The article tells me more about the dangers of overtraining at an advanced age than the usefulness of wearables. The wearable was useful for the researchers after the person had already passed away.
Also, considering the number of such workouts he completed and the fact that it seems he was aware of his deteriorating heart, he carried on? Even despite reported fatigue? Mad respect for his determination.
In 2021-2022 the patient was doing 2-3 workouts a week on a stationary bicycle. And 177 watts for 30 minutes is solid, at age 72.
Going up to 5-6 workouts per week is a step up, but this wasn't an inexperienced rider. And who knows how much outdoor cycling they did, which the Peloton wouldn't have recorded?
My (35) physician explicitly advised me against my 8-10 hour road cycling sessions pointing out changes in the heart wall thickness he was already able to observe.
How is this at all scientifically revealing or interesting? The paper says he developed structural heart failure with several confirmational lab findings then died doing a stress test of his heart basically? Like... yeah? That's what would happen in a 76 year old. ????
The striking detail here is that the patient's post-shift numbers wouldn't have tripped the static alarms we currently use; a resting heart rate moving from 44 to 49 bpm is still "athletic" by population standards. What mattered was the synchronized shift in several metrics relative to his personal baseline.
Perhaps because statistics education attunes us to average over individual differences, any mention of "data" unfortunately makes people instinctively jump to this 'cross-sectional' corner... textbooks explain how the typical human body behaves, and standard education and medical practice respectively emphasize averaging and population-level guidelines.
I think this approach is limited in some fundamental ways, and forces a dilemma: either accept wide and unactionable bands for what counts as normal, or use narrow bands that over-pathologize most individuals as diseased. The natural consequence of this is to start "fixing" deviations, and this is the territory of dangerous over-steering. This is why the medical community is afraid of "incidentalomas" and over-intervention. Tests which are point-in-time and compare one human to others are problematic in this respect. This is especially acute when the body is measured along many axes: like a full body scan. Much like the US Air Force Study of pilots discovered that there is no single pilot compatible with the "average" pilot measurements, comparing metrics from an individual's full body scan to a cohort average is likely to drastically overestimate problems.
In some ways, learned systems (like our physiology which is constantly adapting to our individual environment and experiences) follow the opposite of the Anna Karenina principle: every healthy individual is healthy in their own way. So the real diagnostic leverage isn't comparing an individual to an aggregate human template, but measuring change against their own homeostasis, and this requires high temporal resolution, not wider population comparisons.
I'm currently exploring how to bring this kind of longitudinal tracking to higher-fidelity modalities like wearable imaging. If any clinicians or researchers here are thinking about how to operationalize personal baselines in practice, I'd love to chat (email in profile).
I was a surgical trainee not a cardiologist, but there is something interesting and unusual about the step change here. In the context of structural heart disease, this to me implies a cardiac event ?chorda tendinae rupture or possibly ?ischaemia that suddenly worsened the competency of the heart valves, reducing cardiac output and leading to the change in resting heart rate etc. This later put the patient at risk for sudden cardiac death.
I guess the authors' argument here is that the change in wearable data could alert clinicians to the need for further imaging and/or change management. But the sudden step change here may not be typical or generalisable across all causes of sudden cardiac death - they obviously state this is n=1, but I think it's worth emphasising that the change may be quite specific to this case.
We do generalise in medicine as it speeds up decision making, but we always see vitals in the patient context. Longitudinal data may be able to build a picture of what is normal for a patient - but I would say healthy physiology is fairly consistent. Even with individualised data, the problem will still be signal to noise, and working out when to alert the clinician or patient.
One of the reasons doctors don't like unnecessary scans is they have knock on effects - radiation, unnecessary procedures / biopsies, patient anxiety and extra strain on healthcare systems. A larger problem is reaching patients that are less health-conscious, that are unlikely to wear wearables or access the diagnostic tests they need.
What kind of wearable imaging are you thinking of?
