Which Indices of Cardiorespiratory Fitness Are More Strongly Associated with Brain Health in Children with Overweight/Obesity?

Eero A Haapala; David R Lubans; Timo Jaakkola; Alan R Barker; Abel Plaza-Florido; Luis Gracia-Marco; Patricio Solis-Urra; Cristina Cadenas-Sanchez; Irene Esteban-Cornejo; Francisco B Ortega

doi:10.1111/sms.14549

. Author manuscript; available in PMC: 2025 Oct 25.

Published in final edited form as: Scand J Med Sci Sports. 2023 Dec 13;34(1):e14549. doi: 10.1111/sms.14549

Which Indices of Cardiorespiratory Fitness Are More Strongly Associated with Brain Health in Children with Overweight/Obesity?

Eero A Haapala ^a,^b, David R Lubans ^a,^c, Timo Jaakkola ^a, Alan R Barker ^d, Abel Plaza-Florido ^e,^f, Luis Gracia-Marco ^e,ⁱ, Patricio Solis-Urra ^e,^g,^h, Cristina Cadenas-Sanchez ^e,ⁱ, Irene Esteban-Cornejo ^e,^i,^j, Francisco B Ortega ^e,^i,^a

^aFaculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland

^bInstitute of Biomedicine, School of Medicine, University of Eastern Finland, Kuopio, Finland

^cCentre for Active Living and Learning, School of Education, The University of Newcastle, Callaghan, Australia; Active Living Research Program, Hunter Medical Research Institute, New Lambton Heights, Australia

^dChildren’s Health and Exercise Research Centre, Public Health and Sports Sciences, University of Exeter, Exeter, UK

^eDepartment of Physical Education and Sports, Faculty of Sport Sciences, Sport and Health University Research Institute (iMUDS), University of Granada, Granada, Spain

^fPediatric Exercise and Genomics Research Center, Department of Pediatrics, School of Medicine, University of California at Irvine, Irvine, CA, USA

^gNuclear Medicine Services, “Virgen de Las Nieves”, University Hospital, Granada, Spain

^hFaculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar, Chile

ⁱCIBER de Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, Granada, Spain

^jInstituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain.

AUTHORS’ CONTRIBUTIONS

EAH conceptualised the work, performed statistical analyses, and drafted the first version of the work; AP-F, LG-M, PS-U, CC-S, IE-C collected the data and contributed and revised the intellectual content of the work; DRL, TJ, ARB conceptualised the work and contributed and revised the intellectual content of the work FBO conceptualised the work and contributed and revised the intellectual content of the work and will act as guarantor for the paper; All authors have read and approved the final version of the manuscript, and agree with the order of presentation of the authors.

^✉

Correspondence: Dr. Eero A. Haapala, PhD, Sports & Exercise Medicine, Faculty of Sport and Health Sciences, University of Jyväskylä, Jyväskylä, Finland, eero.a.haapala@jyu.fi, Prof. Francisco B. Ortega, PhD, Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, Granada, Spain. ortegaf@ugr.es

PMCID: PMC12551997 NIHMSID: NIHMS2115068 PMID: 38093459

Abstract

Purpose:

To compare the strength of associations between different indices of cardiorespiratory fitness (CRF) and brain health outcomes in children with overweight/obesity.

Methods:

Participants were 100 children aged 8–11 years. CRF was assessed using treadmill exercise test (peak oxygen uptake [ $\dot{V} O_{2 p e a k}$ ], treadmill time, and $\dot{V} O_{2}$ at ventilatory threshold) and 20-metre shuttle run test (20mSRT, laps, running speed, estimated $\dot{V} O_{2 p e a k}$ using the equations by Léger et al., Mahar et al., and Matsuzaka et al.). Intelligence, executive functions, and academic performance were assessed using validated methods. Total grey matter and hippocampal volumes were assessed using structural MRI.

Results:

$\dot{V} O_{2 p e a k}$ /body mass (β=0.18, 95% CI=0.01 to 0.35) and treadmill time (β=0.18 to 0.21, 95% CI=0.01 to 0.39) were positively associated with grey matter volume. 20mSRT laps were positively associated with executive functions (β=0.255, 95% CI=0.089 to 0.421) and academic performance (β=0.199 to 0.255, 95% CI=0.006 to 0.421), and the running speed was positively associated with executive functions (β=0.203, 95% CI=0.039 to 0.367). Estimated $\dot{V} O_{2 p e a k / Léger et al .}$ was positively associated with intelligence, executive functions, academic performance, and grey matter volume (β=0.205 to 0.282, 95% CI=0.013 to 0.500). Estimated $\dot{V} O_{2 p e a k / Mahar et al .}$ and $\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$ were positively associated with executive functions (β=0.204 to 0.256, 95% CI=0.031 to 0.436).

Conclusion:

Although $\dot{V} O_{2 p e a k}$ is considered the gold-standard indicator of CRF in children, peak performance (laps or running speed) and estimated $\dot{V} O_{2 p e a k / Léger et al .}$ derived from 20mSRT had stronger and more consistent associations with brain health outcomes than other indices of CRF in children with overweight/obesity.

Keywords: Child, Physical fitness, Brain, Cognition, Paediatric obesity

INTRODUCTION

Nearly 30% of children in European countries are living with overweight/obesity, and the prevalence may be increasing.¹ Childhood overweight/obesity is associated with impaired brain health.² Some evidence also suggests that higher levels of cardiorespiratory fitness (CRF) are associated with improved executive functions, academic performance, and enhanced brain characteristics, such as improved functional connectivity and increased hippocampal volume (hereafter referred to as brain health outcomes) in children and adolescents.^3,4 However, the variety of methodologies used to assess CRF^3,4 may have clouded our understanding of the importance of CRF for young people’s brain health outcomes.

Previous research has identified positive associations between CRF assessed using the 20-metre shuttle run (20mSRT) or treadmill exercise tests and brain health outcomes in youth.⁵ Alternatively, studies that have used other measures of CRF, such as peak power output in the cycle ergometer exercise test, have not observed such associations.^6–11 Moreover, CRF assessed by a 1.6 km run, but not CRF assessed by physical work capacity at a heart rate of 170, was positively associated with academic performance in a random-sample of apparently healthy children.⁷ This issue is further complicated with the different equations used to calculate $\dot{V} O_{2 p e a k}$ from indirect submaximal and maximal exercise testing.¹² For example, there are at least 15 different equations used to estimate $\dot{V} O_{2 p e a k}$ using results from the 20mSRT.¹³ The use of different estimation equations may increase the uncertainty in the associations between CRF and brain health outcomes.¹⁰

Most studies that have explored associations between directly measured $\dot{V} O_{2 p e a k}$ and brain health outcomes have used $\dot{V} O_{2 p e a k}$ normalised for body mass (BM).¹⁴ However, the ratio standard approach has been criticised in the literature because it is often invalid in removing the effect of body size on $\dot{V} O_{2 p e a k}$ ¹⁵ leading to underestimated $\dot{V} O_{2 p e a k}$ in children with overweight/obesity.¹⁵ Therefore, normalising the indicators of CRF using allometrically modelled lean body mass (LBM) has been recommended.¹⁶ Some studies suggest no association between $\dot{V} O_{2 p e a k}$ normalised for LBM and brain health outcomes,^6,17 yet others have observed such a link.¹⁸ To our knowledge, the effects of the scaling approach on the associations between directly measured $\dot{V} O_{2 p e a k}$ and brain health outcomes have yet to be comprehensively investigated in children with increased adiposity.

Compared to maximal exercise, submaximal exercise testing is better tolerated, safer and more appropriate for children with increased adiposity.¹⁹ Therefore, submaximal testing may be a more feasible approach for investigating the link between CRF and brain health outcomes in this population, but submaximal indices of CRF have been almost completely omitted in previous studies.^3,18 $\dot{V} O_{2}$ at the ventilatory threshold (VT) is widely used submaximal indicator of CRF²⁰ and could have a different association with brain health outcomes than $\dot{V} O_{2 p e a k}$ . However, to the best of our knowledge, only one study has investigated the associations between $\dot{V} O_{2}$ at the VT and brain health outcomes in youth with normal weight, showing a positive association between $\dot{V} O_{2}$ at the VT and brain health outcomes.¹⁸

While we have previously observed that laps completed in the 20-metre shuttle run test and estimated $\dot{V} O_{2 p e a k}$ are positively associated with brain health outcomes in children with overweight/obesity,^8–11 no previous study has comprehensively investigated the magnitude of the associations between several indices of CRF and a broad set of brain health outcomes. The present study contributes to the existing evidence by comparing the associations of different CRF indices with brain health outcomes, specifically focusing on 1) lab vs field-based; 2) measured vs estimated $\dot{V} O_{2 p e a k}$ ; 3) different equations to estimate $\dot{V} O_{2 p e a k}$ ; 4) different scaling approaches; 5) $\dot{V} O_{2 p e a k}$ vs peak performance; and 6) maximal vs submaximal indices. Therefore, the aim of our study is to compare the strength of associations between different indices of CRF and brain health outcomes, including intelligence, executive function, academic performance, and total grey matter and hippocampal volumes in children with overweight/obesity.

