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How to look for correlations between heart rate and eda

How to look for correlations between heart rate and eda

I am looking for some advice on how to take heart rate (HR) and electro-dermal activity (EDA) data files collected via an Empatica e4 and look for correlation between them.

My research project is looking at emotional responses to artworks in museums. I have individual participant data and what I am finding is that when visualised simply some emotional responses provoke EDA and other HR. What I would like to do is run an analysis to show whether there is a relationship between the two. I suspect not, and that this might be a pointless exercise, but it's something I want to have in my back pocket when questioned about these relationships and to use to defend my approach.

Has any one else done this, and if so, what was the best way?


HRV measures

Measures of heart-rate variability (HRV) are primarily calculated with the Inter-beat interval (IBI), also referred to as RR-intervals or NN-intervals (see this question). Some variables in the time domain are :

  • SDNN/SDANN
  • NN50 / pNN50
  • SDSD
  • rMSSD

And many more (see also the rHRV tutorial, which also describes frequency-domain measures).

EDA measures

Electrodermal activity can be quantified by calculating the galvanic skin level (GSL) or the galvanic skin responses (GSR; see also this question; Bouscein (2012)). GSL is a slow drifting tonic level of conductivity, whereas GSR are spikes in activity after some events. You can quantify these values by determining the average amplitude of the GSL/GSR's within a specific time period, or calculating the amount of GSR's.

Correlations

When you have acquired the variables you can start calculating correlations. You do have to ensure that the time intervals of these variables are the same. E.g. the average values of each variable are calculated over five-second windows. In turn, each five-second period can be considered as a data point.

Here are some results I found in a pilot-study I performed with two participants. Because of the little amount of data, not every correlation may be what you expect. pNN50, SDNN, LFHF, LF and HF are the HRV data. numGSR (amount of spikes), eda_p_filt (mean amplitude of GSR spikes) and eda_t (tonic skin activity) are the electrodermal-activity measures. The IWS is a subjective measure of workload (Kramer, Johnson and Zeilstra, 2016),


References

Bouscein, W., Roth, W. T., Dawson, M. E., & Filion, D. L. (2012). Publication recommendations for electrodermal measurements. Psychophysiology, 49, 1017-1034.

Garcıa, C. A., Otero, A., Vila, X., Méndez, A., Rodrıguez-Linares, L., & Lado, M. J. (2013). Getting started with RHRV.

Kramer, R., Johnson, A., & Zeilstra, M. P. (2016). The Integrated Workload Scale-Translation and validation of a subjective workload scale. Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit.

What is the difference between RR-intervals and NN-intervals in HRV-data?

Open-source software for analyzing Electrodermal activity


College News

Music plays a role in every person’s life. From the casual listener to the avid musician, music can have an emotional and physical impact. These effects inspired Havovi Desai to conduct a specialized research project for the 2018 Denman Research Forum.

Desai, a fifth-year student pursuing a dual degree in psychology and music, was interested in the relationship between resting heart rate variability (HRV) and music listening preferences. With the help of psychology postdoctoral researcher DeWayne Williams and psychology professor Julian Thayer, she used an electrocardiogram to collect participants' HRV data and had them answer a series of questionnaires. Their answers were then correlated to their heart signals.

The research took place over multiple months and the results found that individuals with higher resting HRV did more cognitive music listening. However, there was no significant correlation for emotional music listening and background music listening. The results were different from what Desai had anticipated, but she still found the revelation to be important.

Although conducting research proved to be a time-consuming task, Desai said that analyzing the data and finding correlations made it all worth it. Desai also found it incredibly rewarding to be able to incorporate music into her research.

“Being able to combine research with my passion for music was truly a special experience for me,” Desai said. “I have always loved research and been heavily involved in it on campus, and being able to tie in my musical background to the work I do in my lab was so fun and unique.”

Desai’s love for music is well documented. On top of her musically inclined academic and research pursuits, she is also a two-year flugelhorn player in The Ohio State University Marching Band. She hopes to pursue a career in the field of music therapy and plans on getting her master’s degree after graduating this autumn.

“Understanding the correlation between one's music listening tendencies and their health can be a great tool for therapists to use in the workplace,” Desai said. “If this study is continued and further explored in the future, we could develop new interventions based on cognitive music listening for patients in the clinical setting.”

Desai said the research would not have been possible without the resources and support that Ohio State and the College of Arts and Sciences provided her. Looking back on her undergraduate research experience, she sees the possibilities she had as a gift.

“I've been able to dive into research and find something I'm genuinely passionate about because the opportunities are so endless here,” Desai said. “Everyone here is so passionate and willing to foster my growth as an undergraduate researcher, and I am so appreciative.”


Why It’s an Imperfect Connection

But just because your heart rate is a good predictor of your fat loss, you shouldn’t get too carried away and assume that it’s a perfect indicator of your fat loss.

Why? Because your heart rate can increase for a number of reasons and it won’t always lead to more oxygen being delivered to your muscles and your fat stores.

A good example is the use of an ‘oxygen restriction mask’. This is a type of mask that you can wear in order to limit the amount of oxygen you take in. Unfortunately, this is something of a fad that doesn’t quite do what it promises on the packet. That is to say, that it won’t actually make you more athletic or help you to burn more fat. It will increase your heart rate but that’s only because you have less oxygen in your blood and therefore you need to pump it around your body faster in order to get the same benefits.

Similarly, the strength of your heart as it pumps can also impact on heart rate without it impacting on the amount of fat you burn. As you get fitter, you can deliver more oxygen around your body while actually using fewer heartbeats – that’s because your heart has become stronger! This doesn’t then mean that you are burning fewer calories though.

Likewise, the air pressure, your blood pressure, your size and the way you’re breathing can all have a big impact on your heart rate without necessarily altering the amount of calories you’re burning. If you have a panic attack, then your breathing will become very rapid and shallow and your heart rate will increase at the same time. But that doesn’t mean that you can burn more calories by having anxiety attacks!


Does COVID really affect your heart?

Credit: Unsplash/CC0 Public Domain

Reading a recent article with the headline Setting the Record straight: there is no "COVID heart" teleported me back to 2020. It wasn't a comfortable trip.

In January 2020, I had a bit of a chat about a virus in Wuhan with my local director of public health. By late February, I was spending hours each night doomscrolling Twitter, seeing the disaster in northern Italy unfold.

Cardiologists like me were dealing with an avalanche of COVID. Multiple tweets suggested many COVID patients suffered cardiac complications. Stories of patients who seemed to be having heart attacks only to infect the staff treating them were very common, as were reports of people with heart failure caused by coronavirus infection of the heart muscle.

Seeing a lot of CV complications of #COVID19 in real-time with myocarditis, ventricular arrhythmias and more. Hard to know what the role of MCS should be. @hfcollaboratory @laurenranard @scottdsolomon @NYPCUCVI
A Heart Attack? No, It Was the Coronavirus https://t.co/DMb9KMgNLU

— Silia DeFilippis, MD (@ersied727) March 27, 2020

It was all pretty convincing and not even that surprising. Even pre-COVID, every week my hospital admits people with heart issues put down to viral infections—although often without much definite evidence. Usually these are mild. There's a high chance you've had a scratchy chest pain during a bad cold that was probably a viral inflammation of the outer lining of the heart—a condition known as pericarditis.

