In short, yes we recommend it and we have made this easy to do. Some physiological measurements are particularly helpful when undergoing a cognitive training program.

When you are working with the brain it can be tricky to get the right load for an athlete. We need to apply enough stress to an athlete's brain in order to create adaptation. Without enough stress, we run the risk of the cognitive training plan being ineffective. Soma Analytics allows clubs, teams and coaches to monitor an athlete's cognitive and physiological measures. Both in the traditional manner of viewing the overall grand mean per cognitive task, and with access to Minute on Minute (MoM) performance measures which can help identify an athlete's cognitive and physiological breaking points.

Soma Analytics allows you to monitor an Athletes

  • Reaction Time

  • Speed

  • Variation

  • Accuracy

  • RCS

  • RPE

  • RMF

  • RME

  • BPM

  • rMSSD

  • SDNN

These cognitive and physiological measures are extremely important to build an in-depth picture of your athlete's performance. By monitoring an athlete's autonomic nervous system you can monitor the effect of each cognitive task or cognitive training plan.

HRV, the physiological measure you wont want to go without.

When an athlete first starts their cognitive training plan you may notice their HRV initially drops and over the course of the cognitive training plan their HRV begins to increase as they adapt to the cognitive stress. HRV can be a great indicator if the cognitive tasks you have programmed for the athlete are creating enough cognitive stress on the athlete. Just like when we go to the gym we must create enough physical stress for our bodies to adapt and come back stronger, if we do not create enough physical stress in the training environment, changes are minimal. If we create too much stress we run the risk of over-training. The same rules apply for cognitive training.

HRV gives insights to how well the athlete adapts to the stress of the cognitive training, ensures the cognitive training is well planned, and that the athlete is pushing themselves every session.

By monitoring an athlete's Heart Rate Variability (HRV) you can see the effect of each cognitive task (or cognitive training plan) on their autonomic nervous system. This allows you to see if you are under-loading (i.e., not creating enough mental stress) or over-loading (i.e., creating too much stress), so that you can titrate your athlete’s cognitive training load by increasing and decreasing task demands, respectively. HRV is a key indicator of mental workload and provides you with a data measure to help you optimise your athlete's cognitive training.

In the example below you can see the athlete starts their cognitive training with a 67.95 ms rMMSD, and after 5 minutes their rMMSD has declined to 46.35 ms. This indicates that the task caused parasympathetic nervous system withdrawal or sympathetic nervous system activation. This is typical and shows that the athlete is finding the cognitive training challenging.

HRV MoM

HRV MoM

In the second trend example, you can see from session 1 to session 3, the athlete's HRV is higher even with the increase of task intensity (50% to 60%) This shows that the athlete is adapting to the cognitive demands placed on their brain.

HRV Grand Mean Trend

HRV Grand Mean Trend

Monitoring HRV provides important insights regarding the relation between cognitive and physiological processes. Monitoring and adapting an athlete's cognitive training based on their HRV enables you to determine the right amount of cognitive load. It is important to apply sufficient cognitive stress to an athlete's cognitive training plan to create adaptation but not too much so they cannot recover sufficiently to be ready for the next session. Without enough stress, we run the risk of the training plan being ineffective and with too much stress we might mentally over-train the athlete.


A recent study exploring the link between sports performance and Brain Endurance Training (BET), has demonstrated significant improvements in cognitive abilities. Pre- and post-testing included a 5-minute physical task, followed by a mentally fatiguing task (20-minutes), followed by a repeat of the physical task. Compared to the control group with no training, the BET group improved cognitive performance, as seen by increased task performance with a reduction in mental fatigue and exertion. From only 5 weeks of computerised cognitive task training, lasting 20-minutes per session (20 total sessions), the BET group displayed an increase in heart rate variability, over time, post-training (P<0.05). This indicated less effort required for the physical tasks in post-testing.

Cognitive training in the BET group showed a decline in Mental Exertion compared to the control group during the same 2-back test used in pre and post-testing (P=0.25). These values represent an overall improvement in mental exertion seen by a 30% decrease in the BET group compared to a 23% increase in the control.

Regarding cardiac activity, we noted a significant decrease in participant's average heart rate in the BET group during the physical and novel cognitive tasks compared to the control group. This finding may be explained by the fact that the BET group perceived the tasks as less demanding seen by the increase in heart rate variability, consistent with the concurrent method (C.Ring, N. Dallaway, S.J.E. Lucas, 2017). While heart rate variability is typically correlated with perceived exertion (Shortz et al.), an increased HRV is associated with greater self-control (Zahn et al., 2016) and enhanced performance (Smith et al., 2019).

What is Heart Rate Variability?

Heart rate variability (HRV) is the variance in time between the beats of your heart. The reason it is being discussed more now in terms of performance indicators is due to the link between HRV and the nervous system. Presently HRV is considered to be one of the best measurements with regard to physical fitness and how prepared your body is to perform and possibly how likely it is to perform well.

