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Association of Physiological Performance, Physical Fitness, and Academic Achievement in Secondary School Students

Sports Science Faculty, Tekirdag Namik Kemal University, Suleymanpasa, Tekirdag 59010, Türkiye
Department of Rehabilitation Sciences, College of Health and Rehabilitation Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Yasar Dogu Faculty of Sport Sciences, Ondokuz Mayıs University, Samsun 55270, Turkey
Author to whom correspondence should be addressed.
Children 2024, 11(4), 396;
Submission received: 24 February 2024 / Revised: 23 March 2024 / Accepted: 23 March 2024 / Published: 27 March 2024
(This article belongs to the Special Issue Childhood Physical Activity and Health)


This study aimed to compare the physiological performance and physical fitness based on the academic achievement levels of secondary school students and to explore the effect of gender on the relationship between physiological performance, physical fitness, and academic achievement. In this cross-sectional study, 304 children aged 13–14 years were recruited. To assess physical fitness, students performed a 20 m sprint test, a pro-agility test, a one-mile endurance run/walk test, and a countermovement jump test. At the end of the one-mile endurance run/walk test, the estimated VO2peak value of the participants was calculated. The physiological performance of the students was determined by measuring their resting heart rate and blood pressure. Students were grouped into three categories based on their academic achievement levels. The assessment of academic achievement considered their scores from the previous academic year. The scores were divided into three levels: poor (average score of 69 points or less), average (scores ranging from 70 to 84 points), and good (scores of 85 points or higher). The study revealed a notable disparity among students’ VO2Max measurements based on their academic achievement (F = 8.938, p < 0.001, η2 = 0.056). However, we observed that the group with poor academic achievement displayed lower diastolic blood pressure values than the groups with average and good performances. Finally, no significant gender differences were evident in the relationship between academic achievement and any of the physical and physiological parameters.

1. Introduction

The evaluation of adolescent students’ overall school success often revolves around their academic achievement, which is typically measured through exam scores [1]. It is known that parents and all other dynamics in the education system acknowledge such an assessment approach. However, it is crucial to acknowledge that multiple variables can influence this approach [2]. Previous research has identified several factors, such as learning pace, intelligence, self-esteem, personality traits, self-efficacy, motivation, study habits, parental attitudes, socio-economic status, and school type, that contribute to these academic outcomes [3,4,5,6].
Although there is growing interest in comprehending the variables that affect academic achievement, the ongoing drop in achievement continues to be of great concern to the academic community [7]. In this sense, more evidence is needed regarding the different variables associated with academic achievement.
Physical fitness is recognized as another important element affecting academic achievement or achievement-related variables, in addition to the previously stated variables. Numerous studies have investigated the relationship between physical exercise and academic achievement, cognitive processes, and physical fitness in children and adolescents [8,9,10,11]. Various mechanisms have been proposed to explain the impact of physical activity on the brain structure and function. Additionally, recent evidence highlights that regular exercise or physical activity can lead to improved learning ability, physical fitness, and, subsequently, enhanced brain function and academic performance [12,13,14]. Furthermore, physical activity has been shown to optimize cell capillaries, increase blood flow, enhance brain oxygenation and neurotransmitter and neurotrophic levels, promote nerve cells, and increase brain tissue volume, which in turn leads to new nerve cell connections [15,16,17]. Previous findings suggest that these physiological changes in the brain positively affect concentration, memory, and processing strategies [18] and that such impacts are more likely to contribute to overall school success, as executive functions and cognitive control promote learning [19]. It was reported that physically fit children would have better executive functions and engage in healthier behaviors, ultimately leading to school success [20].
Studies have examined physical fitness and academic achievement in children and young students [21,22,23,24,25,26,27,28,29]. Various investigations involving sample sizes ranging from 134 to 884,715 participants aged 7 to 15 years have been conducted [25,28]. These studies utilized the US Fitnessgram, a comprehensive test suite that includes cardiovascular endurance (PACER), muscular endurance (push-ups and sit-ups), flexibility (sit and reach), and body mass index assessments; the endurance was measured using US Fitnessgram and a battery of tests [25,28]. In a study by Eveland-Sayers et al. [26], the standard PACER test was substituted with a one-mile run. A battery test measures encompassed lung function, a 50 m sprint for muscular power, a 1.6 km run for cardiorespiratory endurance, a standing long jump for muscular power, and sit-ups and push-ups for muscular endurance [29]. One study utilized the evaluation of academic competence by a school official on a five-point scale, while three others employed standardized tests to measure academic performance. Two studies factored in parental education and socio-economic status in their analysis [25,29]. A consistent, moderately positive link was established between physical fitness and academic performance across the studies, with cardiovascular fitness exhibiting the strongest correlation, demonstrated by r values from 0.41 with the PACER test to 0.20 for a lengthier one-mile run. Additionally, correlations between academic achievement and measures of muscular force/power and flexibility have been observed [30]. The findings of our study are anticipated to enrich the existing body of literature in this field. There are only a few studies in the relevant literature that look at the effects of factors like blood pressure and heart rate on young people’s academic achievement. Students with high blood pressure outperform their counterparts in terms of intellectual achievement, according to Berendes et al. [31]. However, other studies disagree with these findings, suggesting that additional evidence is needed to substantiate this relationship [32,33]. Therefore, a comprehensive study exploring the relationship between academic achievement, blood pressure, heart rate, and physical fitness among young students would significantly contribute to the existing literature. Additionally, analyzing the impact of gender on these variables could enhance the scholarly value of this research. This study aims to compare the physical and physiological performance levels of secondary school students based on academic achievement and to examine the influence of gender on the association between physical and physiological performance and academic achievement.