METHODS

Study design and population

We used baseline data from the ActiveBrains trial (ClinicalTrial.gov ID: NCT02295072),²¹ conducted in children aged 8 to 11 years with overweight/obesity. The recruitment occurred mainly at the paediatric units of the two main hospitals in Granada, Spain, from November 1, 2014, to June 30, 2016. The assessments in the study were carried out in three waves and over 5–6 days for different outcome measures in the following order: 1) brain health outcomes, 2) field-based physical fitness testing, 3) cardiometabolic risk factors, 4) maximal incremental treadmill test, 5) laboratory-based strength testing, questionnaires, and body composition (note that in the first wave, consisting 20% of the whole sample, body composition was assessed at the same day than the cardiometabolic risk factors). Children with any medical condition that would affect the results of the evaluations or that limit the normal capacity to do exercise were excluded. A total of 100 children (40 girls) had complete data and were included in the analyses of the present study. For the current analyses, we estimated that 97 observations were needed to observe the correlation of 0.25 at the power of 0.80 when statistical significance level was set at p<0.05. The parents or legal guardians of the children provided written informed consent to participate in the trial. The ActiveBrains project was approved by the ethics committee of the University of Granada (Reference: 848, February 2014).

Assessment of indices of cardiorespiratory fitness

Maximal indices obtained from the incremental treadmill exercise test

$\dot{V} O_{2 p e a k}$ and a treadmill time were assessed during a maximal incremental treadmill test (h/p/cosmos sports and medical gmbh, Nussdorf-Traunstein, Germany) at the Andalusian Centre of Sports Medicine. Respiratory gas exchange was analysed using a calibrated gas analyser (General Electric Corp) and the breath-by-breath data were averaged over 10 seconds. Participants walked on a treadmill at a constant speed (4.8 km/h) with a 6% slope with grade increments of 1% every minute until volitional exhaustion. We defined a maximal effort in the incremental treadmill exercise test as meeting three out of four following criteria: achieving >85% of aged-predicted maximal heart rate, a respiratory exchange ratio of ≥1.0, volitional fatigue (i.e., >8 points in the OMNI scale), and a plateau in the $\dot{V} O_{2}$ during the last two exercise work rates (<2.0 mL x kg BM⁻¹ x min⁻¹).²¹ Heart rate was measured an electrocardiogram. Before the treadmill exercise test, the OMNI scale was explained to children to ensure that they understood the meaning of each category of the scale. However, because of uncertainty about the secondary indicators of maximal effort in children,²² we performed the analysis with the complete sample that performed the incremental treadmill exercise test and provided $\dot{V} O_{2 p e a k}$ values. We also ran sensitivity analyses in the sub-sample of children that met the criteria for maximal effort.

$\dot{V} O_{2 p e a k} (L / \min)$ was ratio scaled for BM ( $\dot{V} O_{2 p e a k} mL \times kg {BM}^{- 1} \times \min^{- 1})$ and allometrically modelled BM $(\dot{V} O_{2 p e a k} mL \times kg lean {mass}^{- b} \times \min^{- 1})$ and LBM $(\dot{V} O_{2 p e a k} mL \times kg lean {mass}^{- b} \times \min^{- 1})$ . Allometric scaling of $\dot{V} O_{2 p e a k}$ was performed by the log-linear regression model²³ with BM or LBM as an independent variable and $\dot{V} O_{2 p e a k}$ as a dependent variable. $\dot{V} O_{2 p e a k}$ , BM and LBM were log-transformed, and least squares regression with the equation ln ( $\dot{V} O_{2 p e a k}) = lnY / b \ln (X)$ was used to obtain the scaling exponent b. The scaling exponent b for BM was 0.70 (95% confidence interval [CI] = 0.60 to 0.81) and for LBM 0.87 (95% CI = 0.77 to 0.98). These power function ratios removed the associations of $\dot{V} O_{2 p e a k}$ with BM (r = −0.023, 95% CI = −0.219 to 0.174, p = 0.810) and LBM (r = −0.016, 95% CI = −0.212 to 0.181, p = 0.872), suggesting the validity of scaling CRF for body size. To test if the slope of the association of BM or LBM with $\dot{V} O_{2 p e a k}$ was similar in boys and girls, we added the interaction term to the model. The interaction of sex with BM or LBM to $\dot{V} O_{2 p e a k}$ was not statistically significant (P > 0.122).

Maximal indices obtained from the 20-metre shuttle run test

Twenty metre SRT was performed at the Sport and Health University Research Institute (iMUDS), University of Granada, and supervised by experienced researchers. Participants were required to run between two lines 20-m apart while keeping pace with a pre-recorded audio. The participants performed the test individually. One researcher ran with the participant to help the keep the pace and reach maximal effort. The initial speed was 8.5 km/h, which was increased by 0.5 km/h each minute (1 min = approximately 1 stage). The CRF was recorded as completed laps and the speed at the final stage. We also estimated $\dot{V} O_{2 p e a k}$ using the equations by Léger et al.,²⁴ Mahar et al..²⁵ and Matsuzaka et al.²⁶ to assess whether using different equations to estimate $\dot{V} O_{2 p e a k}$ can be used interchangeably to investigate the associations between estimated CRF and brain health outcomes. The equation by Léger et al.²⁴ is the most widely used equation to estimate $\dot{V} O_{2 p e a k}$ in youth, allowing comparisons between previous studies. Furthermore, the equations by Mahar et al.²⁵ and Matsuzaka et al.²⁶ have been suggested to provide better prediction accuracy of $\dot{V} O_{2 p e a k}$ than other equations.^13,25

Submaximal indices obtained from the incremental treadmill exercise test

$\dot{V} O_{2}$ at VT was determined using the data acquired during the maximal incremental treadmill test by two sports physicians. The VT was defined as a point where the increase in the ventilatory equivalent for $\dot{V} O_{2}$ occurs without an increment in the ventilatory equivalent for carbon dioxide production. The threshold was confirmed by inspecting the non-linear increase in ventilation relative to oxygen uptake. $\dot{V} O_{2}$ at VT was normalised for BM and allometrically modelled LBM using the $\dot{V} O_{2 p e a k}$ approach.¹⁸

Assessment of brain health outcomes

Brain health outcomes were assessed at the the Mind, Brain, and Behaviour Research Centre, at the University of Granada, by trained researchers. Total intelligence was assessed using the Spanish version of Kaufman Brief Intelligence test measuring verbal and non-verbal intelligence.²¹ Normal scores from verbal and non-verbal intelligence subtests were used to calculate a composite intelligence score.

Executive functions including cognitive flexibility, inhibition, and working memory were assessed using the Design Fluency Test and the Trail Making Test, a modified version of the Stroop Color-Word Test (paper-pencil version), and a modified version of the Delayed Non-Match-to-Sample computerised task, respectively. The executive function composite z score was calculated as the renormalised mean of the z scores for cognitive flexibility, inhibition, and working memory.⁸

Academic performance was assessed using the Spanish version of the Woodcock-Johnson III Tests of Achievement. Total academic performance was defined as an overall performance based on reading, mathematics, and writing.¹¹

Total grey matter volume (cm³) and hippocampal (mm³) volume were assessed by structural magnetic resonance imaging (Siemens Trio de 3T, Magnetom Trio, Siemens Medical Systems, Erlangen, Germany). All images were collected on a 3.0 Tesla Siemens Magnetom Tim Trio scanner (Siemens Medical Solutions, Erlangen, Germany) with a 32-channel head coil. High-resolution T1-weighted images were acquired using a 3D MPRAGE (magnetization-prepared rapid gradient-echo) protocol.⁹ Acquisition parameters were: repetition time (TR) = 2300 ms, echo time (TE) = 3.1 ms, inversion time (TI) = 900 ms, flip angle = 9°, field of view (FOV) = 256 × 256, acquisition matrix = 320 × 320, 208 slices, resolution = 0.8 × 0.8 × 0.8 mm, and scan duration of 6 min and 34 s.

The MRI images were analysed with FreeSurfer software version 5.3.0 (http://surfer.nmr.mgh.harvard.edu) and FMRIB’s Software Library (FSL) version 5.0.7. (FMRIB analysis group, Oxford, UK). We used the standard processing pipeline known as recon-all that has been previously described and well-validated to assess total and grey matter volume^27–29 and a semi-automated model-based subcortical segmentation tool which uses the Bayesian framework from shape and appearance models obtained from manually segmented images of hippocampal volumes described in detail previously.³⁰ Before pre-processing, we visually checked each individual image for acquisition artifacts and four children were excluded due to motion noise. In addition, outputs were visually inspected by two assessors and when an additional opinion was needed, another assessor inspected the outputs.