But sometimes, for reasons we don't understand, viral infections can cause very serious heart problems, mimicking a heart attack, causing rhythm issues, or even fatal heart failure. So a virus like SARS-CoV-2 that attaches to the lining of blood vessels might plausibly cause cardiac complications.

Like many university-based doctors, I went full-time NHS and got ready to face COVID. My colleagues and I started to read everything we could find about COVID's effect on the heart. At the same time, we began to see the virus first hand. A journal asked us to review the emerging data. It seemed like a good idea at the time.

By May 2020, there were several published "case reports" (descriptions of single cases) of COVID patients with a huge range of cardiac complications, and already multiple reviews citing dozens of papers (all published in 2020) describing potential complications and causes of COVID heart. The word "potential" is easily overlooked, but is critical.

By the time we submitted our manuscript in May 2020, a search for "COVID" and "cardiovascular" on PubMed—a website for searching medical and life sciences journal articles—found 653 publications. Our hospital had treated 1,450 COVID patients. Many of them were very ill with terrible lung problems—many died. Although the heart must fail at the end of life, we weren't seeing the avalanche of direct cardiac complications we expected.

By the time we were invited to submit a revised manuscript in July, there were 3,055 publications from the PubMed search, many of which continued to raise significant concerns about high rates of direct cardiac complications with COVID. This included the studies now questioned in the article mentioned above. But also, several high-profile papers had been retracted or revised to correct errors made in the understandably frantic haste to publish.

We found a number of errors. For example, a case series including someone who was on intensive care for a pericardial operation and then contracted the sister virus that causes Mers (Mers-CoV) was erroneously cited several times as evidence of the cardiac complications that can be caused by this family of viruses.

By the time we resubmitted our final manuscript in September 2020, I was already concerned we were adding to the problem writing yet another paper when there were already too many to read. We subtitled our review "many publications, multiple uncertainties."

Although we included reports of complications we had encountered, especially blood clots related to COVID, we concluded "our fears of a large number of severe cardiac complications of COVID have so far not materialized." It's been cited exactly zero times. That PubMed search now finds 6,810 publications, ours among them.

At medical school, I was taught about "Occam's razor"—the idea that the most economical explanation is usually the right one. In medicine, this is often interpreted as an instruction to look for a single cause in someone with multiple issues. But this only works on TV shows like House, in which the preternaturally gifted Dr. House usually discovers the obscure, singular cause of his patient's multiple symptoms.

Patients more often have two or even three common diseases simultaneously than a rare problem that causes all the same issues. But when a coincidence is "interesting" it is far more likely to end up as a case report. Hence the correlations between heart problems and COVID that appeared in the literature.

So there's no COVID heart? Actually, I think there is, but severe forms of it are not as common as I expected and difficult to tell from the indirect effects of severe illness due to any cause: COVID, cancer or car crash.

This article is republished from The Conversation under a Creative Commons license. Read the original article.


  • Exploratory Data Analysis (EDA) is a pre-processing step to understand the data. There are numerous methods and steps in performing EDA, however, most of them are specific, focusing on either visualization or distribution, and are incomplete. Therefore, here, I will walk-through step-by-step to understand, explore, and extract the information from the data to answer the questions or assumptions. There are no structured steps or method to follow, however, this project will provide an insight on EDA for you and my future self.

Cardiovascular diseases (CVDs) or heart disease are the number one cause of death globally with 17.9 million death cases each year. CVDs are concertedly contributed by hypertension, diabetes, overweight and unhealthy lifestyles. You can read more on the heart disease statistics and causes for self-understanding. This project covers manual exploratory data analysis and using pandas profiling in Jupyter Notebook, on Google Colab. The dataset used in this project is UCI Heart Disease dataset, and both data and code for this project are available on my GitHub repository.


How reliable is this study for the relationship between heart rate and calories burned?

It states that within the narrow range of 90-150 bpm, there is a linear correlation between heart rate and calories burned as follows:

However, I have my doubts about this. In particular, how come this formula doesn't factor in the metabolic equivalent of task (MET)? Surely the exercise intensity plays an important role in how calories are burned, or is that just purely reflected through heart rate and V02max?

What I find strange is that it takes very little workout intensity to get my heart rate very high. Just today I ran on the treadmill at a mere 4.7 mph (7.5 kph) for an hour at a sustained heart rate between 145 and 155. Going by the HR formula, I burned around 1033 kcal, whereas running at this speed yields an MET of about 8.3, which yields 772 kcal (my MET is 93). That's not even close.

As another example, a game of basketball has me easily averaging a 160-170 HR (even with all the whistles, timeouts, stop & go, etc) and my HR will sustain 20-30 BPM above normal for at least another 30 mins or so after the game.

I don't think I'm necessarily out of shape. I managed to achieve a VO2max above 50 a week ago via the cooper test. I'm a big guy at 6"6, 245 lbs, roughly 20% body fat. Is it strange that my HR increases so fast from running? Does it make me an outlier with regards to this formula? Should I rely on MET values more than this HR formula for estimating caloric burn during exercise?


Temporal dynamics of arousal and attention in 12-month-old infants

Research from the animal literature suggests that dynamic, ongoing changes in arousal lead to dynamic changes in an individual's state of anticipatory readiness, influencing how individuals distribute their attention to the environment. However, multiple peripheral indices exist for studying arousal in humans, each showing change on different temporal scales, challenging whether arousal is best characterized as a unitary or a heterogeneous construct. Here, in 53 typical 12-month-olds, we recorded heart rate (HR), head movement patterns, electrodermal activity (EDA), and attention (indexed via look duration) during the presentation of 20 min of mixed animations and TV clips. We also examined triggers for high arousal episodes. Using cross-correlations and auto-correlations, we found that HR and head movement show strong covariance on a sub-minute scale, with changes in head movement consistently preceding changes in HR. EDA showed significant covariance with both, but on much larger time-scales. HR and head movement showed consistent relationships with look duration, but the relationship is temporally specific: relations are observed between head movement, HR and look duration at 30 s time-lag, but not at larger time intervals. No comparable relationships were found for EDA. Changes in head movement and HR occurred before changes in look duration, but not for EDA. Our results suggest that consistent patterns of covariation between heart rate, head movement and EDA can be identified, albeit on different time-scales, and that associations with look duration are present for head movement and heart rate, but not for EDA. Our results suggests that there is a single construct of arousal that can identified across multiple measures, and that phasic changes in arousal precede phasic changes in look duration. © 2016 Wiley Periodicals, Inc. Dev Psychobiol 58: 623-639, 2016.

Keywords: arousal attention cross-correlation dynamic infant naturalistic.