2. Materials and Methods

2.1. Study Design and Participants

This study employed a cross-sectional design based on a non-experimental methodology. This research design is a type of research study in which a group of people is observed or certain information is collected at a single point in time or over a short period [34]. The sample consisted of 304 children aged 13–14 years who were enrolled in seven public schools in Tekirdag, Turkey. The schools where the participating groups were located were in the city center, and these schools had similar socio-economic family structures. These students were all studying in the eighth grade of secondary school. The sample size was calculated as at least 276 students, with a margin of error of 5%, a z-score equal to 1.96, and a confidence index level set of 95% [35]. A total of 304 participants participated in the study. We stratified the participants into three groups based on their academic achievement scores (the criteria proposed for classifying academic achievement are specified below). The study’s inclusion criteria were being 13 or 14 years old, in the eighth year of secondary school, and not having a cardiovascular, orthopedic, or neurological condition. The exclusion criteria, on the other hand, were specified as functional illiteracy, a history of attention deficit hyperactivity disorder, intellectual disabilities, visual or hearing impairment without correction, and an inability to participate in physical assessments. However, 23 students who did not meet the stated conditions for participation were excluded from the study. The participants and their parents were informed of the experimental procedures and provided written consent. This study was performed in accordance with the ethical standards of the Declaration of Helsinki and ethical approval granted by the Ethics Committee of Tekirdag Namik Kemal University (The ethical approval code: 2021.85.04.03; Date: 13 April 2021).

2.2. Procedure

All measurements were performed in the same order by the same researchers. All tests were performed at the same time of the day (11:00 a.m.–14:00 p.m.) to avoid the influence of circadian rhythms on the study results. Participants attended two sessions: anthropometric measurements, physical fitness tests, and physiological performance tests. During the first session, the blood pressure, heart rate, and anthropometric measurements were obtained from the participants. In the second session, all physical fitness tests were conducted using the following speed tests: agility test, vertical jump test, and one-mile run/walk test. Prior to testing, the protocols were explained and demonstrated to the participants. Before the high-intensity activities and tests, a standard warm-up consisting of 5 min of jogging followed by 5 min of dynamic stretching was performed. After the tests were completed, the participants performed cooling exercises. A minimum 24 h break was taken between sessions. Figure 1 shows a schematic of the study flow diagram.

2.3. Instruments

2.3.1. Body Composition Assessment

The body composition assessment included the measurement of height using a Mesilife 13539 portable stadiometer; Istanbul, Turkey) [36]. The participants’ body weights were quantified in kilograms to a precision of 0.01 kg utilizing a PoloSmart PSC05 Mood Glass Digital Scale [37]. The body mass index (BMI), expressed in kg/m2, was subsequently calculated by dividing the body weight by the square of the height.

2.3.2. Physiological Performance Tests

Resting heart rate assessment: The participants’ resting heart rates were measured using a telemetric heart rate sensor (Polar Verity Sense; Kempele, Finland). After wearing the heart rate sensor, the participants remained in a sitting position for 20 min, and the lowest heart rate measured in the last 5 min was recorded as the resting heart rate (RHR). While the participants were waiting in a sitting position, they were not allowed to do any activity that could increase their heart rate (talking, playing with their mobile phone, etc.) [38].
Blood pressure measurement: Blood pressure measurements are critical tools for monitoring individuals’ cardiovascular health and detecting potential problems early. The blood pressure was assessed using an Omron HEM 742 automated oscillometric sphygmomanometer (Kyoto, Japan). The protocol was created by the American Heart Society [39] and was used to measure the blood pressure of the participants.

2.3.3. Physical Fitness Tests

20 m sprint test: This is considered an essential performance metric critical to success in sports that involves sprinting due to its ability to measure an athlete’s speed and explosive power over short distances [40,41]. The 20 m distance was chosen for one reason: this distance was part of the subjects’ regular fitness-testing battery [42]. The best time was recorded using a photocell gate (Newtest Powertimer 300-series, Oy, Helsinki, Finland). The assessment was initiated by instructing the students to position their leading foot 0.2 m away from the initial photocell gate while standing. The test was performed twice with a rest interval of 2–3 min between each trial. Statistical analysis considered the shortest recorded time in seconds.
Pro-agility test: This is a popular agility test used to assess an individual’s ability to quickly change direction. The test involves sprinting back and forth between three designated points, measuring the time taken to complete the course. The setup included placing three cones in a straight line five yards apart. The cones are arranged in the following order: Cone A (starting point), Cone B (middle point), and Cone C (finishing point). Before starting the test, we ensured that the participants had an adequate warm-up to prevent injuries. The front foot was aligned with the cone. On the “go” command, the participant sprints as quickly as possible to Cone B, touching the ground or the cone with their hand before changing direction. After touching Cone B, the participant immediately changed direction and sprinted to Cone C, again touching the ground or cone with their hand. Once the participant reached Cone C, the timing was stopped, and their time was recorded [43]. Similarly, we recorded the test times using a photocell gate (Newtest Powertimer 300-series, Oy, Finland). The test was applied 2 times, and a 2 min rest interval was provided between the two tests. The test protocol was demonstrated by researchers in practice. The shortest recorded time represented the agility performance of the participants.
1-Mile Endurance Run/Walk Test: This test is commonly adopted for measuring aerobic endurance and aims to record the fastest possible time to complete a one-mile run. Participants were asked to run as fast as possible in a short time. The time taken to complete the mile was recorded in minutes and seconds [44]. The heart rate variability of the participants during the/run/walk test was monitored using an optical heart rate monitor (Polar Verity Sense, Kempele, Finland). The Bluetooth®, ANT+, internal memory, and Polar Verity Sense are connected to an app, and data are saved in the app.
The following equations were used to estimate the oxygen update (VO2Max):
Male (VO2Max) = 108.844 − 0.1636 W − 1.438 T − 0.1928 HR
Female (VO2Max) = 100.5 − 0.1636 W − 1.438 T − 0.1928 HR
W = weight in kg, T = time for the one-mile run, and HR = heart rate at the end of the run [45].
Countermovement jump test (Determination of Estimated Anaerobic Power): The countermovement jump (CMJ) is commonly used for the assessment of lower-body mechanical capacities. It has been used to monitor sports performance [46], inter-limb asymmetries [47], neuromuscular fatigue [48], and the effectiveness of different training programs [49]. The participants’ countermovement jump heights were measured using a Myotest Pro system accelerometer (Myotest SA, Sion, Switzerland). The device was attached to the belts and fixed vertically. Next, they were instructed to attempt to reach their maximum jump height when hearing the signal sound [50]. They were also instructed to maintain the knee joint at approximately 90° for each jump condition [51]. The highest jump score from the three trials was used for the evaluation. The test protocol was demonstrated by the researchers in practice. The vertical jump test was used to measure anaerobic power, with the participants performing the test protocol three times with 1–2 min rest between each trial [52].
P (kg-m/s) = √4.9 (body weight) × √vertical jump height

