Motivation and emotion/Book/2024/Heart rate variability and mental health

Heart rate variability and mental health:
What is the relationship between HRV and mental health?

Overview

This chapter provides an understanding of heart rate variability (HRV) and its connection to mental health. This chapter argues, HRV and mental health are closely linked; physiological changes reflected in HRV data can influence mental health issues. The chapter also explores various ways to enhance mental health using HRV data.

Key Points

HRV is a Physiological Marker of Autonomic Balance!

HRV scores can provide insights into mental health status!

Understanding HRV

HRV describes the fluctuation in time intervals between consecutive heartbeats. HRV can be measured using various devices like the Apple Watch and WHOOP strap. According to Billman (2011), HRV may serve as a proxy for assessing health and well-being. Research indicates that individuals with greater variability between heartbeats tend to be more resilient to mental health risk factors (Alshami, 2019; Lehrer & Gevirtz, 2014; Thayer et al., 2012). In both clinical and everyday settings, HRV biofeedback has been shown to reduce symptoms of anxiety, depression, and PTSD (Gevirtz, 2013). Recognising downward trends in HRV can enable early interventions to enhance parasympathetic nervous system (PNS) activity and improve overall mental health (Thayer & Lane, 2009).

How is HRV Measured?

HRV measurement and analysis are typically performed using two main techniques:

time-domain analysis;

and frequency-domain analysis.

These methods provide insights into an individual's physiological and psychological state (Berntson et al., 1997). Understanding healthy and unhealthy HRV measurements is complex and context-dependent (Shaffer & Ginsberg, 2017). There are short-term and long-term norms that may indicate susceptibility to mental health issues.

Table 1) Techniques and Analysis Methods to Measure HRV: SDNN and LF/HF Ratio

HRV Measurement Techniques and Analysis
Time domain analysis	Frequency domain analysis
SDNN (Standard Deviation of NN intervals)	LF/HF Ratio

SDNN calculates the standard deviation of all N-RN intervals (normal-to-normal heartbeats) over a recording period. It reflects the overall HRV and includes both short-term high-frequency variations and longer-term low-frequency fluctuations (Shaffer & Ginsberg, 2017). Generally a healthy range is SDNN > 100 milliseconds (ms) for a 24-hour recording and an unhealthy range is SDNN < 50 ms. (see: Tsuji et al.,1996; Umetani et al., 1998)

The LF/HF Ratio assesses the balance between sympathetic and parasympathetic activity. A higher ratio indicates sympathetic dominance, while a lower ratio suggests parasympathetic dominance (Task Force, 1996). Approximately 0.5 to 2.0 ratio would indicate heathy balance; outside this range is associated with stress and potential health risks.

Visualising The Data

Beyond raw numbers, visual representations can make HRV data more accessible. Yu et al. (2017) developed the "StressTree," a tree-shaped visualisation of HRV data (specifically SDNN) for stress. In a study with nine participants, the StressTree was found to be easy to understand and motivated individuals to regulate their breathing for a "healthy-looking" tree. Future research should explore new visualisation methods to provide useful insights for a wider audience.

Biofeedback, HRV and Mental Health

HRV biofeedback is a practical method to reduce mental health symptoms. Wearable devices and smartphone applications enable individuals to monitor HRV and detect patterns indicative of increased allostatic load—the cumulative physiological wear and tear due to chronic stress (McEwen & Stellar, 1993). By using biofeedback, people can proactively manage stress before it escalates into mental health challenges. Studies suggest that HRV biofeedback can effectively reduce symptoms of anxiety, depression, and PTSD, especially when combined with other therapeutic modalities (Gevirtz, 2013).

A crucial factor in using HRV as a mental health marker is monitoring HRV trends over time rather than relying on single measurements (Lehrer & Gevirtz, 2014). Regular HRV tracking can reveal long-term patterns in autonomic function, offering a comprehensive view of how the body responds to cumulative stress (Shaffer et al., 2014). Recognising downward trends allows for early interventions to enhance PNS activity and improve overall mental health (Thayer & Lane, 2009).

HRV Biofeedback Case Study (Schuman et al., 2022)

Schumann et al. (2022) explored the use of HRV biofeedback to reduce depressive rumination and improve mental health outcomes. In this six-week study involving 30 participants with depression, the intervention group received HRV biofeedback training via a smartphone application, engaging in five sessions per week. Results showed increased resting HRV and reduced levels of anxiety, perceived stress, depressive symptoms, and rumination. Significant correlations were found between increases in HRV and reductions in depressive symptoms (p < 0.05). The control group showed no significant changes. These findings support the use of HRV biofeedback as a practical tool for individuals experiencing mental health difficulties.