Assessment of body size and composition

BM (kg) and height (cm) were measured using an electronic scale (SECA861, Hamburg, Germany) and a precision stadiometer (SECA225, Hamburg, Germany), respectively. Both measurements were performed twice and averaged. Dual energy X-ray absorptiometry (DXA) was used to measure whole body fat mass (kg), body fat percentage (BF%), and LBM (kg). The Norland XR-46 (software version 3.9.6, Medical System, Inc., Fort Atkinson, Wisconsin) scanner was used in the first wave (16 participants) while the Hologic Discovery Wi (software version APEX 4.0.2, Hologic Series Discovery QDR, Bedford, Massachusetts) was used in the second and third wave (84 participants). Subsequent analyses were completed by the same researcher following recommendations from the International Society of Clinical Densitometry.³¹ These analyses were performed separately for each DXA device to eliminate the potential error associated with using two different DXA devices.

Other assessments

Somatic maturity status in terms of time to peak height velocity was calculated using the equations by Moore et al.³² The participants were classified as pre- (<−1 years), circa (−1 to 1 years), and post (>1 years) peak height velocity.

Parental education was reported as: no elementary school, elementary school, middle school, high school, and university completed. Parents responses were combined into a trichotomous variable: none, one of the parents or both had a university degree.

Statistical analyses

Statistical analyses were performed using the SPSS statistical software, version 27.0 (IBM corp. Armonk, NY, USA). All continuous variables were checked for normality by observing histograms and using the Kolmogorov-Smirnov test. The associations between the indices of CRF and brain health outcomes were investigated using linear regression analyses adjusted for sex, somatic maturity status, and parental education. These data were further adjusted for BF%. We further investigated the modifying effects of sex and BF% on the associations between indices of CRF and brain health outcomes including sex x CRF or BF% x CRF interaction term to the model. The data were reported using standardised regression coefficients and 95% confidence intervals. We considered standardised regression coefficients between 0.10–0.29, between 0.30–0.49 are medium, and ≥0.50 to describe small, medium, and large effect sizes, respectively.³³

RESULTS

Characteristics of participants

Participants’ characteristics are reported in Table 1. A total of 66 of 100 (%) children met the criteria for maximal effort in the treadmill exercise test. Specifically, 95%, 29%, 88%, and 67% of children met the criteria for heart rate, respiratory exchange ratio, volitional fatigue, and plateau in the $\dot{V} O_{2}$ , respectively. Children who did not meet the criteria for maximal effort did not differ in absolute $\dot{V} O_{2 p e a k}$ , $\dot{V} O_{2 p e a k}$ normalised for kg LBM, treadmill time, absolute or normalised $\dot{V} O_{2}$ at VT, or in maximal OMNI score from those who met the criteria (p>0.115). However, those who did not meet the criteria for maximal effort had lower peak respiratory exchange ratio (mean difference −0.05, 95% CI=−0.07 to −0.03, p<0.001), peak heart rate (mean difference −6.9, 95% CI=−11.7 to −2.1, p=0.006), and higher $\dot{V} O_{2 p e a k}$ normalised for kg BM⁻¹ (mean difference 3.0, 95% CI=1.1 to 4.9, p=0.002) than those who met the criteria.

Table 1.

Characteristics of participants.

	All	Girls	Boys

Age (years)^*	10.1 (9.2 to 11.0)	9.9 (8.9 to 10.5)	10.3 (9.3 to 11.2)
Height (cm)	144.0 (8.3)	142.8 (9.4)	144.7 (7.4)
Weight (kg)	55.8 (11.0)	54.5 (11.5)	56.7 (10.7)
Lean mass (kg)	29.2 (5.2)	27.8 (5.7)	30.1 (4.6)
Fat mass (kg)	24.5 (7.0)	24.6 (7.0)	24.4 (7.1)
Body fat percentage	43.9 (5.6)	45.3 (6.0)	43.0 (5.2)
Prevalence of overweight, %	74.0	75.0	73.3
Prevalence of obesity, %	26.0	25.0	26.7
Time to peak height velocity (years)	−2.3 (1.0)	−1.6 (1.0)	−2.7 (0.8)
Pre-peak height velocity, <-1 years (%)	90	75	100
Circa peak height velocity, −1 to 1 years (%)	10	25	0
Post peak height velocity, >1 years (%)	0	0	0
Parental education (university degree, %)
Neither parent	66	57.5	71.7
One parent	18	20.0	16.7
Both parents	16	22.5	11.7
Cardiopulmonary exercise test
Plateau in $\dot{V} O_{2}$ during incremental treadmill exercise test (%)	67	75	61.7
Peak respiratory exchange ratio	0.96 (0.06)	0.98 (0.06)	0.94 (0.05)
Peak heart rate (beats / minute)	193 (12)	198 (10)	189 (12)
OMNI score (min – Max) ^†	10 (3–10)	10 (3 to 10)	10 (4 to 10)
Proportion of children meeting the criteria for maximal effort (%)¹	66	75	60
$\dot{V} O_{2 p e a k}$ (mL/min⁻¹)	2058 (359)	1984 (351)	2108 (359)
$\dot{V} O_{2 p e a k}$ (mL x BM⁻¹ x min⁻¹)	37.4 (4.7)	36.9 (4.3)	37.7 (5.0)
$\dot{V} O_{2 p e a k}$ (mL x BM^−0.70 x min⁻¹)	123.8 (13.8)	121.4 (12.0)	125.5 (14.8)
$\dot{V} O_{2 p e a k}$ (mL x LBM^−0.87 x min⁻¹)	110.4 (9.8)	111.3 (9.3)	109.8 (10.1)
Treadmill time (min)^*	8.1 (6.4 to 10.0)	8.4 (6.6 to 9.3)	8.0 (6.3 to 11.0)
$\dot{V} O_{2}$ at VT (mL x min⁻¹)	1696 (339)	1.601 (341)	1759 (325)
V̇O2 at VT (mL x min-1) / $\dot{V} O_{2 p e a k}$ (mL/min⁻¹) (%)^*	83.8 (79.8 to 87.7)	81.6 (77.5 to 85.5)	84.7 (80.1 to 88.2)
$\dot{V} O_{2}$ at VT (mL x BM⁻¹ x min⁻¹)	30.8 (4.9)	29.8 (4.8)	31.5 (5.0)
$\dot{V} O_{2}$ at VT (mL x LBM^−0.81 x min⁻¹)^*	110.2 (105.0 to 117.0)	108.5 (104.9 to 113.7)	111.7 (105.0 to 119.7)
20-metre shuttle run test
Peak heart rate during 20 metre shuttle run test, n=98	197 (10.2)	200	195
20-m SRT laps (n)^*	14 (11 to 20.8)	12 (10 to 17)	14.5 (12.0 to 23.8)
20-m SRT speed (minutes)^*	8.5 (8.5 to 9.0)	8.5 (8.5 to 9.0)	8.8 (8.5 to 9.5)
$\dot{V} O_{2 p e a k / Léger et al .}$	40.8 (0.3)	40.7 (2.8)	40.8 (2.8)
$\dot{V} O_{2 p e a k / Mahar et al .}$	34.4 (5.1)	31.8 (4.3)	36.2 (4.9)
$\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$	34.8 (4.6)	33.2 (4.4)	35.7 (4.6)
$\dot{V} O_{2 p e a k / Matsuzaka et al . (laps)}$	29.5 (2.9)	28.8 (2.9)	29.9 (3.0)
Brain health outcomes
Total grey matter volume (cm³)	729.1 (65.0)	692.6 (57.3)	753.5 (58.3)
Hippocampal grey matter volume (mm³)	7050.2 (693.6)	6757.5 (630.1)	7245.3 (669.3)
Total intelligence score	98.0 (11.9)	99.9 (12.0)	96.7 (11.7)
Total executive functions	−0.03 (0.75)	−0.20 (0.70)	0.09 (0.77)
Total academic performance	109.1 (11.8)	109.3 (13.4)	109.0 (10.7)

Open in a new tab

The data are means and standard deviations, *medians and interquartile ranges, or † median and minimum and maximum values. P-Value for the differences between girls and boys from Student’s t-test, Mann–Whitney U-test, or χ²-test. Maximal effort was defined as meeting three out of four following criteria: achieving >85% of aged-predicted maximal heart rate, a respiratory exchange ratio of ≥1.0, volitional fatigue (i.e., >8 points in the OMNI scale) and a plateau in the oxygen uptake during the last two exercise work rates (<2.0 ml/kg/min). The $\dot{V} O_{2 p e a k}$ , peak oxygen uptake; mL, millilitre; BM, body mass; LBM, lean body mass; $\dot{V} O_{2}$ , oxygen uptake; VT, ventilatory threshold; 20-m SRT, 20-metre shuttle run test; $\dot{V} O_{2 p e a k / Léger et al .}$ , $\dot{V} O_{2 p e a k / Mahar et al .}$ , $\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$ , and $\dot{V} O_{2 p e a k / Matsuzaka et al . (laps)}$ , peak oxygen uptake estimated from the 20-metre shuttle run test using the equations by Léger et al.²⁴, Mahar et al.²⁵, and Matsuzaka et al.²⁶, respectively.