Introduction

There is a substantial body of research demonstrating a relationship between low resting heart rate (RHR) and antisocial behavior (Raine & Portnoy, 2012). Low RHR is associated with a range of antisocial outcomes such as aggression (Portnoy et al., 2014), delinquency (Raine, Venables, & Mednick, 1997), psychopathy (Gao, Raine, & Schug, 2012), conduct problems, and criminal offending (Armstrong, Keller, Franklin, & Macmillan, 2009). The relationship has been demonstrated throughout the life-course, in children, adolescents and adults (Armstrong et al., 2009 Van de Weijer, de Jong, Bijleveld, Blokland, & Raine, 2017) in male and female populations (Moffitt, Caspi, Rutter, & Silva, 2001) and using a range of measures of antisocial behavior including clinical diagnoses of conduct disorder, child psychopathology (Raine, Fung, Portnoy, Choy, & Spring, 2014), self-reports (Murray et al., 2016), observational measures (Kindlon et al., 1995) and criminal records (Armstrong, Boisvert, Flores, Symonds, & Gangitano, 2017 Cauffman, Steinberg, & Piquero, 2005 Jennings, Piquero, & Farrington, 2013). The relationship has been replicated internationally using different approaches to measure RHR in general, clinical/treatment-seeking, and correctional populations (Murray et al., 2016 Raine, 2002).

Most of the research in this area has used cross-sectional designs: there are very few longitudinal studies of RHR and antisocial behavior. However, three major meta-analyses examining this relationship have been conducted to date. All three have reached the conclusion that low RHR is a significant, independent correlate of antisocial behavior. The averaged effect sizes (d) were −0.38 (based on 46 effect sizes Lorber, 2004), −0.44 (based on 45 effect sizes Ortiz & Raine, 2004) and −0.20 (based on 115 effect sizes Portnoy & Farrington, 2015). A notable strength of the Portnoy and Farrington meta-analysis was that they overcame several methodological limitations of the previous meta-analyses (see Portnoy & Farrington, 2015, pp. 35–36): they ruled out several study confounds and demonstrated that the effect is not moderated by sex, study design, length of follow-up period, sample age or antisocial behavior type. They concluded that low RHR “should continue to be incorporated into antisocial behavior research and confirm resting heart rate's status [as] an important correlate of antisocial behavior” (Portnoy & Farrington, 2015 p. 42). Research has also examined the association between heart rate reactivity and antisocial outcomes, although the findings to date have been less consistent than those for resting heart rate (Jennings, Pardini, & Matthews, 2017). Because of its sturdiness as a predictor, researchers have argued that RHR should be included in longitudinal studies as a risk factor (Choy, Farrington, & Raine, 2015) and a protective factor for antisocial (Portnoy, Chen, & Raine, 2013) and health outcomes (Jennings et al., 2017). It should also be noted that additional research has ruled out the possibility that antisocial behavior causes low RHR (Moffitt & Caspi, 2001 Raine et al., 1997).

Why is understanding the relationship between RHR, and more generally, autonomic nervous system (ANS) functioning and antisocial behavior important? Van Goozen and Fairchild (2008) argue that having a depressed autonomic nervous system (ANS) produces an insensitivity to stress, and it is this lack of ANS excitation that may attenuate one's fight or flight response and/or ability to process emotional cues. The result is that individuals with a relatively low RHR might be less likely to learn from their experiences or benefit from certain treatments, particularly those that incorporate punishment-based learning (Van Goozen & Fairchild, 2008), thus, making antisocial behavior more likely. An obvious implication is that some individuals might not benefit from traditional treatment programs (e.g., cognitive behavioral) unless they also receive some sort of intervention (e.g., transcranial magnetic stimulation) aimed at their underlying physiology. From a crime prevention perspective, it would therefore be helpful to measure RHR along with other relevant biosocial variables so that resources can be allocated intelligently to those who pose a greater risk for offending (e.g., the risk principle: Andrews & Bonta, 2010). From a theoretical perspective, it is important to isolate the relative strength of individual biological factors from other (e.g., family, peer, neighborhood) factors, and to understand the circumstances under which they are most likely to produce antisocial behavior.

Two recent large-scale studies (Latvala et al., 2016 Latvala, Kuja-Halkola, Almqvist, Larsson, & Lichtenstein, 2015) confirm the growing body of research linking low RHR to antisocial outcomes. What is novel about these studies, however, is that they expanded the range of biosocial predictors to include systolic blood pressure (SBP). In a study with a sample of over 710,264 Swedish men followed up >18 years, Latvala et al. (2015) demonstrated that low RHR predicted a range of rule-breaking behaviors including minor violence, drug-related, property, and traffic crime, but especially serious violence. Study participants in the lowest RHR quintile were 31% more likely to experience unintended injuries and 41% more likely to be injured as a result of an assault compared to those in the topmost quintile, even after adjusting for potential confounding variables. Interestingly, the authors also found similar relationships between SBP and violent and non-violent criminality. An important exception is that no significant association was observed between low RHR/SBP and sexual offending, leading the authors to call on future studies to further clarify this relationship.

In a follow-up study, Latvala et al. (2016) extended this analysis to measure the relationship between RHR and SBP in relation to a range of psychiatric disorders in a prospective longitudinal study. They studied a very large sample of Swedish men whose RHR (n = 1,039,443) and SBP (n = 1,555,979) were measured at military conscription (mean age = 18.3 years) and followed up three decades later, on average. They found that higher RHR and SBP were associated with a greater likelihood of having a diagnosis of OCD, anxiety disorder, or schizophrenia. The strongest effect was found for OCD, where a 10-point increase in RHR increased the chance of this diagnosis by 18%. Conversely, having a lower RHR and SBP were associated with higher rates of substance use disorders and violent criminality, and these risks were even higher when physical fitness was controlled for. Each 10-point decrease in either RHR or SBP increased the risk of being diagnosed with a substance use disorder by 5% and increased the risk of having a violent conviction by 10%.

Aside from the Latvala studies, there is a relatively small research base examining the relationship between SBP and antisocial and psychological outcomes. For example, in a sample of 122 Swedish schoolchildren, Borres, Tanaka, and Thulesius (1998) demonstrated that children with three or more psychosomatic or psychological symptoms had significantly lower SBP compared to children with no symptoms. Rapoza et al. (2014) examined the relationship between child maltreatment and a range of health outcomes in early adulthood and found that higher self-reported anger, but not child maltreatment per se, was significantly correlated with low SBP. Capitalizing on a very large sample from the Western Australian Pregnancy Cohort Study (n = 2900), Louise et al. (2012) found a significant association between low SBP and aggression at age 14 for boys, but not for girls. Finally, in a cross-sectional study, Gower and Crick (2011) extended the analysis to include relational aggression in a sample of preschoolers (aged 43 to 66 months, M = 54.0, SD = 7.0) and found that low SBP was significantly correlated with relational aggression in older, but not younger children.

While it is clear that there is an empirical link between low RHR (and apparently also low SBP) and antisocial behavior, the precise mechanisms mediating these relationships are not precisely known. Two main theories have been proposed to explain the relationship between low RHR and antisocial behavior: 1) fearlessness theory, and 2) sensation-seeking theory. The first asserts that, in the face of anxiety provoking stimuli, some individuals will not experience a rise in their heart rate, which reflects a relative lack of fear. Without internal cues, such as a pounding heart or sweaty palms, to signal that they are about to engage in risky behaviors, individuals are more easily able to follow through on their actions without being inhibited by the fear of potential negative consequences (Raine, 1993, Raine, 2002). The sensation-seeking hypothesis postulates that for some a state of low arousal is uncomfortable, and these individuals will seek out stimulation to increase their arousal to more normative levels (Eysenck, 1997 Quay, 1965 Raine, 2002). Because individuals with a low RHR are in a chronic state of under-arousal, they seek out opportunities to increase stimulation, including participation in antisocial behavior and crime.

