Motivation and emotion/Book/2024/Heart rate variability and emotion regulation

Heart rate variability and emotion regulation:
How can HRV monitoring be used to regulate emotion?

Overview

Figure 1. A girl on stage under a spotlight

Imagine that you're standing on a wide stage (see Figure 1) and the spotlight suddenly shines right on you. Your heart starts to pound heavily and your palms are drenched in sweat. You can feel yourself becoming overwhelmed with stress as your mind goes completely blank. All you can see is a million pairs of eyes staring at you expecting the worst. You tried everything you could right to calm the fast rhythms of you heart, from deep breathing to visualising the crowd in front of you in a funny way, however none of this seemed to help. You stand there, completely frozen.

This is the type of situation where the monitoring of your heart rate variability comes to the rescue.

This chapter introduces how learning about your heart rate variability can assist with emotion regulation. The ability to be flexible and respond to complex changes in an environment is important for adapting to environmental challenges and regulating emotion (Aldao et al., 2015). Responses to situational demands can lead to changes in emotional states and physiological factors such as elevation and reduction of heart rate (Egbuniwe et al., 2023). Heart rate variability (HRV) has been recognised as a useful tool in medical research and psychological investigations and can be considered a transdiagnostic biomarker related with emotion regulation abilities (Franquillo et al., 2021). By learning about heart rate variability, you can gain an understanding of your emotional state and learn how to regulate it.

Focus questions:

What are the theories of heart rate variability?
What is the process model of emotion regulation?
How does the different heart rate variabilities regulate emotions?
What can be used to monitor fluctuations in heart rate?

What is heart rate variability?

The autonomic nervous system (ANS) is studied as a correlate of emotion. A central part of this system is the heart rate variability, referring to a the beat-to-beat change in the heart over time (Quintana & Heathers., 2014). Heart rate variability (HRV for short) is a reflection of many physiological factors that modulate the normal rhythm of the heart. It reflects the hearts[grammar?] ability to adapt to changing circumstances by detecting and quickly responding to unpredictable stimuli and emotions. HRV reflects much the state of the heart as the state of the brain. The heart rate represents the net effect of the parasympathetic nerves which slow heart rate, and the sympathetic nerves, which accelerate it. These changes are influenced by emotions, stress and physical exercise. There are two theories that demonstrate this: the Polyvagal Theory and the Neurovisceral Integration Model (Ernst., 2017).

Polyvagal theory

The Polyvagal Theory establishes that heart rate variability provides a sensitive marker of ones[grammar?] ability to respond and recognise social cues. The theory suggests that the physiological state dictates the range of behaviour and psychological experiences. Reduces variability according to the Polyvagal theory represent a fundamental homeostasis mechanism in a pathological state (Ernst., 2017)[improve clarity].

The polyvagal theory states that the vagus nerve is involved in the inhibition of primitive neural fight and flight mechanisms and the promotion of social behaviour[factual?]. Therefore, increased vagus control is associated with higher HRV as well as emotion expression, social competence and active engagement with the environment (De Witte et al., 2016).

Neurovisceral integration model

The Neurovisceral Integration Model proposes that physiological, emotion and cognitive regulation processes are similar to each other in the service of goal-directed behaviour and adapting to changing environments (Grol & De Raedt., 2020). This model suggests that HRV is an index of the capacity for the central autonomic network, - which includes the brainstem, hypothalamus and prefrontal cortex - to adjust to environmental demands. It summarises the relationship between the central nervous system and cardiac activity as indexed by heart rate variability (Thayer., 2009).

What is emotion regulation?

The term 'emotion regulation' is used to describe a person ability to effectively manage and respond to an emotional experience (see figure 2). It is also a key factor associated with resilience and mental health (Guendelman et al., 2024).

Figure 2. Process model of emotion regulation

It can be defined as the extrinsic and intrinsic processes responsible for monitoring, evaluating and modifying emotional reactions (Fantini-Hauwel et al., 2020). Emotions can be recognised based on a physiological signals such as heart rate. With this, it can be aided to predict human emotion (Balbin et al., 2017).

Adapting to different environmental stressors determines good individual functionality ,but subjects with varying degrees of psychopathology seem to lack this capacity. As said before, since ER is an essential skill for psychological health and it represents one’s ongoing adjustment to continuous environmental stimuli and changes, an adequate emotional ability is crucial for general health since it facilitates the selection of optimal responses by inhibiting and rejecting dysfunctional options (Cattaneo et al., 2021).

