Effects of High-Intensity Interval Training (HIIT) with cognitive stimulation versus traditional HIIT on cognitive performance, glucose utilization, and distance covered in adolescent non-professional athletes: a pilot study

GIOVANNI GUARASCIO1, ALESSANDRO GIORGIO1, ERMINIA COFANO1, ALESSIO CALABRÒ1, VINCENZO PRIGITANO1, ALESSANDRO IMBROGNO1, GIORGIO GASPARINI2,3, SAVERIO ARENA4, MICHELE MERCURIO3, GIAN PIETRO EMERENZIANI1, ATTILIO PARISI6, MARIA GRAZIA TARSITANO5, FILIPPO FAMILIARI2,3

1Department of Experimental and Clinical Medicine, “Magna Graecia” University, Catanzaro, Italy; 2Department of Orthopaedic and Trauma Surgery, “Magna Græcia” University, “Renato Dulbecco” University Hospital, Catanzaro, Italy; 3Research Center on Musculoskeletal Health, MusculoSkeletalHealth@UMG, Magna Graecia University, Catanzaro, Italy; 4Physical Therapist, Private Practice, Catanzaro, Italy; 5Department of Human Science and Promotion of Quality of Life, San Raffaele Open University of Rome, Italy; 6Department of Movement, Human and Health Sciences, University of Rome Foro Italico, Italy.

Summary. Introduction. Effects of High-Intensity Interval Training (HIIT) with cognitive stimulation affect glucose metabolism and neural plasticity. However, no study has examined the chronic effects of HIIT integrated with cognitive stimuli on neuro-metabolic and cognitive adaptations. Methods. In this randomized, blind controlled pilot study, adolescent non-professional football players aged 14-18 years who were randomly assigned to two groups. Group CG was assigned traditional HIIT while Group EG had HIIT with visuo-motor stimulation. All groups underwent training three times a week for eight weeks. Evaluations comprising cognitive, physical performance and glucose consumption were scheduled for T0 and T8. Results. After 8 weeks, the EG demonstrated a significant reduction in glucose consumption compared to the CG. No significant differences were observed in distance covered or cognitive scores between groups over time. Discussion. HIIT combined with cognitive stimulation may improve glucose efficiency in adolescent athletes. These findings highlight the potential of integrating cognitive challenges into HIIT protocols to optimize both cerebral and muscular energy management.

Key words. High-Intensity Interval Training, visuo-motor stimulation, glucose metabolism, cognitive performance, adolescent athletes.

Effetti dell’High-Intensity Interval Training (HIIT) con stimolazione cognitiva rispetto all’HIIT tradizionale sulla performance cognitiva, sull’utilizzo del glucosio e sulla distanza percorsa in giovani atleti adolescenti non professionisti: uno studio pilota

Riassunto. Introduzione. Gli effetti dell’High-Intensity Interval Training (HIIT) associato a stimolazione cognitiva influenzano il metabolismo glucidico e la plasticità neurale. Tuttavia, nessuno studio ha finora esaminato gli effetti cronici di un protocollo HIIT integrato con stimoli cognitivi sugli adattamenti neuro-metabolici e cognitivi. Metodi. In questo studio pilota randomizzato e in cieco, giovani calciatori non professionisti di età compresa tra 14 e 18 anni sono stati assegnati casualmente a due gruppi. Il gruppo CG ha seguito un HIIT tradizionale, mentre il gruppo EG ha svolto HIIT con stimolazione visuo-motoria. Entrambi i gruppi si sono allenati tre volte a settimana per otto settimane. Le valutazioni delle funzioni cognitive, della performance fisica e del consumo di glucosio sono state effettuate a T0 e T8. Risultati. Dopo 8 settimane, il gruppo EG ha mostrato una riduzione significativa del consumo di glucosio rispetto al gruppo CG. Non sono state osservate differenze significative tra i gruppi, nel tempo, né nella distanza percorsa né nei punteggi cognitivi. Discussione. L’HIIT combinato con stimolazione cognitiva potrebbe migliorare l’efficienza nell’utilizzo del glucosio negli atleti adolescenti. Questi risultati evidenziano il potenziale dell’integrazione di compiti cognitivi nei protocolli HIIT al fine di ottimizzare la gestione energetica sia cerebrale sia muscolare.