2.3.4. Academic Achievement

To determine the participants’ academic achievement levels, we considered the average achievement scores from the previous academic year. Typically, this score is calculated by dividing the total final exam grade of all courses by the number of courses. We obtained the participants’ achievement scores from the administrative registry office of their schools. The scores were categorized into three levels of academic achievement: poor (average ≤ 69 points), average (70 points ≤ average > 84 points), and good (≥85 points) [53] (Figure 1). This methodology ensured a thorough and accurate assessment of participants’ academic achievement levels. Academic score averages representing students’ academic achievement levels were given by the school administration.

2.4. Statistical Analyses

Descriptive statistics were used to define the sample characteristics. Next, we considered skewness-kurtosis values to determine whether the research data were normally distributed. As a rule of thumb, Tabachnick and Fidell proposed that the data with skewness-kurtosis values between −1.5 and +1.5 show a normal distribution [54]. For normally distributed data, we analyzed the mean differences between groups using one-way analysis of variance (ANOVA) with the Bonferroni correction and calculated the eta-squared (η2) to reveal the effect size of any significant difference between the variables. Moreover, we calculated Pearson product-moment correlation coefficients to investigate the possible associations between the variables. Finally, we used the Fischer r-to-z transformation to analyze the significant differences between the correlation coefficients by gender. This transformation often seeks to convert correlation coefficients to standardized values (z-values) to make them comparable (the relevant formula is presented below) [55]. All statistical analyses were performed using SPSS version 18.0 (SPSS, Chicago, IL, USA), and the statistical significance was set at p < 0.05.

3. Results

The distribution of the samples according to age and gender is presented in Table 1. The data reveal that the largest proportion of students are at the age of 13. Figure 2 shows the percentage of students based on their academic performance levels.
Table 2 shows the descriptive statistics of the findings concerning the RHR, blood pressure, and physical fitness variables. Additionally, a comparison of the specified variables in terms of academic performance level is presented.
The findings showed no significant difference between the participants’ systolic pressure and heart rate values according to their academic achievement (F = 0.583, p > 0.05; F = 0.717, p > 0.05, respectively). However, we found a significant difference between the groups in their diastolic pressure values based on academic achievement (F = 7.696, p < 0.001, η2 = 0.049). We used the Bonferroni correction to determine the source of the significant difference within the groups, and it was found that the diastolic pressure value of the poor academic achievement group was markedly lower than that of the average academic achievement (66.7 vs. 70.9; p < 0.01) and the good academic performance groups (66.7 vs. 71.5; p < 0.001) (Figure 3).
We could not find a significant difference between the groups in the vertical jump, speed, and anaerobic power values based on academic achievement (F = 0.382, p > 0.05; F = 1.410, p = 0.2; and F = 1.097, p > 0.05, respectively). However, we found a significant difference across the groups in VO2Max by academic achievement (F = 8.938, p < 0.001; η2 = 0.056). Accordingly, the poor academic achievement group had a significantly higher mean VO2Max value than the average academic (47.1 vs. 42%, 6; p < 0.001) and good groups (47.1 vs. 42%, 4; p < 0.001) (Figure 4).
Table 3 shows that relationships between academic achievement and SBP, DBP, RHR, VJ, SP, VO2Max, and AC did not differ by gender (p > 0.05). In other words, we could not conclude that gender had a significant effect on the relationship between academic achievement and the specified variables.