Examples of emotion regulation include decreasing or increasing either negative or positive emotion. However, decreasing negative emotion seems to be the most common (Gross., 2015). The process model of emotion regulation consists of 5 regulatory processes by which responses to emotional experiences might be regulated: situation selection, situation modification, attention deployment, cognitive change and response modulation ( see Table 1).

Table 1. Explains Process Model of Emotion Regulation


Process Model of Emotion Regulation
Process	Description	Example
Situation Selection	Situation selection involves choosing situations based on their likely emotional impact and may be less cognitively taxing or challenging to implement compared to other strategies for emotion regulation (Webb et al., 2018).	Meeting up with a friend after having a bad day so you are guaranteed to have a laugh and have a fun time.
Situation Modification	Situation modification strategy entails purposefully changing our circumstances to advantage (Duckworth et al., 2017).	First, warned by the goddess Circe of the fatal allure of the Sirens, whose island he would sail past on his journey home; Odysseus preemptively plugs the ears of his oarsmen so they will be deaf to the their song (Duckworth et al., 2017).
Attention Deployment (distraction & concentration)	Attention deployment is a strategy where individuals regulate feelings by changing focus of attention towards non-emotional aspects of a situation (Nunez et al., 2020)	Sometimes when you get feedback for your assignment from peers you may take it personally, feeling angry and anxious, however you can redirect your thoughts to the constructive benefits of the feedback such as imagining a successful moment in the future when applying the criticism you received.
Cognitive Change (reappraisal)	Cognitive change involves how one thinks about the current situation to alter how they feel about it (Uusberg et al., 2019).	When your about to apply for colleges and you feel like you didn't get many offers compared to others, but then you realise that you should be grateful because you at least got offers compared to those who didn't.
Response Modulation (suppression	Response modulation refers to the efforts to modify an emotion after it has been fully generated (Gross et al., 2014)	Your having a veyr heated argument with your parents, your screaming and getting defensive and you want to get violent but instead you count to ten to slow your heart rate to avoid conflict and begin to think rationally in a way to avoid implusive reactions.

Case study

Monia is a student who is doing a double degree as well as working a part-time job at a busy cafe. Shes started to realise that the stress is getting to her, she started to bail out on her shifts at the last minute because she forgot to submit an assignment or realises its due very soon.

She decides that it's time to make a change. She realises the best effective method is creating a timetable or a schedule at the start of every semester. She could start by identifying days her assignments are due and tell her boss which days she requires days off and what weeks she needs fewer shifts, especially during exam season. This way she won't be burdened by the overwhelming feelings of stress as well as sending her boss in a panic when she bails out on work

Read the Case Study above and answer the quiz below. Choose your answers and click "Submit":

	This may be a lot of work, but I know if I create a schedule of sorts I can juggle both my work and studies!
	Do I have to do uni? I mean I get paid a decent amount at the cafe so I don't see a reason why I need to labour and stress about uni constantly.
	It's just not possible to do uni work and a part-time job at the same time.
	I can keep bailing out on the last minute if I need to, it doesn't look like my boss gets too bothered by it

	Cognitive Appraisal
	Situation Modification
	Situation Selection
	Attention Deployment

Relationship between HRV and emotion regulation

Several studies have identified the correlation between heart rate variability and emotion regulation (Cattaneo et al, 2021). The ease with which once can transition between high and low arousal states is dependent on heart rate variability and so emotion regulation critically depends on ones ability to adjust physiological arousal on a momentary basis (Appelhans & Luecken., 2006).

Individuals are able to adapt their responses to environmental demands and, in turn, to navigate social interactions depends on the interplay between the two subsystems of the autonomic nervous system (ANS) (Thayer & Lane., 2000). The sympathetic nervous system (SNS) has an excitatory effect on the body and dominates in situations in which the organism is required to use a high level of energy. The parasympathetic nervous system (PNS) on the other hand is responsible for the conservation of resources, returning the organism to homeostasis . Heart rate variability (HRV) is a noninvasive marker of parasympathetic activity and hence of the autonomous flexibility of the organism. An adaptive ANS is characterised by high HRV, whereas reduced HRV indicates an imbalance of the ANS. The polyvagal theory and the neurovisceral integration model both emphasize the role of para sympathetically (vagally) mediated inhibition of arousal for effective emotional responding and suggest HRV as an index thereof (Appelhans & Luecken., 2006).