Parole chiave. High-Intensity Interval Training, stimolazione visuo-motoria, metabolismo del glucosio, performance cognitiva, atleti adolescenti.

Introduction

High-intensity interval training (HIIT) has a strong impact on the peripheral and central physiological systems, representing a strong stimulus for metabolic and cognitive enhancement¹. During high-intensity exercise, skeletal muscle relies predominantly on glycogen as the main energy substrate, while glucose serves as the exclusive fuel for the brain under normal physiological conditions2. The close link between muscle and brain glucose metabolism underscores the role of skeletal muscles as a peripheral reservoir that contributes to brain homeostasis3. Since glucose availability in the brain depends on continuous blood flow and systemic metabolic regulation3, modulation of peripheral glucose consumption during exercise is essential for maintaining optimal neural function3. Beyond its metabolic benefits, HIIT has been shown to induce significant neurobiological adaptations1. The metabolic stress associated with high-intensity exercise enhances the expression of neurotrophic factors like brain-derived neurotrophic factor (BDNF)1, promoting neurogenesis1, synaptic plasticity1, and increased cerebral perfusion1 . Regarding dual task exercise, several studies showed that an improves cognitive functions and promotes neural plasticity5. Performing cognitive tasks simultaneously with motor activity requires increased neuronal activation across frontal and parietal cortical regions responsible for executive control, attention, and decision-making6. Increased neural activity leads to an elevation in glucose metabolism, which augments neuro-energetic and increases cerebral perfusion7. Thus, the combination of physical effort and a cognitive load may enhance the ability of the body to allocate and utilize glucose across both muscular and neural tissues7. However, no study to date has specifically researched the combined effect of HIIT integrated with cognitive stimulation on cognitive and metabolic outcomes. Considering the close interconnection between skeletal muscle and the brain in carbohydrate metabolism, the well-documented impact of HIIT on neural and metabolic adaptations, and the role of dual-task in enhancing brain function and glucose utilization, the present study aimed to evaluate the chronic effects of HIIT combined with cognitive stimulation compared to traditional HIIT without cognitive engagement on cognitive performance, glucose utilization, and physical performance(distance covered) on adolescent non-professional young athletes. We hypothesized that participants doing HIIT along with cognitive stimulation would show more cognitive improvements than those doing regular HIIT. Furthermore, we thought that the metabolic adaptations from HIIT with cognitive challenges would improve neuro-energetic efficiency, indicated by a lower glucose utilization during the final session test. However, we did not expect there to be significant differences between the groups in the distance covered, as the HIIT protocol was not primarily designed to improve endurance performance.

Materials and methods

Participants

In this blind, randomized controlled trial, adolescent non-professional football players were recruited to participate. The inclusion criteria were: (1) age between 14 and 18 years, and (2) regular participation in non-professional athletic activities. The exclusion criteria included: (1) diagnosed cognitive impairment, (2) neurological disorders affecting the upper or lower limbs, (3) a history of musculoskeletal injury, (4) current or previous inflammatory disease, and (5) the presence of ongoing or previous musculoskeletal pain. The study protocol was approved by the local Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participants and their legal guardians before enrolment.

