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Anxiety
Published in Carolyn Torkelson, Catherine Marienau, Beyond Menopause, 2023
Carolyn Torkelson, Catherine Marienau
HeartMath is a way to help you learn to meditate (heartmath.com). This scientifically validated technique is a combination of hands-on and visual. It guides you to a state of relaxation intended to help manage stress, revitalize energy, and restore mental and emotional balance and resilience. When this technology is used, you can see a display of your heart rhythm, measured by heart rate variability, which indicates how your emotional state is affecting your nervous system. Having a visible measuring tool helps some women direct their breath to a calmer state. Heart rate variability can offer a window into the connection between your heart and your brain, which directly impacts how you feel and perform.
Depression, Anxiety, Stress, and Spirituality in Cardiovascular Disease
Published in Stephen T. Sinatra, Mark C. Houston, Nutritional and Integrative Strategies in Cardiovascular Medicine, 2022
Erminia Guarneri, Shyamia Stone
Depression and anxiety may also be correlated with CVD through their influence on altering autonomic vascular tone. This has been most directly studied through the concept of heart rate variability (HRV). HRV is a measurement of R-R intervals and the cyclic variation that reflects autonomic balance between sympathetic and parasympathetic nervous system activity.52 Autonomic nervous system imbalance with increased sympathetic nervous system activation and decreased vagal tone has been correlated with CVD and risk of adverse cardiac events,52,169 through such mechanisms as triggering atherosclerosis and/or platelet aggregation, and leading to changes in lipid metabolism.53 Low HRV is related to poor cardiac outcomes and increased risk of post-MI mortality,54 and is correlated with sudden cardiac death even in those that have not been diagnosed with CVD.55
Metabolic Approaches to the Treatment of Back Pain
Published in Kohlstadt Ingrid, Cintron Kenneth, Metabolic Therapies in Orthopedics, Second Edition, 2018
Carrie Diulus, Patrick Hanaway
On the other side of the spectrum, there is the subset of patients who over-exercise. Over-exercise itself increases the risk of injury. It also increases systemic inflammation, increases oxidative stress, increases cortisol, and can lead to poor sleep. These can all be factors when addressing someone with chronic low back pain. Helping these patients understand the need to exercise less can be as challenging as getting other patient populations to understand the importance of moving more. It is often helpful to fill up the time that the “over-exerciser” would spend exercising with stress-relieving strategies that are very active and require attention and engagement. Heart rate variability training can be especially helpful in this patient population, providing biofeedback for stress reduction while at the same time being a goal-oriented pursuit.
Test–retest reliability of heart-rate variability metrics in individuals with aphasia
Published in Neuropsychological Rehabilitation, 2023
Sameer A. Ashaie, Samantha Engel, Leora R. Cherney
One potential methodology to objectively assess cognitive domains such as executive functioning and attention with minimal reliance on language is heart rate variability (HRV). Heart rate variability refers to variability in the interval between successive heartbeats (RR intervals) and can be used as an index of parasympathetic and sympathetic nervous activity of the autonomic nervous system (Malik et al., 1996; Quintana et al., 2016). Parasympathetic nervous system (PNS) activity decreases heart rate while increasing heart rate variability; in contrast, sympathetic nervous system (SNS) activity increases heart rate while decreasing heart rate variability. Sympathetic nervous system is typically dominant during stressful conditions while PNS is dominant during relaxed and resting conditions. The two systems work together to maintain homeostasis. Moreover, Thayer et al. (2012) in their metanalysis of neuroimaging and HRV studies postulate that resting HRV may be linked to functioning of prefrontal and subcortical (e.g., ventral medial prefrontal cortex and amygdala) structures of the brain. Thayer et al. (2012) further suggest that HRV may measure the degree of integration between medial prefrontal cortex and the brainstem which directly regulates the heart.
The Role of Affect as a Mediator between Coping Resources and Heart Rate Variability among Older Adults
Published in Experimental Aging Research, 2022
Galit Pinto, Lee Greenblatt-Kimron, Ibrahim Marai, Avraham Lorber, Ariela Lowenstein, Miri Cohen
High-frequency heart rate variability. HF-HRV was obtained from ECG data measured using a Holter monitoring device with six electrodes placed on the participant’s chest and back (Norav Medical DL800). The patients were instructed to breathe quietly and naturally, yet no controlled breathing was applied because it does not affect HRV in resting state recordings. Other studies used a similar policy (Åhs, Sollers, Furmark, Fredrikson, & Thayer, 2009; Berna, Ott, & Nandrino, 2014; Denver, Reed, & Porges, 2007). Participants were instructed to sit quietly for 15 minutes. HF-HRV has a range of 0.15 to 0.40 Hz. Its analysis utilizes spectral methods to interpret the RR tachogram and mathematical algorithm, fast Fourier transform, that generates spectral (frequency) components (Mccraty & Shaffer, 2015). Artifacts were identified and edited by Norav Medical DL800 software.
Physiological Monitoring to Enhance Clinical Hypnosis and Psychotherapy
Published in International Journal of Clinical and Experimental Hypnosis, 2020
Heart rate measures the number of heart beats (identified as R-waves in the electrocardiogram) per minute. Heart rate variability is the moment to moment variation in the time interval between heartbeats (Moss & Shaffer, 2016). The healthy heart is not a metronome. Healthy organisms show organized oscillations, reflecting constant internal regulatory loops. Greater heart rate variability reflects positive health and resilience and predicts wellbeing, longevity, and social engagement (Geisler et al., 2013; Singer, 2010). HRV declines with age and in sedentary individuals, and morbidity risk increases with lower HRV (Umetani et al., 1998). Lowered heart rate variability accompanies both medical and emotional illness (Moss & Shaffer, 2016, 2017). Biomedical research has shown that lowered HRV correlates with increased risk for a second myocardial infarction as well as a risk for cardiac morbidity in the general population (Kleiger et al., 1987; Sessa et al., 2018), risk for death by all causes (Dekker et al., 1997; Tsuji et al., 1994), and the development of diabetic neuropathy (Braun & Geisendorfer, 1995). McCraty and Shaffer (2015) summarize a number of additional correlations between low HRV and disease risk.