Associations of indices of cardiorespiratory fitness with brain health outcomes

The associations between indices of CRF and brain health outcomes are presented in Figure 1. $\dot{V} O_{2 p e a k}$ normalised for kg BM⁻¹ or kg BM^−0.70 and a longer treadmill time were positively associated with total grey matter volume. Brain health outcomes were not statistically significantly associated with $\dot{V} O_{2 p e a k}$ or $\dot{V} O_{2}$ at VT measured during the incremental treadmill exercise test.

Laps completed in the 20mSRT were positively associated with executive functions and academic performance. Higher speed at the final stage of the 20mSRT was associated with better executive functions. Estimated $\dot{V} O_{2 p e a k / Léger et al .}$ was positively associated with intelligence, executive functions, academic performance, and grey matter volume. Higher estimated $\dot{V} O_{2 p e a k / Mahar et al .}$ and $\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$ was positively associated with executive functions. All statistically significant associations were small in magnitude (standardised regression coefficient ranging from 0.183 to 0.256).

Most of the abovementioned associations between the indices of CRF with brain health outcomes remained materially unchanged after further adjustment for BF% (Table 2). However, the association of $\dot{V} O_{2 p e a k}$ normalised for kg BM⁻¹ or kg BM^−0.70, a treadmill time, and estimated $\dot{V} O_{2 p e a k / Léger et al .}$ with total grey matter volume were no longer statistically significant after adjustment for BF%.

Table 2.

Associations of indices of cardiorespiratory fitness with brain health outcomes.

	Intelligence	Executive functions	Academic performance	Grey matter volume	Hippocampal volume

$\dot{V} O_{2 p e a k}$ (mL x BM⁻¹ x min⁻¹)	0.019 (−0.280 to 0.241)	0.137 (−0.092 to 0.366)	0.123 (−0.137 to 0.383)	0.082 (−0.058 to 0.321)	−0.058 (−0.325 to 0.208)
$\dot{V} O_{2 p e a k}$ (mL x BM^−0.70 x min⁻¹)	0.000 (−0.238 to 0.238)	0.058 (−0.153 to 0.268)	0.086 (−0.153 to 0.324	0.175 (−0.041 to 0.392)	0.012 (−0.232 to 0.256)
$\dot{V} O_{2 p e a k}$ (mL x LBM^−0.87 x min⁻¹)	0.003 (−0.184 to 0.191)	0.091 (−0.074 to 0.256)	0.107 (−0.080 to 0.294)	0.095 (−0.077 to 0.267)	−0.022 (−0.215 to 0.170)
$\dot{V} O_{2}$ at VT (mL x BM⁻¹ x min⁻¹)	0.025 (−0.191 to 0.240)	0.130 (−0.059 to 0.320)	0.037 (−0.180 to 0.253)	0.041 (−0.158 to 0.239)	−0.153 (−0.373 to 0.066)
$\dot{V} O_{2}$ at VT (mL x LBM^−0.81 x min⁻¹)	0.032 (−0.157 to 0.220)	0.101 (−0.064 to 0.267)	0.035 (−0.154 to 0.224)	0.070 (−0.103 to 0.243)	−0.122 (−0.314 to 0.070)
Treadmill time (min)	−0.064 (−0.301 to 0.174)	0.146 (0.063 to 0.354)	0.082 (−0.157 to 0.320)	0.138 (−0.079 to 0.355)	−0.078 (−0.321 to 0.166)
20-m SRT laps	0.046 (−0.194 to 0.287)	0.364 (0.164 to 0.564)	0.295 (0.061 to 0.530)	−0.004 (−0.226 to 0.218)	0.041 (−0.206 to 0.288)
20-m SRT speed	0.104 (−0.134 to 0.342)	0.297 (0.094 to 0.500)	0.282 (0.049 to 0.515)	0.053 (−0.167 to 0.273)	0.081 (−0.163 to 0.326)
$\dot{V} O_{2 p e a k / Léger et al .}$	0.270 (0.001 to 0.539)	0.291 (0.055 to 0.527)	0.379 (0.115 to 0.644)	0.187 (0.063 to 0.437)	0.167 (−0.113 to 0.444)
$\dot{V} O_{2 p e a k / Mahar et al .}$	0.054 (−0.249 to 0.358	0.496 (0.247 to 0.745)	0.385 (0.091 to 0.680)	−0.089 (−0.368 to 0.190)	−0.008 (−0.320 to 0.303)
$\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$	0.167 (−0.140 to 0.475)	0.444 (0.186 to 0.702)	0.392 (−0.092 to 0.692)	−0.080 (−0.364 to 0.204)	−0.019 (−0.337 to 0.298)
$\dot{V} O_{2 p e a k / Matsuzaka et al . (laps)}$	0.087 (−0.193 to 0.367)	0.043 (−0.206 to 0.292)	0.035 (−0.247 to 0.317)	−0.159 (−0.415 to 0.098)	−0.137 (−0.424 to 0.150)

Open in a new tab

The data are standardised regression coefficients and their 95% confidence intervals adjusted for sex, estimated time to peak height velocity, parental education, and body fat percentage. $\dot{V} O_{2 p e a k}$ , peak oxygen uptake; mL, millilitre; BM, body mass; LBM, lean body mass; $\dot{V} O_{2}$ , oxygen uptake; VT, ventilatory threshold; 20-m SRT, 20-metre shuttle run test; $\dot{V} O_{2 p e a k / Léger et al .}$ , $\dot{V} O_{2 p e a k / Mahar et al .}$ , $\dot{V} O_{2 p e a k / Matsuzaka et al . (speed)}$ , and $\dot{V} O_{2 p e a k / Matsuzaka et al . (laps)}$ , peak oxygen uptake estimated from the 20-metre shuttle run test using the equations by Léger et al.²⁴, Mahar et al.²⁵, and Matsuzaka et al.²⁶, respectively. Statistically significant associations are bolded.

Sex and body fat percentage as moderators of the associations between indices of cardiorespiratory fitness and brain health outcomes

In girls, completed laps and final speed on the 20mSRT, and estimated $\dot{V} O_{2 p e a k / Leger et al .}$ were positively associated with academic performance with medium to large effect sizes (Supplementary table 1). In boys, these associations were statistically non-significant with small effect sizes (Supplementary table 1).

Laps in the 20mSRT were directly associated with executive functions with medium effect sizes in children with lower BF% (below median) but the associations in children with higher BF% (at or above median) were weak and statistically non-significant (Supplementary table 2). Laps and speed at the final stage in the 20mSRT had positive association with medium effect size with academic performance in children with higher BF% but the association in children with lower BF% was weak and statistically non-significant. $\dot{V} O_{2 p e a k / Leger et al .}$ was positively associated with grey matter volume with medium effect size in children with higher BF% but not in children with lower BF%.

Sensitivity analyses

The associations between the indices of CRF and brain health outcomes remained materially unchanged after excluding children who did not reach three of four criteria for maximal effort during the incremental treadmill exercise test from the analyses (Supplementary table 3).

DISCUSSION

Our main findings were that 1) directly measured $\dot{V} O_{2 p e a k}$ and $\dot{V} O_{2}$ at VT were not associated with behavioural brain health outcomes, 2) $\dot{V} O_{2 p e a k}$ estimated from 20mSRT performance using the equation by Leger et al. in 1988²⁴ had stronger and more consistent associations with brain health outcomes than other indices of CRF, and 3) 5 out of 6 indices derived from the 20mSRT were positively associated with executive functions. Collectively, our findings suggest that while CRF is positively associated with brain health in children with overweight/obesity, not all measures of CRF are associated with brain health. Furthermore, the effect sizes for all associations were considered small.

