Heart Rate Variability (HRV) Biofeedback and Athletic Performance: Part One
Rationale and background
In this series of posts, I discuss Heart Rate Variability (HRV) Biofeedback, and in particular:
- Part One: Rationale for using HRV Biofeedback (this post)
- Part Two: Important metrics
- Part Three: Common Protocols
- Part Four: Expected physiological, psychological and performance outcomes
While part Four of this guide is mainly focused on athletic performance, Parts One, Two, and Three are generic and cover aspects applicable to any other population in terms of the benefits of improved emotional self-regulation
But let’s start with the basics.
What’s HRV biofeedback?
Life can be demanding, from both a physical and psychological point of view. Our health and performance can be affected by how we are able to effectively cope with stressful situations and deal with anxiety, or in broader terms, our ability to emotionally self-regulate can be very important.
Through deep breathing exercises, Heart Rate Variability (HRV) Biofeedback can directly affect physiological and psychological factors mediating training adaptation, injury risk, and performance, making it an ideal strategy to help us self-regulate and better cope with stressful situations.
However, physical and cognitive demands differ greatly between individuals, and evidence on the effectiveness of HRV biofeedback is scattered and with conflicting results.
With this series of posts, I will try to provide a critical overview of the effects of HRV biofeedback practice for athletic performance enhancement, as well as to discuss implications for future research and for practitioners.
HRV Biofeedback is a technique that consists of providing an individual with real-time feedback on instantaneous heart rate and respiration changes while being instructed to breathe at low frequencies (Lehrer and Gevirtz, 2014).
From a physiological point of view, we can consider homeostasis as a starting point to understand the rationale behind using HRV Biofeedback. As the body via the autonomic nervous system (ANS) responds to stressful stimuli in an attempt to maintain a state of balance, we can determine how effective this physiological self-regulation process is, by measuring the ANS.
In particular, the ANS is composed of two branches, the sympathetic and the parasympathetic one. The sympathetic branch is normally associated with the fight or flight response and excitatory responses (Minns et al., 2018). On the other hand, the parasympathetic branch is characterized by inhibitory responses and restorative processes, such as lowering heart rate and breathing rate, so that the system can go back to homeostasis after facing a stressor.
For these reasons, in the past fifty years, a vast body of research investigated the link between HRV and various mental and physical stressors, showing consistently reductions in parasympathetic activity when facing physical and psychological stressors (Hjortskov et al., 2004; Plews et al., 2012; Plews et al., 2014). Additionally, reduced parasympathetic activity has been associated with various clinical conditions (e.g. depression and anxiety disorders) as well as higher mortality risk (Stapelberg et al., 2012).
During HRV Biofeedback, an individual is instructed to breathe at low frequencies. Breathing at low frequencies (or deep breathing) causes large oscillations in the instantaneous heart rate, which synchronize with breathing rate. The influence of breathing on heart rate is called Respiratory Sinus Arrhythmia (RSA) and is mostly modulated by the parasympathetic branch of the ANS (Lehrer and Gevirtz, 2014). Hence, deep breathing results in training of the parasympathetic system, which might explain at least part of the positive effects of HRV Biofeedback reported in the literature in the context of reducing stress and anxiety (Goessl, Curtiss, and Hofmann, 2017).
Strengthening the parasympathetic nervous system could also motivate using HRV Biofeedback in athletes, with the potential of improving emotional self-regulation, coping mechanisms, and performance (Khazan, 2016; Pusenjak et al., 2015)
Stressors such as negative life events and intense physical training can result in negative physiological responses such as stress hormone perturbation, immunosuppression, and impaired skeletal muscle repair (Williams and Andersen, 2007; Appaneal and Perna, 2014). All of these aspects can act as mediators for negative outcomes such as increased injury risk or training maladaptation, in both cases resulting in reduced health and performance (Prinsloo, Rauch, and Derman, 2014).
Upon facing a stressor, the ANS responds via two pathways mainly. First, we have an activation of the sympathetic nervous system which is directly innervating most organs. Secondly, we have hormonal responses through the hypothalamic-pituitary-adrenal (HPA) axis which results in cortisol release. Depending on an individual’s ability to cope with a stressor, these responses can last longer and have a stronger negative effect on an individual’s physiology.
