Biofeedback - Listen to your body
How many times have we heard this golden advice:
“listen to your heart”
or this one
“listen to your gut”
Everyday we listen to our own body to navigate through our highly complex psychosocial and physical world. Our daily decisions and actions depend on us judging a variety of external factors and how they affect us. We do this constantly by deciphering our internal messages and attributing them to correct external causes.
We practice this matching between our external world and our inner life every day, without realising, even when we eat; we not only need to both know if a food source is available externally, but we need to “have a feel for” our stomach to understand if we need/want to eat. Based on what our stomach tells us, then we make a decision. Researchers and healthcare professionals who study biofeedback could say that this simple case of deciding to eat is an instance of using biofeedback. Dr. Inna Khazan, a Harvard-trained clinical health and performance psychologist, describes biofeedback as the clearest and most effective way to understand the messages your body is sending you, and know what kinds of changes you need to do to address them. Biofeedback is hardcoded in our survival that depends on us taking action in the outside world to meet our internal needs (Khazan, 2013, 2019).
“Biofeedback is the clearest and most effective way to understand the messages your body is sending you, learn to recognise them before the signals intensify, and know what kinds of changes you need to make and how to make them.” - Dr. Inna Khazan
Our ability to self-regulate normally functions well to adapt us to our complex environment, and maintain our health and social wellbeing throughout our life-span. However, research shows that this ability gets disrupted by major events such as trauma, chronic stress, physical or mental illness and chronic pain. Researchers note that disruption in self-regulation is common and can also be seen in contemporary societal problems such as obesity and addiction (Heatherton & Wagner, 2011). Nearly 40% of deaths in the US are mediated through disruption in self-regulation, and further research shows that those who show better self-regulation have improved social relationships, job success and mental health (Duckworth & Seligman, 2005. Tangney et al., 2004).
Our physiology is an important entry point to self-regulation of our psychological and psychosocial wellbeing . (Niedenthal, 2007, Weerdmeester et al., 2020) dysregulation in our physiological parameters underpins many problems in mental health (American Psychiatric Association, 2013, McKay & Storch, 2011). This is, to the extent that for diagnoses of generalised anxiety disorder and, posttraumatic disorder, physiological dysregulation of the individual serves as an important diagnosticis criteria ((American Psychiatric Association, 2013; Ebner-Priemer et al., 2007). Yet researchers note that a focus on physiology in mental health interventions is largely missing.
Biofeedback describes a methodology, which can be entirely internal (and autonomous), or external (and controlled) using biofeedback devices. Biofeedback devices typically integrate physiological data collection, analysis and feedback to enable individuals to understand and regulate their health and performance.
A biofeedback system first collects data from a variety of physiological signals our body produces, such as breathing, heart rate, muscle tension, finger temperature, through wearable or non-wearable sensors. Examples for these sensors include thermometer, breath pacers or sophisticated devices like EEG headsets. Then, the system analyses these biological signals, and displays to the individual appropriate feedback, in forms of physiological readings reflecting autonomic balance. This display can be either through vision, such as animation and games, or hearing, such as pitch in music, or haptics (Snyder et al., 2015; Yu et al., 2018). After being informed of their effect on physiological parameters of interest, individuals try different intervention methods such as breathing techniques, to bring these physiological readings to the desired range. This iterative process can look like trial and error, with the training results becoming a motivating factor for the participant. (Yu et al., 2018).
This training can be either mentored with a healthcare professional, or done individually, with each session of 3 minutes to months, implemented in a multi-session training programme that can take up from 3 days to 3 months (Yu et al., 2018).
Biofeedback systems are extensively used in stress management as hypothalamus, a key regulator of autonomic nervous system, gets activated in response to stressors. Various studies show that certain biofeedback systems that we will further describe in our next blog posts have pronounced effect in reducing stress and anxiety (Goessl et al., 2017)
Check our earlier blog posts which describe the neural mechanisms involved in stress and particularly chronic system, which substantially affects the balance between sympathetic and parasympathetic nervous systems that make up the autonomic balance.
In our later blog posts we will delve in to the specific modules within biofeedback, but as a start point we will quickly highlight some commonly used biofeedback systems:
- Breathing biofeedback: Done with devices that measure breathing rate and pattern or concentration of oxygen/carbon dioxide levels in your blood.Training involves trying different breathing techniques.
- Heart Rate (HR) and Heart Rate Variability (HRV): This type of training that is especially relevant to stress management and general mental health, uses devices that measure HR heart rate, usually either through a photoplethysmograph on your fingers or electrocardiogram on your chest, and the user training to lower or keep their HR in the desired range through breathing.
