Sports nutrition for cognitive function -
Avoiding Energy Crashes : Sugary snacks and drinks can lead to rapid spikes and subsequent crashes in blood sugar levels, which can negatively impact focus and cognitive function. Omega-3 Fatty Acids: Brain Health Boosters Supporting Cognitive Function : Omega-3 fatty acids, found in fatty fish, chia seeds, flaxseeds, and walnuts, have been associated with improved cognitive function and may enhance mental focus.
Anti-Inflammatory Effects : Omega-3s have anti-inflammatory properties, which can contribute to overall brain health and mental well-being.
Antioxidant-Rich Foods: Protecting Brain Cells Combatting Oxidative Stress : Berries, dark leafy greens, nuts, and seeds are rich in antioxidants, which can help protect brain cells from oxidative damage and support mental clarity.
Vitamins and Minerals : A diet rich in a variety of fruits and vegetables provides essential vitamins and minerals that support overall brain health and function. Hydration: A Key Element of Mental Sharpness Cognitive Function and Hydration : Dehydration can lead to reduced cognitive function, impaired concentration, and decreased alertness.
Proper hydration is essential for mental sharpness. Electrolyte Balance : Maintaining proper electrolyte levels through hydration is crucial for optimal nerve function, which directly impacts mental focus and coordination. Protein for Neurotransmitter Production Amino Acids and Neurotransmitters : Protein sources like lean meats, poultry, fish, dairy, and plant-based options provide the amino acids needed for the production of neurotransmitters that regulate mood and focus.
Similarly, elite amateur athletes performed significantly better and had a significantly steeper learning curve than non-athletes 8. College age non-athletes, who completed 10 NT training sessions, experienced significant improvements in cognitive performance, attention span, memory, and reaction time, in addition to quantitative changes in brain function when comparing baseline and final sessions While the importance of visual processing speed in sport performance is increasingly recognized, the understanding of the variability between individuals is poor.
Individual intake of macronutrients including the dietary ratio of carbohydrates, protein, and fat appear to impact overall cognitive ability. Some studies report mixed effects of high carbohydrate diets that are dependent on the type of carbohydrate consumed.
Simple carbohydrates have been shown to reduce cognitive function as well as lead to pathological cognitive decline through deleterious effects on the brain. Complex carbohydrates with high fiber content are reported to be beneficial for cognition through improved blood glucose control and thus reduce metabolic dysregulation.
Studies also support a balanced protein intake due to evidence that inadequate or overconsumption of protein can negatively impact cognition while adequate intake improves certain cognitive tests.
Additionally, research supports higher intakes in healthy fats. For example, polyunsaturated fats are linked to improved cognitive function while diets high in saturated fatty acids and cholesterol are shown to have a negative correlation with global cognitive function 11 — The purpose of the IONSport study was to examine the influence of free-living nutritional intake on visual perceptual-cognitive performance in young healthy adults.
We employed a cross-sectional design to characterize cognitive performance and dietary intake. Participants kept food logs on each day of cognitive testing to evaluate the effect of variability in a free-living diet on cognitive performance; detailed day nutrient averages were used to represent individual long term nutrient intake.
The NT 3D-MOT software program was used to identify individual perceptual cognitive ability. We are unaware of any studies that have evaluated the relationship of nutrient intake to visual cognitive testing using the NT software.
Individuals with a body mass index BMI less than 18, had a pacemaker, experienced vertigo, or had difficulty with 3D viewing were excluded. Persons who self-reported their inability to distinguish between yellow and orange due to colorblindness on a pre-screening form were also excluded since the training program required the ability to distinguish these colors.
Prior to the start of the study, baseline data including blood pressure BP ; heart rate HR Omron Healthcare, Inc. Bannockburn, IL, United States ; visual acuity using the Snellen Chart; and body composition using the Biometric Beurer BF BIA technology Beurer, Germany were collected Subjects completed the Pittsburgh Sleep Quality Index, medical history, and the Modifiable Activity Questionnaire to provide detailed sleep, health, and physical activity background 28 , Participants came to the lab 10 times over a day period to complete cognitive testing.
Daily data, anthropometrics, and food logs were collected at each visit. Subjects were instructed to continue their usual eating behaviors and physical activity throughout the study.
A registered dietitian nutritionist RDN prepared and emailed five, one-to-two-minute, online instructional videos and provided participants direct guidance on how to accurately assess and document food and beverages consumed.