This is especially true when combined with high intensity and high volume training typical of elite sports (Clow and Huckle- bridge, 2004). For example, in Perna and McDowell (1995) the authors showed how athletes that reported being more stressed, had a long-lasting negative response including increased cortisol level for several hours after exercise, with respect to athletes that did not report high levels of life stress. Park et al. (2012) have also shown how cardiac vagal activity (the activity of the parasympathetic branch of the ANS) is also associated with improved emotional self-regulation and performance in mental tasks.
While a certain level of activation is beneficial (Khazan, 2016), an individual needs to be able to maintain activation within an optimal zone, and avoid deleterious effects. The individual zones of optimal functioning model (IZOF, Hanin, 1978; Hanin, 2007), argues that each individual athlete operates in an optimal zone in terms of various psychological parameters. This model has been widely employed in the scientific literature, showing how poor performance is often associated with anxiety levels perceived above an athlete’s optimal zone (Krane, 1993). While anxiety is not necessarily detrimental to athletic performance, from a cognitive point of view, it is key to performance how anxiety is perceived (Uphill and Jones, 2007). Hence, our ability to emotionally self-regulate and cope with anxiety can potentially influence performance outcomes.
Given the physiological and psychological factors just discussed, HRV Biofeedback is an ideal strategy to help us self-regulate and better cope with stressful situations. HRV Biofeedback can directly affect ANS activity through deep breathing exercises that stimulate parasympathetic activity. Therefore, HRV Biofeedback might directly provide a positive impact on the physiological and psychological factors that mediate health and performance
The scientific literature on HRV Biofeedback has shown positive outcomes on a variety of applications outside of sports, from chronic obstructive pulmonary disease (COPD) to depression, cardiac rehabilitation, and post-traumatic stress disorder (PTSD).
As previously introduced, HRV Biofeedback requires the individual to breathe at low frequencies. Experimental studies have found the highest amplitudes in instantaneous heart rate oscillations when breathing at approximately 0.1 Hz. The frequencies at which amplitude is maximal is often called the resonant frequency of an individual and can vary by 0.5–1 breath/minute between individuals. The resonant frequency of an individual can be established with a protocol that consists of breathing at different frequencies for a few minutes until the frequency that elicits the maximal amplitude is found (Lehrer et al., 2003). At the resonant frequency, heart rate and breathing rate are perfectly synchronized. Additionally, the resonant frequency results in a 180 degrees phase relationship between heart rate and blood pressure, suggesting that the high variations in the instantaneous heart rate are due to the baroreflex. Further research into these mechanisms has shown how biofeedback practice could indeed increase baroreflex gain, which might be a causal pathway explaining why hypertensive disorders can improve using HRV Biofeedback (Vaschillo et al., 2002).
Other pathways have also been proposed to explain the relationship between HRV Biofeedback and positive outcomes. First, we have a potential pathway between the baroreflex and neural control, in particular the amygdala, which could explain why improvements are seen in patients with anxiety and depression when using biofeedback interventions (Lehrer et al., 2003; Karavidas et al., 2007). Secondly, another pathway could involve a strengthening of the parasympathetic nervous system, as shown using electrical vagal stimulation in the context of treating depression (Brown, Gerbarg, and Muench, 2013), which might also occur during HRV Biofeedback.
Combining insights from biopsychosocial models and basic physiology, we can see how HRV Biofeedback has been proposed as a technique that can help athletes to improve emotional self-regulation and coping mechanisms via a strengthening of homeostasis, with the potential of resulting in better health and performance.
Based on the present overview, we can see how the ability to effectively self-regulate emotions and stress can be beneficial. In particular, apart from the potential for direct improvements in health and performance, other pathways could be positively impacted, from a psychological (e.g. anxiety) and physiological (e.g. hormonal response, strengthening of the parasympathetic system) point of view.
Such changes in psychological and physiological factors could then affect other health and performance-related outcomes such as injury risk and recovery
In the next parts of this guide, we’ll learn more about common metrics that can be used to quantify HRV Biofeedback sessions and the impact of such sessions on our physiology, as well as common protocols and expected changes in physiological, psychological and performance outcomes.
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology and human performance.