Preprint Smartphone-based monitoring of heart rate variability and resting heart rate predicts variability in symptom exacerbations..., 2024, Aitken+

[Nightsong]

Abstract:

Background: Complex chronic conditions like Long COVID and Myalgic Encephalomyelitis/Chronic Fatigue Syndrome involve energy limitations and changes in heart rate variability (HRV) and resting heart rate (HR). Mobile health technologies now offer real-time, valid measurements of HRV and HR, advancing symptom monitoring and management. Using a high-density dataset from an observational longitudinal study, we aimed to describe, quantify, and predict within-person co-variations in daily biometric data and subsequent crash, fatigue, and brain fog symptom occurrences.

Methods: Leveraging data collected through a mobile health app (n=4,244), we developed predictive models using mixed-effects linear regression and logistic regression to explore how within-person fluctuations in biometrics (HR, HRV, and respiratory rate) predict dynamic change in symptomology (crash, fatigue, and brain fog). Predictive performance was assessed using 5-fold stratified cross-validation and compared to a 20% holdout set to evaluate model generalizability to new observations and individuals.

Results: Across all symptom domains, within-person changes in HRV and HR consistently emerged as key predictors of symptom change across all models, with higher HR and lower HRV conferring risk for crashes, fatigue, and brain fog. Moreover, 7-day biometric stability (or variable dispersion) was a robust predictor of symptom occurrence and severity. Models trained solely on biometric features achieved moderate predictive performance in the stratified cross-validation set; however, incorporating random effects to capture individual-specific variations and prior-day symptom reports substantially enhanced model accuracy, with AUC values reaching .91.

Discussion and Conclusion: This study is the first to use data-driven models to predict everyday symptom experiences in individuals with complex chronic illnesses based on biometric fluctuations. Findings demonstrate the potential utility of mobile health tools for real-time monitoring of symptoms and highlight the need for further research to refine these predictive models and integrate them into clinical decision-making processes.

Link (ResearchSquare preprint, open access)

 

[Nightsong]

The major limitation seems to be that these were self-reported diagnoses - those with LC were apparently asked to confirm if they met WHO criteria for LC, but this is a very broad definition, there were no specific criteria for ME/CFS, and the sample contained "individuals with other energy-limiting conditions":

Similarly, participants in this study reported that they met the WHO criteria for LC, but there were no standardized criteria for reporting ME/CFS. Additionally, this study included individuals with other energy-limiting conditions, which introduces variability and challenges in defining the sample population. This led us to refer to the dataset as representing individuals with complex chronic illnesses rather than a specific condition

Nonetheless, very interesting to see research using smartphone-derived data.

 

[rvallee]

I have been doing HRV monitoring for about a year now, on and off but with months-long periods of sustained readings. I have never really noticed any benefit from a high HRV. Mine is typically in the 55-65 range, which is excellent. I have had many worse days that weren't proper crashes, and it rarely dips, so it's not very sensitive.

It does, however, really pick up acute illnesses. Which I'm not sure is especially useful as the times I saw a significant dip, I definitely knew I was ill and did not need someone telling me. Same with the few days following a vaccine. And drinking alcohol.

So it can probably be of some use, but as a very adjunct thing that is more like having a flag to show if it's windy, but one that is a bit too heavy so that it only tells you when it's so windy outside that you can't miss it because cows are close to flying. Which has very limited uses.

I'm actually surprised at how consistently good my HRV can be, despite being essentially non-functional, even for periods lasting weeks and months. Which tells me that a high HRV means very little, only a low one.

 

[Trish]

I have just replaced my failing fitbit with one that does HRV. In the first week my HRV is consistently scarily low (mid teens), but I've decided to put that down to old age and not worry about it.

 

[Eleanor]

I have just replaced my failing fitbit with one that does HRV. In the first week my HRV is consistently scarily low (mid teens), but I've decided to put that down to old age and not worry about it.

My Fitbit also gave me scarily low HRV readings, so I looked on their community forums and saw lots of other people saying theirs were also giving scarily low readings compared to what their other devices were telling them. I think it's just a fitbit 'thing' and probably the key is to look at trends over time rather than the numbers.

 

[Snow Leopard]

Note the non random effects model had very poor predictivity - AUC of 0.6-0.61 for crashes or brain fog.

This means in the real world, HRV and HR monitoring is not terribly useful because most people will not have a perfectly fit model, and even if they did, that doesn't tell them much about the specific triggers nor any treatments that prevents the response.