To date, only a handful of studies have tested these two theories. Based on data from 151 university students, Armstrong and Boutwell (2012) used a rational choice framework to study the association between RHR and antisocial behavior. They concluded that fearlessness and not sensation-seeking best explained the relationship between low RHR and participants' perceived probability of conviction for assault. Studies examining RHR in relation to sensation-seeking are relatively more common. For example, using data from a large Dutch prospective population study, Sijtsema et al. (2010) found that the statistical relationship between low RHR and aggression and rule breaking was mediated by a parent-reported measure of sensation seeking in adolescent boys. Portnoy et al. (2014) assessed the influence of RHR on antisocial outcomes in a subsample (N = 335) of boys in the Pittsburgh Youth Study and found that sensation-seeking, and not fearlessness, mediated the relationship between low RHR and antisocial behavior, defined as aggression and non-violent delinquency. A methodological strength of this study was that the researchers included a measure of state fear to more directly test its potential mediating influence for decisions to commit antisocial acts.

This study sought to expand the knowledge base on the relationship between physiological indices of arousal (i.e., RHR and SBP) and antisocial behavior. As noted earlier, aside from the studies by Latvala et al., 2015, Latvala et al., 2016, virtually no work has been done to measure the association between SBP and antisocial outcomes. In addition, although prior research has used official offending as an outcome variable in relation to RHR in adolescent populations (e.g., Cauffman et al., 2005 de Vries-Bouw et al., 2011 Raine, Venables, & Williams, 1990), only one prior study (i.e., Armstrong et al., 2017) has examined the relationship in an adult correctional context using official criminal records. Furthermore, and consistent with Latvala et al. (2015), we subdivided criminal offending into mutually exclusive categories (i.e., person, property, weapons, sexual, drug-related, other) to assess whether the negative relationship between RHR/SBP and criminality held across all crime types. Of interest here was whether we would replicate the non-significant association between RHR/SBP and sexual offending. In summary, the objectives of the study were to: 1.

Assess the relationship between low RHR and measures of antisocial behavior in a sample of incarcerated male adults

Determine whether low SBP is also a significant predictor of antisocial behavior and

Examine whether the aforementioned relationships apply to all types of crime.


Foundations

Niels Birbaumer , Herta Flor , in Comprehensive Clinical Psychology , 1998

1.05.5.3.7 Heart activity and blood pressure

Heart rate (HR) and blood pressure are the most frequently used psychophysiological indicators. HR is used because it is easily recorded from any two electrodes affixed at the more right or left side of the body allowing the heart to be positioned between them (i.e., both hands). Blood pressure has become more popular during recent years when noninvasive continuous measurement was realized with pulse-wave velocity devices. The obvious physiological role of the cardiovascular system explains the high correlations of HR with all kinds of behaviors: motor, mental, perception, attention, and orienting stress, emotion, and motivation personality, social stimuli, brain interactions, and conditioning (cf., Andreassi, 1995 , for a review). The dual sympathetic and parasympathetic nervous control allows separation of the two branches of the ANS: slowing of the heartbeat indicates the chronotopic parasympathic, speeding the increased excitability and contraction force of the sympathetic branch.

For decades psychophysiology was obsessed with discussions of the psychological significance of HR changes: while Obrist (1981) and his co-workers interpreted HR changes as variations of mobilization of blood supply for the somatic musculature (cardiac-somatic coupling), Lacey and Lacey (1970) proposed a close relationship with information processing in the brain: HR deceleration and negative going slow cortical potentials should accompany information intake, acceleration information rejection, and positive going slow cortical potentials. The deceleration in a signaled foreperiod reaction paradigm is caused by phasic blood pressure increase which fires the baroreceptors in the carotis sinus. This leads to parasympathetic slowing of the heart and increased cortical activity because the nucleus of the vagus located in the reticular formation fires the ARAS. Tonic stimulation of the baroreceptors through a specially designed neck cuff, however, produces heart rate slowing and positive going cortical slow waves which causes inhibition of cortical activity and information rejection ( Rau, Pauli, Brody, Elbert, & Birbaumer, 1993 ). The accompanying stress reduction negatively reinforces the preceding blood pressure increase and on a long-term basis causes essential hypertension in genetically prone individuals. Neither Obrist's cardiac-somatic coupling nor Lacey and Lacey's information processing hypothesis was unanimously confirmed. HR slowing clearly appears in orienting, while HR increase after intensive or dangerous stimuli constitutes part of the defensive reaction.

The interaction of stress and blood pressure increase was also confirmed in different personality patterns such as type A and hostility: type A subjects (competitive, driven, impatient) with hostile attitudes toward others, demonstrate large increases in blood pressure during stressful situations and exhibit a high risk for essential hypertension and stroke. Early behavioral intervention effectively prevents this vicious circle of blood pressure increase, stress reduction through cortical inhibition, and aggressive muscular mobilization.


Research shows correlation between adult height, underlying heart disease

Minneapolis Heart Institute Foundation research cardiologist Dr. Michael Miedema is the lead author of a paper published by Circulation -- Cardiovascular Imaging, a journal of the American Heart Association, that suggests a connection between an adult's height and the prevalence of coronary artery calcium (CAC), a direct marker of plaque in the arteries that feed the heart. Coronary artery calcium is a strong predictor of future heart attacks with a nearly 10 fold increase in the risk of coronary heart disease (CHD) in patients with elevated CAC.

The article is based on research in 2,703 patients from the Family Heart Study, a government sponsored study of the relationship between potential risk factors and heart disease and is the first study to examine the relationship between adult height and CAC in a large population. It suggests that taller adults tend to have lower levels of plaque, and thus, a lower risk of CHD. This relationship persisted even after accounting for standard cardiovascular risk factors such as age, smoking, high cholesterol, and diabetes.

"A potential link between height and CHD has been shown in several studies but the mechanism of this relationship has not been clear and our study suggests the relationship is mediated by plaque build up in the coronary arteries," said Michael Miedema, MD, MPH, from the Minneapolis Heart Institute Foundation. There may be as much as 30% lower risk of plaque build-up in the top quarter of tallest adults compared to the bottom quarter. These results had to be adjusted for gender, given the differences in height between men and women, but the relationship was consistent in both men and women."

Why taller individuals develop less plaque is not entirely clear.

"Some studies suggest that taller people have favorable changes in their blood pressure due their height but these changes are quite small and unlikely to be the sole cause of this relationship," Miedema stated. "It may be more likely that this relationship is mediated through a common link, such as childhood nutrition or other environmental factors during childhood, which may be determinants of both adult height as well as future coronary heart disease."


Heartbeat And Breathing Cycles

Heartbeat and breathing cycles can become synchronized, a new study shows.

Looking for patterns in the sequence of human heartbeats is a much studied subject evidence for pattern-revealing characteristics such as chaos and fractal or spiral geometry have been sought. Breathing, which is more under direct conscious control than heartbeat, is much less studied.