Experimental procedure

Participants were randomly assigned to one of two groups using simple randomization (i.e., by drawing lots): the Control Group (CG), performing traditional HIIT, and the Experimental Group (EG), performing HIIT incorporating real-time cognitive stimuli. The HIIT protocol was the same for both groups in terms of exercises, intensity, and, crucially, work and recovery times, eliminating any confounding variables. Each session began with a 10-minute cardio warm-up on a cycle ergometer, followed by the high-intensity phase. The EG received HIIT combined with cognitive stimulation. In this group, the exercise was initiated by a green light and stopped by a red light during training. In the EG, patients received the cognitive stimulus provided by different visual stimuli, integrating it with a simple exercise; the latency period of varying visual stimuli decreased from 0.7 seconds to 0.3 seconds (a decrease of 0.05 seconds on a weekly basis), thus enhancing the cognitive stimulus-response. The control group (CG) received the same HIIT training without cognitive stimulation. The training protocol lasted 8 weeks, consisting of 3 weekly sessions of 1 hour each for both groups. The exercises performed were: squats, bench press exercise, leg press, high skip, low skip, jumping jacks, burpees, lat machine, military press, and Romanian deadlifts. The workload used for both groups was 50% of one repetition maximum (1RM). Each exercise consisted in 4 sets of 10 repetitions, with 30 seconds of recovery between sets of the same exercise.; between the different exercises, 1 minute of recovery was performed. At the start of every training session, 10 minutes of cardio on a treadmill at 50% maximum heart rate (HRmax) was done (table 1).




The crucial difference lies in cognitive integration for the EG. While the CG focuses only on physical execution, the EG receives visual inputs that actively signal the work and recovery phases. This is cognitive stimulation that forces the participant to maintain divided attention between the motor gesture and the processing of the external signal. To ensure progressive difficulty, the protocol stipulates that, from week to week, the latency time of cognitive stimulation (i.e., the time allowed for the participant to process and react to the visual input) is lowered. This gradually intensifies the cognitive load, enhancing processing speed and mental flexibility even under physical stress. The eight-week intervention, with three 1-hour sessions each week, had assessments conducted at the two intervals of baseline assessment (T0) and the post-assessment (T8). In every evaluation session, every subject executed a performance test on a cycle (Taurus UB10.0 Pro), which assesses cognitive performance, glucose utilization, and physical output (distance covered) simultaneously. For 15 minutes, participants were instructed to cover as much distance as possible while also solving the logic-based cognitive tasks provided on the application (Not Not – A Brain-Buster, Altshift, iOS/Android;) responded using a Tablet (Samsung Galaxy Tab A8), held in landscape orientation. The application scored the tasks automatically, giving 1 point for every correct answer and 0 for every incorrect answer. At the end of the 15-minute test, the total cognitive score and distance covered (km) were recorded. In addition, capillary blood glucose was measured immediately before and after the test using a standard glucometer (Accu-Chek Guide®, Roche Diagnostics). Care was taken to ensure that no sweat contamination was present on the fingertips during sampling. Glucose utilization was subsequently calculated as the difference between pre-test and post-test blood glucose concentrations. All participants were instructed to abstain from sugary food or drinks for at least 90 minutes and for at least 24 hours from consuming alcohol and exercising before testing sessions. The study design adopted is reported in figure 1.




Statistical Analysis

An a priori power analysis was conducted using G*Power software (Version 3.1.9.7). The analysis was designed to detect a large interaction effect (f = 0.40) in a 2×2 mixed-design ANOVA, with a significance level (α) of 0.05 and a statistical power (1-β) of 0.80. The analysis indicated that a total sample size of N = 16 participants (8 per group) was required to achieve adequate power. However, due to recruitment constraints and the highly specific nature of the population, a final sample of N 14 participants was enrolled in the study. All statistical analyses were performed using IBM SPSS Statistics 23.0 (SPSS Inc., Chicago, IL, USA). Continuous variables are presented as mean ± standard deviation (SD). Out of the outcome variables, the normal distribution for a multivariate dataset of all variables in the outcome was confirmed using the Shapiro-Wilk test (p > 0.05), which enabled the use of parametric tests. The baseline characteristics were tested for normal distribution to determine the characteristics that existed at the onset of the experiment, which were the Experimental Group (EG) and the Control Group (CG). The primary analysis was performed in order to determine the effects of the intervention on the 2 (Time: T0, T8) × 2 (Group: EG, CG) of the mixed design ANOVA. This model assessed the main effects of Time and Group, as well as the Time × Group interaction effect. Where a significant Time × Group interaction was found, post-hoc analyses were performed using pairwise comparisons with Bonferroni adjustment to control for multiple comparisons. The threshold for statistical significance was set at p ≤ 0.05.