Flexible autonomic nervous system (ANS) allow for rapid generation or modulation of emotional states in accordance with situational demands (Appelhans & Luecken., 2006). Those with high heart rate variability tend to have better emotional well-being in contrast with low heart rate variability. Research indicate emotion regulation and HRV are associated via common brain regions (Thayer et al., 2012).

Lets see how much you know!

How does heart rate variability monitor for emotion regulation?

The brain and heart are connected via the autonomic nervous system which indirectly influences each others behaviour. The connection of the sympathetic nervous system (SNS) and parasympathetic nervous system (PNS) is part of the autonomic nervous system (ANS) thus emotional experience cause some changes in heart rhythm which can be detected through various methods of biofeedback. The individual differences in heart rate variability can serve as an index of an individuals self-regulatory abilities and physiological responses to stressful life events (Carnevali et al., 2018). Identifying differences and patterns in HRV can be used to understand the risk and resilience patterns that impact how individuals adapt to stress. See Table 2 for how different levels of HRV regulates emotion.

Table 2. Different levels of HRV vs Emotion Regulation

	Emotion Regulation
High Heart Rate Variability	In healthy individuals, a high HRV is not just the result of random variability, instead much of it is due to the heart responding to physiological oscillatory signals such as breathing and blood pressure feedback so the heart rate slows down and speeds up in a rhythmic fashion at certain frequencies (Mather and Thayer., 2017). During physical or psychological stress, activity of the sympathetic nervous system becomes dominant, producing physiological arousal to aid in adapting to the challenge. An increased pulse, or heart rate is characteristic of this arousal (Appelhans & Luecken., 2006). Having a high heart rate variability is associated with higher emotional wellbeing, including being correlated with lower levels of worry and rumination, lower anxiety and more regulated emotional responding. Those with high heart rate variability have a better emotional well-being than those with lower hear rate variability. (Mather & Thayer., 2017). Specifically, higher HRV is related to greater openness and less aggression, more accurate emotion recognition, better down regulation of negative emotions, use of adaptive ER strategies, and more flexible emotional responses, as well as better accessibility of ER strategies. A higher HRV further indicates enhanced control of the parasympathetic nervous system. Elevated HRV is often considered a marker of psychological resilience and the capacity to react appropriately and flexibly to ever-changing environmental cues. In contrast, a decreased HRV suggests greater control of the sympathetic nervous system, which is often associated with increased impulsivity and greater difficulties in emotional regulation (Luo et al., 2024).
Low Heart Rate Variability	By contrast with high HRV, those with low HRV are slower from recovering from psychological stress leading to emotional dysregulation (Cattaneo et al., 2021). At rest, active cortical brain areas are indicative of lower emotion dysregulation, resulting in a low HRV. It has been recently shown that depression is associated with reduced heart rate variability (HRV), a measure that reflects both sympathetic and parasympathetic activity. Specifically, Patron et al., (2012) found that patients with depression had significantly lower HRV, especially an altered cardiac vagal tone, at discharge from the hospital compared to non-depressed patients. Lower heart rate variability and difficulties in emotion regulation are characteristics of certain psychopathological states (Thayer & Lane., 2007). Trait anxiety is associated with both lower heart rate variability and emotion regulation capabilities. These are characteristic of major psychopathological disorders such as major depressive disorder, generalised anxiety disorder and post-traumatic stress (Friedman & Thayer., 1998).
Resting Heart Rate Variability	A resting heart rate variability is considered a biomarker of stress resilience and reflects the ability to effectively regulate emotions in a changing environment (Makovac et al., 2022).

Monitoring HRV

During our everyday life we are constantly regulation our emotions, but in some cases emotions can take control and then it becomes too difficult to regulate them effectively, for e.g. anxiety, an emotion characterised by tension, worried thoughts and physical changed liked increased heart rate (Costa et al., 2016).

Biofeedback is valuable because it consistently feeds back relevant information about the current physiological state and reaction evoked by specific stimuli or situations to the individuals. In emotional situations, biofeedback has been used in clinical settings to train individuals to control their heart rate and influence it during later tasks that may cause arousal such as speaking in public .Over the years, it has gained attention as an effective way to reduce symptoms of hostility, depression and anxiety (Peira et al., 2014).

Wearable devices

Among the physiological signals heart rate is the easiest to collect using various devices such as a smart bracelet or electrocardiography etc. Humans can hide emotions and not show up in facial expressions and physical movements, but heart rate can be difficult to control (Chen et al., 2020).

