[00:00:00] Speaker A: Welcome to the heart rate variability podcast. Each week we talk about heart rate variability and how it can be used to improve your overall health and wellness. Please consider the information in this podcast. For your informational use and not medical advice, please see your medical provider to apply any of the strategies outlined in this episode. Heart rate variability. Podcast is a production of optimal LLC and optimal HRV. Check us
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Welcome friends to the heart rate variability. Podcast. I am back here with a friend of the show and somebody I consider a friend as well, mentor in this field, Dr. Fred Schaefer. Dr. Schaefer, welcome back to the show. It's great to have you here and.
[00:00:49] Speaker B: It'S great to be here.
I always enjoy our chats where we get to nerd out about HRV.
I've heard that you have questions about some measurements that we use.
[00:01:08] Speaker A: I do, and I'm excited to explore that. I mean, I can't think of a better person in the world to explore this with. So I've been waiting for this podcast. It's been on my list for so long.
So there's Heart Rate Variability, which I think this is episode it's going to be in the think when we publish this. So we've talked a lot about this topic and then there's another level down and this is where we get into time domains, frequency domains. And to me it can get very complex. I always like to throw out what is RMSB. It is this Wacky equation that I did not get to that level at school with math to have any idea what I'm looking at. Even so, even though I think we feel comfortable with the term heart rate variability in general, the next layer below it, it can feel so complex that even somebody who's, like, nerd out about this written two books about it like I get it and yet I got to keep learning about it to get some level of mastery where maybe at some point I can teach other what a frequency domain is actually. So I would love to start this exploration out if maybe just looking at time domains, frequency domains.
What the heck? What are we doing here Dr. Schaefer?
Help me even ask the right questions, maybe moving forward.
[00:02:45] Speaker B: You're asking some great questions and it's so easy to be intimidated and overwhelmed by the alphabet soup of acronyms and metrics. So let's start with the essentials. We begin in HRV measurement by calculating the time between successive heartbeats. And we call this time which we pray will be different across different heartbeats, the inter beat interval. And really all that comes down to is a column of numbers, typically in milliseconds or thousand of a second.
And so it might be 1015, it might be the next one might be 878 and so forth.
It is from this column of numbers that we calculate our time domain measurements and our frequency domain measurements. So we start with just a column of interbeat intervals.
Now, timed frequency domain measures are looking at different answering different questions, time domain measurements like the RMSD, which I'll explain in just a minute, tell us how much heart rate variability did we see in this series of interbeat intervals? So it's a single metric that gives us an estimate of how much heart rate variability was there over, say, five minutes of recording.
And as you can guess, there are different metrics that may give us different estimates.
Now, the Armssd is widely used, it's used in Fitbit, it's used in the Apple Watch.
Optimal HRB uses it.
And what it is, is and this will sound like Greek or Klingon, it's the root mean square of successive differences between normal heartbeats.
Yeah.
So what you're doing is you're calculating the difference between successive interbeat intervals.
You are ultimately squaring these so that you can add it together.
And so each difference is squared.
And then you average the results and then you finally take the square root of the total. And no one has to do that. We have so you don't have to get lost in the calculation.
But essentially it's looking at the difference in the time intervals between successive beats.
And it is a pretty good beat by beat estimate of heart rate variability.
It is better than the standard deviation of the normal to normal interval, which I'll explain in a minute, in part because it better estimates the contribution of the parasympathetic system, the Vagal nerve to heart rate variability.
[00:07:04] Speaker A: And can I ask a clarifying question there? Yeah, because this is where I think I love it, I nerd out about it. And yet if I had to put into words, I think I would struggle saying RMSD measures parasympathetic.
I get that heart rate variability is we're measuring Vagal break if I pass the test, at least somewhat. So when we look at RMSSD specifically because I don't want to move on until you're ready to move on there.
How specifically is that as a time domain measuring parasympathetic? I think you said activity.
[00:07:53] Speaker B: Yeah, it's considered vaguely mediated HRV.