4. Discussion

Accordingly, our findings revealed that the systolic–diastolic blood pressures and heart rates of the groups were within the reference values [56]. Therefore, it was demonstrated that all three academic achievement groups have blood pressure values within the normal limits. It can be stated that only the group with lower diastolic baseline values within these three groups exhibited lower academic achievement. This situation could contribute to viewing research conducted on the subject from a different perspective. This is because research tends to focus on the relationship between high blood pressure and cognitive function. For example, previous research has suggested that higher blood pressure is associated with decreased cognitive performance among children and young adults [57,58] and cognitive decline in adults [59,60]. Similarly, the existing literature presents studies that establish a link between hypertension and decreased cognitive function [32,61,62,63]. In this study, no statistically significant differences in systolic blood pressure or heart rate were found between the groups. However, it was determined that the poor academic achievement group had significantly lower diastolic pressure compared to the average and good academic performance groups. Additional experimental research may be necessary to further investigate this topic.
Additionally, our results indicated that there were no significant variations in the vertical jump, speed, or anaerobic power values between the groups based on their academic achievement. In contrast, Zhai et al. discovered that individuals in the high academic achievement group outperformed other groups in several physical fitness assessments, including boys excelling in the 50 m sprint, standing long jump, push-ups, and 1000 m run, and girls demonstrated superior performance in the timed shuttle and 800 m jog [11]. Furthermore, Zhai et al. revealed a positive association between endurance/explosive strength and academic achievement among male and female undergraduate students [11]. Matejek and Planinšec found that children with better academic achievement performed better on most physical fitness tests than those with poor academic performance [64]. These inconsistencies across different age groups, including children, adolescents, and young adults, could potentially be attributed to age-related factors, as both muscles and the brain undergo rapid development during childhood and adolescence [65,66]. This rapid development may contribute to the difficulty of observing a discernible relationship between these variables and academic success. Hence, we posit that the observed inconsistency between our findings regarding muscular fitness and the existing literature may be attributed to the disparity in the age groups studied.
Specifically, we discovered that the poor achievement group exhibited significantly higher VO2Max values than the average and good achievement groups, potentially indicating that individuals in the poor achievement group devoted more time to physical activities rather than studying. However, these results contradict the findings reported in the literature. Numerous previous studies have consistently demonstrated a correlation between aerobic fitness and academic performance [67,68,69]. In addition, a good cardiorespiratory level has been suggested to be associated with high academic achievement [70]. Longitudinal research from Germany, Sweden, Spain, Taiwan, and the United States [71,72] encompassed a cohort of 234,474 children and adolescents and evaluated their academic proficiency and aerobic capacity. The follow-up periods ranged from 1.5 to 3 years and demonstrated positive correlations between aerobic fitness and academic performance, irrespective of the duration between the initial and subsequent evaluations. No negative association was observed [73]. These findings are also consistent with the idea that cardiovascular fitness improves cognition through increased circulating factors that contribute to brain plasticity and cognitive function [16].
In this study, no significant gender differences were evident in the relationships between academic achievement and SBP, DBP, HBR, VJ, SP, VO2Max, and AC in our study. A few studies have shown that overall physical fitness has a positive impact on academic performance only among boys [22,69,74]. Gender differences may be influenced by several factors, such as dissimilarities in biological development and the neural pathways associated with motor and language skills in boys and girls. [11]. Another study found a significant correlation between physical fitness and reading in girls; however, this association was negative and low among boys [75]. The findings of another study suggest that the correlation between academic achievement and overall physical fitness in male and female undergraduate students is consistent. The researchers proposed that participants’ desire to achieve success in all areas might be a contributing factor [11].
The collection of data from seven secondary schools with varying socio-economic statuses and the utilization of objective measures of physiological and physical fitness can be recognized as strengths of the current study. However, there are several limitations to be considered in this research. First, given that the study had a cross-sectional design, the causation and underlying mechanism cannot be derived from the findings of this study. Second, we did not control for the effects of students’ physical activity levels at the beginning of the research. Future studies should employ a longitudinal design. Lastly, prospective researchers could consider utilizing additional instruments to collect the demographic characteristics, nutrition styles, and sleep habits of the sample.

5. Strengths and Limitations

The primary strength of this research lies in examining the impact of physiological factors on students’ academic achievement. This topic has been explored in only a limited number of studies. Additionally, the study specifically focused on students in the early stages of adolescence, which adds to its strength. However, this study had a few limitations. First, factors such as participants’ socio-economic status, family education level, and involvement in physical activity were not controlled for. Furthermore, this study failed to identify the cognitive functions that are believed to have a significant influence on academic achievement.

6. Conclusions

Our study had several major findings that emerged from our research. First, compared to the other groups based on academic achievement, the group with poor academic achievement exhibited noticeably lower diastolic blood pressure. However, additional long-term research is necessary to explore the significance of this situation regarding academic achievement and the potential application of this discovery in predicting academic achievement while taking into account student and environmental factors. Second, only the VO2Max level was higher in the poor academic achievement group than in the other groups among the physical performance measurements investigated. This might mean that students who struggle academically prefer physical pursuit to study. Finally, as a result of determining how the relationship between academic achievement, physical fitness, and physiological performance elements differs in terms of gender, it was determined that there was no significant difference. It was revealed that gender did not affect the relationship between the parameters in our sample group. Overall, the findings highlight the complexity of the relationship between physiological measures, physical performance, and academic achievement, emphasizing the need for comprehensive longitudinal studies and a consideration of various factors in understanding this relationship.

Author Contributions

Conceptualization, U.C. and M.I.A.; methodology, U.C. and H.K.; formal analysis, U.C.; investigation, U.C. and M.I.A.; data curation, U.C.; writing—original draft preparation, U.C. and M.I.A.; writing—review and editing, U.C. and M.I.A. All authors have read and agreed to the published version of the manuscript.


This research was funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2024R 286), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki, while the Research Ethics Committee of the University of Tekirdag Namık Kemal issued a favorable report 2021.85.04.03 [Date: 13 April 2021].

Informed Consent Statement

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

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions related to ongoing work for further publications.