Consistent with some previous studies in children,^3,7,34 we found positive associations of CRF with brain health outcomes, particularly with executive functions. The high malleability of executive functions and their sensitivity to changes in CRF in childhood^3,35,36 may explain why we observed the most consistent associations between CRF and executive functions. Nevertheless, supporting the evidence from one previous study,⁷ we observed that peak performance in the field-based running tests is more strongly associated with behavioural brain health outcomes than laboratory-measured indices of CRF. Different determinants of peak performance and measured $\dot{V} O_{2 p e a k}$ may explain our findings. Maximal stroke volume and cardiac output are the strongest determinants of $\dot{V} O_{2 p e a k}$ , whereas a combination of $\dot{V} O_{2 p e a k}$ , body composition, agility, motivation, and self-regulation determining 20mSRT performance may contribute to its stronger associations with brain health outcomes.³⁷ However, $\dot{V} O_{2 p e a k}$ normalised for body mass has been positively associated with executive functions and academic performance in children with overweight and low levels of CRF (mean $\dot{V} O_{2 p e a k} = 27 mL \times kg {BM}^{−1} \times \min^{- 1})$ .¹⁴ While the reason for these contrasting findings is unclear, a higher directly measured $\dot{V} O_{2 p e a k}$ may contribute to brain health, particularly in children with very low CRF. Furthermore, the larger sample size may have contributed to the statistically significant associations found in the study by Davis and Cooper.¹⁴

$\dot{V} O_{2 p e a k}$ normalised for BM, treadmill time, and estimated $\dot{V} O_{2 p e a k / Léger et al .}$ were the only indices of CRF positively associated with grey matter volume. However, the associations of $\dot{V} O_{2 p e a k}$ normalised for BM and treadmill time with grey matter volume were explained by BF%. As we also observed that $\dot{V} O_{2 p e a k}$ normalised for LBM was not associated with grey matter volume, our results suggest that body adiposity is an important confounder for the associations between CRF and brain health outcomes in children with overweight/obesity. Previous studies in children and adults reporting positive associations between CRF and grey matter volume after controlling for several confounding factors partially supported our findings.^9,38,39 However, it is important to note that none of these studies considered DXA-derived BF% in their analyses. Nevertheless, peak performance in the 20mSRT quantified by laps or running speed was not associated with grey matter volume. Grey matter plays an important role in the controlling functions related to memory, emotions, and movement.^40,41 However, it also has complicated maturation-related development and associations with behavioural brain health outcomes, such as executive functions.^40,41 Although our results suggest that a phenotype with high peak performance and favourable body composition may benefit brain health, further longitudinal studies with larger sample sizes clarifying the role of different indices of CRF in the grey matter volume are warranted.

In contrast to some previous findings,^42,43 we found no associations between the different indices of CRF and hippocampal volume. Although the hippocampus is important for learning and memory functions and maybe sensitive to changes in CRF,^42,44 also some previous studies have also found weak associations between CRF and hippocampal volume.⁴² However, the reason for these weak and non-significant associations is unknown. While Aghjayan et al.⁴² speculated that these mixed findings could be related to the assessment of CRF, we showed non-significant associations between indices of CRF and hippocampal volume using a variety of CRF measures. Therefore, the association between CRF and hippocampal volume requires further clarification.

Our findings indicate that using different equations to estimate $\dot{V} O_{2 p e a k}$ from 20mSRT performance influences the associations between CRF and brain health outcomes. Therefore, our results suggest that the direct measures from the 20mSRT, such as laps or maximal running speed, are more appropriate for examining associations between CRF and brain health outcomes. Nevertheless, if any equation is to be used in relation to brain health outcomes, our findings showed that the Leger equation provides the most consistent associations with different measures of brain health. The reason for different findings in our population may be due to the different variables in each equation. Whereas the equation by Léger et al.²⁴ uses only age and maximal running speed in the equation, the equations by Mahar et al.²⁵ and Matzuzaka et al.²⁶, developed in populations including mainly children with normal weight, also include body mass index. Including a measure of adiposity in the equation may introduce a larger error in the estimation of $\dot{V} O_{2 p e a k}$ among children with overweight/obesity.

Contrary to our previous study among adolescents and young adults,¹⁸ we found no associations between $\dot{V} O_{2}$ at VT and brain health outcomes. Submaximal indices of CRF may be differently related to brain health outcomes in adolescents and young adults than in children. Moreover, it is also possible that different tasks used to assess brain health may influence findings. Nevertheless, absolute $\dot{V} O_{2}$ at VT, but not relative, has been positively associated with grey matter volume in adults.³⁹ However, the association between absolute $\dot{V} O_{2}$ at VT and grey matter volume was weaker than the associations with the maximal indices of CRF.³⁹ Our findings suggest that $\dot{V} O_{2}$ at VT is less important for brain health compared to peak performance in children.

Performance in the 20mSRT and estimated $\dot{V} O_{2 p e a k / Léger et al .}$ was positively associated with academic performance in girls but not boys. While the reason for these sex-differences is unclear, girls may have better motivation towards achieving high peak performance and academic performance than boys. Moreover, we also found that better performance in the 20mSRT was associated with better academic performance and a higher estimated $\dot{V} O_{2 p e a k / Léger et al .}$ with larger grey matter volume in children with higher BF%. These findings suggest that peak performance is more important for brain health than directly measured $\dot{V} O_{2 p e a k}$ and that the importance of peak performance is accentuated in children with high BF%. It can be speculated that peak performance may protect against obesity-induced deterioration of brain health in children by decreasing, e.g., insulin resistance and systemic low-grade inflammation and improving cerebral blood flow.^45,46 However, the 20mSRT performance was more strongly associated with executive functions in children with lower BF% than their peers with higher BF%. Executive functions refer to higher-order cognitive processes essential for goal-directed behaviour.³⁶ As such, while this association is counter-intuitive, the association may be related to reverse causality, as children with lower BF% and better executive functions may be more motivated and willing to run longer in the 20mSRT. However, this finding should be interpreted with caution because the association was not uniform across all measures derived from the 20mSRT.

The strengths of our study include the comprehensive assessment of CRF using laboratory- and field-based measures. Moreover, we also utilised a comprehensive assessment of brain health outcomes, including behavioural measures and structural brain imaging. However, some potential limitations should be acknowledged. The limitations of our study include the possibility that indices of CRF would be differently associated with other brain structures than total grey matter and hippocampal volume used in this study. However, a detailed analysis of brain structures was beyond the scope of this study. Grey matter and hippocampal volumes have been positively associated with academic performance.^9,47 We also investigated the associations of indices of CRF with brain health outcomes in a sample of children with overweight/obesity. Whether the associations would be similar among children with lower levels of adiposity or clinical conditions is unknown. Only 66% of the children met the predetermined criteria for the maximal effort in the treadmill exercise test used to assess $\dot{V} O_{2 p e a k}$ . Therefore, it is possible that all children did not achieve their true maximal cardiorespiratory capacity on this test. However, the results remained materially unchanged in the sensitivity analyses, which included only those children who met the predetermined criteria for maximal effort, suggesting that the primary analyses provided robust results. Furthermore, the sample size was relatively small particularly in the sex-stratified analyses. Finally, our study was cross-sectional, precluding any causal interpretations.

In conclusion, we found that better peak performance in the 20mSRT, expressed as laps and final speed achieved, were positively associated with executive functions and academic performance in children with overweight/obesity. Alternatively, directly measured $\dot{V} O_{2 p e a k}$ normalised for either BM or LBM had weak, if any, associations with brain health outcomes. Compared with the other equations, $\dot{V} O_{2 p e a k}$ estimated using Leger and colleagues’ equation²⁴ provided the strongest associations with brain health outcomes. We did not find any associations between brain health outcomes and sub-maximal indicators of CRF (e.g., $\dot{V} O_{2}$ at VT). Finally, our results suggest that different measures of CRF cannot be used interchangeably in studies investigating associations between CRF and brain health in children. Future longitudinal studies are warranted to investigate whether our results can be replicated. Moreover, studies investigating the mechanisms how different indices of CRF influence brain health are needed.

PERSPECTIVE

Even though previous studies have shown that higher CRF is associated with improved brain health in children, various methodologies used to assess CRF may have clouded our understanding of the importance of CRF for young people’s brain health outcomes. Thus, studies using different field tests and equations to estimate peak oxygen uptake from these tests and inappropriate scaling procedures to normalise measured peak oxygen uptake have provided evidence that children with better ability to run prolonged periods may have better brain health than other children. Our findings that peak performance measured using the 20-metre endurance shuttle run test had consistent positive associations with brain health outcomes. Moreover, these results also indicate that true CRF as a measure of cardiovascular capacity to deliver oxygen and skeletal muscle aerobic capacity may not be relevant for brain health in children and that a physical ability combining endurance, motor skills, and body composition may be beneficial for brain health. These findings suggest that while CRF is positively associated with brain health in children with overweight/obesity, not all measures of CRF are associated with brain health.