And HRV is driven by the parasympathetic system under most conditions, heart rate variability measurements and resting condition has no significant sympathetic component.
Right. And so what is driving, in fact, when we get into frequency domain measurements, there has been controversy over the sympathetic contribution. But here, now that we're just staying safely in the time domain area, it's all parasympathetic and maybe blood pressure receptor reflex driven, the barrel receptor reflex driven.
So this is the workhorse measure because it is less affected by extreme values called outliers than some of the other measures and also is somewhat less vulnerable to artifacts.
[00:09:26] Speaker A: Because we got these equations. So is something in the equation because if I'm understanding artifact let me try to find this. You can grade me on it's sort of likely I sneezed I moved there's something in the individual's, something created an artificial artifact score that really isn't telling us anything meaningful is going to throw off. So how does the equation, which I would encourage our audience to Google, what it actually looks like because it's really a huge confusing equation. How does an equation itself account for help to eliminate some of that artifact?
[00:10:19] Speaker B: I'll give you an analogy that may make it clear.
What two ways that we can report the typical value of a set of scores are the mean and the median.
The mean is just the average. My students know it as their GPA.
The mean is heavily affected. It is yanked up and down by extreme scores.
[00:10:52] Speaker A: Okay.
[00:10:53] Speaker B: The median is not right.
In the same way the Rmsd's calculation minimizes the opportunity for extreme scores to pull it either up or down. It doesn't eliminate it, but the very method of calculation minimizes the influence of extreme scores.
[00:11:22] Speaker A: Fabulous. Awesome.
I think I got it.
[00:11:30] Speaker B: I know you have it.
And so this is the reason that the RMS SSD is widely used, at least in consumer grade trackers.
Now this is the point in which I have to put in a caveat or caution.
Consumer grade equipment is only as good as the conditions in which the recordings are made.
Movement artifact, for example, is going to be going to make any kind of adjustment darn near impossible. So that what you have to do when you are doing research is you need to export your individual IDIS.
You then need to clean up the data when there are false values artifacts and then you can do the math.
[00:12:50] Speaker A: Okay.
[00:12:51] Speaker B: With enough activity, the calculation of RMSD is not robust enough to, for example, compensate for a hyperactive adult or child.
[00:13:08] Speaker A: Yeah.
[00:13:09] Speaker B: So someone who's highly kinetic is going to create so much movement artifact that you'll get false beats and missing beats.
[00:13:17] Speaker A: So Arm SSD, could we say, helps with artifacts but doesn't address the full range. Our artifacts can still pull it and make it inaccurate, still can be pulled.
[00:13:29] Speaker B: It still can be invalid under the worst case.
[00:13:32] Speaker A: Yeah, but it's a little bit more robust if that word that's exactly robust.
[00:13:39] Speaker B: In statistics means that even if the assumptions of your statistical test are not perfectly satisfied, the calculation won't be garbage. It will give you a meaningful result. So the RMSD tends to be fairly tolerant of some mild deviations from normal recording.
[00:14:11] Speaker A: Excellent.
So RMSD gold standard in the time domains, would that be a fair thing to still I saw that in the research as I was looking at different measures. Is that still pretty true?
[00:14:30] Speaker B: It depends on what you're using it for.
It is one of the gold standards. Now, if instead we're thinking of risk assessment for like heart attack deaths, the Sdn, which is calculated a different way, is a better predictor of cardiac risk when recorded over 24 hours.
So not five minutes right, but 24 hours.
And this is from the task force report in 1996, which is really one of the most influential documents for the field.
[00:15:27] Speaker A: I have it up on my other screen as we talk.
[00:15:32] Speaker B: So here you use 24 hours monitoring and if you have Sdn values below 50 milliseconds or 50,000 of a second, a person would be classified as unhealthy. If it falls between 51 hundred milliseconds, they're considered compromised, and above 100 milliseconds they're healthy.
Now, the issue here is if you are in an unhealthy category, your risk over the next 31 months might very well be 5.3 times greater than someone who is in the healthy category.