Part of the problem with searching for a breathing-heartbeat correlation is that these systems have very different rhythms. The heart normally beats 60 to 70 times per minute, while the breathing rate is about one-fifth of that. Furthermore, the heart and breathing phenomena are complex consequently at least for periods of awakeness or rapid-eye-movement (REM) sleep little or no phase synchrony (that is, breathing and heartbeat recurring with a consistent relation to each other) can be found.

However, solid evidence has now been found for a breathing-heartbeat correlation for periods of deep sleep. Some signs of phase synchrony have been found before, but only in small samples of a dozen or so subjects. By contrast, the study performed by scientists at Bar-Ilan University (Israel), and the Martin-Luther University and the Philipps University (both in Germany), includes 112 healthy subjects of varying ages, men and women, for a variety of sleep stages.

The researchers conclude, for one thing, that the breathing rate affects the heart rate but not the other way around. Both the breathing oscillation and heartbeat oscillation are disturbed by the kinds of noise superimposed by higher brain activity present, such as in REM sleep. Jan Kantelhardt is sure enough of the heart-breathing correlation that he believes the sleep stages could now be determined by measuring heartbeat rather than measuring brain waves.

The researchers are also hoping to establish careful heart-breathing correlations for patients with heart problems, the better to develop diagnostic devices.

Story Source:

Materials provided by American Institute Of Physics. Note: Content may be edited for style and length.


Does COVID really affect your heart?

Credit: Unsplash/CC0 Public Domain

Reading a recent article with the headline Setting the Record straight: there is no "COVID heart" teleported me back to 2020. It wasn't a comfortable trip.

In January 2020, I had a bit of a chat about a virus in Wuhan with my local director of public health. By late February, I was spending hours each night doomscrolling Twitter, seeing the disaster in northern Italy unfold.

Cardiologists like me were dealing with an avalanche of COVID. Multiple tweets suggested many COVID patients suffered cardiac complications. Stories of patients who seemed to be having heart attacks only to infect the staff treating them were very common, as were reports of people with heart failure caused by coronavirus infection of the heart muscle.

Seeing a lot of CV complications of #COVID19 in real-time with myocarditis, ventricular arrhythmias and more. Hard to know what the role of MCS should be. @hfcollaboratory @laurenranard @scottdsolomon @NYPCUCVI
A Heart Attack? No, It Was the Coronavirus https://t.co/DMb9KMgNLU

— Silia DeFilippis, MD (@ersied727) March 27, 2020

It was all pretty convincing and not even that surprising. Even pre-COVID, every week my hospital admits people with heart issues put down to viral infections—although often without much definite evidence. Usually these are mild. There's a high chance you've had a scratchy chest pain during a bad cold that was probably a viral inflammation of the outer lining of the heart—a condition known as pericarditis.

But sometimes, for reasons we don't understand, viral infections can cause very serious heart problems, mimicking a heart attack, causing rhythm issues, or even fatal heart failure. So a virus like SARS-CoV-2 that attaches to the lining of blood vessels might plausibly cause cardiac complications.

Like many university-based doctors, I went full-time NHS and got ready to face COVID. My colleagues and I started to read everything we could find about COVID's effect on the heart. At the same time, we began to see the virus first hand. A journal asked us to review the emerging data. It seemed like a good idea at the time.

By May 2020, there were several published "case reports" (descriptions of single cases) of COVID patients with a huge range of cardiac complications, and already multiple reviews citing dozens of papers (all published in 2020) describing potential complications and causes of COVID heart. The word "potential" is easily overlooked, but is critical.

By the time we submitted our manuscript in May 2020, a search for "COVID" and "cardiovascular" on PubMed—a website for searching medical and life sciences journal articles—found 653 publications. Our hospital had treated 1,450 COVID patients. Many of them were very ill with terrible lung problems—many died. Although the heart must fail at the end of life, we weren't seeing the avalanche of direct cardiac complications we expected.

By the time we were invited to submit a revised manuscript in July, there were 3,055 publications from the PubMed search, many of which continued to raise significant concerns about high rates of direct cardiac complications with COVID. This included the studies now questioned in the article mentioned above. But also, several high-profile papers had been retracted or revised to correct errors made in the understandably frantic haste to publish.

We found a number of errors. For example, a case series including someone who was on intensive care for a pericardial operation and then contracted the sister virus that causes Mers (Mers-CoV) was erroneously cited several times as evidence of the cardiac complications that can be caused by this family of viruses.

By the time we resubmitted our final manuscript in September 2020, I was already concerned we were adding to the problem writing yet another paper when there were already too many to read. We subtitled our review "many publications, multiple uncertainties."

Although we included reports of complications we had encountered, especially blood clots related to COVID, we concluded "our fears of a large number of severe cardiac complications of COVID have so far not materialized." It's been cited exactly zero times. That PubMed search now finds 6,810 publications, ours among them.

At medical school, I was taught about "Occam's razor"—the idea that the most economical explanation is usually the right one. In medicine, this is often interpreted as an instruction to look for a single cause in someone with multiple issues. But this only works on TV shows like House, in which the preternaturally gifted Dr. House usually discovers the obscure, singular cause of his patient's multiple symptoms.

Patients more often have two or even three common diseases simultaneously than a rare problem that causes all the same issues. But when a coincidence is "interesting" it is far more likely to end up as a case report. Hence the correlations between heart problems and COVID that appeared in the literature.

So there's no COVID heart? Actually, I think there is, but severe forms of it are not as common as I expected and difficult to tell from the indirect effects of severe illness due to any cause: COVID, cancer or car crash.

This article is republished from The Conversation under a Creative Commons license. Read the original article.


How reliable is this study for the relationship between heart rate and calories burned?

It states that within the narrow range of 90-150 bpm, there is a linear correlation between heart rate and calories burned as follows:

However, I have my doubts about this. In particular, how come this formula doesn't factor in the metabolic equivalent of task (MET)? Surely the exercise intensity plays an important role in how calories are burned, or is that just purely reflected through heart rate and V02max?

What I find strange is that it takes very little workout intensity to get my heart rate very high. Just today I ran on the treadmill at a mere 4.7 mph (7.5 kph) for an hour at a sustained heart rate between 145 and 155. Going by the HR formula, I burned around 1033 kcal, whereas running at this speed yields an MET of about 8.3, which yields 772 kcal (my MET is 93). That's not even close.

As another example, a game of basketball has me easily averaging a 160-170 HR (even with all the whistles, timeouts, stop & go, etc) and my HR will sustain 20-30 BPM above normal for at least another 30 mins or so after the game.

I don't think I'm necessarily out of shape. I managed to achieve a VO2max above 50 a week ago via the cooper test. I'm a big guy at 6"6, 245 lbs, roughly 20% body fat. Is it strange that my HR increases so fast from running? Does it make me an outlier with regards to this formula? Should I rely on MET values more than this HR formula for estimating caloric burn during exercise?


  • Exploratory Data Analysis (EDA) is a pre-processing step to understand the data. There are numerous methods and steps in performing EDA, however, most of them are specific, focusing on either visualization or distribution, and are incomplete. Therefore, here, I will walk-through step-by-step to understand, explore, and extract the information from the data to answer the questions or assumptions. There are no structured steps or method to follow, however, this project will provide an insight on EDA for you and my future self.