 

[Yann04]

I used visible (which is where the data for this study comes from) for about 4 months and I think there’s two problems with a study like this:

1) Expectancy Bias
Visible is literally based around the idea your HRV impacts your PEM threshold. They give you specific scores every day based on your HRV, so someone that got a “bad” HRV score in the morning might subconsciously be likely to report worse symptoms in the end of day questionnaire, for the simple reason they got a bad score.

2) Self-Selection Bias
For people like me, HRV didn’t seem to predict much, so I stopped using the app. The app will likely select for long term users who believe HRV impacts their symptoms.

For a study like this to work, since it relies on PROMs, HRV data would have to be blinded.

 

[Kiristar]

The pattern is more complicated for me and it's about degree and pattern of change not absolutes or high being good low being bad.

I have noticed my hrv actually shoots up just as I am entering pem before then tanking and gradually working it's way up to mid range as pem slowly subsides. So for me either unusually high or low in range scores are a bad sign. It's run at similar absolute scores with exactly the same pem indicator pattern on two different brand devices now (polar then garmin) over several years.

Two pem episodes are underlined in pink here and the red and orange is the impact of the Covid infection I recently had.

 

[MrMagoo]

I used visible (which is where the data for this study comes from) for about 4 months and I think there’s two problems with a study like this:

1) Expectancy Bias
Visible is literally based around the idea your HRV impacts your PEM threshold. They give you specific scores every day based on your HRV, so someone that got a “bad” HRV score in the morning might subconsciously be likely to report worse symptoms in the end of day questionnaire, for the simple reason they got a bad score.

2) Self-Selection Bias
For people like me, HRV didn’t seem to predict much, so I stopped using the app. The app will likely select for long term users who believe HRV impacts their symptoms.

For a study like this to work, since it relies on PROMs, HRV data would have to be blinded.

I like visible, people seem to really fixate on their daily score and it’s a bit prominent so you can’t have see why.

Then there is all this next step they want to do - predict a crash. I absolutely don’t see the point of pursuing that. Sometimes there’s a trauma. Sometimes there’s no discernible reason. I don’t believe that a crash is as simple as HRV + past performance = cancel your plans for the next 3 days. However, people want to believe it is and they want to be able to make plans, I guess that idea is enchanting.

I don’t think we know enough about ME/CFS or LC to be predicting how it behaves in future. If I’m wrong I’ll eat my words but I think it’s a fools errand. It’s like when people follow the car map thing and drive into a river because it said to, ignoring what their eyes and ears see, in favour of the computer.

 

[SNT Gatchaman]

I have never really noticed any benefit from a high HRV. Mine is typically in the 55-65 range, which is excellent. I have had many worse days that weren't proper crashes, and it rarely dips, so it's not very sensitive.

I'm actually surprised at how consistently good my HRV can be, despite being essentially non-functional, even for periods lasting weeks and months

I'm routinely around 18-20, sometimes as high as 30, which is quite different to you.

I wonder if we have enough people here with HRV trackers whether it's worth doing an indicative poll. Within the limits of our limited and biased sample, the hypothesis to interrogate would be: "A low HRV is protective of cognitive function" / "HRV is inversely correlated with cognitive function". (Perhaps also neuro symptoms more generally including hypersensitivities rather than just brain fog).

Two example and opposing data points prompting this question are myself vs rvallee as above. If I'm not mistaken, one of us has significant OI but no brain fog and the other significant brain fog but little OI — but otherwise I suspect we're broadly similar in terms of demographics/pre-illness health/education etc (I am a bit older though).

The idea behind this relates to —

I have wondered whether the OI is a mechanism the brain has (or can) invoke to protect itself. For example if one of the underlying problems were to be endothelial dysfunction and glycocalyx damage, impairing the blood-brain barrier, it might be protective to try and reduce shear effects from fast flow by dropping cerebral blood flow/velocity.