[00:16:32] Speaker A: Fascinating.
[00:16:33] Speaker B: And so it is predictive of both heart attacks as well as heart attack deaths. But only if you've done 24 hours recording and then cleaned up the data.
[00:16:49] Speaker A: Got you. Is there a lot of that? I don't hear much about 24/7 monitoring.
Is that very specific to heart conditions that you might do that?
Okay.
[00:17:06] Speaker B: It's very specialized.
It is not widely used, as you can imagine. It is quite intensive in the sense you have to get the client to wear it for 24 hours.
Then you have the data and you have 24 hours of data that you need to clean up. And while there are algorithms that can do a good initial job, then you need an experienced technician to do what the algorithm can't.
And so, for example, my friends at the Institute of Heart Math will take data from a first beat bodyguard two, which is a relatively inexpensive 24 hours monitoring, scenic, and you can upload that information to their service and then they will use an algorithm and have staff give you analysis of your data.
But this is not widely used. It would be used, some clinicians might use it pre post.
It might be used in research because there's variability we don't catch across the 24 hours day, particularly when you're asleep, where you're actually going to have greater heart rate variability.
[00:18:53] Speaker A: Yeah.
[00:18:54] Speaker B: And again, to explain that Sdn is the standard deviation of the inter beat interval, which I've already explained, of normal sinus beats. And normal sinus beats is just code for these are not abnormal beats.
They originate where nature intended and not some other part.
[00:19:26] Speaker A: Yeah.
[00:19:27] Speaker B: So sino atrial node as opposed to the atrial ventricular other areas of the heart.
[00:19:34] Speaker A: Okay, so if we look at SDNN, which is pretty popular, I would say RMSD, it would be number two. We'll get to the frequency as I know they compete to be up there as well.
If I gave Fred my RMSD and my Sdn score, would you be looking for different things?
What would you be looking at? I give you those two scores from, let's say a ten minute reading where I'm being a good boy, I'm sin still.
I'm giving you good data. Let's just pretend I'm artifact free.
I wonder how you would see those two data points, do they mean anything to you in comparison to each other?
What are you looking at with your expert eye at those two data points?
[00:20:39] Speaker B: Well, the first thing I need to confess is I'm not a clinician, nor am I an optimal performance coach.
So what that means is I don't know a lot. I'm an academic.
[00:20:59] Speaker A: By the way, Dr. Schaefer, when I ask, who should I get for this episode, everybody tells me, you got to call Fred. So I just want to throw that out. There is your reputation is incredibly maybe nobody else wants to do it, but I got to say, everybody respects the hang. People I respect respect you. In this arena.
[00:21:24] Speaker B: My suspicion is the brightest people are way too smart than to do this. And so Richard Gerbertz and Paul Lair, got to talk to Fred, just are not available.
[00:21:47] Speaker A: Everybody says, I got to talk to.
[00:21:48] Speaker B: You, my friend, so let's talk about it. What you do is you're very correct in saying you need to tell me the conditions in which the recordings were obtained.
For example, were you sitting?
Were you standing? Had you just done aerobic exercise? You need to explain that. You need to tell me whether you had feedback or not during this time.
A good baseline would not did you have special instructions for breathing? Again, a good baseline would just allow you to breathe normally, and I need to know how long. And you told me ten minutes.
And your audience might wonder, well, why do we care whether it's five or ten? And the answer is that longer recording periods tend to give us larger values.
And so you need to compare apples with apples.
Now, having said all that, the way I, as an academic would look at it is I'd compare it to age and fitness appropriate norms, and these are published. And the norms clearly show the publications in which the journal articles in which they've been published clearly state the conditions in which these norms were obtained and the characteristics of the participants. So, for example, while I would compare you to aerobically fit norms, I would not compare you to couch potato norms.
[00:24:01] Speaker A: Thank you.
[00:24:02] Speaker B: Okay.
[00:24:03] Speaker A: Yeah.
[00:24:03] Speaker B: Okay.