Heart rate variability is the variance in time between your heartbeats (we measure this in milliseconds). When we measure our pulse, we tend to assume the heart is beating once every second or so, and it can feel as though the heart is beating at a steady pattern, like a metronome, never going off beat. However, your heartbeat varies and the level of variation gives insight into the functioning of your nervous system. A higher variation between beats indicates that the body is relaxed, alert, and ready to perform.

So why is this?

The direct link between HRV and the nervous system is what gives us this insight. HRV can change BEFORE your heart rate does, as a response to stress, illness, and even over-training. That is what makes it such a powerful tool in measuring recovery and readiness. HRV gets the message to us sooner. HRV is our canary down the mine.

There are situations that cause an increase in the variation between heartbeats (high HRV) and there are other situations that do the opposite, where the HRV is low and the heart beats at a more constant rhythm.

These periods of time between successive heartbeats are known as RR intervals.

How Heart Rate Variability Reflects Your Nervous System

HRV is linked to the autonomic nervous system (ANS) With HRV we get an indication of the balance between the parasympathetic (rest-and-digest) and sympathetic (fight-or-flight) portions of the nervous system. Good ANS balance means you have enough of both when you need them. Like a well-balanced tug of war, where nobody is totally winning or losing, simply moving in one direction or the other as required, resulting in a constant fluctuation in the time between heartbeats. The heart slows down for rest-and-digest. This means the HRV is higher as there is more time for variability between beats. In fight-or-flight, your system speeds your heart up. This means there is less time and space for the heart to have variability, as the heartbeats are closer together.

Generally speaking,

High HRV can indicate, rest-and-digest, general fitness, and that you are well recovered

Low HRV can indicate, fight-or-flight, stress, sickness (maybe you are coming down with something), or overtraining (accumulation of training fatigue)

What is the average Heart Rate Variability (HRV)?

Every persons HRV is unique to them. Genetic factors explain roughly 30 percent of your HRV as they do with many other parts and systems in your body, however, you can influence and improve your individual HRV. When you improve your fitness, stress levels, and optimise recovery, you can make positive changes to your HRV.

It is important to keep in mind that comparison to other people’s HRV values is not going to be a significant indication of where you are at.

What influences Heart Rate Variability (HRV)?

Some of these factors are under our control, and others are not. When we are looking at HRV improvement we need to work within our own sphere of influence, working on improving the things we can.

  • Workout intensity and volume

  • Breathing

  • Recovery from workouts (rest)

  • Hormones

  • Genetics

  • Health problems or illness

  • Metabolism

  • Stress levels

  • Age

  • Gender

  • Food, medication

  • Sleep

  • Hydration

  • Temperature

What are the advantages of Tracking Heart Rate (HR) as an athlete?

Heart rate is a quick and easy measure to check. Most athletes should know their own average resting heart rate and their maximum heart rate. Coupled with general subjective feelings of well-being, heart rate can indicate recovery, as well as how hard your athlete is working. If their heart rate is unusually high during a training session that is not normally particularly difficult, and the athlete is feeling a little flat, it suggests they are not recovered enough to push as hard usual, or suggests the need to incorporate recovery measures as part of training.

What are the advantages of Tracking HRV as an athlete?

HRV measurements can give more in-depth information and give you a heads up BEFORE your heart rate is affected and you start feeling flat. Particularly with athletes, it can be hard to tell the difference between mental fatigue and physical fatigue, not feeling like doing something today as opposed to having genuine limits on performance. HRV offers a means to capture how your nervous system is doing and thereby help head problems off at the pass.

  • How your HRV trends over weeks can help you identify if you’re recovering from session to session.

  • How your HRV trends over months can reveal how you are dealing with competition, stress, even if your performance should be improving.

It is very important to understand that regular and consistent measurements are required to give you a proper picture of HRV. You need to know your own TREND. As it is with many measurements when it comes to the body, a one-off snapshot is not always a great indicator.

Will HRV help me structure my athlete's cognitive training better?

Yes. HRV is a great way to monitor cognitive training loads and recovery on the athlete's nervous system. Just like tracking your nutrition without knowing your macro split you can run the risk of not hitting your goals. Cognitive training is a deliberate fatiguing of the brain and therefore the nervous system responds to that. With physical training, we tend to feel if muscles are sore, or the body feels tired but mental fatigue can be more subtle and can be misinterpreted for other things. HRV can assist with load management and make that interpretation more objective.

Why does Heart Rate Variability (HRV) matter for Cognitive Training?

By monitoring an athlete's Heart Rate Variability (HRV) you can see the effect of each cognitive task (or cognitive training plan) on your athlete’s autonomic nervous system. This can enable you to see if you are under-loading (i.e., not creating enough mental stress) or over-loading (i.e., creating too much stress) so that you can titrate your athlete’s cognitive training load by increasing and decreasing task demands, respectively. HRV is a key indicator of workload and provides you with another data measure to help you optimise your athlete's cognitive training.


Learn More

🔗 Cognitive Task Selection For Sports Performance.

🔗 How To Integrate Cognitive Training.

🔗 How To Manipulate Cognitive Load With Task Duration.

🔗 How To Manipulate Cognitive Load With Task Intensity.

🔗 Understanding your Athlete's Cognitive Data.

