Next Article in Journal
Relationship between the College Student and the Campus Club: An Evolutionary Game Theory Analysis
Next Article in Special Issue
The Relationship between Live Sports Learning and Exercise Behavior in College Students: A Serial Mediation Model
Previous Article in Journal
Preventive Effect of a 7-Week App-Based Passive Psychoeducational Stress Management Program on Students
Previous Article in Special Issue
Effect of Achievement Motivation and Self-Efficacy on General Well-Being among Students at Normal Universities in Ningxia: The Mediating Role of Time Management
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Behavioral Sciences
Volume 14
Issue 3
10.3390/bs14030181
behavsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

Influence of Relative Age on Physical Condition and Academic Performance in Adolescents

by
Luis Miguel Fernández-Galván
1,2,
Noelia Belando-Pedreño
3,
Benito Yañez-Araque
4 and
Jorge Sánchez-Infante
3,5,6,*
1
Faculty of Sport Science, Jaime I University, 12006 Castellon de la Plana, Spain
2
Health and Sport Sciences University School (EUSES), Rovira i Virgili University, 43002 Amposta, Spain
3
Faculty of Physical Activity and Sport Science, European University, 28670 Madrid, Spain
4
Department of Business Administration, School of Industrial and Aerospace Engineering, University of Castilla-La Mancha Toledo, 13071 Ciudad Real, Spain
5
Faculty of Physical Therapy and Nursing, University of Castilla-La Mancha, 45071 Toledo, Spain
6
Faculty of Health Sciences, Universidad Francisco de Vitoria, 28223 Madrid, Spain
*
Author to whom correspondence should be addressed.
Behav. Sci. 2024, 14(3), 181; https://doi.org/10.3390/bs14030181
Submission received: 19 January 2024 / Revised: 16 February 2024 / Accepted: 22 February 2024 / Published: 25 February 2024
(This article belongs to the Special Issue The Effects of Sports on Individual Cognition, Motivation and Mental Health)
Browse Figures
Review Reports Versions Notes

Abstract

:
Annual age grouping is a common organizational strategy in academics and sports. This strategy could promote the relative effects of age, which refers to the (dis)advantages that subjects who were born in the first or last months of the year may suffer. The consequences could be minimized, resulting in better physical and/or academic results. The objective of the study was to evaluate the influence of the quarter of birth and examine the correlation between physical condition variables and academic performance. The sample included 79 students (51.90% females) 13.46 ± 0.21 years old in the second year of Obligatory Secondary Education. The physical variables of this study were explosive power, cardiorespiratory capacity, speed, flexibility, and muscle strength. They were evaluated using the horizontal jump tests, Cooper test, 50 m sprint, sit-and-reach test, and medicine ball exercises, respectively. The academic variables were obtained from the average academic grade, grouped by key competencies. An Analysis of Covariance (ANCOVA), controlling for gender, body mass, and height, revealed significant differences between male students born in the first quarter compared to those born in subsequent quarters in all physical condition variables (except for cardiorespiratory capacity). For female students, significant differences were observed only in the explosive power variable. No statistically significant intra-sex differences were evident in academic performance at any time of measurement. Additionally, moderate to large correlations were found in the physical condition and academic variables. It is concluded in relation to the need to implement diverse strategies regarding the process of developing physical conditions in adolescence that satisfy the growth needs of students based on age and sex.

1. Introduction

The school organization policy for grouping students in the same academic year is based on the date of birth. In general, students born in the same year are grouped into the same course [1]. This grouping of students in the academic year varies in different countries; thus, in Spain, the grouping takes place from 1 January to 31 December; however, in other countries, such as England, students born between 1 September and 31 August of the following year are grouped in the same course [2]. This difference in age between individuals within the same age group is referred to as relative age, and its consequences are known as the Relative Age Effect (RAE) [3].
These inequalities could lead to (dis)advantages both in academic performance [4] and in physical performance [5,6]. Most of the published research on RAE and its impact on academic performance originates from Norway [7], Britain [8], Belgium [9], or the United States [10]. Despite the variations among these studies, including differences in participant ages, backgrounds, students with special educational needs, and varying sample sizes, a consistent trend emerges: younger students within the school year face more difficulties than relatively older students [11]. This pattern becomes more significant with younger students but reverses upon reaching higher education.
At a sporting and physical performance level, the effects of RAE have been studied since 1985, when Barnsley and Thompson [12] found that 40% of hockey players were born in the first three months of the year. The effect of RAE has a great effect on the physical conditions of children, affecting, for example, the selection of male and female athletes for international competitions [13]. Some physical abilities increase with growth and maturation, such as strength [14] and resistance [15]. Currently, this effect has been shown in other sports, such as basketball [16], handball [17], or soccer, where a recent systematic review showed the effects described above [18]. This means that those born in the first months of the year have more options to play on elite teams [19]. It seems that this advantage in performance is given by the greater maturational growth of those born in the first months of the year, which leads to greater body development that could result in a greater application of force at a higher speed [5]. However, in sports such as gymnastics, an overrepresentation of those born in recent months has been found (contrary to the RAE), since this increase in height and weight could harm the flexibility and speed of rotation of the most mature ones [20].
The literature reviewed focuses on the representation of students in the first months of the year, both in sports and at school. However, there are few studies that have delved into the existence of differences in physical and academic conditions linked to the month of birth. Therefore, the objective of this study was to analyze the influence of the quarter of birth on physical condition and academic performance and examine the relationship between variables. Thus, it was hypothesized that the quarter of birth would influence the level of physical condition and the academic performance of the students; likewise, differences would be observed depending on sex.