Linking HRV and Mental Health

Connecting HRV to mental health is complex because both are significantly influenced by various conscious and unconscious physiological functions (Thayer & Lane, 2009). Yet, changes in HRV reflect alterations in autonomic functioning and have been associated with mental illnesses such as anxiety, schizophrenia, and depression (Agelink et al., 2002; Chalmers et al., 2014; Clamor et al., 2016; Licht et al., 2009; Schumann et al., 2022). To understand the link between these two core concepts the following theories and systems are important to understand.

The Neurovisceral Theory

The Neurovisceral Integration Theory, developed by Thayer and Lane (2000), articulates a link between HRV and mental health. This theory proposes that a central autonomic network (CAN)—including brain regions like the prefrontal cortex, amygdala, hypothalamus, and the heart—coordinates autonomic responses and shapes emotional and cognitive functions. Research suggests that individuals with higher HRV demonstrate better emotional regulation, reduced anxiety, and greater flexibility in coping with stress due to adaptive top-down control over emotional responses (Appelhans & Luecken, 2006; Thayer et al., 2009).

The Polyvagal Theory

The Polyvagal Theory (Porges, 1995) posits that the vagus nerve—the primary pathway linking the heart to the brain—is influenced by social and environmental demands, reflecting autonomic functioning. Lower HRV is consistently linked to vagal dysfunction, suggesting an unbalanced autonomic nervous system and limited psychological resilience (Beauchaine & Thayer, 2015; Porges, 2007).

Gray’s BIS/BAS Theory

Gray’s Behavioural Inhibition System (BIS) and Behavioural Activation System (BAS) theory (Gray & McNaughton, 2000) distinguishes between two core neurobiological systems:

BIS) Associated with sensitivity to punishment and anxiety-provoking stimuli;

and BAS) Responds to rewards and goal-oriented behaviours.

Research has shown that individuals with high BIS sensitivity tend to have lower HRV and mental heath issues (Scholten et al., 2006; Thayer et al., 2009).

Autonomic Nervous System (ANS)

HRV provides a non-invasive approach to understanding how the ANS responds to physiological and environmental stimuli. The ANS consists of two branches:

Sympathetic Nervous System) Acts as the body's gas pedal, activating the "fight-or-flight" response during stress.
Parasympathetic Nervous System) Functions as the brake, promoting relaxation and recovery.

HRV reflects the balance between these two branches, indicating the body's adaptability to internal and external stimuli (Billman, 2011). Higher HRV suggests a flexible and responsive ANS, associated with positive emotional regulation and cognitive functioning. Low HRV indicates reduced adaptability and has been linked to mental health disorders (Agelink et al., 2002; Clamor et al., 2016; Schumann et al., 2022).

Table 2) Overview of the Autonomic Nervous System (ANS) Branches

ANS Branches
Sympathetic Nervous System (SNS)	Parasympathetic Nervous System (PNS)
Activates the "fight-or-flight" response, increasing heart rate and preparing the body for action.	Promotes the "rest-and-digest" response, reducing heart rate and facilitating energy conservation and restorative processes.

Heart-Brain Axis

Within the ANS, the brain and heart communicate extensively (Simats et al., 2022). The heart, with its intrinsic nervous system comprising approximately 40,000 neurons, transmits information to the brain via the vagus nerve, influencing cognitive processes, emotional regulation, and decision-making (Armour, 2003).

Interesting Fact

Some research suggests that the heart sends more signals to the brain than vice versa, highlighting the significance of heart-brain communication (Alshami, 2019).

Hypothalamic-Pituitary-Adrenal (HPA) Axis

The HPA axis, the body's primary stress response system, regulates hormones crucial for stress management, such as cortisol. Activation of the HPA axis during acute stress leads to cortisol release, which can decrease HRV when overactivated. Persistent elevated cortisol levels disrupt healthy SNS and PNS activity (Licht et al., 2009; Thayer et al., 2012). Consequently, HRV serves as a valuable biomarker for assessing autonomic regulation and hormonal stress responses.