Supplementary Material

Supplementary Table 3

NIHMS2115068-supplement-Supplementary_Table_3.docx^{(17.1KB, docx)}

Supplementary Table 1

NIHMS2115068-supplement-Supplementary_Table_1.docx^{(13.8KB, docx)}

Supplementary Table 2

NIHMS2115068-supplement-Supplementary_Table_2.docx^{(14.5KB, docx)}

SUPPLEMENTAL DIGITAL CONTENT

Supplementary table 1.docx

Supplementary table 2.docx

Supplementary table 3.docx

ACKNOWLEDGEMENTS

The present study was mainly supported by grants from the Spanish Ministry of Economy and Competitiveness (DEP2013-47540, DEP2016-79512-R, and DEP2017-91544-EXP), European Regional Development Fund (ERDF), the European Commission (667302), and by the Alicia Koplowitz Foundation. Supplementary funding was obtained from the Andalusian Operational Programme supported with ERDF (FEDER in Spanish, B-CTS-355-UGR18). This study was also supported by the University of Granada, Plan Propio de Investigación, Visiting Scholar grants and Excellence actions: Units of Excellence; Unit of Excellence on Exercise, Nutrition and Health (UCEENS) and by the Junta de Andalucía, Consejería de Conocimiento, Investigación y Universidades and the ERDF (SOMM17/6107/UGR). IE-C is supported by the Spanish Ministry of Science and Innovation (RyC2019-027287-1). PS-U is supported by a grant from ANID/BECAS Chile/72180543 and through a Margarita Salas grant from the Spanish Ministry Universities. EAH was supported by the Juho Vainio Foundation. AP-F contribution was funded in part by NIH grant #: U01 TR002004 (REACH project). The study sponsors had no role in study design, the data collection, analysis, interpretation of the data, the writing of the report, or the decision to submit the manuscript for publication.

Footnotes

Conflict of interest disclosure

The authors declare that the results of this study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare the work described has not been published previously.

Ethics approval statement

The ActiveBrains project was approved by the ethics committee of the University of Granada (Reference: 848, February 2014).

Participant consent statement

The parents or legal guardians of the children provided written informed consent to participate in the trial.

Clinical trial registration

ClinicalTrial.gov ID: NCT02295072

Data availability statement

The data that support the findings of this study are available from the principal investigator, [FBO], upon reasonable request.

REFERENCES

1.European Childhood Obesity Surveillance Initiative (‎COSI)‎. Report on the Fifth Round of Data Collection, 2018–2020: Available at: https://www.who.int/europe/publications/i/item/WHO-EURO-2022-6594-46360-67071
2.Likhitweerawong N, Louthrenoo O, Boonchooduang N, Tangwijitsakul H, Srisurapanont M. Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta-analysis of longitudinal studies. Obes Rev. 2022;23(8):e13458. [DOI] [PubMed] [Google Scholar]
3.Donnelly JE, Hillman CH, Castelli D, et al. Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children: A Systematic Review. Med Sci Sports Exerc. 2016;48(6):1197–1222. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Esteban-Cornejo I, Stillman CM, Rodriguez-Ayllon M, et al. Physical fitness, hippocampal functional connectivity and academic performance in children with overweight/obesity: The ActiveBrains project. Brain Behav Immun. 2021;91:284–295. [DOI] [PubMed] [Google Scholar]
5.Marques A, Santos DA, Hillman CH, Sardinha LB. How does academic achievement relate to cardiorespiratory fitness, self-reported physical activity and objectively reported physical activity: a systematic review in children and adolescents aged 6–18 years. Br J Sports Med. 2018;52(16):1039–1039. [DOI] [PubMed] [Google Scholar]
6.Haapala EA, Lintu N, Väistö J, et al. Associations of Physical Performance and Adiposity with Cognition in Children. Med Science Sports Exerc. 2015;47(10):2166. [DOI] [PubMed] [Google Scholar]
7.Dwyer T, Sallis JF, Blizzard L, Lazarus R, Dean K. Relation of Academic Performance to Physical Activity and Fitness in Children. Pediatr Exerc Sci. 2001;13(3):225–237. [Google Scholar]
8.Mora-Gonzalez J, Esteban-Cornejo I, Cadenas-Sanchez C, et al. Physical Fitness, Physical Activity, and the Executive Function in Children with Overweight and Obesity. J Pediatr. 2019;208:50–56.e1. [DOI] [PubMed] [Google Scholar]
9.Esteban-Cornejo I, Cadenas-Sanchez C, Contreras-Rodriguez O, et al. A whole brain volumetric approach in overweight/obese children: Examining the association with different physical fitness components and academic performance. The ActiveBrains project. NeuroImage. 2017;159:346–354. [DOI] [PubMed] [Google Scholar]
10.Cadenas-Sanchez C, Migueles JH, Erickson KI, Esteban-Cornejo I, Catena A, Ortega FB. Do fitter kids have bigger brains? Scand J Med Sci Sports. 2020;30(12):2498–2502. [DOI] [PubMed] [Google Scholar]
11.Cadenas-Sanchez C, Migueles JH, Esteban-Cornejo I, et al. Fitness, physical activity and academic achievement in overweight/obese children. J Sports Sci. 2020;38(7):731–740. [DOI] [PubMed] [Google Scholar]
12.Ferrar K, Evans H, Smith A, Parfitt G, Eston R. A Systematic Review and Meta-Analysis of Submaximal Exercise-Based Equations to Predict Maximal Oxygen Uptake in Young People. Pediatr Exerc Sci. 2014;26(3):342–357. [DOI] [PubMed] [Google Scholar]
13.de Menezes FJ, de Jesus ÍC, Leite N. Predictive equations of maximum oxygen consumption by shuttle run test in children and adolescents: a systematic review. Rev Paul Pediatr. 2019;37(2):241–251. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Davis CL, Cooper S. Fitness, fatness, cognition, behavior, and academic achievement among overweight children: Do cross-sectional associations correspond to exercise trial outcomes? Prev Med. 2011;52(Suppl 1):S65–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]
15.Welsman J, Armstrong N. Interpreting Aerobic Fitness in Youth: The Fallacy of Ratio Scaling. Pediatr Exerc Sci. 2019;31(2):184–190. [DOI] [PubMed] [Google Scholar]
16.Loftin M, Sothern M, Abe T, Bonis M. Expression of VO2peak in Children and Youth, with Special Reference to Allometric Scaling. Sports Med. 2016;46(10):1451–1460. [DOI] [PubMed] [Google Scholar]
17.Raine LB, Khan NA, Drollette ES, Pontifex MB, Kramer AF, Hillman CH. Obesity, Visceral Adipose Tissue, and Cognitive Function in Childhood. J Pediatr. 2017;187:134–140.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Skog H, Lintu N, Haapala HL, Haapala EA. Associations of cardiorespiratory fitness, adiposity, and arterial stiffness with cognition in youth. Physiol Rep. 2020;8(18):e14586. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Rowland TW. Cardiopulmonary Exercise Testing in Children and Adolescents. Human Kinetics, Inc.; 2017. [Google Scholar]
20.Mahon AD, Cheatham CC. Ventilatory Threshold in Children: A Review. Pediatr Exerc Sci. 2002;14(1):16–29. [Google Scholar]
21.Cadenas-Sánchez C, Mora-González J, Migueles JH, et al. An exercise-based randomized controlled trial on brain, cognition, physical health and mental health in overweight/obese children (ActiveBrains project): Rationale, design and methods. Cont Clin Trials. 2016;47:315–324. [DOI] [PubMed] [Google Scholar]
22.Sansum KM, Weston ME, Bond B, et al. Validity of the Supramaximal Test to Verify Maximal Oxygen Uptake in Children and Adolescents. Pediatr Exerc Sci. 2019;31(2):213–222. [DOI] [PubMed] [Google Scholar]
23.Tolfrey K, Barker A, Thom JM, Morse CI, Narici MV, Batterham AM. Scaling of maximal oxygen uptake by lower leg muscle volume in boys and men. J Appl Physiol. 2006;100(6):1851–1856. [DOI] [PubMed] [Google Scholar]
24.Léger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci. 1988;6(2):93–101. [DOI] [PubMed] [Google Scholar]
25.Mahar MT, Guerieri AM, Hanna MS, Kemble CD. Estimation of Aerobic Fitness from 20-m Multistage Shuttle Run Test Performance. Am J Prev Med. 2011;41(4, Supplement 2):S117–S123. [DOI] [PubMed] [Google Scholar]
26.Matsuzaka A, Takahashi Y, Yamazoe M, et al. Validity of the Multistage 20-M Shuttle-Run Test for Japanese Children, Adolescents, and Adults. Pediatr Exerc Sci. 2004;16(2):113–125. [Google Scholar]
27.Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Nat Acad Sci USA. 2000;97(20):11050–11055. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Fischl B, Sereno MI, Dale AM. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage. 1999;9(2):195–207. [DOI] [PubMed] [Google Scholar]
29.Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage. 1999;9(2):179–194. [DOI] [PubMed] [Google Scholar]
30.Migueles JH, Cadenas-Sanchez C, Esteban-Cornejo I, et al. Associations of Objectively-Assessed Physical Activity and Sedentary Time with Hippocampal Gray Matter Volume in Children with Overweight/Obesity. J Clin Med. 2020;9(4):1080. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Crabtree NJ, Arabi A, Bachrach LK, et al. Dual-Energy X-Ray Absorptiometry Interpretation and Reporting in Children and Adolescents: The Revised 2013 ISCD Pediatric Official Positions. J Clin Densitom. 2014;17(2):225–242. [DOI] [PubMed] [Google Scholar]
32.Moore S, McKay H, Macdonald H, et al. Enhancing a Somatic Maturity Prediction Model. Med Sci Sports Exerc. 2015;47:1755–1764. [DOI] [PubMed] [Google Scholar]
33.Cohen J Statistical Power Analysis for the Behavioral Sciences. 2nd ed. L. Erlbaum Associates; 1988. [Google Scholar]
34.Van Waelvelde H, Vanden Wyngaert K, Mariën T, Baeyens D, Calders P. The relation between children’s aerobic fitness and executive functions: A systematic review. Infant Child Devel. 2020;29(3):e2163. doi: 10.1002/icd.2163 [DOI] [Google Scholar]
35.Zelazo PD, Carlson SM. Hot and Cool Executive Function in Childhood and Adolescence: Development and Plasticity. Child Devel Perspect. 2012;6(4):354–360. [Google Scholar]
36.Diamond A Executive Functions. Ann Rev Psychol. 2013;64(1):135–168. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Armstrong N, Welsman J. Clarity and Confusion in the Development of Youth Aerobic Fitness. Front Physiol. 2019;10. doi: 10.3389/fphys.2019.00979 [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Ruotsalainen I, Renvall V, Gorbach T, et al. Aerobic fitness, but not physical activity, is associated with grey matter volume in adolescents. Behav Brain Res. 2019;362:122–130. [DOI] [PubMed] [Google Scholar]
39.Wittfeld K, Jochem C, Dörr M, et al. Cardiorespiratory Fitness and Gray Matter Volume in the Temporal, Frontal, and Cerebellar Regions in the General Population. Mayo Clin Proc. 2020;95(1):44–56. [DOI] [PubMed] [Google Scholar]
40.Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30(6):718–729. [DOI] [PubMed] [Google Scholar]
41.Walhovd KB, Tamnes CK, Fjell AM. Brain structural maturation and the foundations of cognitive behavioral development. Curr Opin Neurol. 2014;27(2):176–184. [DOI] [PubMed] [Google Scholar]
42.Aghjayan SL, Lesnovskaya A, Esteban-Cornejo I, Peven JC, Stillman CM, Erickson KI. Aerobic exercise, cardiorespiratory fitness, and the human hippocampus. Hippocampus. 2021;31(8):817–844. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Valkenborghs SR, Noetel M, Hillman CH, et al. The Impact of Physical Activity on Brain Structure and Function in Youth: A Systematic Review. Pediatrics. 2019;144(4):e20184032. [DOI] [PubMed] [Google Scholar]
44.Knierim JJ. The hippocampus. Curr Biol. 2015;25(23):R1116–R1121. [DOI] [PubMed] [Google Scholar]
45.Haapala EA, Kuronen E, Ihalainen JK, et al. Cross-sectional associations between physical fitness and biomarkers of inflammation in children—The PANIC study. Scand J Med Sci Sports. 2023;33:1000–1009. [DOI] [PubMed] [Google Scholar]
46.Haapala EA, Wiklund P, Lintu N, et al. Cardiorespiratory Fitness, Physical Activity, and Insulin Resistance in Children. Med Sci Sports Exerc. 2020;52:1144–1152. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Cadenas-Sanchez C, Esteban-Cornejo I, Migueles JH, et al. Differences in Brain Volume between Metabolically Healthy and Unhealthy Overweight and Obese Children: The Role of Fitness. J Clin Med. 2020;9(4):1059. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Table 3