I can't believe so many people think these wearables are "accurate"; then they wonder why they aren't losing or gaining weight when they go by the expenditure estimates on these.
A 2020 systematic review by Fuller et al. found that consumer wearables often perform poorly at estimating energy expenditure, even when they measure steps and heart rate reasonably well. Across brands like Apple, Garmin, Polar, and Withings, calorie burn estimates frequently showed substantial error, often in the 15–40% range and sometimes exceeding 50%. Wearables can still be useful for tracking trends, but not for precise calorie calculations.
protip: if you actually want to do better, look into "adaptive TDEE".
This comment confuses me. Why would we expect calorie burn to be related to steps and heart rate? How could a wearable measure energy expenditure? What if someone was wearing a heavy backpack? The energy to move the backpack has to come from somewhere but the wearable can't tell that the person has one.
Is it really that confusing? Heart rate is correlated with oxygen consumption which is correlated with metabolic activity. The wearable would see the increased heart rate when a person is moving with a heavy backpack versus moving without one.
Now whether we can do that accurately with today’s wearables and without calibration to each individual is up in the air as far as I’m concerned. But it seems strange to me that you’re confused as to how we could even directionally infer these things.
> The wearable would see the increased heart rate when a person is moving with a heavy backpack versus moving without one.
I'm sure it would notice the heartrate was up, but how is it supposed to translate that into energy consumption? Eg, my heartrate is 80 bpm. How much energy am I burning in an hour? There isn't enough information to say, even if we add in data points like a gps measure of my velocity and elevation change.
It's not super far fetched to come to approximation, conservation of energy and all that, the main variability people in energy expenditure comes from activity.
Get population averages of base metabolic rate based on sex/weight/age etc. Get base heart rate. When heart rate increases due to activity,estimate what activity and intensity and use weight to estimate activity expenditure on top of base. A lot of assumptions of course which make these tend to be inaccurate but if the assumptions are right then methodology is not that unsound.
> I'm sure it would notice the heartrate was up, but how is it supposed to translate that into energy consumption?
Well, you need more oxygen to burn more nutrients. This means faster heart rate.
The body normally doesn't want to waste energy, so the heart rate at any given moment is just enough to cover the requirements.
Precisely calibrating that is another story. You can go into one of the places that directly measure the oxygen use and get the baseline data (e.g. https://www.dexafit.com/services/vo2-max-testing )
> Why would we expect calorie burn to be related to steps and heart rate? How could a wearable measure energy expenditure?
A lot of exercise equipment manufacturers will display very approximate numbers, based on assumptions the user can adjust if they want (but they seldom do).
If you jump on a treadmill at the gym and set it to a brisk walk, it'll show you a 'calories burned' count that goes up by 7 calories per minute based on your speed and an average adult's weight.
It doesn't have to be all that accurate to motivate the user to just do 5 more minutes, and to put a checkmark on a product feature list.
The article is a case report. They’re almost always N=1. They’re just meant to contribute to the sum total of general knowledge of practicing medicine.
Why is it so surprising that continuous measurement is likely to catch a failing system? The more surprising belief is that sporadic random measurements at random times give you good enough predictive powers. Like all the blood work and vitals we do at the doctor’s office - I’m sure it captures some problems but more subtle deviations from baseline are much more difficult to suss out.
Human's own warning system is awful and we've already replaced it. E.g. without using technology to measure LDL you might have no idea you are at high risk of myocardial infarction.
Actual title: "Longitudinal Changes in Wearable-Captured Physiology Before Sudden Cardiac Death"
The article tells me more about the dangers of overtraining at an advanced age than the usefulness of wearables. The wearable was useful for the researchers after the person had already passed away.
Also, considering the number of such workouts he completed and the fact that it seems he was aware of his deteriorating heart, he carried on? Even despite reported fatigue? Mad respect for his determination.