Table 3) HRV Levels and Stress Adaptation connection table

High HRV	Low HRV
Indicates the body is effectively managing stress	Reflects poor stress adaptation and a higher risk for stress-induced mental health disorders.

Non-Biofeedback Techniques for Improving HRV and Mental Health

Beyond biofeedback, individuals can influence HRV and build resilience to mental health issues through various practices:

Breathing techniques

Techniques such as box breathing involve inhaling, holding, exhaling, and pausing for equal counts, helping to regulate autonomic balance and manage stress. These practices can increase HRV (Jerath et al., 2015).

Mindfulness

Research indicates mindfulness improves emotional regulation and reducing anxiety by reducing sympathetic activity and enhancing parasympathetic response (Krygier et al., 2013). Mindfulness practice, which emphasizes being present in the moment without judgment, can increase HRV.

Exercise

Activities like aerobic exercise, yoga, and strength training positively affect HRV by boosting cardiovascular health and promoting resilience to stress (Stanley et al., 2013). Regular physical activity improves HRV and contributes to positive mental health.

Sleep

Adequate and quality sleep is essential for maintaining healthy HRV levels. Sleep deprivation can lead to decreased HRV, indicating increased stress and reduced recovery capacity (Cellini et al., 2017). Improving sleep hygiene by maintaining a regular sleep schedule and creating a restful environment can enhance HRV and overall mental health.

Test your knowledge

Do you want to understand more about connection between HRV and Mental health?

You should check out the chat bot below, which can answer questions about HRV and its connection to mental health. If you provide a quality prompt, you should receive valuable information. https://chatgpt.com/g/g-UH2YiyWON-heart-rate-variability-hrv-coach

Conclusion

HRV reflects a person's physiological and psychological adaptability, acting as both a marker and a modifiable factor in mental health. As research continues to evolve, HRV stands as a valuable tool for clinicians and individuals, providing insights into autonomic balance and offering pathways to strengthen mental resilience through biofeedback and other evidence-based practices.

References

Agelink, M. W., Boz, C., Ullrich, H., & Andrich, J. (2002). Relationship between major depression and heart rate variability. Clinical consequences and implications for antidepressive treatment. Psychiatry Research, 113(1–2), 139–149. https://doi.org/10.1016/s0165-1781(02)00225-1

Alshami, A. M. (2019). Pain: Is It All in the Brain or the Heart? Current Pain and Headache Reports, 23(12), 88. https://doi.org/10.1007/s11916-019-0827-4

Billman, G. E. (2011). Heart rate variability—A historical perspective. Frontiers in Physiology, 2, 86. https://doi.org/10.3389/fphys.2011.00086

Chalmers, J. A., Quintana, D. S., Abbott, M. J.-A., & Kemp, A. H. (2014). Anxiety Disorders are Associated with Reduced Heart Rate Variability: A Meta-Analysis. Frontiers in Psychiatry, 5, 80. https://doi.org/10.3389/fpsyt.2014.00080

Clamor, A., Lincoln, T. M., Thayer, J. F., & Koenig, J. (2016). Resting vagal activity in schizophrenia: Meta-analysis of heart rate variability as a potential endophenotype. The British Journal of Psychiatry, 208(1), 9–16. https://doi.org/10.1192/bjp.bp.114.160762

Ernst, G. (2017). Heart-Rate Variability—More than Heart Beats? Frontiers in Public Health, 5. https://doi.org/10.3389/fpubh.2017.00240

Howard E. LeWine. (2011, June 15). Understanding the stress response. Harvard Health. https://www.health.harvard.edu/staying-healthy/understanding-the-stress-response

Kamath, M. V., Watanabe, M. A., & Upton, A. R. M. (2016). Heart rate variability (HRV) signal analysis: Clinical applications. In Heart Rate Variability (HRV) Signal Analysis: Clinical Applications.