🔗 How to Interpret your Athlete’s Cognitive Data.


Research

🔗 Mental fatigue and heart rate variability (HRV): The time-on-task effect

This study investigated time-on-task effects on heart rate variability (HRV) and its relationship with self-reported and cognitive performance. Data were collected from 19 volunteers aged between 18 and 24 years, who performed a Go/NoGo task for 50 min. The inter-beat intervals recording at resting baseline and along with the task were assessed by electrocardiogram NeXus-10 apparatus. HRV data (time and frequency domain), self-reported scores, and cognitive performance were compared along the task. The variables’ reactivity during mental fatigue (fifth minus first block values) were also correlated. Results indicated that time-on-task effects cause a decrease in parasympathetic activity (rMSSD and pNN50), self-report scales (attention, drowsiness, and motivation) and cognitive impairment (RT and error frequency). Only frequency domain (LF/HF) had relation with self-report measures, suggesting a link between the HRV and psychometric measurement of mental fatigue. These results suggest that time-on-task effects on HRV are related to decrease in parasympathetic activity, in which time domain indices have more reliability to monitoring autonomic changes during mental fatigue induction while the frequency domain is related to psychological symptoms of mental fatigue.

🔗 Mental fatigue and heart rate variability (HRV): The time-on-task effect.

This study investigated time-on-task effects on heart rate variability (HRV) and its relationship with self-reported and cognitive performance. Data were collected from 19 volunteers aged between 18 and 24 years, who performed a Go/NoGo task for 50 min. The interbeat intervals recording at resting baseline and along with the task were assessed by electrocardiogram NeXus-10 apparatus. HRV data (time and frequency domain), self-reported scores, and cognitive performance were compared along the task. The variables’ reactivity during mental fatigue (fifth minus first block values) were also correlated. Results indicated that time-on-task effects cause a decrease in parasympathetic activity (rMSSD and pNN50), self-report scales (attention, drowsiness, and motivation) and cognitive impairment (RT and error frequency). Only frequency domain (LF/HF) had relation with self-report measures, suggesting a link between the HRV and psychometric measurement of mental fatigue. These results suggest that time-on-task effects on HRV are related to decrease in parasympathetic activity, in which time domain indices have more reliability to monitoring autonomic changes during mental fatigue induction while the frequency domain is related to psychological symptoms of mental fatigue.

🔗 Detection of mental fatigue state using heart rate variability and eye metrics during simulated flight

Pilot mental fatigue is a growing concern in the aviation field due to its significant contributions to human errors and aviation accidents. Long work hours, sleep loss, circadian rhythm disruption, and workload are well-known reasons, but there is a need to accurately detect pilot mental fatigue to improve aviation safety. However, due to the highly restricted cockpit environment and the complex nature of mental fatigue, feasible in-flight detection remains under-investigated. The objective of this study is to define a promising approach for mental fatigue detection based on psychophysiological measurements in flying-relevant environments. Eleven participants engaged in a simulated flight experiment, where several conventional heart rate variability (HRV) and ocular indices were examined to study their relevance to mental fatigue. Additionally, a Toeplitz Inverse Covariance-Based Clustering (TICC) method was performed to determine the ground truth, after which supervised machine learning was adopted to enable automated mental fatigue detection using HRV and eye metrics. Results showed that HRV and eye metrics were sensitive to the mental fatigue induced by prolonged flight-relevant tasks. The TICC method helped determine the ground truth for mental fatigue and identify its three distinct levels. Furthermore, a supervised learning-based detection of mental fatigue was achieved using a support vector machine with the greatest detection accuracy of 91.8%. The findings and methodology of this study provide new insights into the fatigue countermeasures in restricted cockpit environment and lay the groundwork for further explorations into the mental fatigue induced by prolonged flight missions.

🔗 Predicting Changes in Cognitive Performance Using Heart Rate Variability

In this paper, we propose a low-invasive framework to predict changes in cognitive performance using only heart rate variability (HRV). Although a lot of studies have tried to estimate cognitive performance using multiple vital data or electroencephalogram data, these methods are invasive for users because they force users to attach a lot of sensor units or electrodes to their bodies. To address this problem, we proposed a method to estimate cognitive performance using only HRV, which can be measured with as few as two electrodes. However, this can't prevent loss of worker productivity because the workers' productivity had already decreased even if their current cognitive performance had been estimated as being at a low level. In this paper, we propose a framework to predict changes in cognitive performance in the near future. We obtained three principal contributions in this paper: (1) An experiment with 45 healthy male participants clarified that changes in cognitive performance caused by mental workload can be predicted using only HRV. (2) The proposed framework, which includes a support vector machine and principal component analysis, predicts changes in cognitive performance caused by mental workload with 84.4 % accuracy. (3) Significant differences were found in some HRV features for test participants, depending on whether or not their cognitive performance changes had been predicted accurately. These results lead us to conclude that the framework has the potential to help both workers and managerial personnel predict what their performances will be in the near future. This will make it possible to proactively suggest rest periods or changes in work duties to prevent losses in productivity caused by decreases of cognitive work performance.





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