Subjects were asked to log all food and beverages consumed on each of the 10 cognitive training days. Each food log was reviewed daily by an RDN and, if necessary, additional details were collected to ensure logs were complete and accurate. NutriBase 19 Pro Edition, v.
Participants completed 15 cognitive training sessions using the NT 3D software program over 10 training days that included alternating single and double training sessions with 4—5 interspersed days of no training 15 total days of the study.
During the first training session, the cognitive testing procedures were explained to participants. They wore active 3D glasses that directly interacted with 3D software and noise-canceling headphones to minimize distractions.
Participants who relied on glasses for corrected vision wore them under the 3D glasses when training. Each training session included tracking the spatial location of four pre-identified target spheres that were initially a distinct color from the other four spheres. Once identified, these spheres became identical in color to the four other spheres.
All eight identical spheres moved among each other at a given speed within a 3D virtual space, passing in front of or behind each other, colliding with each other or the edges of the screen and changing directions. If one or more spheres was missed, the speed of sphere movement decreased for the next trial in a 0.
The final speed threshold for each training session and the progression over 15 sessions were the primary determinants of cognitive performance. Each of the 10 cognitive training days included collection of recent physical activity, fluid intake, most recent urine color validated urine color scale , blood pressure, heart rate, readiness to perform, body composition, level of sleepiness Stanford Sleepiness questionnaire and actual hours of sleep the previous night 32 , IBM SPSS NB was used to calculate the day food log mean of individual nutrients which was used in cognitive performance analyses.
Visual cognitive performance was assessed using the day NT speed threshold. Speed threshold analysis included: 1 evaluating mean and maximal performance and 2 comparing baseline sessions 1—3 to the final 3 sessions.
The relationship of nutritional intake and cognitive performance was evaluated based on sex and comparison of top and poor performers on the NT cognitive tests. A repeated measures analysis of variance ANOVA was performed to compare the effect of nutrient intake on visual cognitive performance.
Nutrients which showed suggestive positive or negative associations to cognitive performance parameters were further analyzed by categorizing intakes according to recommended levels and natural groupings within the data.
Stepwise linear regression was used to determine the independent effect of cognitive performance using variables that continued to show a consistent effect on cognitive performance parameters. Table 1 shows the characteristics of all male and female participants.
There were significant differences among male and female participants in age, height, weight, lean mass, BMI, and percent body fat. Regarding blood pressure, there was a significant difference in the systolic but not diastolic blood pressure.
There was also a significant difference in hydration status as indicated by urine color. There was no significance between the hours of sleep or responses on the Stanford Sleepiness scale.
There was variance between men and women in both average dietary intake and cognitive performance outcomes as displayed in Table 2. VCP was significantly better for males than the females and males performed significantly better on the three baseline and three final cognitive performance sessions.
However, the change from baseline to the final sessions was similar between males and females. A repeated measures ANOVA compared the effect of the percent of calories from carbohydrate and protein consumption on visual cognitive performance in Figures 1 , 2 , respectively.
Figure 1. Figure 2. There were also differences in average dietary intake and cognitive performance when comparing the top performers to poor performers.
Top performers were defined as the 7 males and 7 females who performed best on 15 cognitive training sessions, while the poor performers were the 8 males and 8 females who performed the worst on the same number of sessions.
Table 3. Top performers compared to poor performers nutrient intake and cognitive performance. However, both groups improved at the same rate. Figure 3. A repeated measures ANOVA compared the effect of vitamin B2 riboflavin consumption on visual cognitive performance Figure 4.
Dietary intake of 1. low B2 riboflavin consumption based on the average dietary intake for women While the recommended dietary intake for men and women ages 19—51 is 1.
Figure 4. Vitamin B2 intake. Although carbohydrate intake positively influenced cognitive performance in the repeated measures ANOVA model, it was not retained in the linear regression models High protein intake was a significant negative predictor of both the mean Table 4 and the maximal Table 5 NT speed threshold.
Similarly, there was a significant negative relationship between age and the maximal Table 5 NT speed threshold. Younger subjects had significantly better maximal NT speed threshold than older participants; however, there was not a significant relationship between age and the mean NT speed threshold.
Vitamin B2 riboflavin had a significantly positive relationship with both the mean Table 4 and the maximal Table 5 NT speed threshold.
