This is a cool new study from UCSD research group. 11–19% mARD is interesting, although not quite accurate enough to displace CGMs (the FDA-cleared ones have ~8% mARD).
But the sweat angle is could add signal alongside other techniques like Raman or MIR spectroscopy, and maybe a combination of these and an ML system would be accurate enough to use in practice.
Definitely agreed. I'm rooting for Apple, Samsung, Oura, etc to finally crack this problem and some of the techniques in the article (MIR, Raman spectroscopy, wearable foundation models) seem like they're leading in the right direction.
Unfortunately this particular intervention wasn't successful (not much difference between treatment and control groups). But conceivably, afib triggers is something that varies from person to person and perhaps a future design would tease this out.
Interesting to see dehydration on there. I never even thought of that as a possible trigger. It's good to see this is specifically looking at Paroxysmal AFIB patients as that's what I fall under. Mine is very mild and self-corrects within hours to a few days.
Biggest triggers are caffeine, alcohol, and sleep deprivation (correlated with my sleep apnea events) for me. I see exercise is only reported as being slightly higher than caffeine here, but exercise (stationary bike, strength training) usually causes my episodes to self-correct; I usually attribute this to the more controlled breathing during exercise, but I have no proof. I also seem to have a much larger threshold of triggering AFIB with alcohol than I do coffee. I have to reach the moderately drunk stage to induce a flare with alcohol. If I keep things near a light-buzz, I can typically get away with no issues.
Doctors blamed my first afib episode on dehydration. I had been very sick, could not eat or drink for days, and just experienced the highest fever of my adult life, nearly 105F. A few hours after the fever finally broke, I went into afib.
At that time, I was on an opioid pain killer for an abscess. The next time I was on an opioid pain killer, years later, I also had an afib episode. So I wonder if maybe I have some sensitivity to opioids.
dehydration impacts electrolyte balance, the muscle cells of the heart work through moving these ions through their membrane. changes in the concentrations changes both the rhythm and rate depending on how the concentrations of each ion is shifted
This is the first oral PCSK9 inhibitor; it cuts LDL cholesterol (or ApoB) levels by 50-60%. It uses a different mechanism than a statin so you can layer them to get an 80% or so reduction overall.
It also reduces Lp(a), the strongest hereditary risk factor for heart disease, by 28%.
Previous PCSK9 inhibitors like Repatha were injectables (similar to GLP-1s). Only about 1% of people eligible for injectable PCSK9 inhibitors use them, so having a convenient daily pill is a potentially huge win for prevention.
Likely uses pulse transit time and the shape of the pulse wave to infer changes in blood pressure. I wrote a bit about how this works (in the context of Apple Watch's hypertension notifications) here: https://www.empirical.health/blog/apple-watch-blood-pressure...
How does it transform blood speed to pressure? Doesn't it require a different calibration for each person? Your article says something about deep learning, but IIUC it's to detect unusual peaks in one person.
The speed of the pressure wave is one signal that correlates with blood pressure. It's a bit like a string being pulled taut -- waves travel faster with higher pressure. The shape of the wave also gives clues. For example, the rise time of the wave tells you something about the resistance encountered, which is a function of blood pressure.
Rather than hand-engineering these features, most modern systems are built on a wearable foundation model that's been trained reconstruct the signal (similar to how an LLM is trained to predict the next word). Those foundation models are picking up on these signals and likely others.
You're right that calibration with a cuff is required of all systems currently on the market.
The Signal Ring folks' claim they can do a blood pressure number without calibration, which is quite novel and seems to be their "secret sauce". They did run a clinical study as well, so presumably more details will come out whenever that's published.
> The speed of the pressure wave is one signal that correlates with blood pressure.
Can it distinguish 200/180 from 120/100? I expect it to depend more on the difference than the absolute value, but fluid dynamic inside flexible tubes is a nightmare.
> They did run a clinical study as well, so presumably more details will come out whenever that's published.
To be eligible, you need either a BMI of >=35, or a BMI of >=27 and a set of specific health conditions (uncontrolled hypertension, chronic kidney disease, pre-diabetes, etc). You also can't be qualified for GLP-1s under Medicare's previous Part D coverage.
In practice these criteria are narrower than they look. Of the 13 million Medicare beneficiaries with overweight or obesity, about 4 million actually qualify, because most are excluded for already having a diagnosis, like type 2 diabetes or sleep apnea, that covers a GLP-1 another way.
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