If you look at supplemental table S6 (which is admittedly inconvenient because it's a separately downloaded MS Word document) you get this:
S6. Summary of indoor cycling (Peloton) workouts by year (2018–2025)
In 2021-2022 the patient was doing 2-3 workouts a week on a stationary bicycle. And 177 watts for 30 minutes is solid, at age 72.Going up to 5-6 workouts per week is a step up, but this wasn't an inexperienced rider. And who knows how much outdoor cycling they did, which the Peloton wouldn't have recorded?
76 (already a good age) with an existing heart condition and increases HIIT exercise frequency by 25%.
I wonder if they consulted their physician before doing that, and what they recommended?
My (35) physician explicitly advised me against my 8-10 hour road cycling sessions pointing out changes in the heart wall thickness he was already able to observe.
How is this at all scientifically revealing or interesting? The paper says he developed structural heart failure with several confirmational lab findings then died doing a stress test of his heart basically? Like... yeah? That's what would happen in a 76 year old. ????
The authors are saying that there was a step change in his wearable data, that might alert clinicians to the need for further imaging or intervention.
Beautifully formatted paper by the way.
Love the colors and graphs. They transport a lot of data without looking bloated-by-design.
The striking detail here is that the patient's post-shift numbers wouldn't have tripped the static alarms we currently use; a resting heart rate moving from 44 to 49 bpm is still "athletic" by population standards. What mattered was the synchronized shift in several metrics relative to his personal baseline.
Perhaps because statistics education attunes us to average over individual differences, any mention of "data" unfortunately makes people instinctively jump to this 'cross-sectional' corner... textbooks explain how the typical human body behaves, and standard education and medical practice respectively emphasize averaging and population-level guidelines.
I think this approach is limited in some fundamental ways, and forces a dilemma: either accept wide and unactionable bands for what counts as normal, or use narrow bands that over-pathologize most individuals as diseased. The natural consequence of this is to start "fixing" deviations, and this is the territory of dangerous over-steering. This is why the medical community is afraid of "incidentalomas" and over-intervention. Tests which are point-in-time and compare one human to others are problematic in this respect. This is especially acute when the body is measured along many axes: like a full body scan. Much like the US Air Force Study of pilots discovered that there is no single pilot compatible with the "average" pilot measurements, comparing metrics from an individual's full body scan to a cohort average is likely to drastically overestimate problems.
In some ways, learned systems (like our physiology which is constantly adapting to our individual environment and experiences) follow the opposite of the Anna Karenina principle: every healthy individual is healthy in their own way. So the real diagnostic leverage isn't comparing an individual to an aggregate human template, but measuring change against their own homeostasis, and this requires high temporal resolution, not wider population comparisons.
I'm currently exploring how to bring this kind of longitudinal tracking to higher-fidelity modalities like wearable imaging. If any clinicians or researchers here are thinking about how to operationalize personal baselines in practice, I'd love to chat (email in profile).
I was a surgical trainee not a cardiologist, but there is something interesting and unusual about the step change here. In the context of structural heart disease, this to me implies a cardiac event ?chorda tendinae rupture or possibly ?ischaemia that suddenly worsened the competency of the heart valves, reducing cardiac output and leading to the change in resting heart rate etc. This later put the patient at risk for sudden cardiac death.
I guess the authors' argument here is that the change in wearable data could alert clinicians to the need for further imaging and/or change management. But the sudden step change here may not be typical or generalisable across all causes of sudden cardiac death - they obviously state this is n=1, but I think it's worth emphasising that the change may be quite specific to this case.
We do generalise in medicine as it speeds up decision making, but we always see vitals in the patient context. Longitudinal data may be able to build a picture of what is normal for a patient - but I would say healthy physiology is fairly consistent. Even with individualised data, the problem will still be signal to noise, and working out when to alert the clinician or patient.
One of the reasons doctors don't like unnecessary scans is they have knock on effects - radiation, unnecessary procedures / biopsies, patient anxiety and extra strain on healthcare systems. A larger problem is reaching patients that are less health-conscious, that are unlikely to wear wearables or access the diagnostic tests they need.