Cardiovascular diseases (CVDs) or heart disease are the number one cause of death globally with 17.9 million death cases each year. CVDs are concertedly contributed by hypertension, diabetes, overweight and unhealthy lifestyles. You can read more on the heart disease statistics and causes for self-understanding. This project covers manual exploratory data analysis and using pandas profiling in Jupyter Notebook, on Google Colab. The dataset used in this project is UCI Heart Disease dataset, and both data and code for this project are available on my GitHub repository.


Why It’s an Imperfect Connection

But just because your heart rate is a good predictor of your fat loss, you shouldn’t get too carried away and assume that it’s a perfect indicator of your fat loss.

Why? Because your heart rate can increase for a number of reasons and it won’t always lead to more oxygen being delivered to your muscles and your fat stores.

A good example is the use of an ‘oxygen restriction mask’. This is a type of mask that you can wear in order to limit the amount of oxygen you take in. Unfortunately, this is something of a fad that doesn’t quite do what it promises on the packet. That is to say, that it won’t actually make you more athletic or help you to burn more fat. It will increase your heart rate but that’s only because you have less oxygen in your blood and therefore you need to pump it around your body faster in order to get the same benefits.

Similarly, the strength of your heart as it pumps can also impact on heart rate without it impacting on the amount of fat you burn. As you get fitter, you can deliver more oxygen around your body while actually using fewer heartbeats – that’s because your heart has become stronger! This doesn’t then mean that you are burning fewer calories though.

Likewise, the air pressure, your blood pressure, your size and the way you’re breathing can all have a big impact on your heart rate without necessarily altering the amount of calories you’re burning. If you have a panic attack, then your breathing will become very rapid and shallow and your heart rate will increase at the same time. But that doesn’t mean that you can burn more calories by having anxiety attacks!


Heartbeat And Breathing Cycles

Heartbeat and breathing cycles can become synchronized, a new study shows.

Looking for patterns in the sequence of human heartbeats is a much studied subject evidence for pattern-revealing characteristics such as chaos and fractal or spiral geometry have been sought. Breathing, which is more under direct conscious control than heartbeat, is much less studied.

Part of the problem with searching for a breathing-heartbeat correlation is that these systems have very different rhythms. The heart normally beats 60 to 70 times per minute, while the breathing rate is about one-fifth of that. Furthermore, the heart and breathing phenomena are complex consequently at least for periods of awakeness or rapid-eye-movement (REM) sleep little or no phase synchrony (that is, breathing and heartbeat recurring with a consistent relation to each other) can be found.

However, solid evidence has now been found for a breathing-heartbeat correlation for periods of deep sleep. Some signs of phase synchrony have been found before, but only in small samples of a dozen or so subjects. By contrast, the study performed by scientists at Bar-Ilan University (Israel), and the Martin-Luther University and the Philipps University (both in Germany), includes 112 healthy subjects of varying ages, men and women, for a variety of sleep stages.

The researchers conclude, for one thing, that the breathing rate affects the heart rate but not the other way around. Both the breathing oscillation and heartbeat oscillation are disturbed by the kinds of noise superimposed by higher brain activity present, such as in REM sleep. Jan Kantelhardt is sure enough of the heart-breathing correlation that he believes the sleep stages could now be determined by measuring heartbeat rather than measuring brain waves.

The researchers are also hoping to establish careful heart-breathing correlations for patients with heart problems, the better to develop diagnostic devices.

Story Source:

Materials provided by American Institute Of Physics. Note: Content may be edited for style and length.


College News

Music plays a role in every person’s life. From the casual listener to the avid musician, music can have an emotional and physical impact. These effects inspired Havovi Desai to conduct a specialized research project for the 2018 Denman Research Forum.

Desai, a fifth-year student pursuing a dual degree in psychology and music, was interested in the relationship between resting heart rate variability (HRV) and music listening preferences. With the help of psychology postdoctoral researcher DeWayne Williams and psychology professor Julian Thayer, she used an electrocardiogram to collect participants' HRV data and had them answer a series of questionnaires. Their answers were then correlated to their heart signals.

The research took place over multiple months and the results found that individuals with higher resting HRV did more cognitive music listening. However, there was no significant correlation for emotional music listening and background music listening. The results were different from what Desai had anticipated, but she still found the revelation to be important.

Although conducting research proved to be a time-consuming task, Desai said that analyzing the data and finding correlations made it all worth it. Desai also found it incredibly rewarding to be able to incorporate music into her research.

“Being able to combine research with my passion for music was truly a special experience for me,” Desai said. “I have always loved research and been heavily involved in it on campus, and being able to tie in my musical background to the work I do in my lab was so fun and unique.”

Desai’s love for music is well documented. On top of her musically inclined academic and research pursuits, she is also a two-year flugelhorn player in The Ohio State University Marching Band. She hopes to pursue a career in the field of music therapy and plans on getting her master’s degree after graduating this autumn.

“Understanding the correlation between one's music listening tendencies and their health can be a great tool for therapists to use in the workplace,” Desai said. “If this study is continued and further explored in the future, we could develop new interventions based on cognitive music listening for patients in the clinical setting.”

Desai said the research would not have been possible without the resources and support that Ohio State and the College of Arts and Sciences provided her. Looking back on her undergraduate research experience, she sees the possibilities she had as a gift.

“I've been able to dive into research and find something I'm genuinely passionate about because the opportunities are so endless here,” Desai said. “Everyone here is so passionate and willing to foster my growth as an undergraduate researcher, and I am so appreciative.”


Temporal dynamics of arousal and attention in 12-month-old infants

Research from the animal literature suggests that dynamic, ongoing changes in arousal lead to dynamic changes in an individual's state of anticipatory readiness, influencing how individuals distribute their attention to the environment. However, multiple peripheral indices exist for studying arousal in humans, each showing change on different temporal scales, challenging whether arousal is best characterized as a unitary or a heterogeneous construct. Here, in 53 typical 12-month-olds, we recorded heart rate (HR), head movement patterns, electrodermal activity (EDA), and attention (indexed via look duration) during the presentation of 20 min of mixed animations and TV clips. We also examined triggers for high arousal episodes. Using cross-correlations and auto-correlations, we found that HR and head movement show strong covariance on a sub-minute scale, with changes in head movement consistently preceding changes in HR. EDA showed significant covariance with both, but on much larger time-scales. HR and head movement showed consistent relationships with look duration, but the relationship is temporally specific: relations are observed between head movement, HR and look duration at 30 s time-lag, but not at larger time intervals. No comparable relationships were found for EDA. Changes in head movement and HR occurred before changes in look duration, but not for EDA. Our results suggest that consistent patterns of covariation between heart rate, head movement and EDA can be identified, albeit on different time-scales, and that associations with look duration are present for head movement and heart rate, but not for EDA. Our results suggests that there is a single construct of arousal that can identified across multiple measures, and that phasic changes in arousal precede phasic changes in look duration. © 2016 Wiley Periodicals, Inc. Dev Psychobiol 58: 623-639, 2016.

Keywords: arousal attention cross-correlation dynamic infant naturalistic.