Combination of wearable devices and HRV-based emotion analysis can realise convenient emotion monitoring and even promote the realisation of real-time feedback regulation that will of great significance for determining emotional arousal and attention process. The realisation of real-time feedback regulation for relevant high-risk groups (such as the unemployed population, pregnant women, empty nest elderly, people with mental disorders, etc), or even ordinary individuals, is also valuable for avoiding the effects of negative emotion on physical and mental health (Bettis et al., 2022).

PPG

Photoplethysmography, (PPG for short) is a non-invasive technology using a light source and photodetector on the skins surface to measure variation in blood circulation.

Figure 3. Sensors on 3 different Apple Watches

On all smart devices, the PPG sensors is located on the back of the device making contact with the skin, such as the apple watch sensor (see Figure 3). When the sensor is functioning, the light sources emits light to the skin and the photodetector measures the reflected light from the tissue, thus inferring changes in blood volume by measuring changes in light absorption. Light absorption is proportional to blood volume variation caused by the beating heart. This pulse to pulse intervals are equivalent to intervals from an electrocardiogram (Li et al., 2023).

This method has also been verified during sleep or rest conditions in multiple studies.

Other examples

Other examples includes Abtahi et al., (2015) who developed a wearable knitted garment with an HRV biofeedback system, which can help to improve the HRV and autonomic balance. Wu et al., (2012) designed a wearable biofeedback system based on a multi-biosensor platform combined with a resonance frequency training biofeedback strategy for stress management and emotional control of unemployed people in daily life.

Conclusion

This chapter discussed how the interplay of the relationship between heart rate variability and emotion regulation helps identify the importance of physiological and psychological mechanisms. The different process of emotion regulation was discussed and how an individual may implement one of these strategies to regulate their feelings (Guendelman et al., 2024). Furthermore HRV is discussed to be biomarker that shows how the autonomic nervous systems adapts to external challenges and stressors therefore exhibiting how one manages their emotional responses (Franquillo et al., 2021). This chapter further discuss how individuals who exhibit high heart rate variability have a greater emotion regulation ability. They know how to navigate their emotions appropriately as well as improving the psychological willpower (Mather & Thayer., 2017). In contrast, those with low heart rate variability lead to emotion dysregulation thus leading to characteristics of major depressive disorder, generalised anxiety disorder and post-traumatic stress (Friedman & Thayer., 1998).

Taking in all this information and realising the importance of HRV in terms of regulating emotion, biofeedback is recommended as a method to monitor HRV, which provides us valuable information about emotional arousal (Peira et al., 2014). Specifically, smart devices such as Apple Watches or even Fitbits have a PPG sensor enabling an individual to easily track their heart rate variability (Li et al., 2023).

Key points:

Emotion Regulation [use sentence casing] has a process model to increase or decrease negative or positive emotion
Measuring HRV enables us to better regulate emotion
The transition between high and low arousal states is dependent on HRV
Different patterns of HRV means different things for emotion
Biofeedback, such as smart devices can help monitor HRV, consistently giving back information on ones individual physiological state

References

Abtahi, F., Ji, G., Lu, K., Rodby, K., & Seoane, F. (2015). A Knitted Garment using Intarsia Technique for Heart Rate Variability Biofeedback: Evaluation of Initial Prototype. Engineering in Medicine and Biology Society, 3121–3124. https://dx.doi.org/10.1109/EMBC.2015.7319053

Aldao, A., Sheppes, G., & J. Gross. (2015). Emotion Regulation Flexibility. Cognitive Therapy and Research, 39(1), 263-278. https://doi.org/10.1007/s10608-014-9662-4

Appelhans, M. B., & Luecken, J. L. (2006). Heart Rate Variability as an Index of Regulated Emotional Responding. Review of General Psychology, 10(3), 229–240. https://doi.org/10.1037/1089-2680.10.3.229

Balbin, R.J., Pinugu, J. N. J., Basco, A. JS., Cabanada, M. B., Gonzales, P, MV., Marasigan, J. CC., & Sejera, M. M. (2017). Development of scientific system for assessment of post-traumatic stress disorder patients using physiological sensors and feature extraction for emotional state analysis. International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management. https://doi.org/10.1109/HNICEM.2017.8269424

Bettis, A. H., Burke, T. A., Nesi, J., & Liu, R. T. (2022). Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review. Clinical Psychology Science, 10(1), 3–26. https://doi.org/10.1177/21677026211011982