In the same way, we have norms for a number of the metrics for age, at least by decade, for biological sex at birth.
So these are helpful. And so you ask, how are the values close to these norms or significantly below or above?
And you might, for example, decide that perhaps two standard deviations above or below are significant.
[00:24:54] Speaker A: Does that give you if I could ask you when we talk about standard deviations, what I have seen at least is those are pretty let me say what I understand and see if I'm tracking right with your knowledge, is that standard deviations are pretty wide on these. They're pretty big, meaning that the average person can have a wide span and still be in the one standard deviation due to genetic differences between individuals.
Am I in the ballpark with that statement?
[00:25:33] Speaker B: There are many reasons for larger standard deviations.
Referencing the task force report, just as an example, the mean RMSD was 27 milliseconds. It's rather fortunate that both Sdn and RMSD are expressed in milliseconds or thousandths of a second.
But 27 milliseconds was the average for 144 healthy subjects, but the standard deviation was twelve milliseconds.
[00:26:21] Speaker A: Yeah.
[00:26:22] Speaker B: So it's a large chunk, 27.
So the takeaway is yes, standard deviations can be quite large. Yeah. And so, you know, you might use two standard deviations above or below as a way of comparing but again, with so many published norms, you need to compare apples with apples. Apples would be, first of all, the recording time period.
Apples would be the type of people participants we are comparing you to.
The task force values were for 24 hours, just like the 24 hours recordings done by umatami, for 260 healthy participants who ranged from ten to 99 years of age.
[00:27:44] Speaker A: Which unfortunately doesn't give you I mean, even though the overall in is fairly big, when you start to break those down by age group, it's very small to get smaller and smaller numbers.
Yeah. So even that is like I appreciate the work because it gives us a reference point.
You start to look at the ends behind these norms and they're relatively small, at least in my statistical mind, which I have to admit hasn't been active probably since grad school. All that highly.
[00:28:22] Speaker B: Your statistical mind is still firing on all cylinders.
[00:28:28] Speaker A: Thank you.
[00:28:30] Speaker B: Because yes, that speaks to how representative are these values?
Smaller sample sizes mean that we really may not have a good idea of representativeness.
But I will mention one other metric that I think that clinicians and optimal performance coaches may find useful in education, and that's heart rate max.
Minus heart rate min.
[00:29:13] Speaker A: Yeah.
[00:29:14] Speaker B: And what this is, is just the average difference between the highest and lowest heart rate during each breathing cycle.
And you start out with the idea that when you inhale heart rate speeds and then when you exhale because the parasympathetic brake is applied, heart rate slows.
[00:29:42] Speaker A: Yes.
[00:29:43] Speaker B: And so you should, in a physically active individual, see a difference that might be, for my students, might be 510 beats per minute.
And if someone had a compromised cardiovascular system, it might be zero beats per minute, or it might just be one or two.
[00:30:20] Speaker A: Yeah.
[00:30:20] Speaker B: And so this is one way of explaining variability, because people have a hard time conceptualizing variability. But when you use an example of fastest versus slowest, calculate the difference, that is something they can wrap their heads around.
[00:30:42] Speaker A: Okay, I want to make sure I get this.
When we look at the max and the min, let's say if I'm even off with this, let's say we're doing a ten minute reading, the score that I get as max min gives me a score. If I'm correct, it doesn't say your min was and your max was.
What the heck am I looking at with that score?
I think I understand what it's telling me, but what's that number?
[00:31:25] Speaker B: The average heart rate difference between your fastest and slowest heart rate.
So it calculates the first difference from the fastest and slowest, and that'll give us a value in beats per minute. And then it will calculate the next and add that to the moving average. So at the end of ten minutes, you will have an overall average difference in heart rate variability. That will be beats per minute.
So Yevgeny Vashilo, a remarkable researcher who collaborated with Paul Lehre and colleagues at Rutgers.
Yevgeni, when he worked with Soviet era Cosmonauts, trained them to increase heart rate max minus heart rate min to values like 50 beats per minute. So the difference between fastest and slowest, 50 beats per minute.
