Antunes Lima 3
; Mireille N M van Poppel 1
1 Institute of Human Movement Science, Sport and Health, University of Graz, 8010 Graz, Austria;
2 Department of Pediatrics and Adolescent Medicine, LKH Hochsteiermark, 8700 Leoben, Austria;
3 Research, Innovation and Teaching Unit, Parc Sanitari Sant Joan de Déu, CIBERSAM, Sant Boi de Llobregat, 08830 Barcelona, Spain;