The lining of blood vessels is constantly exposed to mechanical forces exerted by blood flow against the endothelium. Endothelial cells detect these tangential forces (i.e., shear stress), initiating a host of intracellular signaling cascades that regulate vascular physiology. Thus, vascular health is tethered to the endothelial cells’ capacity to transduce shear stress. Indeed, the mechanotransduction of shear stress underlies a variety of cardiovascular benefits, including some of those associated with increased physical activity. However, endothelial mechanotransduction is impaired in aging and disease states such as obesity and type 2 diabetes, precipitating the development of vascular disease. Understanding endothelial mechanotransduction of shear stress, and the molecular and cellular mechanisms by which this process becomes defective, is critical for the identification and development of novel therapeutic targets against cardiovascular disease.

a better hypothesis could be that cerebral autoregulation etc is principally aiming to preserve and protect the BBB. If it's damaged that might prompt the brain to use all the tools at its disposal to try and reduce CBF to repair or protect against further damage. That could include the other aspects beyond autoregulation: chemoregulation, neuronal regulation, and endothelium-dependent regulation (reviewed here).

Increased sympathetic activity will cause cerebral vasoconstriction and there are inputs via the trigeminal nerve too. Maybe reduced parasympathetic inputs reduces cerebral vasodilatation. Effects of each may be small, but the brain has quite a few mechanisms to use in parallel.

So low HRV might be indicating that such compensating mechanisms are at play, at least in terms of the autonomic nervous system. Obviously there are people with significant OI and brain fog but their level of disease severity may have exceeded any beneficial compensation.

@MelbME any possibility you could you evaluate this question with the data you're gathering in eg the saline study?

 

[SNT Gatchaman]

Thread by David Putrino

https://bsky.app/profile/did:plc:6mjkfnedlglksv5gpivxrldd/post/3lcdzfpftck2h

So excited to finally see this study out in pre-print! This study is the largest of its kind to date: Using data from 4,244 people with Long COVID, ME/CFS, and other complex chronic illnesses, we took hundreds of thousands of data points across hundreds of days to see if we could predict crashes using physiological and self-reported data.

Turns out - yes we can. Data related to variations in HRV could predict crashes in individuals with quite good accuracy. The caveat: your data was good at predicting your patterns of crashing, but not good at predicting other people's crash patterns: every person with , and other complex chronic illnesses has unique physiology and requires a personalized approach.

So if we can (in an individual) predict crashes from their morning physiological data —

The diagram illustrates the model structure, where morning biometric scores (e.g., HRV) predict same-day evening symptom severity. This approach allows for within-day analysis of the relationship between physiological measures and symptom changes.

Meanwhile Paul Garner's thoughts are —

Of course PEM is a "physical response". As is blushing , tachcardia from a frightening movie, vomiting at the sight of a hospital in people who have had chemo: subconscious, influenced by expectations, and involve classical conditioning. Same with PEM.

So not only is PG missing the whole point that there's a significant delay to symptoms, unlike immediate classical conditioning responses; he's also missing the point that the effect can be measured before his "cause" has even occurred.

 

[Eleanor]

Garner's understanding of classical conditioning seems to be that the dog's salivating causes the food to arrive.

 

[Sean]

It’s like when people follow the car map thing and drive into a river because it said to, ignoring what their eyes and ears see, in favour of the computer.

+1

 

[Snow Leopard]

I don’t think we know enough about ME/CFS or LC to be predicting how it behaves in future. If I’m wrong I’ll eat my words but I think it’s a fools errand. It’s like when people follow the car map thing and drive into a river because it said to, ignoring what their eyes and ears see, in favour of the computer.

I agree, I'd much rather people learn to listen to their body than overly focus on heart rate and HRV.

 

[MrMagoo]

I'm routinely around 18-20, sometimes as high as 30, which is quite different to you.

I wonder if we have enough people here with HRV trackers whether it's worth doing an indicative poll. Within the limits of our limited and biased sample, the hypothesis to interrogate would be: "A low HRV is protective of cognitive function" / "HRV is inversely correlated with cognitive function". (Perhaps also neuro symptoms more generally including hypersensitivities rather than just brain fog).

Two example and opposing data points prompting this question are myself vs rvallee as above. If I'm not mistaken, one of us has significant OI but no brain fog and the other significant brain fog but little OI — but otherwise I suspect we're broadly similar in terms of demographics/pre-illness health/education etc (I am a bit older though).

The idea behind this relates to —









So low HRV might be indicating that such compensating mechanisms are at play, at least in terms of the autonomic nervous system. Obviously there are people with significant OI and brain fog but their level of disease severity may have exceeded any beneficial compensation.

@MelbME any possibility you could you evaluate this question with the data you're gathering in eg the saline study?

I think it would still be a subjective thing - you’re asking people with cognitive dysfunction to self report their cognitive dysfunction and link it to their heart rate variability? But I’m not a scientist.