What kind of wearable imaging are you thinking of?
I can't believe so many people think these wearables are "accurate"; then they wonder why they aren't losing or gaining weight when they go by the expenditure estimates on these.
A 2020 systematic review by Fuller et al. found that consumer wearables often perform poorly at estimating energy expenditure, even when they measure steps and heart rate reasonably well. Across brands like Apple, Garmin, Polar, and Withings, calorie burn estimates frequently showed substantial error, often in the 15–40% range and sometimes exceeding 50%. Wearables can still be useful for tracking trends, but not for precise calorie calculations.
protip: if you actually want to do better, look into "adaptive TDEE".
This comment confuses me. Why would we expect calorie burn to be related to steps and heart rate? How could a wearable measure energy expenditure? What if someone was wearing a heavy backpack? The energy to move the backpack has to come from somewhere but the wearable can't tell that the person has one.
Is it really that confusing? Heart rate is correlated with oxygen consumption which is correlated with metabolic activity. The wearable would see the increased heart rate when a person is moving with a heavy backpack versus moving without one.
Now whether we can do that accurately with today’s wearables and without calibration to each individual is up in the air as far as I’m concerned. But it seems strange to me that you’re confused as to how we could even directionally infer these things.
> The wearable would see the increased heart rate when a person is moving with a heavy backpack versus moving without one.
I'm sure it would notice the heartrate was up, but how is it supposed to translate that into energy consumption? Eg, my heartrate is 80 bpm. How much energy am I burning in an hour? There isn't enough information to say, even if we add in data points like a gps measure of my velocity and elevation change.
It's not super far fetched to come to approximation, conservation of energy and all that, the main variability people in energy expenditure comes from activity.
Get population averages of base metabolic rate based on sex/weight/age etc. Get base heart rate. When heart rate increases due to activity,estimate what activity and intensity and use weight to estimate activity expenditure on top of base. A lot of assumptions of course which make these tend to be inaccurate but if the assumptions are right then methodology is not that unsound.
> I'm sure it would notice the heartrate was up, but how is it supposed to translate that into energy consumption?
Well, you need more oxygen to burn more nutrients. This means faster heart rate.
The body normally doesn't want to waste energy, so the heart rate at any given moment is just enough to cover the requirements.
Precisely calibrating that is another story. You can go into one of the places that directly measure the oxygen use and get the baseline data (e.g. https://www.dexafit.com/services/vo2-max-testing )
> Why would we expect calorie burn to be related to steps and heart rate? How could a wearable measure energy expenditure?
A lot of exercise equipment manufacturers will display very approximate numbers, based on assumptions the user can adjust if they want (but they seldom do).
If you jump on a treadmill at the gym and set it to a brisk walk, it'll show you a 'calories burned' count that goes up by 7 calories per minute based on your speed and an average adult's weight.
It doesn't have to be all that accurate to motivate the user to just do 5 more minutes, and to put a checkmark on a product feature list.
Isn’t that in line with the comment, that they aren’t accurate?
N = 1?
The article is a case report. They’re almost always N=1. They’re just meant to contribute to the sum total of general knowledge of practicing medicine.
Why is it so surprising that continuous measurement is likely to catch a failing system? The more surprising belief is that sporadic random measurements at random times give you good enough predictive powers. Like all the blood work and vitals we do at the doctor’s office - I’m sure it captures some problems but more subtle deviations from baseline are much more difficult to suss out.
By definition a case report describes a single patient (n = 1).
Yes, this is a „case study“.
So basically this is advocating to replace a human's own warning system by technology? Sounds like a lost cause ...
Human's own warning system is awful and we've already replaced it. E.g. without using technology to measure LDL you might have no idea you are at high risk of myocardial infarction.
No, read the abstract.
This is to decrease time to clinical eval and intervention.