My own former student Alex Kangelosi was just off the plane in Venice at a Bifeedback Foundation of Europe meeting, and we measured him with jet lag during our workshop. And Alex clocked in at 40 beats per minute. Average difference.
[00:33:23] Speaker A: Wow.
[00:33:25] Speaker B: Yes.
[00:33:26] Speaker A: So I'm struggling here a little with the backsmith. So the 40 beats per minute is good, right?
Yeah. I was like, Boy, if you're jet.
[00:33:46] Speaker B: Realize it was close to cosmonaut level.
[00:33:49] Speaker A: Yeah.
[00:33:50] Speaker B: Suffering from jet.
[00:33:57] Speaker A: Again. I want to break this down into Matt so Matt's brain can chew on it a little bit.
So I'm looking at the inhale. Obviously, I'm releasing the brake. You got the speed up there, the exhale, the vego brake applies. I'm slowing down. So max min is taking the averages of the speed up versus the averages of the slow down.
Help me get right the difference between.
[00:34:38] Speaker B: The average of the difference between the fastest heart rate versus the slowest heart rate during each breathing cycle.
[00:34:49] Speaker A: Okay. During each breathing cycle.
So inhale, exhale. So the inhale the exhale breathing cycle. Right. Okay.
[00:34:59] Speaker B: Yes.
[00:35:03] Speaker A: I think I could maybe guess at this, but it seems like that might be telling you something complementary but different than RMS.
[00:35:15] Speaker B: Yeah, it's complementary. It's major value. It's not used as a clinical indicator, it's not used diagnostically, it's not used in most research.
But as a clinician or as an optimal performance trainer, it's very easy to say, you know, you started in training here and your difference started out at eight beats per minute, and now we've worked together for, say, eight sessions, and now it's 20.
That is something that clients can wrap their heads around, because where RMSD and Sdn are extremely abstract, they're algebraic. This is really simple. This is something people can conceptualize.
[00:36:23] Speaker A: So let me ask a question this way before I do. Are there any more I think we're having a time domain episode here, and I'll have to beg you to come back for a frequency domain if you're okay with that? Because I've got questions on the frequencies. So are there any other time domains that you feel we need to cover that are important for folks? I think we've hit the big ones, but I want to make sure I don't think so.
[00:36:54] Speaker B: And I'll tell you why.
These are the ones that are most widely used in the research that I read.
Now, when our center for Applied Psychophysiology at Truman State University does research, we calculate probably over 25, 30 different variables, some time domain, some frequency, some nonlinear.
But really the ones that are most important that our audience really wants to hear about are values like RMSD and SSDN. Yeah, Sdn.
[00:37:46] Speaker A: So here's the question. I want to give you plenty of space. It may be a one word answer or two, but it may be something that I'm really cautiously excited about because I don't know if it's meaningful or not. But we're getting to the point as I play around with Chat GBT four. And now if you're not familiar with audience members, you can upload spreadsheets and it will analyze it for you. And I'm not saying it's perfect, but we're getting to a point, I believe, where even if I ask you as one of the world's experts on this, or at least let all the world experts tell me you're the world expert, whether you like that or not, I'll let you take I don't like it at all.
We could look at this data in a very deep way. And let's say we have the Fred AI that is omniscient around time domain variables, and I'll take that ten minute perfect reading, and I get all the information for I get a perfect Sdn score, RMSD score, max Min score. So the data is great. And I've got these scores.
If you could create the Fred AI that is all omniscient and can give me feedback within 30 seconds is those scores. And let's say I'll give Fred AI omniscient. You also know the past year of my scores as well. So you have good baselines on all these metrics. I've been a good boy. Every morning I take my ten minute reading. I'm still and now you got these three data sets. You've got all that history, and what would you want to know? What could we know if we're omniscient in the way that really Chat TB four is starting to look around? Data analysis.
What would you like to know? What would you like to know with those three metrics? What could they tell us maybe about parasympathetic versus I doubt sympathetic, but maybe more eventual break?
