FREE SHIPPING over $40
OVER 10000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
BMC psychology2026; 14(1); 843; doi: 10.1186/s40359-026-04596-1

Riding toward better math: executive functions mediate the link between horseback activities and children’s mathematical achievement.

Abstract: Research directly examining the cognitive and academic correlates of horseback-riding in childhood remains limited. This study examined the relationships among monthly horseback-riding hours, executive function (EF), and mathematics achievement in children aged 7–9 years. A sample of 262 participants from Saudi Arabia was assessed using parent interviews and official educational records, with data collected from sports clubs and horse stables. EF was modeled as a latent construct—including organization and planning, working memory, emotion regulation, self-control, and motivation—`using structural equation modeling in R (lavaan 0.6–12). Model fit was excellent (CFI ≥ 0.98, TLI ≥ 0.98, RMSEA ≤ 0.05, SRMR ≤ 0.03), and multivariate normality was supported by nonsignificant Mardia’s coefficients. In the direct-effects model, horseback-riding hours ( = 0.175,  = 0.004) and age ( = 0.156,  = 0.011) significantly predicted mathematics scores. When EF was introduced as a mediator, both predictors significantly predicted EF—which, in turn, strongly predicted mathematics achievement ( = 0.958,  < 0.001); however, the direct effects became nonsignificant. The strongest EF loadings were observed for organization and planning (≈ 0.88) and self-control (≈ 0.85). In the final full-mediation model, horseback-riding hours ( = 0.170,  = 0.007) and age ( = 0.151,  = 0.017) influenced mathematics exclusively through EF ( = 0.971,  < 0.001). The results show that EF statistically mediates the association between horseback-riding hours and mathematics achievement in the present model, highlighting the potential relevance of cognitively rich physical activities for academic outcomes.
Get Full Text
Publication Date: 2026-04-25 PubMed ID: 42035204PubMed Central: PMC13248452DOI: 10.1186/s40359-026-04596-1Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

Overview

  • This study investigated how horseback-riding relates to children’s math achievement and the role executive functions (EF) play in this relationship.
  • The research found that time spent horseback-riding is linked to better math scores through improvements in executive functions such as planning, self-control, and working memory.

Research Background and Purpose

  • There has been limited research directly connecting horseback-riding activities in childhood with cognitive and academic outcomes.
  • The study aimed to explore whether the amount of horseback-riding children engage in monthly predicts their executive functions and, in turn, their mathematics achievement.
  • Executive functions (EF) refer to mental processes including organization, planning, working memory, emotion regulation, self-control, and motivation that support goal-directed actions.

Participants and Methods

  • Sample: 262 children aged 7–9 years from Saudi Arabia participated in the study.
  • Data Collection: Information was gathered via parent interviews and official academic records, as well as from sports clubs and horse stables to verify horseback-riding hours.
  • Statistical Approach: Structural equation modeling (SEM) was conducted using the lavaan package in R to analyze relationships among horseback-riding, EF, age, and math achievement.
  • EF was treated as a latent construct measured by multiple observed indicators related to organization, planning, working memory, emotion regulation, self-control, and motivation.

Key Statistical Findings

  • Model Fit: The SEM model showed excellent fit indices (CFI and TLI ≥ 0.98, RMSEA ≤ 0.05, SRMR ≤ 0.03), indicating the model accurately represented the data.
  • Initial Direct Effects: Both horseback-riding hours and age had significant positive direct effects on math achievement when EF was not included as a mediator.
  • Mediated Effects: After introducing EF as a mediator, horseback-riding hours and age significantly predicted EF, which in turn had a strong effect on math achievement.
  • Direct pathways from horseback-riding and age to math achievement became nonsignificant, suggesting full mediation by EF.
  • Strongest EF Indicators: Organization and planning (~0.88 factor loading) and self-control (~0.85 factor loading) showed the strongest associations with the EF latent variable.

Interpretation and Implications

  • Executive functions fully mediate the relationship between horseback-riding time and children’s mathematics achievement.
  • This suggests that horseback-riding contributes to improving cognitive processes critical for academic success rather than directly influencing math skills.
  • Physically and cognitively engaging activities like horseback-riding may enhance underlying cognitive abilities (EF), which then support better performance in subjects like mathematics.
  • The findings highlight the potential educational benefits of integrating cognitively rich physical activities into children’s routines.