Introduction

There is a substantial body of research demonstrating a relationship between low resting heart rate (RHR) and antisocial behavior (Raine & Portnoy, 2012). Low RHR is associated with a range of antisocial outcomes such as aggression (Portnoy et al., 2014), delinquency (Raine, Venables, & Mednick, 1997), psychopathy (Gao, Raine, & Schug, 2012), conduct problems, and criminal offending (Armstrong, Keller, Franklin, & Macmillan, 2009). The relationship has been demonstrated throughout the life-course, in children, adolescents and adults (Armstrong et al., 2009 Van de Weijer, de Jong, Bijleveld, Blokland, & Raine, 2017) in male and female populations (Moffitt, Caspi, Rutter, & Silva, 2001) and using a range of measures of antisocial behavior including clinical diagnoses of conduct disorder, child psychopathology (Raine, Fung, Portnoy, Choy, & Spring, 2014), self-reports (Murray et al., 2016), observational measures (Kindlon et al., 1995) and criminal records (Armstrong, Boisvert, Flores, Symonds, & Gangitano, 2017 Cauffman, Steinberg, & Piquero, 2005 Jennings, Piquero, & Farrington, 2013). The relationship has been replicated internationally using different approaches to measure RHR in general, clinical/treatment-seeking, and correctional populations (Murray et al., 2016 Raine, 2002).

Most of the research in this area has used cross-sectional designs: there are very few longitudinal studies of RHR and antisocial behavior. However, three major meta-analyses examining this relationship have been conducted to date. All three have reached the conclusion that low RHR is a significant, independent correlate of antisocial behavior. The averaged effect sizes (d) were −0.38 (based on 46 effect sizes Lorber, 2004), −0.44 (based on 45 effect sizes Ortiz & Raine, 2004) and −0.20 (based on 115 effect sizes Portnoy & Farrington, 2015). A notable strength of the Portnoy and Farrington meta-analysis was that they overcame several methodological limitations of the previous meta-analyses (see Portnoy & Farrington, 2015, pp. 35–36): they ruled out several study confounds and demonstrated that the effect is not moderated by sex, study design, length of follow-up period, sample age or antisocial behavior type. They concluded that low RHR “should continue to be incorporated into antisocial behavior research and confirm resting heart rate's status [as] an important correlate of antisocial behavior” (Portnoy & Farrington, 2015 p. 42). Research has also examined the association between heart rate reactivity and antisocial outcomes, although the findings to date have been less consistent than those for resting heart rate (Jennings, Pardini, & Matthews, 2017). Because of its sturdiness as a predictor, researchers have argued that RHR should be included in longitudinal studies as a risk factor (Choy, Farrington, & Raine, 2015) and a protective factor for antisocial (Portnoy, Chen, & Raine, 2013) and health outcomes (Jennings et al., 2017). It should also be noted that additional research has ruled out the possibility that antisocial behavior causes low RHR (Moffitt & Caspi, 2001 Raine et al., 1997).

Why is understanding the relationship between RHR, and more generally, autonomic nervous system (ANS) functioning and antisocial behavior important? Van Goozen and Fairchild (2008) argue that having a depressed autonomic nervous system (ANS) produces an insensitivity to stress, and it is this lack of ANS excitation that may attenuate one's fight or flight response and/or ability to process emotional cues. The result is that individuals with a relatively low RHR might be less likely to learn from their experiences or benefit from certain treatments, particularly those that incorporate punishment-based learning (Van Goozen & Fairchild, 2008), thus, making antisocial behavior more likely. An obvious implication is that some individuals might not benefit from traditional treatment programs (e.g., cognitive behavioral) unless they also receive some sort of intervention (e.g., transcranial magnetic stimulation) aimed at their underlying physiology. From a crime prevention perspective, it would therefore be helpful to measure RHR along with other relevant biosocial variables so that resources can be allocated intelligently to those who pose a greater risk for offending (e.g., the risk principle: Andrews & Bonta, 2010). From a theoretical perspective, it is important to isolate the relative strength of individual biological factors from other (e.g., family, peer, neighborhood) factors, and to understand the circumstances under which they are most likely to produce antisocial behavior.

Two recent large-scale studies (Latvala et al., 2016 Latvala, Kuja-Halkola, Almqvist, Larsson, & Lichtenstein, 2015) confirm the growing body of research linking low RHR to antisocial outcomes. What is novel about these studies, however, is that they expanded the range of biosocial predictors to include systolic blood pressure (SBP). In a study with a sample of over 710,264 Swedish men followed up >18 years, Latvala et al. (2015) demonstrated that low RHR predicted a range of rule-breaking behaviors including minor violence, drug-related, property, and traffic crime, but especially serious violence. Study participants in the lowest RHR quintile were 31% more likely to experience unintended injuries and 41% more likely to be injured as a result of an assault compared to those in the topmost quintile, even after adjusting for potential confounding variables. Interestingly, the authors also found similar relationships between SBP and violent and non-violent criminality. An important exception is that no significant association was observed between low RHR/SBP and sexual offending, leading the authors to call on future studies to further clarify this relationship.

In a follow-up study, Latvala et al. (2016) extended this analysis to measure the relationship between RHR and SBP in relation to a range of psychiatric disorders in a prospective longitudinal study. They studied a very large sample of Swedish men whose RHR (n = 1,039,443) and SBP (n = 1,555,979) were measured at military conscription (mean age = 18.3 years) and followed up three decades later, on average. They found that higher RHR and SBP were associated with a greater likelihood of having a diagnosis of OCD, anxiety disorder, or schizophrenia. The strongest effect was found for OCD, where a 10-point increase in RHR increased the chance of this diagnosis by 18%. Conversely, having a lower RHR and SBP were associated with higher rates of substance use disorders and violent criminality, and these risks were even higher when physical fitness was controlled for. Each 10-point decrease in either RHR or SBP increased the risk of being diagnosed with a substance use disorder by 5% and increased the risk of having a violent conviction by 10%.

Aside from the Latvala studies, there is a relatively small research base examining the relationship between SBP and antisocial and psychological outcomes. For example, in a sample of 122 Swedish schoolchildren, Borres, Tanaka, and Thulesius (1998) demonstrated that children with three or more psychosomatic or psychological symptoms had significantly lower SBP compared to children with no symptoms. Rapoza et al. (2014) examined the relationship between child maltreatment and a range of health outcomes in early adulthood and found that higher self-reported anger, but not child maltreatment per se, was significantly correlated with low SBP. Capitalizing on a very large sample from the Western Australian Pregnancy Cohort Study (n = 2900), Louise et al. (2012) found a significant association between low SBP and aggression at age 14 for boys, but not for girls. Finally, in a cross-sectional study, Gower and Crick (2011) extended the analysis to include relational aggression in a sample of preschoolers (aged 43 to 66 months, M = 54.0, SD = 7.0) and found that low SBP was significantly correlated with relational aggression in older, but not younger children.