Koenig, J., & Thayer, J. F. (2016). Sex differences in healthy human heart rate variability: A meta-analysis. Neuroscience & Biobehavioral Reviews, 64, 288–310. https://doi.org/10.1016/j.neubiorev.2016.03.007

Krygier, J. R., Heathers, J. A. J., Shahrestani, S., Abbott, M., Gross, J. J., & Kemp, A. H. (2013). Mindfulness meditation, well-being, and heart rate variability: A preliminary investigation into the impact of intensive Vipassana meditation. International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology, 89(3), 305–313. https://doi.org/10.1016/j.ijpsycho.2013.06.017

Lehrer, P. M., & Gevirtz, R. (2014). Heart rate variability biofeedback: How and why does it work? Frontiers in Psychology, 5. https://doi.org/10.3389/fpsyg.2014.00756

Licht, C. M. M., de Geus, E. J. C., van Dyck, R., & Penninx, B. W. J. H. (2009). Association between anxiety disorders and heart rate variability in The Netherlands Study of Depression and Anxiety (NESDA). Psychosomatic Medicine, 71(5), 508–518. https://doi.org/10.1097/PSY.0b013e3181a292a6

Matsuura, Y., & Ochi, G. (2023). The Potential of Heart Rate Variability Monitoring for Mental Health Assessment in Top Wheel Gymnastics Athletes: A Single Case Design. Applied Psychophysiology and Biofeedback, 1. https://doi.org/10.1007/s10484-023-09585-3

Scholten, M. R. M., van Honk, J., Aleman, A., & Kahn, R. S. (2006). Behavioral inhibition system (BIS), behavioral activation system (BAS) and schizophrenia: Relationship with psychopathology and physiology. Journal of Psychiatric Research, 40(7), 638–645. https://doi.org/10.1016/j.jpsychires.2006.03.005

Schumann, A., Helbing, N., Rieger, K., Suttkus, S., & Bär, K.-J. (2022). Depressive rumination and heart rate variability: A pilot study on the effect of biofeedback on rumination and its physiological concomitants. Frontiers in Psychiatry, 13, 961294. https://doi.org/10.3389/fpsyt.2022.961294

Shaffer, F., & Ginsberg, J. P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health, 5, 258. https://doi.org/10.3389/fpubh.2017.00258

Simats, A., Sager, H. B., & Liesz, A. (2024). Heart–brain axis in health and disease: Role of innate and adaptive immunity. Cardiovascular Research, cvae185. https://doi.org/10.1093/cvr/cvae185

Stanley, J., Peake, J. M., & Buchheit, M. (2013). Consecutive days of cold water immersion: Effects on cycling performance and heart rate variability. European Journal of Applied Physiology, 113(2), 371–384. https://doi.org/10.1007/s00421-012-2445-2

Thayer, J. F., Ahs, F., Fredrikson, M., Sollers, J. J. 3rd, & Wager, T. D. (2012). A meta-analysis of heart rate variability and neuroimaging studies: Implications for heart rate variability as a marker of stress and health. Neuroscience and Biobehavioral Reviews, 36(2), 747–756. https://doi.org/10.1016/j.neubiorev.2011.11.009

Thayer, J. F., & Lane, R. D. (2009). Claude Bernard and the heart-brain connection: Further elaboration of a model of neurovisceral integration. Neuroscience and Biobehavioral Reviews, 33(2), 81–88. https://doi.org/10.1016/j.neubiorev.2008.08.004

What is a Good HRV? | Heart Rate Variability | WHOOP. (n.d.). Retrieved November 5, 2024, from https://www.whoop.com/au/en/thelocker/what-is-a-good-hrv/

Yu, B., Funk, M., Hu, J., & Feijs, L. (2017, June 10). StressTree: A Metaphorical Visualization for Biofeedback-assisted Stress Management. https://doi.org/10.1145/3064663.3064729

Zaccaro, A., Piarulli, A., Laurino, M., Garbella, E., Menicucci, D., Neri, B., & Gemignani, A. (2018). How Breath-Control Can Change Your Life: A Systematic Review on Psycho-Physiological Correlates of Slow Breathing. Frontiers in Human Neuroscience, 12, 353. https://doi.org/10.3389/fnhum.2018.00353

	The heart's response to exercise
	The balance between the sympathetic and parasympathetic branches of the autonomic nervous system
	The heart's ability to pump blood
	The emotional state of an individual

	Time-domain analysis
	Frequency-domain analysis
	Echocardiogram
	LF/HF ratio

	Poorer emotional regulation
	Decreased resilience to stress
	Enhanced cognitive functions
	Increased anxiety levels

	Decreasing parasympathetic activity only
	Increasing sympathetic activation during stress
	Enhancing the individual's ability to regulate autonomic functions
	Suppressing the hypothalamic-pituitary-adrenal (HPA) axis

	The circulatory system
	The vagus nerve
	The lymphatic system
	The somatic nervous system

	Improved stress management
	Enhanced overall health
	Potential onset of stress-related disorders
	Higher energy levels