- Respiratory Sinus Arrhythmia biofeedback (or resonance frequency feedback (RFF)). A special emphasis, in the domain of HRV, which is concerned with the changes in HR frequency.This type of biofeedback is shown to be effective in increasing HRV, and primarily aimed at alleviating psychiatric and physiological disorders which are associated with low HRV, ranging from post-traumatic stress disorder to dementia (McCraty et al., 2009; Sarabia-Cobo, 2015; Wu et al., 2012; Zucker et al., 2009). The technique involves breathing at a breathing frequency that creates resonance with the heart rate: it is on average 6 breaths per minute with individualised differences due to heart and vessel physiologies. At this frequency, the heart rate can synchronize with the breathing rate at 0.1 Hz, with heart rate at 1 Hz equaling to 60 beats per minute, and the synchronisation between the peaks in the blood volume created by both breathing and heart pumping in the blood vessels allows for higher amplitudes in blood flow, resulting in more pronounced variations in HR yielding to a higher HRV (Lehrer & Gevirtz, 2014). This is also referred to as cardiac coherence.
- Muscle tension biofeedback: This type of biofeedback is done for purposes of chronic pain management and general muscle relaxation and posture correction. It is done with devices that measure the electrical activity of the muscles (usually through surface electromyography) that informs the user of the amount of non-activity the muscle exhibits when at rest, with the individual trying to lower this.
- Skin conductance: This type of biofeedback is usually employed to improve in managing acute stress and reactions to emotional stimuli, as it measures variations in individual’s arousal levels, through changes in the skin conductivity. Skin conductivity is measured by the amount of sweat produced by glands in your fingers or palm of your hand, as sweat production is shown to be controlled by autonomic nervous system thus tightly coupled to acute autonomic response to stressors.
- Neurofeedback: It requires sophisticated devices that measure different frequencies of oscillation in brain and overall activation power in certain brain regions that are involved in areas of interest for biofeedback, such as frontal cortex to self-regulate executive control or attention, or entire cerebral cortex to track changes in oscillations during sleep to assess sleep quality.
- American Psychiatric Association. (2013). Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition). American Psychiatric Association. https://doi.org/10.1176/appi.books.9780890425596
- Ebner-Priemer, U. W., Welch, S. S., Grossman, P., Reisch, T., Linehan, M. M., & Bohus, M. (2007). Psychophysiological ambulatory assessment of affective dysregulation in borderline personality disorder. Psychiatry Research, 150(3), 265–275. https://doi.org/10.1016/j.psychres.2006.04.014
- Goessl, V. C., Curtiss, J. E., & Hofmann, S. G. (2017). The effect of heart rate variability biofeedback training on stress and anxiety: A meta-analysis. Psychological Medicine, 47(15), 2578–2586. https://doi.org/10.1017/S0033291717001003
- Khazan, I. (2019). Biofeedback and Mindfulness in Everyday Life: Practical Solutions for Improving Your Health and Performance. W. W. Norton & Company.
- McCraty, R., Atkinson, M., Lipsenthal, L., & Arguelles, L. (2009). New hope for correctional officers: An innovative program for reducing stress and health risks. Applied Psychophysiology and Biofeedback, 34(4), 251–272. https://doi.org/10.1007/s10484-009-9087-0
- Sarabia-Cobo, C. M. (2015). Heart coherence: A new tool in the management of stress on professionals and family caregivers of patients with dementia. Applied Psychophysiology and Biofeedback, 40(2), 75–83. https://doi.org/10.1007/s10484-015-9276-y
- Snyder, J., Matthews, M., Chien, J., Chang, P. F., Sun, E., Abdullah, S., & Gay, G. (2015). MoodLight: Exploring Personal and Social Implications of Ambient Display of Biosensor Data. CSCW: Proceedings of the Conference on Computer-Supported Cooperative Work. Conference on Computer-Supported Cooperative Work, 2015, 143–153. https://doi.org/10.1145/2675133.2675191
- Wu, W., Gil, Y., & Lee, J. (2012). Combination of Wearable Multi-Biosensor Platform and Resonance Frequency Training for Stress Management of the Unemployed Population. Sensors (Basel, Switzerland), 12(10), 13225–13248. https://doi.org/10.3390/s121013225
- Yu, B., Funk, M., Hu, J., Wang, Q., & Feijs, L. (2018). Biofeedback for Everyday Stress Management: A Systematic Review. Frontiers in ICT, 5. https://doi.org/10.3389/fict.2018.00023
- Zucker, T. L., Samuelson, K. W., Muench, F., Greenberg, M. A., & Gevirtz, R. N. (2009). The effects of respiratory sinus arrhythmia biofeedback on heart rate variability and posttraumatic stress disorder symptoms: A pilot study. Applied Psychophysiology and Biofeedback, 34(2), 135–143. https://doi.org/10.1007/s10484-009-9085-2