Conclusion

  • Engagement in horseback-riding is positively linked to children’s math achievement via improvements in executive functions.
  • Educational and developmental programs might consider supporting participation in horseback-riding or similar activities as a means to foster cognitive functions related to learning.
  • Future research could explore other physical activities and their mediated effects on different academic or cognitive outcomes.

Cite This Article

APA
Almutairi S. (2026). Riding toward better math: executive functions mediate the link between horseback activities and children’s mathematical achievement. BMC Psychol, 14(1), 843. https://doi.org/10.1186/s40359-026-04596-1

Publication

BMC psychology
ISSN: 2050-7283
NlmUniqueID: 101627676
Country: England
Language: English
Volume: 14
Issue: 1
PII: 843

Researcher Affiliations

Almutairi, Seham
  • Department of Psychology, College of Education, King Saud University, Riyadh, Saudi Arabia. sealmutairi@ksu.edu.sa.

Conflict of Interest Statement

Declarations. Ethics approval and consent to participate: Ethical approval was obtained from the Scientific Research and Publication Ethics Committee of King Saud University (No. KSU-HE-25-997). Voluntary informed consent was obtained from all participants. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

References

This article includes 42 references
  1. World Health Organization. WHO guidelines on physical activity and sedentary behaviour. Geneva: World Health Organization; 2020.
    pubmed: 33369898
  2. Donnelly JE, Hillman CH, Castelli D, Etnier JL, Lee S, Tomporowski P. Physical activity, fitness, cognitive function, and academic achievement in children: A systematic review. Med Sci Sports Exerc 2016;48:1197–222.
    doi: 10.1249/MSS.0000000000000901pmc: PMC4874515pubmed: 27182986google scholar: lookup
  3. Poitras VJ, Gray CE, Borghese MM, Carson V, Chaput JP, Janssen I. Systematic review of the relationships between objectively measured physical activity and health indicators in school-aged children and youth. Appl Physiol Nutr Metab 2016;41:S197–239.
    doi: 10.1139/apnm-2015-0663pubmed: 27306431google scholar: lookup
  4. Hillman CH, Erickson KI, Kramer AF. Be smart, exercise your heart: exercise effects on brain and cognition. Nat Rev Neurosci 2008;9:58–65.
    doi: 10.1038/nrn2298pubmed: 18094706google scholar: lookup
  5. Best JR. Effects of physical activity on children’s executive function: Contributions of experimental research on aerobic exercise. Dev Rev 2010;30:331–51.
    doi: 10.1016/J.DR.2010.08.001pmc: PMC3147174pubmed: 21818169google scholar: lookup
  6. Diamond A. Executive functions. Ann Rev Psychol 2013;64:135–68.
    doi: 10.1146/annurev-psych-113011-143750pmc: PMC4084861pubmed: 23020641google scholar: lookup
  7. Sember V, Jurak G, Kovač M, Morrison SA, Starc G. Children’s Physical Activity, Academic Performance, and Cognitive Functioning: A Systematic Review and Meta-Analysis. Front Public Health 2020;8:536635.
    doi: 10.3389/FPUBH.2020.00307/BIBTEXpmc: PMC7372103pubmed: 32760689google scholar: lookup
  8. Miyake A, Friedman NP, Emerson MJ, Witzki AH, Howerter A, Wager TD. The Unity and Diversity of Executive Functions and Their Contributions to Complex Frontal Lobe Tasks: A Latent Variable Analysis. Cogn Psychol 2000;41:49–100.
    doi: 10.1006/COGP.1999.0734pubmed: 10945922google scholar: lookup
  9. Best JR, Miller PH, Naglieri JA. Relations between executive function and academic achievement from ages 5 to 17 in a large, representative national sample. Learn Individ Differ 2011;21:327–36.
    doi: 10.1016/j.lindif.2011.01.007pmc: PMC3155246pubmed: 21845021google scholar: lookup
  10. Vazou S, Pesce C, Lakes K, Smiley-Oyen A. More than one road leads to Rome: A narrative review and meta-analysis of physical activity intervention effects on cognition in youth. Int J Sport Exerc Psychol 2019;17:153–78.
    doi: 10.1080/1612197X.2016.1223423pmc: PMC6615761pubmed: 31289454google scholar: lookup
  11. Angevaren M, Vanhees L, Wendel-Vos W, Verhaar HJJ, Aufdernkarnpe G, Aleman A. Intensity, but not duration, of physical activities is related to cognitive function. Eur J Cardiovasc Prev rehabilitation 2007;14:825–30.
    doi: 10.1097/HJR.0b013e3282ef995bpubmed: 18043306google scholar: lookup
  12. Peng P, Namkung J, Barnes M, Sun C. A meta-analysis of mathematics and working memory: Moderating effects of working memory domain, type of mathematics skill, and sample characteristics. J Educ Psychol 2016;108:455–73.
    doi: 10.1037/EDU0000079google scholar: lookup
  13. Pascual AC, Moyano N, Robres AQ. The relationship between executive functions and academic performance in primary education: Review and meta-analysis. Front Psychol 2019.
    doi: 10.3389/fpsyg.2019.01582pmc: PMC6638196pubmed: 31354585google scholar: lookup
  14. Yeniad N, Malda M, Mesman J, Van Ijzendoorn MH, Pieper S. Shifting ability predicts math and reading performance in children: A meta-analytical study. Learn Individ Differ 2013;23:1–9.
    doi: 10.1016/J.LINDIF.2012.10.004google scholar: lookup
  15. Charitaki G, Soulis SG, Alevriadou A. Early numeracy skills in children with intellectual disabilities between age groups 4–7 years: developmental patterns and interrelationships. Int J Dev Disabil 2025;71:306–20.
    doi: 10.1080/20473869.2023.2232555pmc: PMC11843637pubmed: 39990084google scholar: lookup
  16. Charitaki G, Alevriadou A, Soulis SG. Early numeracy profiles in young children with intellectual disabilities: The role of cognitive functions.. J Intellect Disabil 2024;28:48–66.
    doi: 10.1177/17446295221117021pubmed: 35917110google scholar: lookup
  17. Bull R, Lee K. Executive functioning and mathematics achievement.. Child Dev Perspect 2014;8:36–41.
    doi: 10.1111/CDEP.12059google scholar: lookup
  18. Menon V. Working memory in children’s math learning and its disruption in dyscalculia.. Curr Opin Behav Sci 2016;10:125–32.
    doi: 10.1016/J.COBEHA.2016.05.014pmc: PMC10785441pubmed: 38223497google scholar: lookup
  19. Van der Ven SHG, Kroesbergen EH, Boom J, Leseman PPM. The development of executive functions and early mathematics: A dynamic relationship.. Br J Educ Psychol 2012;82:100–19.
    doi: 10.1111/J.2044-8279pubmed: 22429060google scholar: lookup
  20. Fuhs MW, Nesbitt KT, Farran DC, Dong N. Longitudinal associations between executive functioning and academic skills across content areas.. Dev Psychol 2014;50:1698–709.
    doi: 10.1037/A0036633pubmed: 24749550google scholar: lookup
  21. Diamond A, Lee K. Interventions shown to aid executive function development in children 4 to 12 years old.. Sci (1979) 2011;333:959–64.
    doi: 10.1126/SCIENCE.1204529/SUPPL_FILE/DIAMOND.SOM.REV2.PDFpmc: PMC3159917pubmed: 21852486google scholar: lookup
  22. Cragg L, Gilmore C. Skills underlying mathematics: The role of executive function in the development of mathematics proficiency.. Trends Neurosci Educ 2014;3:63–8.
    doi: 10.1016/j.tine.2013.12.001google scholar: lookup
  23. Spiller J, Clayton S, Cragg L, Johnson S, Simms V, Gilmore C. Higher level domain specific skills in mathematics; The relationship between algebra, geometry, executive function skills and mathematics achievement.. PLoS ONE 2023;18:e0291796.
    doi: 10.1371/JOURNAL.PONE.0291796pmc: PMC10627446pubmed: 37931003google scholar: lookup
  24. Qi Y, Chen Y, Yu X, Yang X, He X, Ma X. The relationships among working memory, inhibitory control, and mathematical skills in primary school children: Analogical reasoning matters.. Cogn Dev 2024;70:101437.
    doi: 10.1016/J.COGDEV.2024.101437google scholar: lookup
  25. Clayton HM, Hobbs SJ. The role of biomechanical analysis of horse and rider in equitation science.. Appl Anim Behav Sci 2017;190:123–32.
    doi: 10.1016/j.applanim.2017.02.011google scholar: lookup
  26. Bass MM, Duchowny CA, Llabre MM. The effect of therapeutic horseback riding on social functioning in children with autism.. J Autism Dev Disord 2009;39:1261–7.
    doi: 10.1007/S10803-009-0734-3/METRICSpubmed: 19350376google scholar: lookup
  27. Kaiser L, Smith KA, Heleski CR, Spence LJ. Effects of a therapeutic riding program on at-risk and special education children.. J Am Vet Med Assoc 2006;228:46–52.
    doi: 10.2460/JAVMA.228.1.46pubmed: 16426165google scholar: lookup
  28. Bachi K, Terkel J, Teichman M. Equine-facilitated psychotherapy for at-risk adolescents: The influence on self-image, self-control and trust.. Clin Child Psychol Psychiatry 2012;17:298–312.
    doi: 10.1177/1359104511404177pubmed: 21757481google scholar: lookup
  29. Smith-Osborne A, Selby A. Implications of the literature on equine-assisted activities for use as a complementary intervention in social work practice with children and adolescents.. Child Adolesc Soc Work J 2010;27:291–307.
    doi: 10.1007/S10560-010-0201-1/METRICSgoogle scholar: lookup
  30. Haverkamp BF, Wiersma R, Vertessen K, van Ewijk H, Oosterlaan J, Hartman E. Effects of physical activity interventions on cognitive outcomes and academic performance in adolescents and young adults: A meta-analysis.. J Sports Sci 2020;38:2637–60.
    doi: 10.1080/02640414.2020.1794763pubmed: 32783695google scholar: lookup
  31. James J, Pringle A, Mourton S, Roscoe CMP. The Effects of Physical Activity on Academic Performance in School-Aged Children: A Systematic Review. Children 2023;10:1019.
    doi: 10.3390/CHILDREN10061019/S1pmc: PMC10297707pubmed: 37371251google scholar: lookup
  32. Uljarević M, Cai RY, Hardan AY, Frazier TW. Development and validation of the Executive Functioning Scale. Front Psychiatry 2023;13:1078211.
    doi: 10.3389/FPSYT.2022.1078211/BIBTEXpmc: PMC9872120pubmed: 36704728google scholar: lookup
  33. World Health Organization. Global scales for early development v1.0: adaptation and translation guide. World Health Organization 2023.
  34. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural equation modeling 1999;6(1).
  35. Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika 1973;38:1–10.
    doi: 10.1007/bf02291170google scholar: lookup
  36. Akaike H. A New Look at the Statistical Model Identification. IEEE Trans Automat Contr 1974;19:716–23.
    doi: 10.1109/TAC.1974.1100705google scholar: lookup
  37. Yao YC. Estimating the number of change-points via Schwarz’ criterion. Stat Probab Lett 1988;6:181–9.
    doi: 10.1016/0167-7152(88)90118-6google scholar: lookup
  38. Sclove SL. Application of model-selection criteria to some problems in multivariate analysis. Psychometrika 1987;52:333–43.
    doi: 10.1007/BF02294360/METRICSgoogle scholar: lookup
  39. oBonett DG, Woodward JA, Randall RL. Estimating p-values for Mardia’s coefficients of multivariate skewness and kurtosis. Comput Stat 2002;17:117–22.
    doi: 10.1007/s001800200094google scholar: lookup
  40. Studies R, Mardia KV. Applications of Some Measures of Multivariate Skewness and Kurtosis in Testing Normality. Sankhyā: Indian J Stat Ser B 1974;36:115–28.
  41. Toplak ME, West RF, Stanovich KE. Practitioner review: do performance-based measures and ratings of executive function assess the same construct?. J Child Psychol Psychiatry 2013;54:131–43.
    doi: 10.1111/jcpp.12001pubmed: 23057693google scholar: lookup
  42. Barkley RA. Executive functions: what they are, how they work, and why they evolved. .

Citations

This article has been cited 0 times.
Subscribe to our newsletter
Join 106,245 horse owners like you who receive our email updates on horse health and nutrition.