What could we know comparing all that data, assuming we could do it halfway, quickly and accurately?
[00:40:18] Speaker B: I think that there's some practical questions that we can even answer with the data from optimal HRV app. And let me frame it this way.
What is most important, I think, is not just the absolute value, but the trend. Yes, you have to know the history. And that's part of what chap four and other systems will do, is look at a large time series of values.
You want to see if there are any important departures from your typical values. And let me tell you why that can be important.
Without making prognostic claims for any HRV measurements, it is possible that a sharp decline in RMSD, again, assuming we have valid measurements, may reflect COVID, may reflect influenza.
For an athlete who has been doing intensive training, it might reflect exhaustion in the need for more recovery time.
So in some ways I'll give you yet another analogy. This one, it won't be a mathy one.
There is, of course, controversy over the PSA test for men for prostate cancer risk.
And we know that a single PSA measurement needs to be interpreted within the context of a person's history, and so it gains greater predictive power if we were to see a significant change or departure from the previous trends.
The same thing is true for HRV when we're looking at time domain measures, you just want to see if they're and typically they'll graphically be obvious.
You don't have to do the stats, they'll graphically be obvious that there is a hiccup here.
And I think that's the point at which we explore, we ask the client to explore is it a health issue, is it a psychological issue, is it an overtraining issue?
It doesn't tell you that it's any of these things, but it invites.
[00:43:53] Speaker A: If I let's say we had that history, and I take my morning reading for today and I get an SDNN score, an RMSSD score, a max min score, would you or would you want Fred AI to compare? Am I getting is my because when I look at my scores, they trend mostly with each other. There's a real nice thing when I look at the dashboard, they're different numbers because they're different measures, but they ebb and flow pretty consistently with each other. But let's say are you looking for something specific if you compare RMSD to max min for.
[00:44:43] Speaker B: I don't. And again, an important nuance in all this is the time of day in which you take the measurements matters. So I would not compare morning to evening measurements.
[00:45:00] Speaker A: Absolutely.
[00:45:01] Speaker B: That's again an apple and some other fruit or vegetable comparison. Yeah, but no, I think you have to know what makes sense for you as you see your values and know what your patterns are.
And then when you see departure from the patterns either in a good or bad way, then that's the opportunity for you to start to brainstorm about what may be driving this.
[00:45:36] Speaker A: I love that. And so if you looked at like, now let's look historically, is there anything like that? The history of max men versus the history of RMSD, is there any difference there? I mean, obviously we want them both to go up so we want to see that and if they go down we can ask questions against it. But is there anything like, let's say my max min is going good, my RMSD probably is also doing good as well. Is there anything you're looking for? Historically, again, we want to basically even though there's a ton of complexity, the higher the better.
[00:46:22] Speaker B: As long as it's driven by healthy cardiovascular activity, it is possible, particularly in the people, older adults or people with conduction disorders, it is possible to have abnormal firing cardiac arrhythmias to generate heart rate variability. That is actually a bad sign.
So if it is being generated by a healthy conduction system, then higher heart rate variability ah is better.
Adele within limits. Now let me suggest some of the other possible contributors to lower time domain measurements.
Poor sleep, and I'm not talking just about hours, but also architecture, particularly loss of if you have sleep that's broken up, you might have less stage three sleep, slow wave sleep or REM sleep.
Another factor could be anxiety or depression.
Given that these can be can fluctuate quite a bit.
Depression will lower can be associated with lower HRV, as can anxiety as they look at an older population pain.
Increased pain can lower HRV.
There are probably several ways that it can do it. One of those ways can be the increasing your breathing rate. A person experiencing acute pain may be breathing 25 30 breaths per minute.
It's also one of the indicators that pet owners should look at when, say a canine is breathing more rapidly than God intended. That could be an indicator of pain.
[00:48:51] Speaker A: Fascinating.
I got one more maybe quick question for you.
When I was first looking and googling about heart rate variability, and I believe we have the same article up in front of us, this March 1996 article came up and I just remember is there's got to be something newer?
Why am I in the guess maybe that's my academic brain is you don't want to go too much past ten years back when you're looking but it just seems like this article is AAPB journal. I see it there. I see it referenced so much.
Have we just kind of hit that we found the time domains and we don't need anymore?
It's kind of fascinating to me that we're still going back to this article that says a lot of great things about the work done behind this article.
But you would assume, what are we 30 years like 20 years later, 30 years later that there would be a new Bible for us to be looking at. But there's this beautiful no do we not need to progress? What's your thoughts just so we can tell grad students you found it? You don't have to keep searching like Matt did, thinking he's missing something.
[00:50:18] Speaker B: I don't think you ever want to say that you found it. I think the task force report was. A watershed event.
It brought together some of the best and brightest of the field, including, again, my colleague and collaborator on one of my earliest review articles.
And this was Roland McCready, who's the chief scientist for the Institute of Heart Math.
I would say that there are many questions that have not been definitively answered.
I would love to see a successor to the task force report.
Instead, we're getting separate review articles and research reports, including reports on norms, but not that integrative consensus document.
And I don't know the politics and I don't know the events and the interpersonal dynamics that led that made that report possible.
[00:51:38] Speaker A: Yeah.
[00:51:40] Speaker B: And I don't know if that magic can be bottled and replicated for the next one, but I know that there are many questions left to answer. And I'll give you yet another analogy. And this is something that most everyone listening will have heard about.
And this is the apocryphal story of blind sages in India, touching different, feeling different parts of an elephant, and only being able to report on what they see. I think of the different HRV metrics very much the same way. They're all measuring different parts of the elephant, which in this context is HRP.
No single one represents the elephant faithfully in its entirety.
[00:52:45] Speaker A: Yeah.
[00:52:47] Speaker B: And I wanted to see research doing a deep dive as to how are these different metrics different at a physiological level.
[00:53:00] Speaker A: Right.
[00:53:01] Speaker B: Yes, we know they should move together in the same direction, but what pieces of the puzzle does one supply as opposed to another? Yeah, we're better able to answer that question with the frequency domain measures than we can with the time domain.
[00:53:23] Speaker A: Okay, well, within that analogy, kind of to wrap up here, because I can't wait to get back with you for frequency domains, but we have taken 60 minutes about time domains, which doesn't really surprise me because it's such a fascinating topic. But in that analogy, could we say, like, the time domains, maybe they're measuring a different part of the foot, so to speak, of the elephant, that there's commonalities enough within these that they're kind of showing us somewhat similar things, where if we compare RMSD to SDNN, we're not getting a whole lot of additional data.
You want it to be higher than yesterday and see that trend, but we're not quite there yet with the say, okay, that's great. Now I know when we get in the frequency domains, adding those will expand this to the wider elephant, which I cannot wait to tackle with you at a future date.
[00:54:30] Speaker B: Yeah. As a teaser for your listeners, the frequency domain will give us a sense of the sources of heart rate variability, of what's driving the variability that we measure with the time domain.
[00:54:51] Speaker A: Awesome. I can't wait.
So our audience knows you at this point, friend of the show. So I want to exit here with and this is coming out of the blue, but I don't think I'm setting you up here for anything but a softball. Right.
Tell people, because I've been doing it. Why should our listeners, dr. Schaeffer, think about joining we're I think this will probably come out in December. So. New year, new thinking.
I'm a huge advocate for APB. I'm cautious to remind people this podcast, even though it might seem like it's sponsored by it is not associated with it directly, but I know you could speak to that. Why would our listener who's interested in this, what would be some of the benefits they get from this organization? I know you're so invested in essential, too.
[00:55:51] Speaker B: Yes. I'm finishing my second year as president, and I've been a member of APB, I think, since 1977.
[00:56:01] Speaker A: That is amazing.
[00:56:02] Speaker B: I've gone to, I think, virtually all of the meetings since that time.
I think the number one reason is a sense of community.
When you have personal experience attending AAP, even if this is your first meeting, and APB will have its annual meeting in Denver, pretty close to where you live, just down the street in May and May of this coming year.
And let's suppose you're a first timer, you're going to be welcomed. People will be available. People will help you network.
If you're students, if you are practitioners, you will find colleagues who will welcome you and will be this is the family I've often thought of app as the family I choose.
[00:57:11] Speaker A: Yeah. I love.
[00:57:14] Speaker B: So I think one of the strongest reasons that many of us go to AAPP is to see our dear friends at least once a year.
And then there's so many amazing presentations.
So you're going to learn a great deal at the level over the topics that you're probably very interested in.
There will be HRV. Content. There will be Neurofeedback content, the major modalities. And, of course, going to the meeting, you will get to meet our awesome vendors, of which optimal is one. Although your mission is very different from many of our vendors do generously support the field, and they do promote education.
But our vendors will tell you what's available.
They'll help you get greater use out of the equipment you already have purchased.
And so getting into the vendor space and learning from them and working with them is extremely valuable.
And then you held up, of course, the Journal of Applied Psychophysiology and Biofeedback, which is edited by my dear colleague, Dr. Paul Lair, professor Emeritus at Rutgers.
It is a wonderful summary of recent studies in the then, of course, APB four times a year publishes Biofeedback magazine, and Biofeedback Magazine is awesome.
For the longest time, I don't have the accurate number of years, but for certainly over 13, it could have been in the 20s.
My colleague Don Moss was the editor.
Don is no longer paid as editor, but still comes back to help us with special.
And so you have this extremely accessible publication app just published evidence based practice in biofeedback and Neurofeedback, your collaborator and dear friend, Dr. Ina Khazan was the lead editor for this effort.
And this was over three and a half, four years in the making. It is over 500 pages, the largest edition that we've ever done.
And I'm very proud of the team effort here. So these are things that APB continues to do.
We have an amazing executive director.
Leslie Shivers focuses on customer service.
She takes the time to help provide the assistance that our members or even the public ask for.
We aren't limited to a five minute rule of if we can't deal with your inquiry in five minutes, we need to put the phone down if it takes 20 minutes. If it takes 30 minutes, she will give that time.
And this is a culture of service that I'm very proud of.
[01:01:46] Speaker A: I love it. And I just got to say, from a newbie to the organization, I hate journal articles.
This one is a much the APB journal is great.
The special editions for the biofeedback as well.
I can't ever remember reading a journal cover to cover, and it's pretty much occasionally I'm more in the biofeedback than Neurofeedback side, but even those give me an idea. I feel like they keep me up on the field, and I'll just put a plug in here is that one of the things I just appreciate about the AAPB conference? It's still small, family oriented, where if you want to pick Dr. Fred Schaefer's brain about time domains, he'll sit down with you. Gerbertz will sit down.
There's almost an informal acceptance mentorship that's just available. And I know not all vendors do this, but you can talk to me at the booth at break. But I'm heading to the workshops because I get to hear and I'm so fortunate to interview and bring the luminaries of the field into this podcast. But you get to hear them speak, you get about their research, and you get students work, which is, I think, just a great dynamic in pushing the field forward. And that next generation that baton is handed over to give them the stage as well. And then somebody like last year, Stephen Porges shows up, and you can tell he's enjoying it as much as I am. So I'll definitely throw that out there. And with that, Dr. Schaefer, thank you so much for your time here. We were going to cover frequency domains, but I'll be honest, I'm a little excited that we get a whole nother section for that because I got questions there, too. So I'm looking forward to hopefully exploring that in the near future with you as well. But thank you so much for your time and your expertise to tackle this really difficult topic. I really appreciate you and your work.
[01:04:00] Speaker B: My pleasure.
Thank you so much.
[01:04:03] Speaker A: Thank you, everybody. As always, you can find show notes at heart rate variability
[email protected], and we'll be back in your feed next week. Thank you all so much. Bye.
