Changes in cognitive processing are manifested by variations in cardiac activity (i.e., HRV). For instance, Mukherjee et al. showed that mental workload affected HRV (the greater the cognitive load, the lower the HRV). The relation between cognitive performance and heart rate variability changes as the performer learns and adapts to the task demands. This 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.

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.

When an athlete first starts their cognitive training plan you may notice their HRV initially drops, and then 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 become 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 to 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.

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.

Soma Analytics allows coaches to apply a specialized training mode to Soma NPT’s cognitive tasks that automatically increases or decreases task difficulty based on the athlete's live HRV. Adaptive Heart Rate Variability Mode tracks variations in HRV every 15s and adjusts task difficulty in order to titrate the cognitive stress placed on the athlete’s brain. This specialized training mode helps coaches avoid cognitively overloading their athletes.

We suggest you try applying Adaptive Heart Rate Variability Mode in a couple of situations: during the in-season when cognitive and physical loads are at highest, or during a deload week when aiming to reduce the overall cognitive load.

🔗 Adaptive Heart Rate Variability Mode


Frequently Asked Questions

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.


🔗 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.

🔗 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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