While it is clear that there is an empirical link between low RHR (and apparently also low SBP) and antisocial behavior, the precise mechanisms mediating these relationships are not precisely known. Two main theories have been proposed to explain the relationship between low RHR and antisocial behavior: 1) fearlessness theory, and 2) sensation-seeking theory. The first asserts that, in the face of anxiety provoking stimuli, some individuals will not experience a rise in their heart rate, which reflects a relative lack of fear. Without internal cues, such as a pounding heart or sweaty palms, to signal that they are about to engage in risky behaviors, individuals are more easily able to follow through on their actions without being inhibited by the fear of potential negative consequences (Raine, 1993, Raine, 2002). The sensation-seeking hypothesis postulates that for some a state of low arousal is uncomfortable, and these individuals will seek out stimulation to increase their arousal to more normative levels (Eysenck, 1997 Quay, 1965 Raine, 2002). Because individuals with a low RHR are in a chronic state of under-arousal, they seek out opportunities to increase stimulation, including participation in antisocial behavior and crime.

To date, only a handful of studies have tested these two theories. Based on data from 151 university students, Armstrong and Boutwell (2012) used a rational choice framework to study the association between RHR and antisocial behavior. They concluded that fearlessness and not sensation-seeking best explained the relationship between low RHR and participants' perceived probability of conviction for assault. Studies examining RHR in relation to sensation-seeking are relatively more common. For example, using data from a large Dutch prospective population study, Sijtsema et al. (2010) found that the statistical relationship between low RHR and aggression and rule breaking was mediated by a parent-reported measure of sensation seeking in adolescent boys. Portnoy et al. (2014) assessed the influence of RHR on antisocial outcomes in a subsample (N = 335) of boys in the Pittsburgh Youth Study and found that sensation-seeking, and not fearlessness, mediated the relationship between low RHR and antisocial behavior, defined as aggression and non-violent delinquency. A methodological strength of this study was that the researchers included a measure of state fear to more directly test its potential mediating influence for decisions to commit antisocial acts.

This study sought to expand the knowledge base on the relationship between physiological indices of arousal (i.e., RHR and SBP) and antisocial behavior. As noted earlier, aside from the studies by Latvala et al., 2015, Latvala et al., 2016, virtually no work has been done to measure the association between SBP and antisocial outcomes. In addition, although prior research has used official offending as an outcome variable in relation to RHR in adolescent populations (e.g., Cauffman et al., 2005 de Vries-Bouw et al., 2011 Raine, Venables, & Williams, 1990), only one prior study (i.e., Armstrong et al., 2017) has examined the relationship in an adult correctional context using official criminal records. Furthermore, and consistent with Latvala et al. (2015), we subdivided criminal offending into mutually exclusive categories (i.e., person, property, weapons, sexual, drug-related, other) to assess whether the negative relationship between RHR/SBP and criminality held across all crime types. Of interest here was whether we would replicate the non-significant association between RHR/SBP and sexual offending. In summary, the objectives of the study were to: 1.

Assess the relationship between low RHR and measures of antisocial behavior in a sample of incarcerated male adults

Determine whether low SBP is also a significant predictor of antisocial behavior and

Examine whether the aforementioned relationships apply to all types of crime.


Foundations

Niels Birbaumer , Herta Flor , in Comprehensive Clinical Psychology , 1998

1.05.5.3.7 Heart activity and blood pressure

Heart rate (HR) and blood pressure are the most frequently used psychophysiological indicators. HR is used because it is easily recorded from any two electrodes affixed at the more right or left side of the body allowing the heart to be positioned between them (i.e., both hands). Blood pressure has become more popular during recent years when noninvasive continuous measurement was realized with pulse-wave velocity devices. The obvious physiological role of the cardiovascular system explains the high correlations of HR with all kinds of behaviors: motor, mental, perception, attention, and orienting stress, emotion, and motivation personality, social stimuli, brain interactions, and conditioning (cf., Andreassi, 1995 , for a review). The dual sympathetic and parasympathetic nervous control allows separation of the two branches of the ANS: slowing of the heartbeat indicates the chronotopic parasympathic, speeding the increased excitability and contraction force of the sympathetic branch.

For decades psychophysiology was obsessed with discussions of the psychological significance of HR changes: while Obrist (1981) and his co-workers interpreted HR changes as variations of mobilization of blood supply for the somatic musculature (cardiac-somatic coupling), Lacey and Lacey (1970) proposed a close relationship with information processing in the brain: HR deceleration and negative going slow cortical potentials should accompany information intake, acceleration information rejection, and positive going slow cortical potentials. The deceleration in a signaled foreperiod reaction paradigm is caused by phasic blood pressure increase which fires the baroreceptors in the carotis sinus. This leads to parasympathetic slowing of the heart and increased cortical activity because the nucleus of the vagus located in the reticular formation fires the ARAS. Tonic stimulation of the baroreceptors through a specially designed neck cuff, however, produces heart rate slowing and positive going cortical slow waves which causes inhibition of cortical activity and information rejection ( Rau, Pauli, Brody, Elbert, & Birbaumer, 1993 ). The accompanying stress reduction negatively reinforces the preceding blood pressure increase and on a long-term basis causes essential hypertension in genetically prone individuals. Neither Obrist's cardiac-somatic coupling nor Lacey and Lacey's information processing hypothesis was unanimously confirmed. HR slowing clearly appears in orienting, while HR increase after intensive or dangerous stimuli constitutes part of the defensive reaction.

The interaction of stress and blood pressure increase was also confirmed in different personality patterns such as type A and hostility: type A subjects (competitive, driven, impatient) with hostile attitudes toward others, demonstrate large increases in blood pressure during stressful situations and exhibit a high risk for essential hypertension and stroke. Early behavioral intervention effectively prevents this vicious circle of blood pressure increase, stress reduction through cortical inhibition, and aggressive muscular mobilization.


Research shows correlation between adult height, underlying heart disease

Minneapolis Heart Institute Foundation research cardiologist Dr. Michael Miedema is the lead author of a paper published by Circulation -- Cardiovascular Imaging, a journal of the American Heart Association, that suggests a connection between an adult's height and the prevalence of coronary artery calcium (CAC), a direct marker of plaque in the arteries that feed the heart. Coronary artery calcium is a strong predictor of future heart attacks with a nearly 10 fold increase in the risk of coronary heart disease (CHD) in patients with elevated CAC.

The article is based on research in 2,703 patients from the Family Heart Study, a government sponsored study of the relationship between potential risk factors and heart disease and is the first study to examine the relationship between adult height and CAC in a large population. It suggests that taller adults tend to have lower levels of plaque, and thus, a lower risk of CHD. This relationship persisted even after accounting for standard cardiovascular risk factors such as age, smoking, high cholesterol, and diabetes.

"A potential link between height and CHD has been shown in several studies but the mechanism of this relationship has not been clear and our study suggests the relationship is mediated by plaque build up in the coronary arteries," said Michael Miedema, MD, MPH, from the Minneapolis Heart Institute Foundation. There may be as much as 30% lower risk of plaque build-up in the top quarter of tallest adults compared to the bottom quarter. These results had to be adjusted for gender, given the differences in height between men and women, but the relationship was consistent in both men and women."

Why taller individuals develop less plaque is not entirely clear.

"Some studies suggest that taller people have favorable changes in their blood pressure due their height but these changes are quite small and unlikely to be the sole cause of this relationship," Miedema stated. "It may be more likely that this relationship is mediated through a common link, such as childhood nutrition or other environmental factors during childhood, which may be determinants of both adult height as well as future coronary heart disease."