Cattaneo, L. A., Franquillo, A, C., Grecucci, A., Beccia, L., Caretti, C., & Dadomo, H. (2021). Is Low Heart Rate Variability Associated with Emotional Dysregulation, Psychopathological Dimensions, and Prefrontal Dysfunctions? An Integrative View. Journal of Personalized Medicine, 11(9), 872. https://doi.org/10.3390/jpm11090872

Carnevali, L., Koeing, J., Sgoifo, A., & Ottaviani, C. (2018). Autonomic and Brain Morphological Predictors of Stress Resilience. Frontiers in Neuroscience, 12, 228. https://doi.org/10.3389/fnins.2018.00228

Chen, W., Yu, Y., Shu, L., Hua, H., Li, Q., Jin, J., & Xu, X. (2020). Wearable Emotion Recognition Using Heart Rate Data from a Smart Bracelet. Sensors (Basel), 20(3), 718. https://doi.org/10.3390/s20030718

Costa, J., Adams, A. T., Jung, A. F., Guimbretiere, F., Choudhury, T. (2016). Emotion Check: Leveraging Bodily Signals and False Feedback to Regulate our Emotions. UBICOMP. https://dl.acm.org/doi/pdf/10.1145/2971648.2971752

De Witte, N. J., Sutterline, S., Braet, C., & Mueller, S. C. (2016). Getting to the Heart of Emotion Regulation in Youth: The Role of Interoceptive Sensitivity. Heart Rate Variability, and Parental Psychopathology. Plos One, 11(10), 1–17. https://doi.org/10.1371/journal.pone.0164615

Duckworth, A. L., Gendler, TS., & Gross, J. J. (2016). Situational Strategies for Self-Control. Perspectives Psychological Science, 11(1), 33–55. https://doi.org/10.1177/1745691615623247

Egbuniwe, I., Tupitsa, E., Lloyd, W. K., Puertollano, M., Macdonald, B., Joanknecht, K., Sakaki, M., & van Reekum, CM. (2023). Heart rate variability covaries with amygdala functional connectivity during voluntary emotion regulation. NeuroImage, 274, 2–10. https://doi.org/10.1016/j.neuroimage.2023.120136

Ernst, G. (2017). Heart-Rate Variability–More than Heart Beats?. Frontiers in Public Health, 5(1), 240. https://doi.org/10.3389/fpubh.2017.00240

Fantini-Hauwel, C., Battle, E., Gois, C., & Noel, X. (2020). Emotion Regulation Difficulties Are Not Always Associated With Negative Outcomes on Women: The Buffer Effect of HRV. Frontiers in Psychology, 11(697) https://doi.org/10.3389/fpsyg.2020.00697

Franquillo, A. C., Grecucci, A., Cattaneo, L. A., Beccia, L., & Dadomo, H. (2021). Psychophysiological perspectives on emotion regulation. Predicting Psychopathological Onset: Early Signs of Neuropsychiatric Diseases, 11(9), 872. https://doi.org/10.3390/jpm11090872

Fredrikson, M., Peira, N., & Pourtois, G. (2014). Controlling the emotional heart: Heart rate biofeedback improves cardiac control during emotional reactions. International Journal of Psychophysiology, 91(3), 225–231. https://doi.org/10.1016/j.ijpsycho.2013.12.008

Friedman, B. H., & Thayer, J. F. (1998). Autonomic balance revisited: panic anxiety and heart rate variability. Journal of Psychosomatic Research, 44(1), 133–151. https://doi.org/10.1016/S0022-3999(97)00202-X

Gatchel, R., & Proctor, J. (1976). Physiological Correlates of Learned Helpness in Man. Journal of Abnormal Psychology, 85, 27–34. https://dx.doi.org/10.1037/0021-843X.85.1.27

Grol, M., & De Raedt, R. (2020). The link between resting heart rate variability and affective flexibility. Cognitive, Affective, & Behavioural Neuroscience, 20, 746–756. https://doi.org/10.3758/s13415-020-00800-w

Gross, J. J. (2015). Emotion Regulation: Current Status and Future Prospects. Psychological Injury, 26(1), 1–26. https://doi.org/10.1080/1047840X.2014.940781

Gross, J. J., Goldin, P. R., Jazaieri, H. (2014). Social Anxiety (3rd Ed) Academic Press. https://doi.org/10.1016/b978-0-12-394427-6.00017-0

Guendelman, S., Kaltwasser, L., Bayer, M., Gallese, V., & Dziobek, I. (2024). Brain mechanisms underlying the modulation of heart rate variability when accepting and reappraising emotions. Scientific Reports, 14(18756), 1–14. https://doi.org/10.1038/s41598-024-68352-4