Results

We enrolled 14 individuals (females n=7, males n=7) who met the inclusion and exclusion criteria. The athletes were then randomly assigned to the Experimental Group (EG, n = 7) and Control Group (CG, n = 7). Every subject went through the experimental processes. The baseline features of participants in both groups are presented in table 2.




A 2 (Time: T0, T8) × 2 (Group: EG, CG) mixed-design ANOVA on distance (km) revealed a significant main effect of Time, F (1, 12) = 9.70, p = 0.01, ηp² = 0.45, with participants improving from T0 to T8 (see table 3 for descriptive statistics). Neither the main effect of Group, F (1, 12) = 0.16, p = 0.69, ηp² = 0.01, nor the Time × Group interaction, F (1, 12) = 0.003, p = 0.95, ηp² < 0.01, reached significance.




A 2 × 2 mixed-design ANOVA on cognitive performance score showed a significant increase over time for each participant, F(1, 12) = 5.96, p = 0.03, ηp² = 0.33 (see table 4 for descriptive statistics), as participants progressed from baseline to post-intervention.




Also, a significant main effect of Group was noted, F(1, 12) = 4.78, p = 0.05, ηp² = 0.28, where the experimental group (EG) was observed to perform better than the control group (CG) throughout the time intervals. The Time × Group interaction was not significant, F(1, 12) = 1.87, p = 0.20, ηp² = 0.14, indicating that improvement over time was similar for both groups of participants.

A 2 x 2 mixed-design ANOVA analyzing glucose utilization showed no significant changes over time, F (1, 12) = 1.53, p = 0.24, ηp² = 0.11, and no significant effects of Group, F (1, 12) = 3.02, p = 0.11, ηp² = 0.20. There was, however, a notable Time x Group interaction F (1, 12) = 8.57, p = 0.01, ηp² = 0.42. This interaction suggested that the two groups diverged in changes over time (see table 5 for descriptive statistics) in stark contrast to their changes over time.




To decompose this interaction, simple effects analyses with a Bonferroni adjustment were conducted. These analyses revealed that, although there was no significant change in the control group (p > 0.05), the experimental group showed a non-significant trend towards improvement from T0 to T8 (p = 0.07). Importantly, the groups did not differ at T0 (p > 0.05); however, at T8, the experimental group demonstrated significantly better glucose utilisation than the control group (p < 0.01) (see figures 2 and 3).