We would like to thank Princess Nourah bint Abdulrahman University for supporting this project. Thank you to all the participants for giving us their time.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. Ozgur, I. Handicapped Children and Education; Karahan Publishing: Adana, Turkey, 2004. [Google Scholar]
  2. Bacak, G. The Releationship between Examination Average and the Respiration Function Tests on the Students Who Prepare for Student Selection Exam. Master’s Thesis, Ataturk University, Erzurum, Turkey, 2007. Available online: (accessed on 1 December 2022).
  3. Howie, S.J.; Pietersen, J.J. Mathematics literacy of final year students: South African realities. Stud. Educ. Eval. 2001, 27, 7–25. [Google Scholar] [CrossRef]
  4. Suna, H.E.; Gür, B.S.; Gelbal, S.; Özer, M. Science high school students’ socioeconomic background and their preferences regarding their transition into higher education. J. High. Educ. 2020, 10, 356–370. [Google Scholar]
  5. Suna, H.E.; Ozer, M. The Achievement gap between schools and relationship between achievement and socioeconomic status in Turkey. J. Meas. Eval. Educ. Psychol. 2021, 12, 55–71. [Google Scholar] [CrossRef]
  6. Wang, D.B. Family background factors and mathematics success: A comparison of Chinese and US students. Int. J. Educ. Res. 2004, 41, 40–54. [Google Scholar] [CrossRef]
  7. Pishghadam, R.; Faribi, M.; Shadloo, F.; Gholami, M.; Shayesteh, S. Intelligence, emotional intelligence, and emo-sensory intelligence: Which one is a better predictor of university students’ academic success? Front. Psychol. 2022, 13, 995988. [Google Scholar] [CrossRef]
  8. Donnelly, J.E.; Hillman, C.H.; Castelli, D.; Etnier, J.L.; Lee, S.; Tomporowski, P.; Szabo-Reed, A.N. Physical activity, fitness, cognitive function, and academic achievement in children: A systematic review. Med. Sci. Sports Exerc. 2016, 48, 1197–1222. [Google Scholar] [CrossRef]
  9. Esteban-Cornejo, I.; Tejero-Gonzalez, C.M.; Sallis, J.F.; Veiga, O.L. Physical activity and cognition in adolescents: A systematic review. J. Sci. Med. Sport 2015, 18, 534–539. [Google Scholar] [CrossRef]
  10. Haapala, E.A.; Poikkeus, A.-M.; Tompuri, T.; Kukkonen-Harjula, K.; Leppänen, P.H.T.; Lindi, V.; Lakka, T.A. Associations of motor and cardiovascular performance with academic skills in children. Med. Sci. Sports Exerc. 2014, 46, 1016–1024. [Google Scholar] [CrossRef] [PubMed]
  11. Zhai, X.; Ye, M.; Gu, Q.; Huang, T.; Wang, K.; Chen, Z.; Fan, X. The relationship between physical fitness and academic performance among Chinese college students. J. Am. Coll. Health 2022, 70, 395–403. [Google Scholar] [CrossRef]
  12. Hillman, C.H.; Pontifex, M.B.; Castelli, D.M.; Khan, N.A.; Raine, L.B.; Scudder, M.R.; Kamijo, K. Effects of the FITKids randomized controlled trial on executive control and brain function. Pediatrics 2014, 134, e1063–e1071. [Google Scholar] [CrossRef]
  13. Ishihara, T.; Sugasawa, S.; Matsuda, Y.; Mizuno, M. Relationship of tennis play to executive function in children and adolescents. Eur. J. Sport Sci. 2017, 17, 1074–1083. [Google Scholar] [CrossRef] [PubMed]
  14. Morita, N.; Nakajima, T.; Okita, K.; Ishihara, T.; Sagawa, M.; Yamatsu, K. Relationships among fitness, obesity, screen time and academic achievement in Japanese adolescents. Physiol. Behav. 2016, 163, 161–166. [Google Scholar] [CrossRef] [PubMed]
  15. Cooke, S.F.; Bliss, T.V. Plasticity in the human central nervous system. Brain 2006, 129, 1659–1673. [Google Scholar] [CrossRef] [PubMed]
  16. Cotman, C.W.; Berchtold, N.C.; Christie, L.A. Exercise builds brain health: Key roles of growth factor cascades and inflammation. Trends Neurosci. 2007, 30, 464–472. [Google Scholar] [CrossRef]
  17. Van Praag, H.; Gage, F.H.; Lombroso, P.J. Genetics of childhood disorders: XXXVI. stem cell research, part 1: New neurons in the adult brain. J. Am. Acad. Child. Adolesc. Psychiatry 2002, 41, 354–356. [Google Scholar] [CrossRef]
  18. Trudeau, F.; Shephard, R.J. Physical education, school physical activity, school sports and academic performance. Int. J. Behav. Nutr. Phys. Act. 2008, 5, 10. [Google Scholar] [CrossRef] [PubMed]
  19. Duncan, G.J.; Dowsett, C.J.; Claessens, A.; Magnuson, K.; Huston, A.C.; Klebanov, P.; Japel, C. School readiness and later achievement. Dev. Psychol. 2007, 43, 1428. [Google Scholar] [CrossRef] [PubMed]
  20. Ishihara, T.; Morita, N.; Nakajima, T.; Okita, K.; Yamatsu, K.; Sagawa, M. Direct and indirect relationships of physical fitness, weight status, and learning duration to academic performance in Japanese schoolchildren. Eur. J. Sport Sci. 2018, 18, 286–294. [Google Scholar] [CrossRef]
  21. Chomitz, V.R.; Slining, M.M.; McGowan, R.J.; Mitchell, S.E.; Dawson, G.F.; Hacker, K.A. Is there a relationship between physical fitness and academic achievement? Positive results from public school children in the northeastern United States. J. Sch. Health 2009, 79, 30–37. [Google Scholar] [CrossRef]
  22. Kwak, L.; Kremers, S.P.J.; Bergman, P.; Ruiz, J.R.; Rizzo, N.S.; Sjöström, M. Associations between physical activity, fitness, and academic achievement. J. Pediatr. 2009, 155, 914–918. [Google Scholar] [CrossRef]
  23. Van Dusen, D.P.; Kelder, S.H.; Kohl, H.W.; Ranjit, N.; Perry, C.L. Associations of physical fitness and academic performance among schoolchildren. J. Sch. Health 2011, 81, 733–740. [Google Scholar] [CrossRef]
  24. Wittberg, R.A.; Northrup, K.L.; Cottrell, L.A. Children’s aerobic fitness and academic achievement: A longitudinal examination of students during their fifth and seventh grade years. Am. J. Public Health 2012, 102, 2303–2307. [Google Scholar] [CrossRef] [PubMed]
  25. Castelli, D.M.; Hillman, C.H.; Buck, S.M.; Erwin, H.E. Physical fitness and academic achievement in third-and fifth-grade students. J. Sport Exerc. Psychol. 2007, 29, 239–252. [Google Scholar] [CrossRef] [PubMed]
  26. Eveland-Sayers, B.M.; Farley, R.S.; Fuller, D.K.; Morgan, D.W.; Caputo, J.L. Physical fitness and academic achievement in elementary school children. J. Phys. Act. Health 2009, 6, 99–104. [Google Scholar] [CrossRef] [PubMed]
  27. London, R.A.; Castrechini, S.A. Longitudinal examination of the link between youth physical fitness and academic achievement. J. Sch. Health 2011, 81, 400–408. [Google Scholar] [CrossRef] [PubMed]
  28. Grissom, J.B. Physical fitness and academic achievement. J. Exerc. Physiol. Online 2005, 8, 11–25. [Google Scholar]
  29. Dwyer, T.; Sallis, J.F.; Blizzard, L.; Lazarus, R.; Dean, K. Relation of academic performance to physical activity and fitness in children. Pediatr. Exerc. Sci. 2001, 13, 225–237. [Google Scholar] [CrossRef]
  30. Keeley, T.J.H.; Fox, K.R. The impact of physical activity and fitness on academic achievement and cognitive performance in children. Int. Rev. Sport Exerc. Psychol. 2009, 2, 198–214. [Google Scholar] [CrossRef]
  31. Berendes, A.; Meyer, T.; Hulpke-Wette, M.; Herrmann-Lingen, C. Association of elevated blood pressure with low distress and good quality of life: Results from the nationwide representative german health interview and examination survey for children and adolescents. Psychosom. Med. 2013, 75, 422–428. [Google Scholar] [CrossRef]
  32. Elias, M.F.; Goodell, A.L.; Dore, G.A. Hypertension and cognitive functioning: A perspective in historical context. Hypertension 2012, 60, 260–268. [Google Scholar] [CrossRef]
  33. Lande, M.B.; Kupferman, J.C.; Adams, H.R. Neurocognitive alterations in hypertensive children and adolescents. J. Clin. Hypertens. 2012, 14, 353–359. [Google Scholar] [CrossRef] [PubMed]
  34. Thomas, J.R.; Nelson, J.K.; Silverman, S. Research Methods in Physical Activity, 7th ed.; Human Kinetics: Champaign, IL, USA, 2015. [Google Scholar]
  35. Cingi, H. Sampling Theory, 2nd ed.; Hacettepe University Press: Ankara, Turkey, 1994. [Google Scholar]
  36. Bosy-Westphal, A.; Later, W.; Hitze, B.; Sato, T.; Kossel, E.; Glüer, C.-C.; Heller, M.; Muller, M.J. Accuracy of bioelectrical impedance consumer devices for measurement of body composition in comparison to whole body magnetic resonance imaging and dual X-ray absorptiometry. Obes. Facts 2008, 1, 319–324. [Google Scholar] [CrossRef] [PubMed]
  37. Tekdemir, I.; Akın, G.; Bektaş, Y.; Gültekin, T.; Emre Erol, A.E. Anthropometry and Sport; Alter Publications: Ankara, Turkey, 2013. [Google Scholar]
  38. Erturk, C.; Can, I.; Bayrakdaroglu, S. Investigating the effects of some physiological and motoric characteristics on shooting performance of air rifle athletes. J. Sport Sci. Res. 2022, 7, 281–293. [Google Scholar]
  39. Pickering, T.G.; Hall, J.E.; Appel, L.J.; Falkner, B.E.; Graves, J.; Hill, M.N.; Jones, D.W.; Kurtz, T.; Sheps, S.G.; Roccella, E.J. Recommendations for blood pressure measurement in humans and experimental animals: Part 1: Blood pressure measurement in humans: A statement for professionals from the Subcommittee of Professional and Public Education of the American Heart Association Council on High Blood Pressure Research. Circulation 2005, 111, 697–716. [Google Scholar] [CrossRef] [PubMed]
  40. Devore, P.; Hagerman, P. A pregame soccer warm-up. Strength Cond. J. 2006, 28, 14–18. [Google Scholar] [CrossRef]
  41. Fletcher, I.M. The effect of different dynamic stretch velocities on jump performance. Eur. J. Appl. Physiol. 2010, 109, 491–498. [Google Scholar] [CrossRef] [PubMed]
  42. Turki, O.; Chaouachi, A.; Behm, D.G.; Chtara, H.; Chtara, M.; Bishop, D.; Chamari, K.; Amri, M. The effect of warm-ups incorporating different volumes of dynamic stretching on 10-and 20-m sprint performance in highly trained male athletes. J. Strength Cond. Res. 2012, 26, 63–72. [Google Scholar] [CrossRef] [PubMed]
  43. Harman, E.; Garhammer, J.; Pandorf, C. Administration, scoring and interpretation of selected tests. In Essentials of Strength and Conditioning; Baechle, T.R., Earle, R.W., Eds.; Human Kinetics: Champaign, IL, USA, 2000; pp. 249–292. [Google Scholar]
  44. McSwegin, P.J.; Plowman, S.A.; Wolff, G.M.; Guttenberg, G.L. The validity of a one-mile walk test for high school age individuals. Meas. Phys. Educ. Exerc. Sci. 1998, 2, 47–63. [Google Scholar] [CrossRef]
  45. George, J.D.; Vehrs, P.R.; Allsen, P.E.; Fellingham, G.W.; Fisher, A.G. VO2max estimation from a submaximal 1-mile track jog for fit college-age individuals. Med. Sci. Sports Exerc. 1993, 25, 401–406. [Google Scholar] [CrossRef]
  46. McMahon, J.J.; Murphy, S.; Rej, S.J.; Comfort, P. Countermovement-Jump-Phase Characteristics of Senior and Academy Rugby League Players. Int. J. Sports Physiol. Perform. 2017, 12, 803–811. [Google Scholar] [CrossRef]
  47. Lake, J.P.; Mundy, P.D.; Comfort, P.; Suchomel, T.J. Do the peak and mean force methods of assessing vertical jump force asymmetry agree? Sports Biomech. 2020, 19, 227–234. [Google Scholar] [CrossRef] [PubMed]
  48. Claudino, J.G.; Cronin, J.; Mezêncio, B.; McMaster, D.T.; McGuigan, M.; Tricoli, V.; Amadio, A.C.; Serrão, J.C. The countermovement jump to monitor neuromuscular status: A meta-analysis. J. Sci. Med. Sport 2017, 20, 397–402. [Google Scholar] [CrossRef] [PubMed]
  49. Berton, R.; Lixandrão, M.E.; Pinto e Silva, C.M.; Tricoli, V. Effects of weightlifting exercise, traditional resistance and plyometric training on countermovement jump performance: A meta-analysis. J. Sports Sci. 2018, 36, 2038–2044. [Google Scholar] [CrossRef] [PubMed]
  50. Driller, M.; Tavares, F.; McMaster, D.; O’Donnell, S. Assessing a smartphone application to measure counter-movement jumps in recreational athletes. Int. J. Sports Sci. Coach. 2017, 12, 661–664. [Google Scholar] [CrossRef]
  51. Nygaard Falch, H.; Guldteig Rædergård, H.; Van den Tillaar, R. Relationship of performance measures and muscle activity between a 180 change of direction task and different countermovement jumps. Sports 2020, 8, 47. [Google Scholar] [CrossRef] [PubMed]
  52. Fox, E.L.; Bowers, R.W.; Foss, M.L. Translation: Mesut Cerit. In The Physiological Basis of Physical Education and Sport; Spor Publishing: Ankara, Turkey, 2012. [Google Scholar]
  53. Available online: (accessed on 11 November 2022).
  54. Tabachnick, B.G.; Fidell, L.S. Using Multivariate Statistics; Allyn and Bacon: Boston, MA, USA, 2013. [Google Scholar]
  55. Field, A. Discovering Statistics Using SPSS; Sage Publication: London, UK, 2005. [Google Scholar]
  56. Firat, S. A current view of childhood hypertension. Akdeniz Med. J. 2021, 7, 463–473. [Google Scholar] [CrossRef]
  57. Waldstein, S.R.; Jennings, J.R.; Ryan, C.M.; Muldoon, M.F.; Shapiro, A.P.; Polefrone, J.M.; Fazzari, T.V.; Manuck, S.B. Hypertension and neuropsychological performance in men: Interactive effects of age. Health Psychol. 1996, 15, 102–109. [Google Scholar] [CrossRef]
  58. Walker, K.A.; Power, M.C.; Gottesman, R.F. Defining the relationship between hypertension, cognitive decline, and dementia: A review. Curr. Hypertens. Rep. 2017, 19, 24. [Google Scholar] [CrossRef]
  59. Gottesman, R.F.; Schneider, A.L.; Albert, M.; Alonso, A.; Bandeen-Roche, K.; Coker, L.; Coresh, J.; Knopman, D.; Power, M.C.; Rawlings, A.; et al. Midlife hypertension and 20-year cognitive change: The atherosclerosis risk in communities neurocognitive study. JAMA Neurol. 2014, 71, 1218–1227. [Google Scholar] [CrossRef] [PubMed]
  60. Hughes, T.M.; Sink, K.M. Hypertension and its role in cognitive function: Current evidence and challenges for the future. Am. J. Hypertens. 2016, 29, 149–157. [Google Scholar] [CrossRef]
  61. Friedman, J.I.; Tang, C.Y.; de Haas, H.J.; Changchien, L.; Goliasch, G.; Dabas, P.; Wang, V.; Fayad, Z.A.; Fuster, V.; Narula, J. Brain imaging changes associated with risk factors for cardiovascular and cerebrovascular disease in asymptomatic patients. JACC Cardiovasc. Imaging 2014, 7, 1039–1053. [Google Scholar] [CrossRef]
  62. Singh-Manoux, A.; Ferrie, J.E.; Lynch, J.W.; Marmot, M. The role of cognitive ability (intelligence) in explaining the association between socioeconomic position and health: Evidence from the Whitehall II prospective cohort study. Am. J. Epidemiol. 2005, 161, 831–839. [Google Scholar] [CrossRef] [PubMed]
  63. Tzourio, C.; Laurent, S.; Debette, S. Is hypertension associated with an accelerated aging of the brain? Hypertension 2014, 63, 894–903. [Google Scholar] [CrossRef] [PubMed]
  64. Matejek, Č.; Planinšec, J. The relationship between academic achievement and physical fitness in preadolescent Children. Croat. J. Educ. Hrvat. Časopis Za Odgoj. I Obraz. 2022, 24, 97–126. [Google Scholar]
  65. Matsudaira, I.; Kawashima, R.; Taki, Y. Structural brain development in healthy children and adolescents. Brain Nerve 2017, 69, 539–545. [Google Scholar] [CrossRef]
  66. Uchiyama, T.; Nakayama, T.; Kuru, S. Muscle development in healthy children evaluated by bioelectrical impedance analysis. Brain Dev. 2017, 39, 122–129. [Google Scholar] [CrossRef] [PubMed]
  67. Bartee, R.T.; Heelan, K.A.; Dority, B.L. Longitudinal evaluation of aerobic fitness and academic achievement among schoolchildren. J. Sch. Health 2018, 88, 644–650. [Google Scholar] [CrossRef]
  68. Raine, L.B.; Biggan, J.R.; Baym, C.L.; Saliba, B.J.; Cohen, N.J.; Hillman, C.H. Adolescent changes in aerobic fitness are related to changes in academic achievement. Pediatr. Exerc. Sci. 2018, 30, 106–114. [Google Scholar] [CrossRef]
  69. Santana, C.C.A.; Azevedo, L.B.; Cattuzzo, M.T.; Hill, J.O.; Andrade, L.P.; Prado, W.L. Physical fitness and academic performance in youth: A systematic review. Scand. J. Med. Sci. Sports 2017, 27, 579–603. [Google Scholar] [CrossRef]
  70. Torrijos-Niño, C.; Martínez-Vizcaíno, V.; Pardo-Guijarro, M.J.; García-Prieto, J.C.; Arias-Palencia, N.M.; Sánchez-López, M. Physical fitness, obesity, and academic achievement in schoolchildren. J. Pediatr. 2014, 165, 104–109. [Google Scholar] [CrossRef]
  71. Suchert, V.; Hanewinkel, R.; Isensee, B. Longitudinal relationships of fitness, physical activity, and weight status with academic achievement in adolescents. J. Sch. Health 2016, 86, 734–741. [Google Scholar] [CrossRef] [PubMed]
  72. Chen, L.J.; Fox, K.R.; Ku, P.W.; Taun, C.Y. Fitness change and subsequent academic performance in adolescents. J. Sch. Health 2013, 83, 631–638. [Google Scholar] [CrossRef] [PubMed]
  73. Alvarez-Bueno, C.; Hillman, C.H.; Cavero-Redondo, I.; Sanchez-Lopez, M.; Pozuelo-Carrascosa, D.P.; Martinez-Vizcaino, V. Aerobic fitness and academic achievement: A systematic review and meta-analysis. J. Sports Sci. 2020, 38, 582–589. [Google Scholar] [CrossRef] [PubMed]
  74. Kyan, A.; Takakura, M.; Miyagi, M. Does physical fitness affect academic achievement among Japanese adolescents? A hybrid approach for decomposing within-person and between-persons effects. Int. J. Environ. Res. Public Health 2018, 15, 1901. [Google Scholar] [CrossRef]
  75. Sigmundsson, H.; Englund, K.; Haga, M. Associations of physical fitness and motor competence with reading skills in 9-and 12-year-old children: A longitudinal study. SAGE Open 2017, 7, 2158244017712769. [Google Scholar] [CrossRef]
Figure 1. Schematic representation of the study flow diagram.
Figure 1. Schematic representation of the study flow diagram.
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Figure 2. Sample distribution by academic performance.
Figure 2. Sample distribution by academic performance.
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Figure 3. Systolic and diastolic blood pressure (SBP and DBP, respectively) and heart rate were measured by academic achievement (* denotes p < 0.05).
Figure 3. Systolic and diastolic blood pressure (SBP and DBP, respectively) and heart rate were measured by academic achievement (* denotes p < 0.05).
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Figure 4. Comparison of fitness profile across different levels of academic achievement (* denotes p < 0.05).
Figure 4. Comparison of fitness profile across different levels of academic achievement (* denotes p < 0.05).
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Table 1. Sample distribution by age and gender and descriptive data of anthropometric features.
Table 1. Sample distribution by age and gender and descriptive data of anthropometric features.
13 Years14 YearsTotalAnthropometric Features X ¯ ± SD
Boys14246.7206.616253.3Height (cm)158.05 ± 5.12
Girls12641.5165.214246.7Body mass (kg)52.95 ± 4.25
Total26888.23611.8304100BMI (kg·m−2)14.98 ± 4.16
Table 2. Comparison of the physical fitness and cardiorespiratory profile across the academic performance levels.
Table 2. Comparison of the physical fitness and cardiorespiratory profile across the academic performance levels.
VariablesAcademic Achievement Levels X ¯ ± SD95% Confidence Intervalp-Value
SBP (mmHg)Poor116.1 ± 15.1113.0119.20.06
Average118.4 ± 17.0115.3121.5
Good117.5 ± 14.5114.5120.5
DBP (mmHg)Poor66.7 ± 9.3664.868.60.04
Average70.9 ± 7.969.472.3
Good71.5 ± 10.669.373.7
RHR (bpm)Poor86.1 ± 14.583.289.10.48
Average88.2 ± 12.385.990.4
Good88.3 ± 15.485.191.5
VJ (cm)Poor31.8 ±
Average30.9 ± 8.129.432.4
Good31.8 ± 9.229.933.7
SP (s)Poor4.3 ±
Average4.4 ±
Good4.4 ±
VO2Max (mL/kg/min)Poor47.1 ± 8.545.448.90.05
Average42.6 ± 9.040.944.2
Good42.4 ± 8.940.544.3
(kg m/s)
Poor65.1 ± 15.761.968.30.33
Average68.2 ± 16.965.071.3
Good67.9 ± 15.964.671.2
Note. SD = std. deviation; SBP = systolic blood pressure; DBP = diastolic blood pressure; RHR = resting heart rate; VJ = vertical jump; SP = sprint; AC= anaerobic power.
Table 3. Correlations between blood pressure, RHR, physical fitness parameters, and academic achievement scores by gender.
Table 3. Correlations between blood pressure, RHR, physical fitness parameters, and academic achievement scores by gender.
Academic Achievement
Total (n = 304)Boys (n = 162)Girls (n = 142)
DBP0.261 **0.265 **0.129
VJ−0.0180.166 *0.105
VO2Max−0.282 **−0.082−0.017
AC0.1020.265 **0.061
SBP = systolic blood pressure; DBP = diastolic blood pressure; RHR = resting heartbeat rate; VJ = vertical jump; SP = sprint; AC = anaerobic power. * Correlation is significant at the 0.05 level. ** Correlation is significant at the 0.01 level.
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Canli, U.; Aldhahi, M.I.; Küçük, H. Association of Physiological Performance, Physical Fitness, and Academic Achievement in Secondary School Students. Children 2024, 11, 396.

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Canli U, Aldhahi MI, Küçük H. Association of Physiological Performance, Physical Fitness, and Academic Achievement in Secondary School Students. Children. 2024; 11(4):396.

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Canli, Umut, Monira I. Aldhahi, and Hamza Küçük. 2024. "Association of Physiological Performance, Physical Fitness, and Academic Achievement in Secondary School Students" Children 11, no. 4: 396.

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