Makovac, E., Carnevali, L., Medina, S., Sgoifo, A., Petrocchi, N., & Ottaviani, C. (2021). Safe in my heart: resting heart rate variability longitudinally predicts emotion regulation, worry, and sense of safeness during COVID-19 lockdown. The International Journal on the Biology of Stress, 25(1), 9–13. https://doi.org/10.1080/10253890.2021.1999408

Nunez, N., Rojas-Libano, D., & Salas, C. E. (2020). Exploring the Neuropsychology of Attention Deployment. International Neuropsychological Science, 2. https://www.researchgate.net/publication/342690375_Exploring_the_Neuropsychology_of_Attention_Deployment

Patron, E., Benvenuti, S. M., Favretto, G., Valfre, C., Bonfa, C., Gasparotto, R., & Palomba, D. (2012). Association between depression and heart rate variability in patients after cardiac surgery: A pilot study. Journal of Psychosomatic Research, 73(1), 42–46. https://doi.org/10.1016/j.jpsychores.2012.04.013

Quintana, D. S., & Heather, J. A. J. (2014). Considerations in the assessment of heart rate variability in biobehavioral research. Frontiers in Psychology, 5(805), 1–10. https://doi.org/10.3389/fpsyg.2014.00805 .

Thayer, J. F. (2009). Heart Rate Variability: A Neurovisceral Integration Model. Encyclopedia of Neuroscience, 1041–1047. https://doi.org/10.1016/B978-008045046-9.01991-4

Thayer, J. F., & Lane, R. D. (2000). A model of neurovisceral integration in emotion regulation and dysregulation. Journal of Affective Disorders, 61(3), 201–216. https://doi.org/10.1016/S0165-0327(00)00338-4

Thayer, J. F., & Lane, R. D. (2007). The role of vagal function in the risk for cardiovascular disease and mortality. Biological Psychology, 74(2), 224–242. https://doi.org/10.1016/j.biopsycho.2005.11.013

Thayer, J. F., Ahs, F. F. M., Sollers, J. J, 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 Biobehavioral Review, 36(2), 747–756. https://doi.org/10.1016/j.neubiorev.2011.11.009

Mather, M., & Thayer, J. F. (2019). How heart rate variability affects emotion regulation brain networks. Current Opinion in Behavioral Sciences, 19, 98–104. https://doi.org/10.1016/j.cobeha.2017.12.017

Peira, N., Fredrikson, M., & Pourtois, G. (2014). Controlling the emotional heart: Heart rate biofeedback improves cardiac control during emotional reactions. International Journal of Psychophysiology, 91(3), 225–231. https://doi.org/10.1016/j.ijpsycho.2013.12.008 https://www.sciencedirect.com/science/article/abs/pii/S016787601300336X

Uusberg, A., Tazer, J. L, Yih, J., Uusberg, H., & Gross, J. J. (2019). Reappraising Reappraisal. International Society for Research on Emotion, 11(4), 267–282. https://doi.org/10.1177/1754073919862617

Webb, T. L., Lindquist, K. A., Jones, K., Avishai, A., & Sheeran, P. (2018). Situation selection is a particularly effective emotion regulation strategy for people who need help regulating their emotions. Cognition and Emotion, 32(2), 231–248 https://doi.org/10.1080/02699931.2017.1295922

Wu, W., Gil, J, & Lee, J. (2012). Combination of wearable multi-biosensor platform and resonance frequency training for stress management of the unemployed population. Biosensors, 12(10), 13225–13248. https://doi.org/10.3390/s121013225

Li, K., Cardoso, C., Moctezuma-Ramirez, A., Elgalad, A., & Perin, E. (2023). Heart rate variability measurement through a smart wearable device: another breakthrough for personal health monitoring? International Journal of Environmental Research and Public Health, 20(24), 7146. https://doi.org/10.3390/ijerph20247146

Luo, X., Wang, R., Zhou, Y., & Xie, W. (2024). The relationship between emotional disorders and heart rate variability: A Mendelian randomization study. Plos One, 19(3), 1–13. https://doi.org/10.1371/journal.pone.0298998

External links

Heart Rate Variability - What this Measure Means for Emotions (imotions.com)
Heart rate variability could be the key to improving your body's response to stress (fortune.com)
The stress test: What heart rate variability can tell you about your mental health (sciencefocus.com)