Discussion

This study aimed to evaluate the effects of HIIT with cognitive stimulation compared to traditional HIIT without cognitive stimulation on glucose utilization, physical performance, and cognitive function. The most important findings were that the EG group showed a significant reduction in glucose consumption after 8 weeks of HIIT training with cognitive stimuli compared to the group without cognitive stimuli, while there were no significant changes in distance covered or cognitive score between the groups over time. These results could indicate a more efficient use of energy at both the muscular and cerebral levels. The observed reduction in glucose consumption in HIIT with cognitive stimulation training could indicate a metabolic adaptation. In fact, both dual-task training 5,6 and HIIT2,3,7 have been shown to have an impact on glucose metabolism. Consequently, we hypothesize that the combined effects of HIIT with cognitive stimuli strongly impact carbohydrate metabolism. Additionally, chronic exposure (eight weeks) to these intense cognitive stimuli, resulting from HIIT training and dual tasking, could enhance the body’s efficiency in utilizing glucose during motor tasks. In this line, separate sessions of physical exercise and cognitive training in chronics showed an improvement in both brain metabolism and cognitive functions8. In the athletic population, it has been observed that performing acquired motor skills requires less glucose consumption9,10 . However, in our study, only the EG showed a reduction in glucose consumption after eight weeks of training. This effect is likely due to the fact that the CG did not improve glucose utilization efficiency, as the post-intervention test (T8) was performed on a cycle ergometer while simultaneously requiring responses to cognitive stimuli presented through the app. Participants in the CG were not accustomed to these types of cognitive tasks, since their training sessions did not include such stimuli. In contrast, the EG underwent HIIT sessions regularly combined with cognitive stimulation, which closely resembled the dual-task conditions applied during the T8 test, likely resulting in better neuro-metabolic adaptation and more efficient glucose utilization. In support of this result, it is well known that cognitive training leads to increased brain efficiency11,12 and, consequently, to lower glucose utilisation12. Furthermore, it should be noted that many other factors could have influenced blood sugar levels, such as hormonal13,14, nutritional factors15,16, factors related to the growth of adolescents17,18, etc. Regarding cognitive function improvement, contrary to what we had hypothesized, our results did not show a significant improvement in cognitive scores in the EG compared to the CG after 8 weeks of training, but rather an improvement in both groups at T8 compared to T0. The improvement in cognitive performance observed in both groups was consistent with previous evidence demonstrating that physical exercise19-21, specifically HIIT1,22 enhances cognitive function. However, we expected that after 8 weeks of HIIT training with cognitive stimuli, the EG group would improve their cognitive score compared to T0 and compared to the CG. As athletes, they probably already had high cognitive functions23 for there to be a significant improvement. In fact, many studies show cognitive improvement after cognitive training in elderly4,6 or clinical populations24-26, but not in athletic populations. With regard to the distance covered, the results confirmed our initial hypothesis, which was that neither group would undergo training. The initial hypothesis was based on the fact that this type of training was not designed to improve the distance covered. As mentioned above, in terms of cognitive functions, since the participants were already trained athletes and were already covering good distances in a set period of time, no improvement was observed in any group. Furthermore, the training sessions carried out by the athletes were not aimed at improving this physical parameter. In fact, Midgley et al.27 have highlighted training techniques for improving distance covered, but these techniques are very different from the protocol used in our study. This study has several limitations. First, the small sample size. Larger cohorts should be included to support these preliminary findings. Second, the assessment of blood glucose levels, using capillary samples at only two time points (pre-test and post-test), only provides a limited snapshot of metabolic dynamics. Use of a continuous glucose monitoring (CGM) system would be the best means to assess glucose utilization during the entire exercise and recovery period. Third, the indirect nature of the interpretation should be noted as the observed reduction in utilization of glucose suggests possible improvement in cerebral metabolic efficiency. Future research can provide more definitive answers than these indirect methods by using PET and fMRI to assess brain glucose metabolism and neuroenergetics.

Conclusions

This pilot study provides evidence that HIIT combined with cognitive stimulation tasks can promote more efficient glucose utilisation compared to traditional HIIT without cognitive stimulation. The reduction in glucose consumption observed in the EG group after eight weeks of training suggests a possible metabolic adaptation, which could indicate an improvement in coordination between peripheral and central glucose metabolism. These findings support the hypothesis that the chronic combination of cognitive load and HIIT training improves neurometabolic efficiency, potentially leading to more economical glucose utilisation in the brain. Although both groups improved their cognitive performance, no significant differences were found between them, probably due to the high baseline cognitive level of the participants, who were trained athletes. Similarly, no changes in distance covered were detected, which is consistent with the primary goal of the HIIT protocol, which is not specifically designed to improve endurance. Overall, these results highlight the potential of HIIT with cognitive stimulation as an intervention to enhance brain efficiency.

Conflicts of interest. The authors declare that there is no conflict of interest.

Authors’ contributions. Conceptualization, AG and SA; Data curation, AC and AG; Formal analysis, GG and VP; Investigation, AG, EC and SA; Methodology, AP, AI, GG and EC; Project administration, GPE, AP, FF and MGT; Resources, G Gasparini and MM; Supervision, GPE, FM, MGT and G Gasparini; Visualization, AP, GG, AG, VP and AC; Writing-original draft, GG and MGT; Writing-review and editing, GG and MGT. G. Guarascio and AG equally contributed to this work.

Ethics approval. The study protocol was approved by the Local Ethic Committee and conducted in accordance with the Helsinki Declaration.

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