2. Materials and Methods

2.1. Research Design

A quantitative methodology research study was carried out [21], with a descriptive approach (collection, analysis, and presentation of data through quantitative measures) and a correlational (non-experimental relationship between closely linked variables) [22]. For the design of the study and the treatment of data, the ethical guidelines of the Declaration of Helsinki of 1964 (latest revision of Fortaleza, Brazil, 2013) were considered. This study was approved by the ethical committee of Rey Juan Carlos University, Madrid, Spain (code: 3103202314923).

2.2. Participants

The sample was made up of 79 adolescent students, of which 41 were females (51.9%) with an average age of 13.24 ± 0.19 and 38 were males (48.1%) with an average age of 13.41 ± 0.24 who were studying the second year of Obligatory Secondary Education (ESO). Non-probabilistic convenience sampling was used [23], organizing the total sample over a four-month period, as proposed by various authors [24,25]. The sample was categorized according to the quarter (Q) of birth: Q1 from January to April (n = 27), Q2 from May to August (n = 25), and Q3 from September to December (n = 27). The sample size was calculated using the G*Power software (version 3.1.4), with a total number of 19 individuals estimated, a statistical power (1−β) of 0.95 for the t test between two dependent means (corresponding pair), an α type error of 0.05, and a large effect size of 0.8 [26].

2.3. Sample Selection Criteria

All participants had to be enrolled in the second year of ESO and could not be “repeaters” of that same course, with the aim of not influencing the maturation effect. Another exclusion criterion was the diagnosis of metabolic health problems (e.g., neuroendocrine pathologies), osteoarticular and muscular problems, among others, as well as mental health problems, in order to avoid their influence on the physical condition of the sample.

2.4. Procedure

The director of the educational center was contacted by sending a formal letter stating the objectives of the investigation. Once the approval of the educational center was obtained, the informed consents were delivered and signed by the students, their parents, or their legal guardians. Subsequently, the students’ academic grades were accessed and recorded in a database. Likewise, the results of the physical condition tests were collected in that database.

2.5. Variables and Measurements

Academic performance. Taking as reference the key specific competencies established by Organic Law 3/2020 modifying the Organic Law of Education 2/2006 (LOMLOE) [27] regulated in Royal Decree 107/2022 for the Valencian Community, the subjects were grouped, in consideration of their connections, into (a) multilingual competence in which the subjects of Spanish, Valencian, and English language were included; (b) mathematical competence and in science, technology and engineering in which the subjects of physics and chemistry, technology and mathematics were included; (c) competence in cultural awareness and expression in which the subject of plastic, visual and audiovisual education was included; and (d) citizen competence in which the subject of geography and history was included.
To assess academic performance, grades ranging from 0 to 10 were collected for the subjects included in each of the four competencies considered, and the average was calculated. These grades were obtained during the second semester of the 2021/2022 academic year.
Physical condition. The physical condition variables were assessed over a 2-week period during scheduled physical education classes (Table 1). All evaluations were conducted outdoors; in cases of inclement weather, assessments took place in the school gymnasium. Prior to the measurement, the same type of warm-up lasting 15 min was carried out, managing the intensity with the Borg scale (RPE, perception of effort) [28]. This initial part of the fitness assessment session consisted of 5 min of continuous running at a moderate intensity (RPE 5–6 out of 10), 5 min of dynamic stretching, and 5 min of progressive sprints (50%, 60%, and 70% of the maximum self-perceived speed). All assessments were carried out twice, following a recovery period of 3 min of rest to ensure complete recovery [29].

2.6. Statistical Analysis

Statistical analysis was performed using the free software Jeffrey’s Amazing Statistics Program (JASP), version 0.17.1. The descriptive characteristics of the study sample are presented as means and ± standard deviations. The normality of the variables was checked through the Shapiro-Wilk tests and the Levene test to verify the homoscedasticity of the variances. A one-way ANCOVA was performed to evaluate the effect of the quarter of birth on the physical condition and academic performance variables. The analysis was adjusted for sex, weight, and height. When necessary, Tukey’s post hoc test was carried out to observe differences between means (MD) between the quarters. The ANCOVA effect sizes were expressed with omega squared (ω2), considering values of <0.06 (small), 0.06 to 0.014 (medium), and >0.014 (large). Cohen’s d effect size (ES) was calculated using the formula ES = t/√(n) [35]. The interpretation of the ES was 0.2 (irrelevant), 0.2 to 0.6 (small), 0.6 to 1.2 (moderate), 1.2 to 2.0 (large), and >2.0 (very large) [36]. Subsequently, to examine the relationship between variables, the Pearson correlation test was carried out. Correlations were interpreted as small (0.10–0.29), moderate (0.30–0.49), large (0.50–0.69), very large (0.70–0.89), or extremely large (≥0.90) [36]. A 95% confidence interval (CI) was considered, and the significance level was p < 0.05.

3. Results

The descriptive statistics for the demographic variables of physical condition and academic performance are shown in Table 2.
An ANCOVA adjusted for weight and height was carried out to evaluate the effect of the quarter of birth. Statistically significant differences of large magnitude were observed in the explosive power variable (F (2.76) = 19.28, p = <0.001, ω2 = 0.32) between Q1 and Q2 (MD = 0.22 cm 95%CI [0.09, 0.35], d = 4.18, p = <0.001) and between Q1 and Q3 (MD = 0.31 cm 95%CI [0.19, 0.44], d = 6.05, p = <0.001). Statistically significant differences of large magnitude were also observed in the muscle strength variables (F(2.76) = 6.96, p = <0.001, ω2 = 0.13) between Q1 and Q3 (MD = 0.82 cm, 95%CI [0.29, 1.35], d = 3.71, p = <0.001). Statistically significant differences of small magnitude in the cardiorespiratory capacity variables (F(2.76) = 3.63, p = 0.030, ω2 = 0.06) between Q1 and Q3 (MD = 251.67 m 95%CI [12.33, 491.01], d = 2.51, p = 0.040) and in the speed variable (F(2.76) = 3.41, p = 0.040, ω2 = 0.06) between Q1 and Q3 (MD = −0.49 s 95%CI [0.02, 0.21], d = −2.32, p = 0.050) (see Figure 1). Regarding the academic performance variables, no statistically significant differences were found in any of the competencies.
Regarding the analysis of the sample by gender (see Table 3), the results show statistically significant differences of large magnitude in males in the explosive power variable (F(2.35) = 17.59, p = <0.001, ω2 = 0.47) between Q1 vs. Q2 (MD = 0.34 cm 95%CI [0.16, 0.52], d = 4.61, p = <0.001) and Q1 vs. Q3 (MD = 0.40 cm 95%CI [0.22, 0.57], d = 5.51, p = <0.001). Furthermore, statistically significant differences of large magnitude were observed in the speed variable (F(2.35) = 4.57, p = 0.020, ω2 = 0.16) between Q1 and Q2 (MD = −0.82 cm 95%CI [−1.49, −0.16], d = 3.02, p = 0.010). And statistically significant of large magnitude in the muscle strength variable (F(2.35) = 7.10, p = <0.001, ω2 = 0.24) between Q1 and Q3 (MD = 1.16 cm, 95%CI [0.39, 1.92], d = 3.71, p = <0.001).
For their part, in females, only statistically significant differences of large magnitude were found in the explosive power variable (F(2.38) = 7.98, p = <0.001, ω2 = 0.25) between Q1 and Q3 (MD = 0.23 cm, 95%CI [0.09, 0.38], d = 3.99, p = <0.001) (see Table 3). No statistically significant differences were observed in the academic performance variables between males and females. Additionally, significant differences were observed between genders, with females obtaining a better result in the sit-and-reach test in the 3 quarters (all p = <0.001). On the other hand, males had a better result in the horizontal jump test, 50 m linear sprint, and medicine ball throw tests in Q1 (p = <0.001), in the medicine ball throw test in Q2 (p = 0.03), and in the 50 m linear sprint in Q3 (p = <0.001).
Finally, a Pearson correlation analysis was carried out, in which correlations from moderate (r = 0.50 to 0.58) to large (r = 0.64 to 0.65) were found between the physical condition variables, except for the variable of flexibility, which presented small correlations with the rest of the variables (Figure 2).
For their part, in the academic performance variables, high correlations are observed between the different competencies (all r = >0.715) (Figure 3).

4. Discussion

The objective of this study was to analyze the effect of the quarter of birth on the physical condition and academic performance of second-year ESO students, as well as the correlation between variables. The main finding of our study was that there were significant differences in the physical condition variables in the males, specifically in the horizontal jump test between Q1 vs. Q2 and Q1 vs. Q3, in the 50 m sprint test between Q1 and Q2, in the sit-and-reach test between Q1 and Q3, and in the medicine ball throw test between Q1 and Q3. However, in females, we only found significant differences in horizontal jumping capacity between Q1 and Q3. Furthermore, there were no differences in academic performance between the different quarters in the same gender, but there were between-gender differences, finding significant differences in favor of females in mathematical competence in Q1, Q2, and Q3, in awareness competence in Q2 and Q3, and in multilingual and citizenship skills in Q3.
These findings agree with previous research, where it was found that those born in the first months of the year obtained better results in physical condition variables [37]. For example, De la Rubia Riaza and Lorenzo Calvo [16] observed that handball athletes who were born in the first four months of the year had advantages over others due to their maturational development; this meant that the teams were nourished by these males and females to increase the team’s performance. This phenomenon is observed both in team sports [38] and in individual sports [13].
The results obtained show statistically significant differences in the variables of explosive power, speed, and muscle strength in males. A possible explanation would be given by the maturational hypothesis, which maintains that children born in the first months of the year, assuming that growth rates are similar, will show physical and cognitive differences within a period of 12 months that will contribute to having more strength and, consequently, to being more effective in its application and to obtaining better physical results [39]. Furthermore, it must be taken into account that the age of the sample (~13 years) coincides with the approach to the maximum altitude speed, in which the subject experiences structural adaptations due to the direct influence on the metabolic system that is intimately related to the amount of surrounding androgenic hormones [40]. These hormones play an essential role in the processes of muscle glycogen synthesis and hypertrophy, which could contribute to improving the ability to generate strength in its different manifestations [41]. Another explanation for the superior performance of those born in the first quarter is that they could have up to one more year of sporting experience, which is known as the “initial performance advantage” effect [42]. Other authors attribute this superior performance to the “Pygmalion effect,” which describes how the achievements of individuals are a product of the expectations placed on them [43].
However, in the variable to estimate cardiorespiratory capacity, no statistically significant differences were found, despite the fact that it is known that in adolescence, there are changes in metabolic, anatomical, and hematological factors, as well as improvements in running economy [39]. These results agree with another observational study carried out on 69 young soccer players from Portugal [44], as well as with the study of Deprez and Coutts [45], where no significant differences were shown between young elite soccer players. In both cases, the explanation was based on the fact that the sample was made up of selected players and, therefore, the group was homogeneous; in our case, Q1 obtained ~15% better results in the test than Q2 and Q3, which would show the heterogeneity of the sample.
As for females, no statistically significant differences were found in the physical condition variables, except for the horizontal jump test. These results agree with other studies [46] and are expected since females, on average, mature 2 years earlier than males [47]. Therefore, at the age of 13, we could find females who are above the maximum growth speed, and therefore, no statistically significant differences are observed.
On the other hand, our results did not show statistically significant differences in the academic performance variables among students born in different quarters. These findings were unexpected, as previous observations have indicated that students born in the later months of the year tend to have significantly lower average academic grades compared to their peers [7]. In addition, these students are 70–80% more likely to repeat a course [9,48]. Furthermore, in other countries, such as the United Kingdom, where the grouping system is different (1 September to 31 August), significant differences were found [2]. Thus, the study carried out by Cobley and McKenna [2] analyzed 657 students between 11 and 14 years old and showed that older students (born between September and December) obtained higher grades in all subjects than younger students (born between January and August), so there was an overrepresentation in gifted and talented programs. In the Gledhill and Ford [8] study, a cross-sectional analysis was conducted with a sample of 8036 students. The results revealed a statistically significant correlation between the time of birth and educational challenges. Specifically, individuals born in the last term of the academic year (the youngest) demonstrated a greater susceptibility to learning difficulties compared to their counterparts born in the early term (the oldest). These findings are consistent with those of another retrospective observational cohort study involving 2768 students, which found lower academic performance in reading, mathematics, and science. Additionally, significant relationships were identified between time of birth and the rate of diagnosis of specific learning difficulties (10% higher) and the rate of grade repetition (25% higher)” [10]. A possible explanation for our results could be that, although there are differences between students born in different quartiles of the year, these decrease over time and with the experience accumulated at the center, and it is possible that at the age of 13–14 years, this difference is not significant.
Finally, a correlation analysis was carried out between the variables of physical condition and academic performance. The horizontal jump test was observed to be moderately correlated with the 50 m sprint test. These results were expected since it is known that the neuromuscular power of the lower hemisphere correlates with the 5 m and 15 m sprints [49]. Specifically, horizontal jumps are better predictors of the 20 m sprint than vertical jumps [30].
Regarding the relationship between physical condition variables and academic variables, there are data that contain the Cooper test and all academic variables. These data are consistent with other studies that showed that students who scored higher on aerobic fitness tests had higher academic scores, specifically in reading and math, while those who were not aerobically fit scored lower [50]. It seems that when the child exerts himself physically, there is greater serum availability of oxygen and nutrients in the brain, which allows optimal brain function for a longer time [51]. In addition, aerobic exercise improves neural activity and synapses (communication between neurons), which favors concentration during learning [51]. Therefore, aerobic exercise could influence the student’s learning ability [52]. Although these statements are in line with other studies, strategies should be sought to enhance the learning of males and females through physical activity, since the practice of physical activity in adolescents improves their level of learning [53].
Among the limitations of the study is the selection of the sample, which was based on accessibility rather than randomization, thus compromising the external validity of the study. Additionally, the sample size of students is small, which limits the ability to draw scientifically valid deductions. It is important to note that no effort was made to account for potential confounding factors such as cognitive ability, socioeconomic status, or students who may have completed part of their education in different school districts or states. On the other hand, the main strength of this study was the homogeneous sample in terms of age and gender. Subsequent studies might consider creating longitudinal research projects that track students from primary education through secondary education while taking into account potential confounding variables. This approach could lead to more definitive outcomes and provide insight into the idea that differences in academic achievement among students of varying relative ages may initially stem from maturation, with this effect decreasing as maturation becomes less significant. Failing to address this issue could result in unfortunate outcomes for younger students who are biologically and developmentally less mature.

5. Conclusions

In conclusion, the results of this study suggest that adolescent students born in the first quarter of the year were associated with better results in physical condition variables than those born in subsequent months. However, no statistically significant differences were observed in academic performance. Our findings underscore the importance of considering the month of birth in the assessment of physical condition tests. Schools might explore the possibility of gradually adjusting the timing, format, and content of students’ physical assessments based on their relative age. One possible solution could be to consider two groups (January to June and July to December).

Author Contributions

Conceptualization, L.M.F.-G., J.S.-I. and B.Y.-A.; methodology, B.Y.-A.; software, N.B.-P.; validation, L.M.F.-G.; formal analysis, J.S.-I. and N.B.-P.; investigation, L.M.F.-G. and J.S.-I.; resources, L.M.F.-G.; data curation, L.M.F.-G.; writing—original draft preparation, L.M.F.-G., J.S.-I. and B.Y.-A.; writing—review and editing, J.S.-I. and N.B.-P.; visualization, B.Y.-A. and N.B.-P.; supervision, J.S.-I. and N.B.-P. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Board of Rey Juan Carlos University, Madrid, Spain (protocol code 3103202314923, 15 May 2023).

Informed Consent Statement

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

Data Availability Statement

All data sets used in this study can be accessed through a request to the author Luis Miguel Fernández-Galván (galvan@uji.es).

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Mavilidi, M.F.; Marsh, H.W.; Xu, K.M.; Parker, P.D.; Jansen, P.W.; Paas, F. Relative Age Effects on Academic Achievement in the First Ten Years of Formal Schooling: A Nationally Representative Longitudinal Prospective Study. J. Educ. Psychol. 2022, 114, 308–325. [Google Scholar] [CrossRef]
  2. Cobley, S.; McKenna, J.; Baker, J.; Wattie, N. How Pervasive Are Relative Age Effects in Secondary School Education? J. Educ. Psychol. 2009, 101, 520–528. [Google Scholar] [CrossRef]
  3. Caye, A.; Petresco, S.; de Barros, A.J.D.; Bressan, R.A.; Gadelha, A.; Gonçalves, H.; Manfro, A.G.; Matijasevich, A.; Menezes, A.M.B.; Miguel, E.C.; et al. Relative Age and Attention-Deficit/Hyperactivity Disorder: Data From Three Epidemiological Cohorts and a Meta-analysis. J. Am. Acad. Child. Adolesc. Psychiatry 2020, 59, 990–997. [Google Scholar] [CrossRef] [PubMed]
  4. Dhuey, E.; Lipscomb, S. What makes a leader? Relative age and high school leadership. Econ. Educ. Rev. 2008, 27, 173–183. [Google Scholar] [CrossRef]
  5. Cobley, S.; Baker, J.; Wattie, N.; McKenna, J. Annual age-grouping and athlete development: A meta-analytical review of relative age effects in sport. Sports Med. 2009, 39, 235–256. [Google Scholar] [CrossRef] [PubMed]
  6. Musch, J.; Grondin, S. Unequal competition as an impediment to personal development: A review of the relative age effect in sport. Dev. Rev. 2001, 21, 147–167. [Google Scholar] [CrossRef]
  7. Lien, L.; Tambs, K.; Oppedal, B.; Heyerdahl, S.; Bjertness, E. Is relatively young age within a school year a risk factor for mental health problems and poor school performance? A population-based cross-sectional study of adolescents in Oslo, Norway. BMC Public Health 2005, 5, 102. [Google Scholar] [CrossRef]
  8. Gledhill, J.; Ford, T.; Goodman, R. Does Season of Birth Matter?: The Relationship between Age within the School Year (Season of Birth) and Educational Difficulties among a Representative General Population Sample of Children and Adolescents (Aged 5–15) in Great Britain. Res. Educ. 2002, 68, 41–47. [Google Scholar] [CrossRef]
  9. Verachtert, P.; De Fraine, B.; Onghena, P.; Ghesquière, P. Season of birth and school success in the early years of primary education. Oxf. Rev. Educ. 2010, 36, 285–306. [Google Scholar] [CrossRef]
  10. Martin, R.P.; Foels, P.; Clanton, G.; Moon, K. Season of birth is related to child retention rates, achievement, and rate of diagnosis of specific LD. J. Learn. Disabil. 2004, 37, 307–317. [Google Scholar] [CrossRef]
  11. Navarro, J.-J.; García-Rubio, J.; Olivares, P.R. The Relative Age Effect and Its Influence on Academic Performance. PLoS ONE 2015, 10, e0141895. [Google Scholar] [CrossRef]
  12. Barnsley, R.H.; Thompson, A.H.; Barnsley, P.E. Hockey success and birthdate: The relative age effect. J. Can. Assoc. Health Phys. Educ. Recreat. 1985, 51, 23–28. [Google Scholar]
  13. Rubia, A.; Bjørndal, C.T.; Sánchez-Molina, J.; Yagüe, J.M.; Calvo, J.L.; Maroto-Izquierdo, S. The relationship between the relative age effect and performance among athletes in World Handball Championships. PLoS ONE 2020, 15, e0230133. [Google Scholar] [CrossRef]
  14. Behringer, M.; vom Heede, A.; Yue, Z.; Mester, J. Effects of Resistance Training in Children and Adolescents: A Meta-analysis. Pediatrics 2010, 126, e1199–e1210. [Google Scholar] [CrossRef] [PubMed]
  15. Sun, T.R. Analysis of sex and age differences in performance of young Canadian freestyle swimmers. J. Hum. Sport. Exerc. 2022, 17, 640–654. [Google Scholar] [CrossRef]
  16. De la Rubia Riaza, A.; Lorenzo Calvo, J.; Mon-López, D.; Lorenzo, A. Impact of the relative age effect on competition performance in basketball: A qualitative systematic review. Int. J. Env. Res. Public Health 2020, 17, 8596. [Google Scholar] [CrossRef]
  17. Wrang, C.M.; Rossing, N.N.; Diernæs, R.M.; Hansen, C.G.; Dalgaard-Hansen, C.; Karbing, D.S. Relative Age Effect and the Re-Selection of Danish Male Handball Players for National Teams. J. Hum. Kinet. 2018, 63, 33–41. [Google Scholar] [CrossRef]
  18. Sierra-Díaz, M.; González-Víllora, S.; Pastor-Vicedo, J.; Serra-Olivares, J. Soccer and Relative Age Effect: A Walk among Elite Players and Young Players. Sports 2017, 5, 5. [Google Scholar] [CrossRef]
  19. Williams, A.M.; Ford, P.R.; Drust, B. Talent identification and development in soccer since the millennium. J. Sports Sci. 2020, 38, 1199–1210. [Google Scholar] [CrossRef]
  20. Hancock, D.J.; Starkes, J.L.; Ste-Marie, D.M. The relative age effects in female gymnastics: A flip-flop phenomenon. Int. J. Sports Med. 2015, 46, 714. [Google Scholar] [CrossRef]
  21. Hernández, R.; Fernández, C.; Baptista, P. Metodología de la Investigación, 6th ed.; McGraw-Hill: Mexico City, Mexico, 2014. [Google Scholar]
  22. Montero, I.; León, O.G. A guide for naming research studies in Psychology. Int. J. Clin. Health 2007, 7, 847–862. [Google Scholar]
  23. Check, J.; Schutt, R.K. Research Methods in Education; Sage Publications: Thousand Oaks, CA, USA, 2011. [Google Scholar]
  24. Isorna-Folgar, M.; Vaquero-Cristóbal, R.; Vinuesa-González, J.; Rial-Boubeta, A. The effect of relative age on participation in the national sport training plan and the achievement of sporting success in canoeing. Apunts. Ef YD 2014, 1, 46–53. [Google Scholar]
  25. Gutiérrez Díaz del Campo, D. Review of relative age effects and potential ways to reduce them in sport and education. Retos 2015, 23, 51–63. [Google Scholar] [CrossRef]
  26. Cohen, J. Statistical Power Analysis for the Behavioral Sciences; Lawrence Erlbaum Associates: Hillsdle, NJ, USA, 1988. [Google Scholar]
  27. Vazquez Ramil, R.; Ucha, P.; Serafin, A. Cross-curricular teaching, citizenship and values education: From LOGSE (1990) to LOMLOE (2020). Innovación Educ. 2020, 30, 113–125. [Google Scholar]
  28. Shariat, A.; Cleland, J.A.; Danaee, M.; Alizadeh, R.; Sangelaji, B.; Kargarfard, M.; Ansari, N.N.; Sepehr, F.H.; Tamrin, S.B.M. Borg CR-10 scale as a new approach to monitoring office exercise training. Work 2018, 60, 549–554. [Google Scholar] [CrossRef]
  29. Nicol, C.; Avela, J.; Komi, P.V. The stretch-shortening cycle: A model to study naturally occurring neuromuscular fatigue. Sports Med. 2006, 36, 977–999. [Google Scholar] [CrossRef]
  30. Maulder, P.; Cronin, J. Horizontal and vertical jump assessment: Reliability, symmetry, discriminative and predictive ability. Phys. Ther. Sport 2005, 6, 74–82. [Google Scholar] [CrossRef]
  31. Cooper, K.H. A Means of Assessing Maximal Oxygen Intake: Correlation Between Field and Treadmill Testing. JAMA J. Am. Med. Assoc. 1968, 203, 201–204. [Google Scholar] [CrossRef]
  32. Di Prampero, P.E.; Fusi, S.; Sepulcri, L.; Morin, J.B.; Belli, A.; Antonutto, G. Sprint running: A new energetic approach. J. Exp. Biol. 2005, 208, 2809–2816. [Google Scholar] [CrossRef]
  33. Mayorga-Vega, D.; Merino-Marban, R.; Viciana, J. Criterion-related validity of sit-and-reach tests for estimating hamstring and lumbar extensibility: A meta-analysis. J. Sports Sci. Med. 2014, 13, 1–14. [Google Scholar]
  34. Gabbett, T.; Georgieff, B.; Domrow, N. The use of physiological, anthropometric, and skill data to predict selection in a talent-identified junior volleyball squad. J. Sports Sci. 2007, 25, 1337–1344. [Google Scholar] [CrossRef] [PubMed]
  35. Lakens, D. Calculating and reporting effect sizes to facilitate cumulative science: A practical primer for t-tests and ANOVAs. Front. Physiol. 2013, 4, 863. [Google Scholar] [CrossRef] [PubMed]
  36. Hopkins, W.G.; Marshall, S.W.; Batterham, A.M.; Hanin, J. Progressive statistics for studies in sports medicine and exercise science. Med. Sci. Sports Exerc. 2009, 41, 3–13. [Google Scholar] [CrossRef]
  37. Lovell, R.; Towlson, C.; Parkin, G.; Portas, M.; Vaeyens, R.; Cobley, S. Soccer player characteristics in English lower-league development programmes: The relationships between relative age, maturation, anthropometry and physical fitness. PLoS ONE 2015, 10, e0137238. [Google Scholar] [CrossRef]
  38. Delorme, N.; Boiché, J.; Raspaud, M. The relative age effect in elite sport: The French case. Res. Q. Exerc. Sport 2009, 80, 336–344. [Google Scholar] [CrossRef]
  39. Lloyd, R.S.; Oliver, J.L. The youth physical development model: A new approach to long-term athletic development. Strength Cond. J. 2012, 34, 61–72. [Google Scholar] [CrossRef]
  40. Martínez-Pérez, P.; Vaquero-Cristóbal, R. Systematic review of strength training in preadolescent and adolescent football players. Retos 2021, 41, 272–284. [Google Scholar]
  41. Reyes, A.C.; Chaves, R.; Baxter-Jones, A.D.G.; Vasconcelos, O.; Tani, G.; Maia, J. A mixed-longitudinal study of children’s growth, motor development and cognition. Design, methods and baseline results on sex-differences. Ann. Hum. Biol. 2018, 45, 376–385. [Google Scholar] [CrossRef] [PubMed]
  42. Helsen, W.F.; van Winckel, J.; Williams, A.M. The relative age effect in youth soccer across Europe. J. Sports Sci. 2005, 23, 629–636. [Google Scholar] [CrossRef]
  43. Rejeski, W.; Darracott, C.; Hutslar, S. Pygmalion in Youth Sport: A Field Study. J. Sport Psychol. 1979, 1, 311–319. [Google Scholar] [CrossRef]
  44. Malina, R.M.; Ribeiro, B.; Aroso, J.; Cumming, S.P. Characteristics of youth soccer players aged 13-15 years classified by skill level. Br. J. Sports Med. 2007, 41, 290–295. [Google Scholar] [CrossRef]
  45. Deprez, D.; Coutts, A.J.; Fransen, J.; Deconinck, F.; Lenoir, M.; Vaeyens, R.; Philippaerts, R. Relative age, biological maturation and anaerobic characteristics in elite youth soccer players. Int. J. Sports Med. 2013, 34, 897–903. [Google Scholar] [CrossRef]
  46. Apadopoulou, S.D.; Papadopoulou, S.K.; Rosemann, T.; Knechtle, B.; Nikolaidis, P.T. Relative Age Effect on Youth Female Volleyball Players: A Pilot Study on Its Prevalence and Relationship with Anthropometric and Physiological Characteristics. Front. Physiol. 2019, 10, 2737. [Google Scholar] [CrossRef]
  47. Malina, R.M.; Bouchard, C. Growth, Maturation, and Physical Activity. JSAMS 1992, 24, 841. [Google Scholar] [CrossRef]
  48. Pedraja-Chaparro, F.; Santín, D.; Simancas, R. Determinants of grade retention in France and Spain: Does birth month matter? J. Policy Model. 2015, 37, 820–834. [Google Scholar] [CrossRef]
  49. Yanci, J.; García Huerta, A.; Castillo Alvira, D.; Rivero Benito, L.A.; Los Arcos Larumbe, A. Evaluación y relación entre distintos parámetros de condición física en futbolistas semi profesionales (Evaluation and relationship among different fitness parameters in semi professional soccer players). Retos 2014, 26, 114–117. [Google Scholar] [CrossRef]
  50. 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]
  51. Meeusen, R. Exercise, nutrition and the brain. Sports Med. 2014, 44 (Suppl. S1), S47–S56. [Google Scholar] [CrossRef]
  52. Lambourne, K.; Hansen, D.M.; Szabo, A.N.; Lee, J.; Herrmann, S.D.; Donnelly, J.E. Indirect and direct relations between aerobic fitness, physical activity, and academic achievement in elementary school students. Ment. Health Phys. Act. 2013, 6, 165–171. [Google Scholar] [CrossRef] [PubMed]
  53. Donnelly, J.E.; Hillman, C.H.; Castelli, D.; Etnier, J.L.; Lee, S.; Tomporowski, P.; Lambourne, K.; 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] [PubMed]
Behavsci 14 00181 g001
Figure 1. ANCOVA analysis shows the differences between the quarters of birth in the physical condition variables after adjusting for weight and height. Note. Q1 (January to April); Q2 (May to August); Q3 (September to December). $ indicates that it is significant between genders in the same quarter; * indicates that it is significant vs. Q2 in the same gender; # indicates that it is significant vs. Q3 in the same gender.
Figure 1. ANCOVA analysis shows the differences between the quarters of birth in the physical condition variables after adjusting for weight and height. Note. Q1 (January to April); Q2 (May to August); Q3 (September to December). $ indicates that it is significant between genders in the same quarter; * indicates that it is significant vs. Q2 in the same gender; # indicates that it is significant vs. Q3 in the same gender.
Behavsci 14 00181 g001
Behavsci 14 00181 g002
Figure 2. Pearson correlation analysis illustrates the interrelationships among physical condition variables. Individual data points indicate the correlations across different subjects, adjusted for weight and height. The solid line signifies the central tendency, while the dashed lines represent confidence intervals.
Figure 2. Pearson correlation analysis illustrates the interrelationships among physical condition variables. Individual data points indicate the correlations across different subjects, adjusted for weight and height. The solid line signifies the central tendency, while the dashed lines represent confidence intervals.
Behavsci 14 00181 g002
Behavsci 14 00181 g003
Figure 3. Pearson correlation analysis illustrates the interrelationships among various academic performance measures. Individual data points indicate the correlations across different subjects, adjusted for weight and height. The solid line signifies the central tendency, while the dashed lines represent confidence intervals.
Figure 3. Pearson correlation analysis illustrates the interrelationships among various academic performance measures. Individual data points indicate the correlations across different subjects, adjusted for weight and height. The solid line signifies the central tendency, while the dashed lines represent confidence intervals.
Behavsci 14 00181 g003
Table 1. Physical condition variables.
Table 1. Physical condition variables.
VariableProcedureAuthor
Explosive powerThe participants performed a horizontal jump; to do so, they stood behind the starting line with their feet shoulder-width apart and bent their knees to an angle of ~120°. To measure the distance jumped, a tape measure oriented perpendicular to the starting line and fixed to the ground was used. The maximum distance in centimeters (cm) achieved by the subject was taken into account.Maulder and Cronin [30]
Cardiorespiratory capacityThe participants performed the Cooper test on an athletic track, which consisted of running the greatest distance possible in a time of 12 min. The distance traveled in meters (m) was taken into account.Cooper [31]
SpeedThe participants performed the 50 m straight-line sprint test on an athletic track. To measure the time taken, a radar gun placed on a tripod 10 m behind the subject and at a height of ~80 cm was used. The minimum time in seconds (s) taken by the subject was considered.Di Prampero, Fusi [32]
FlexibilityThe participants performed the trunk anterior flexion test (sit-and-reach test). To do this, they took off their shoes and sat on the floor with their legs fully extended and the soles of their feet resting against the box. Then, participants clasped their hands over each other and slowly leaned and stretched forward as far as possible, sliding their fingers along the top of the box and holding the final position for 2–3 s. The maximum distance in cm achieved by the subject was taken into account.Mayorga-Vega, Merino-Marban [33]
Muscle strengthThe participants performed a throw with a 3 kg medicine ball. To do this, they stood behind the starting line, took the ball with both hands, carried it back to gain momentum, and threw it at an angle of ~45° as far as possible. Chalk was used to mark the throw on the ground. The maximum distance in cm achieved by the subject was taken into account.Gabbett, Georgieff [34]
Table 2. Characteristics of the study sample by quarters.
Table 2. Characteristics of the study sample by quarters.
VariablesQ1 (n = 27)Q2 (n = 25)Q3 (n = 27)
Demographics
 Age13.72 ± 0.1513.48 ± 0.2313.18 ± 0.11
 Weight (kg)55.16 ± 7.8753.89 ± 9.4154.01 ± 8.78
 Height (cm)160.62 ± 5.11158.87 ± 7.89155.89 ± 6.58
Physical condition
 Explosive power (Horizontal jump) (cm)1.71 ± 0.24 *#1.49 ± 0.101.40 ± 0.19
 Cardiorespiratory capacity (Cooper) (m)2255.93 ± 361.67 #2043.44 ± 284.652004.26 ± 435.87
 Speed (Sprint 50 m) (s)8.59 ± 0.70 #9.06 ± 0.769.08 ± 0.87
 Flexibility (Sit-and-reach) (cm)27.37 ± 10.1023.84 ± 8.4822.37 ± 11.81
 Muscle Strength (Medicine ball) (cm)5.02 ± 0.97 #4.54 ± 0.674.20 ± 0.76
Academics performance
 Multilingual competence6.84 ± 1.536.13 ± 1.805.99 ± 1.95
 Mathematical competence7.80 ± 1.357.08 ± 2.036.87 ± 1.58
 Consciousness competence6.67 ± 1.985.81 ± 2.226.02 ± 1.84
 Citizenship competence7.37 ± 1.336.40 ± 1.756.93 ± 1.55
Note. Data are presented as means and ± standard deviations. Q1 (January to April); Q2 (May to August); Q3 (September to December). * Indicates that it is significant vs. Q2. # Indicates that it is significant vs. Q3.
Table 3. Characteristics of the study sample by gender and quarters (n = 79).
Table 3. Characteristics of the study sample by gender and quarters (n = 79).
Q1 (n = 27)Q2 (n = 25)Q3 (n = 27)
VariablesMales (n = 13)Females (n = 14)Males (n = 12)Females (n = 13)Males (n = 13)Females (n = 14)
Demographics
 Age13.79 ± 0.4113.51 ± 0.0913.41 ± 0.2413.24 ± 0.1913.12 ± 0.2313.09 ± 0.22
 Weight (kg)58.28 ± 5.8753.24 ± 6.1755.48 ± 4.1751.19 ± 3.8753.28 ± 6.8750.98 ± 4.87
 Height (cm)159.89 ± 6.87162.58 ± 5.24157.89 ± 5.98159.68 ± 6.58156.58 ± 4.21155.65 ± 5.87
Physical condition
 Explosive power (cm)1.86 ± 0.19 $*#1.58 ± 0.21 #1.52 ± 0.121.47 ± 0.071.46 ± 0.221.34 ± 0.15
 Cardiorespiratory (m)2385 ± 2992135 ± 3822061 ± 5342027 ± 2522134 ± 5341883 ± 289
 Speed (s)8.19 ± 0.38 $*8.95 ± 0.739.01 ± 0.849.10 ± 0.728.57 ± 0.74 $9.55 ± 0.72
 Flexibility (cm)23.15 ± 11.65 #31.29 ± 6.66 $19.00 ± 5.9428.31 ± 8.15 $14.92 ± 6.6529.29 ± 11.46 $
 Muscle strength (cm)5.59 ± 0.86 $#4.49 ± 0.764.84 ± 0.75 $4.26 ± 0.454.44 ± 0.763.98 ± 0.72
Academics performance
 Multilingual6.82 ± 1.716.86 ± 1.406.08 ± 1.816.18 ± 1.865.49 ± 2.016.45 ± 1.83 $
 Math7.54 ± 1.278.04 ± 1.42 $6.42 ± 2.367.69 ± 1.51 $6.15 ± 1.527.54 ± 1.38 $
 Awareness6.64 ± 2.086.69 ± 1.965.47 ± 2.276.13 ± 2.22 $5.54 ± 1.866.48 ± 1.77 $
 Citizenship7.35 ± 1.457.39 ± 1.266.17 ± 1.486.62 ± 2.016.65 ± 1.617.18 ± 1.50 $
Note. Data are presented as means and ± standard deviations. Differences between gender and quarter are examined through independent samples (ANCOVA) after adjusting for weight and height. Q1 (January to April); Q2 (May to August); Q3 (September to December). $ indicates that it is significant between genders in the same quarter; * indicates that it is significant vs. Q2 in the same gender; # indicates that it is significant vs. Q3 in the same gender.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Fernández-Galván, L.M.; Belando-Pedreño, N.; Yañez-Araque, B.; Sánchez-Infante, J. Influence of Relative Age on Physical Condition and Academic Performance in Adolescents. Behav. Sci. 2024, 14, 181. https://doi.org/10.3390/bs14030181

AMA Style

Fernández-Galván LM, Belando-Pedreño N, Yañez-Araque B, Sánchez-Infante J. Influence of Relative Age on Physical Condition and Academic Performance in Adolescents. Behavioral Sciences. 2024; 14(3):181. https://doi.org/10.3390/bs14030181

Chicago/Turabian Style

Fernández-Galván, Luis Miguel, Noelia Belando-Pedreño, Benito Yañez-Araque, and Jorge Sánchez-Infante. 2024. "Influence of Relative Age on Physical Condition and Academic Performance in Adolescents" Behavioral Sciences 14, no. 3: 181. https://doi.org/10.3390/bs14030181

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

Article Metrics

Back to TopTop