Based on absolutely no evidence and just my opinion (!) I don’t think it’s a straightforward quid pro quo I think it’s more complex. I feel like you’d be trying to work out the way a supermarket logistics picking system works by following the journey of a pallet of oranges.

The giant sticking point in any female study - my heart rate has a crazy boost about a week before my period, then drops back down over a few days, so my HRV will change due to that. I will also be entering the “the back of my head has opened and my brain has rolled out” phase of PMT.

I absolutely love Visible for many things, but I really can’t be bothered to let it dominate my life, I tried the Facebook group but it was awful, more like when I’ve done weight watchers, people obsessed about points, what they tried to do not to use points, how they accidentally burned their points….I generally just live my life and check it at the end of the day tbh, use it to show other people what sitting up does to my heart, do my FUNCAP and daily symptoms.

 

[hibiscuswahine]

Visible is not available in NZ. I monitor HRV with the app Welltory (or through the Apple Health App) for a couple of years. Initially it was to make sense of my OI and OH and being stuck in PEM for the longest period so far but in the end it was more useful for stress management when needed as is tied in with the breathing app on my Apple Watch. Welltory, annoyingly keeps telling me to breath, meditate and exercise when a nice rest works better so the later makes it not suitable for pwME, imho.

Welltory has moved recently to a Wellness score which compares your HRV to previous readings and to population norms (but that could be the population using the app). I am comfortably in the population norm. It did predict a nasty URTI I got but while I was in PEM following that it did not show that. I have a few brief periods of PEM since and it is debatable if it has predicted it. Maybe Visible has different metrics.

I would definitely not use an HRV monitor to predict a crash or to advise on whether to exercise in the next heart rate threshold, I have apps specially for that, just for academic interest, and the scores rarely reflect the reality of my ME and PEM. To a degree, heart rate is more reflective of my body's state than HRV. I do still use Welltory as one of may biggest problems is sleep and I find the graphics useful which includes a body battery.

But health apps, although interesting, I do not think should replace learning to recognise one's own symptoms and how they relate to PEM, basically like other's have said listening to your body.

I have both OI and cognitive problems and my average HRV is 20ms, it has gone down from about 30 a few years ago. I have had a few readings of 0 which didn't reflect my assessment of my body's state at all. A poll would be interesting for academic interest @SNT Gatchaman, maybe linked to severity with a FUNCAP score comparing different dimensions of symptoms.

 

[SNT Gatchaman]

Yeah we may find it's easier and quicker to get answers to what's underlying, driving some of these symptoms, long before we work out the mechanisms of certain downstream derangements.

 

[RobH]

Been wearing a Garmin with HRV monitoring for a couple of years. My HRV reliably drops when I've had a stressful day or consume alcohol. Haven't noticed any correlation to crashes. N=1 though.

 

[MrMagoo]

To be fair it’s my data they used in this study, I’ve just realised.

 

[MelbME]

I'm routinely around 18-20, sometimes as high as 30, which is quite different to you.

I wonder if we have enough people here with HRV trackers whether it's worth doing an indicative poll. Within the limits of our limited and biased sample, the hypothesis to interrogate would be: "A low HRV is protective of cognitive function" / "HRV is inversely correlated with cognitive function". (Perhaps also neuro symptoms more generally including hypersensitivities rather than just brain fog).

Two example and opposing data points prompting this question are myself vs rvallee as above. If I'm not mistaken, one of us has significant OI but no brain fog and the other significant brain fog but little OI — but otherwise I suspect we're broadly similar in terms of demographics/pre-illness health/education etc (I am a bit older though).

The idea behind this relates to —









So low HRV might be indicating that such compensating mechanisms are at play, at least in terms of the autonomic nervous system. Obviously there are people with significant OI and brain fog but their level of disease severity may have exceeded any beneficial compensation.

@MelbME any possibility you could you evaluate this question with the data you're gathering in eg the saline study?

HRV is typically shown to be lower during stress, so the idea that people have been using is that low HRV is predictive of feeling worse.

We are looking to do some similar work here but a little different than single morning HRV measures. It's encouraging to see that it was accurate. The recall numbers I couldn't see but I think they aren't as impressive.

Recall is most important though, that is telling you how often you could predict a crash that actually happened. For ME/CFS you really want 100% recall, you want to predict a crash everytime they have a crash. That way you could give warning a crash was coming. A simple way to do this would be to tell someone evey day that they will have a crash, but obviously that's also not helpful. You want to get every crash day right with the most amount of predicted non-crash days.