Previous investigations have found that carbohydrates contribute to athletic performance and endurance, but there has been limited emphasis on the relationship between carbohydrate intake and cognitive ability While carbohydrate recommendations for endurance athletes vary based on their intensity and length of performance, recent studies demonstrated a positive relationship between carbohydrate mouth rinses and improved accuracy and precision in cognitive processing 14 , However, there is limited research evaluating the relationship of carbohydrate consumption and cognitive performance 15 , A small study that evaluated the relationship between three different mouth rinses containing 1.
The carbohydrate rinse also decreased the rating of perceived exertion RPE when compared to the other rinses Another small study that evaluated the impact of maltodextrin mouth rinses also reported that fatigued fencing athletes displayed enhanced accuracy in this skill-based sport after using a maltodextrin mouth rinse Brain imaging of participants using 1.
However, more research on the relationship between habitual carbohydrate consumption and cognitive performance is needed. Our research showed a significantly negative linear relation between total protein intake and the mean and maximal speed threshold in cognitive testing.
Previous investigations reporting a positive relationship between dietary protein consumption and cognitive response were conducted in aging persons, which often consume less protein than the needed protein for maintaining their health 37 , However, our population was younger, and many consumed above the RDA for protein, which may have influenced the conflict with the present result.
In a study that examined the impact of dietary protein on cognitive performance compared the impact of carbohydrates alone to a protein and carbohydrate mixture, no significant differences in cognitive ability were demonstrated after a min period of exercise Studies evaluating the impact of protein on cognitive performance most often focus on branched chain amino acids 40 whereas we evaluated total protein consumption.
These percentages also align with other research studies that support the positive relationship of complex carbohydrates with cognitive performance 11 , Many diets promote extreme ratios of macronutrients for distinct reasons including, but not limited to, weight loss and athletic performance However, these diets have not evaluated the impact of the varying macronutrient ratios on cognitive performance suggesting additional investigations of macronutrient ratios and cognitive performance is warranted.
Our data suggest a positive relationship between vitamin B2 riboflavin and visual cognitive performance. Additionally, dietary riboflavin has been shown to serve as a protective factor for global cognitive ability Vitamin B2 is essential for macronutrient metabolism, energy generation, and many other physiological functions including but not limited to antioxidant and anti-inflammatory roles Limitations of this study include the subject population and the length of the study.
The young, college-enrolled population limits the generalization of the results to other age and demographic groups. However, we provided baseline participant education on the level of detail required for reporting and handouts with portion size estimates were given to participants.
Food logs were also checked upon submission for detail. Additionally, 10 food records greatly exceed the standard 3 food records for dietary assessment and may capture more of the variability and reflect longer term intake better. Furthermore, this study is limited by the lack of biochemical assessment indicators such as serum analysis and macular pigment optical density for objective evaluation 42 , 45 — Athletes who often manipulate macronutrient ratios for muscular performance gains may consider optimizing this balance of protein for muscular gains and carbohydrate for both high energy metabolism and visual cognitive performance.
Advanced Search. Home About FAQ My Account Accessibility Statement. Privacy Copyright. Skip to main content Home About FAQ My Account. Authors Ewa Karpecka , University School of Physical Education in Krakow, Poland Follow Barbara Fraczek , Department of Sports Medicine and Human Nutrition, Faculty of Physical Education and Spor, University School of Physical Education in Krakow, Poland Follow.
Abstract Proper nutrition is a key factor in physical performance, but there are also indications of an impact of dietary components on the brain function. Recommended Citation Karpecka E, Fraczek B. Creative Commons License This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.
DOWNLOADS Since March 15, Included in Health and Physical Education Commons , Sports Medicine Commons , Sports Sciences Commons , Sports Studies Commons.
Introduction: The purpose Sports nutrition for cognitive function this study was to examine the influence of nutritional intake on visual Diabetic nephropathy kidney function performance Foor in young healthy adults. Food logs cignitive extensive Multivitamin for weight management measures including fujction composition, cardiovascular health, sleep and exercise patterns, and general readiness to perform were collected. Mean intake from 10 food logs collected over the 15 days were analyzed using Nutribase software. Statistical analyses were performed in SPSS using repeated measures ANOVA including significant covariates when appropriate. Results: Males consumed significantly more calories, macronutrients, cholesterol, choline, and zinc and performed significantly better on VCP than the females.
Ich finde mich dieser Frage zurecht. Ist fertig, zu helfen.
Sie nicht der Experte, zufällig?