NIHMS2115068-supplement-Supplementary_Table_3.docx^{(17.1KB, docx)}

Supplementary Table 1

NIHMS2115068-supplement-Supplementary_Table_1.docx^{(13.8KB, docx)}

Supplementary Table 2

NIHMS2115068-supplement-Supplementary_Table_2.docx^{(14.5KB, docx)}

Data Availability Statement

The data that support the findings of this study are available from the principal investigator, [FBO], upon reasonable request.

[R1] 1.European Childhood Obesity Surveillance Initiative (‎COSI)‎. Report on the Fifth Round of Data Collection, 2018–2020: Available at: https://www.who.int/europe/publications/i/item/WHO-EURO-2022-6594-46360-67071

[R2] 2.Likhitweerawong N, Louthrenoo O, Boonchooduang N, Tangwijitsakul H, Srisurapanont M. Bidirectional prediction between weight status and executive function in children and adolescents: A systematic review and meta-analysis of longitudinal studies. Obes Rev. 2022;23(8):e13458. [DOI] [PubMed] [Google Scholar]

[R3] 3.Donnelly JE, Hillman CH, Castelli D, et al. Physical Activity, Fitness, Cognitive Function, and Academic Achievement in Children: A Systematic Review. Med Sci Sports Exerc. 2016;48(6):1197–1222. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R4] 4.Esteban-Cornejo I, Stillman CM, Rodriguez-Ayllon M, et al. Physical fitness, hippocampal functional connectivity and academic performance in children with overweight/obesity: The ActiveBrains project. Brain Behav Immun. 2021;91:284–295. [DOI] [PubMed] [Google Scholar]

[R5] 5.Marques A, Santos DA, Hillman CH, Sardinha LB. How does academic achievement relate to cardiorespiratory fitness, self-reported physical activity and objectively reported physical activity: a systematic review in children and adolescents aged 6–18 years. Br J Sports Med. 2018;52(16):1039–1039. [DOI] [PubMed] [Google Scholar]

[R6] 6.Haapala EA, Lintu N, Väistö J, et al. Associations of Physical Performance and Adiposity with Cognition in Children. Med Science Sports Exerc. 2015;47(10):2166. [DOI] [PubMed] [Google Scholar]

[R7] 7.Dwyer T, Sallis JF, Blizzard L, Lazarus R, Dean K. Relation of Academic Performance to Physical Activity and Fitness in Children. Pediatr Exerc Sci. 2001;13(3):225–237. [Google Scholar]

[R8] 8.Mora-Gonzalez J, Esteban-Cornejo I, Cadenas-Sanchez C, et al. Physical Fitness, Physical Activity, and the Executive Function in Children with Overweight and Obesity. J Pediatr. 2019;208:50–56.e1. [DOI] [PubMed] [Google Scholar]

[R9] 9.Esteban-Cornejo I, Cadenas-Sanchez C, Contreras-Rodriguez O, et al. A whole brain volumetric approach in overweight/obese children: Examining the association with different physical fitness components and academic performance. The ActiveBrains project. NeuroImage. 2017;159:346–354. [DOI] [PubMed] [Google Scholar]

[R10] 10.Cadenas-Sanchez C, Migueles JH, Erickson KI, Esteban-Cornejo I, Catena A, Ortega FB. Do fitter kids have bigger brains? Scand J Med Sci Sports. 2020;30(12):2498–2502. [DOI] [PubMed] [Google Scholar]

[R11] 11.Cadenas-Sanchez C, Migueles JH, Esteban-Cornejo I, et al. Fitness, physical activity and academic achievement in overweight/obese children. J Sports Sci. 2020;38(7):731–740. [DOI] [PubMed] [Google Scholar]

[R12] 12.Ferrar K, Evans H, Smith A, Parfitt G, Eston R. A Systematic Review and Meta-Analysis of Submaximal Exercise-Based Equations to Predict Maximal Oxygen Uptake in Young People. Pediatr Exerc Sci. 2014;26(3):342–357. [DOI] [PubMed] [Google Scholar]

[R13] 13.de Menezes FJ, de Jesus ÍC, Leite N. Predictive equations of maximum oxygen consumption by shuttle run test in children and adolescents: a systematic review. Rev Paul Pediatr. 2019;37(2):241–251. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Davis CL, Cooper S. Fitness, fatness, cognition, behavior, and academic achievement among overweight children: Do cross-sectional associations correspond to exercise trial outcomes? Prev Med. 2011;52(Suppl 1):S65–S69. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R15] 15.Welsman J, Armstrong N. Interpreting Aerobic Fitness in Youth: The Fallacy of Ratio Scaling. Pediatr Exerc Sci. 2019;31(2):184–190. [DOI] [PubMed] [Google Scholar]

[R16] 16.Loftin M, Sothern M, Abe T, Bonis M. Expression of VO2peak in Children and Youth, with Special Reference to Allometric Scaling. Sports Med. 2016;46(10):1451–1460. [DOI] [PubMed] [Google Scholar]

[R17] 17.Raine LB, Khan NA, Drollette ES, Pontifex MB, Kramer AF, Hillman CH. Obesity, Visceral Adipose Tissue, and Cognitive Function in Childhood. J Pediatr. 2017;187:134–140.e3. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Skog H, Lintu N, Haapala HL, Haapala EA. Associations of cardiorespiratory fitness, adiposity, and arterial stiffness with cognition in youth. Physiol Rep. 2020;8(18):e14586. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Rowland TW. Cardiopulmonary Exercise Testing in Children and Adolescents. Human Kinetics, Inc.; 2017. [Google Scholar]

[R20] 20.Mahon AD, Cheatham CC. Ventilatory Threshold in Children: A Review. Pediatr Exerc Sci. 2002;14(1):16–29. [Google Scholar]

[R21] 21.Cadenas-Sánchez C, Mora-González J, Migueles JH, et al. An exercise-based randomized controlled trial on brain, cognition, physical health and mental health in overweight/obese children (ActiveBrains project): Rationale, design and methods. Cont Clin Trials. 2016;47:315–324. [DOI] [PubMed] [Google Scholar]

[R22] 22.Sansum KM, Weston ME, Bond B, et al. Validity of the Supramaximal Test to Verify Maximal Oxygen Uptake in Children and Adolescents. Pediatr Exerc Sci. 2019;31(2):213–222. [DOI] [PubMed] [Google Scholar]

[R23] 23.Tolfrey K, Barker A, Thom JM, Morse CI, Narici MV, Batterham AM. Scaling of maximal oxygen uptake by lower leg muscle volume in boys and men. J Appl Physiol. 2006;100(6):1851–1856. [DOI] [PubMed] [Google Scholar]

[R24] 24.Léger LA, Mercier D, Gadoury C, Lambert J. The multistage 20 metre shuttle run test for aerobic fitness. J Sports Sci. 1988;6(2):93–101. [DOI] [PubMed] [Google Scholar]

[R25] 25.Mahar MT, Guerieri AM, Hanna MS, Kemble CD. Estimation of Aerobic Fitness from 20-m Multistage Shuttle Run Test Performance. Am J Prev Med. 2011;41(4, Supplement 2):S117–S123. [DOI] [PubMed] [Google Scholar]

[R26] 26.Matsuzaka A, Takahashi Y, Yamazoe M, et al. Validity of the Multistage 20-M Shuttle-Run Test for Japanese Children, Adolescents, and Adults. Pediatr Exerc Sci. 2004;16(2):113–125. [Google Scholar]

[R27] 27.Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Nat Acad Sci USA. 2000;97(20):11050–11055. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Fischl B, Sereno MI, Dale AM. Cortical Surface-Based Analysis: II: Inflation, Flattening, and a Surface-Based Coordinate System. NeuroImage. 1999;9(2):195–207. [DOI] [PubMed] [Google Scholar]

[R29] 29.Dale AM, Fischl B, Sereno MI. Cortical Surface-Based Analysis: I. Segmentation and Surface Reconstruction. NeuroImage. 1999;9(2):179–194. [DOI] [PubMed] [Google Scholar]

[R30] 30.Migueles JH, Cadenas-Sanchez C, Esteban-Cornejo I, et al. Associations of Objectively-Assessed Physical Activity and Sedentary Time with Hippocampal Gray Matter Volume in Children with Overweight/Obesity. J Clin Med. 2020;9(4):1080. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Crabtree NJ, Arabi A, Bachrach LK, et al. Dual-Energy X-Ray Absorptiometry Interpretation and Reporting in Children and Adolescents: The Revised 2013 ISCD Pediatric Official Positions. J Clin Densitom. 2014;17(2):225–242. [DOI] [PubMed] [Google Scholar]

[R32] 32.Moore S, McKay H, Macdonald H, et al. Enhancing a Somatic Maturity Prediction Model. Med Sci Sports Exerc. 2015;47:1755–1764. [DOI] [PubMed] [Google Scholar]

[R33] 33.Cohen J Statistical Power Analysis for the Behavioral Sciences. 2nd ed. L. Erlbaum Associates; 1988. [Google Scholar]

[R34] 34.Van Waelvelde H, Vanden Wyngaert K, Mariën T, Baeyens D, Calders P. The relation between children’s aerobic fitness and executive functions: A systematic review. Infant Child Devel. 2020;29(3):e2163. doi: 10.1002/icd.2163 [DOI] [Google Scholar]

[R35] 35.Zelazo PD, Carlson SM. Hot and Cool Executive Function in Childhood and Adolescence: Development and Plasticity. Child Devel Perspect. 2012;6(4):354–360. [Google Scholar]

[R36] 36.Diamond A Executive Functions. Ann Rev Psychol. 2013;64(1):135–168. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Armstrong N, Welsman J. Clarity and Confusion in the Development of Youth Aerobic Fitness. Front Physiol. 2019;10. doi: 10.3389/fphys.2019.00979 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Ruotsalainen I, Renvall V, Gorbach T, et al. Aerobic fitness, but not physical activity, is associated with grey matter volume in adolescents. Behav Brain Res. 2019;362:122–130. [DOI] [PubMed] [Google Scholar]

[R39] 39.Wittfeld K, Jochem C, Dörr M, et al. Cardiorespiratory Fitness and Gray Matter Volume in the Temporal, Frontal, and Cerebellar Regions in the General Population. Mayo Clin Proc. 2020;95(1):44–56. [DOI] [PubMed] [Google Scholar]

[R40] 40.Lenroot RK, Giedd JN. Brain development in children and adolescents: Insights from anatomical magnetic resonance imaging. Neurosci Biobehav Rev. 2006;30(6):718–729. [DOI] [PubMed] [Google Scholar]

[R41] 41.Walhovd KB, Tamnes CK, Fjell AM. Brain structural maturation and the foundations of cognitive behavioral development. Curr Opin Neurol. 2014;27(2):176–184. [DOI] [PubMed] [Google Scholar]

[R42] 42.Aghjayan SL, Lesnovskaya A, Esteban-Cornejo I, Peven JC, Stillman CM, Erickson KI. Aerobic exercise, cardiorespiratory fitness, and the human hippocampus. Hippocampus. 2021;31(8):817–844. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Valkenborghs SR, Noetel M, Hillman CH, et al. The Impact of Physical Activity on Brain Structure and Function in Youth: A Systematic Review. Pediatrics. 2019;144(4):e20184032. [DOI] [PubMed] [Google Scholar]

[R44] 44.Knierim JJ. The hippocampus. Curr Biol. 2015;25(23):R1116–R1121. [DOI] [PubMed] [Google Scholar]

[R45] 45.Haapala EA, Kuronen E, Ihalainen JK, et al. Cross-sectional associations between physical fitness and biomarkers of inflammation in children—The PANIC study. Scand J Med Sci Sports. 2023;33:1000–1009. [DOI] [PubMed] [Google Scholar]

[R46] 46.Haapala EA, Wiklund P, Lintu N, et al. Cardiorespiratory Fitness, Physical Activity, and Insulin Resistance in Children. Med Sci Sports Exerc. 2020;52:1144–1152. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Cadenas-Sanchez C, Esteban-Cornejo I, Migueles JH, et al. Differences in Brain Volume between Metabolically Healthy and Unhealthy Overweight and Obese Children: The Role of Fitness. J Clin Med. 2020;9(4):1059. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Which Indices of Cardiorespiratory Fitness Are More Strongly Associated with Brain Health in Children with Overweight/Obesity?

Eero A Haapala

David R Lubans

Timo Jaakkola

Alan R Barker

Abel Plaza-Florido

Luis Gracia-Marco

Patricio Solis-Urra

Cristina Cadenas-Sanchez

Irene Esteban-Cornejo

Francisco B Ortega

Abstract

Purpose:

Methods:

Results:

Conclusion:

INTRODUCTION

METHODS

Study design and population

Assessment of indices of cardiorespiratory fitness

Maximal indices obtained from the incremental treadmill exercise test

Maximal indices obtained from the 20-metre shuttle run test

Submaximal indices obtained from the incremental treadmill exercise test

Assessment of brain health outcomes

Assessment of body size and composition

Other assessments

Statistical analyses

RESULTS

Characteristics of participants

Table 1.

Associations of indices of cardiorespiratory fitness with brain health outcomes

Figure 1.

Table 2.

Sex and body fat percentage as moderators of the associations between indices of cardiorespiratory fitness and brain health outcomes

Sensitivity analyses

DISCUSSION

PERSPECTIVE

Supplementary Material

ACKNOWLEDGEMENTS

Footnotes

Data availability statement

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases