Next Steps in Wearables

By MedTech Intelligence Staff
June 29, 2022


• Categories: 01-Digital Health, 03-Manufacturing Execution, Connected Care, Home Health, Patient Engagement, Product Design

After more than 30 years working in the diabetes space, John Mastrototaro, Ph.D., biomedical engineer and CEO of Movano is now focused on improving the utility and data accuracy of consumer wearables. We spoke with John to learn more about the challenges in collecting accurate data in real-life conditions and the coming convergence of consumer wearables and medical remote monitoring.

MedTech Intelligence (MTI): What led you to move from medical-grade monitoring technologies to consumer wearables?

Mastrototaro: I have a Ph.D. in biomedical engineering and have been developing medical devices for more than 30 years. Most of my work was in the diabetes space, where I led the team at MiniMed—which was later acquired by Medtronic—that developed the first continuous glucose monitoring system that the FDA cleared back in 1999. Most of our products were designed for people with type 1 diabetes who are on insulin. We developed an insulin pump with real-time alerts to alert patients when their blood sugars are getting too high or too low. And, ultimately we created the first baby steps for the artificial pancreas to have insulin delivery automatically suspended if the glucose levels were starting to drop too low.

Near the tail end of my time at Medtronic I got very into digital health, remote monitoring and understanding how we could leverage data from people in their homes to help us understand how well they were living with chronic conditions and whether their situations were starting to deteriorate or not, so that we could take more immediate action.

MTI: What are you currently working on at Movano?

Mastrototaro: Movano has a few different things going on. First, we have a proprietary radiofrequency (RF) technology that we are using to see if we can accurately monitor glucose noninvasively as well as blood pressure without a cuff. We’re looking at different form-factors right now and running clinical trials to see if this RF energy is able to measure blood pressure or glucose noninvasively.

As we started developing a solution for those two measures, we also realized that there was a lot of value in measuring many things at the same time. If you really want to have a good understanding of someone’s health, ideally you would also monitor their heart rate and heart rate variability, their oxygen and respiration, and their activity levels and sleep patterns. So, we embarked on gathering different sensors that would allow us to measure those things as well.

MTI: What are some of the challenges in ensuring that you are receiving accurate data from a consumer wearable?

Mastrototaro: A lot of the companies that are in the wearables market today buy a chip that has the sensor built into it and also an algorithm that calculates the metric that is being measured. For example, there are chips that measure heart rate, and those chips are very accurate when someone is at rest. When you get a nice clean signal from the sensor you’re going to get a very accurate representation of your heart rate.

However, if the wearer is involved in certain forms of physical activity that can cause a lot of “noise” in the signal that is being caught by the sensor. As the sensor pushes that signal into the algorithm that is trying to determine the heart rate, it gets confused because it can’t tease out the signal from the background noise. As a result, the device may report a heart rate that is completely inaccurate.

So, when we look at sensors to monitor heart rate, one of the things we are doing is using sensors where we get raw data. This allows the sensor to look at the raw data. Then, we do signal processing to determine whether we can extract the signal from the noise. If so, we will provide a heart rate measure. If we cannot because the signal is too noisy, the device simply will not report a data point. You will get an unable to read response rather than an inaccurate reading.

MTI: What factors needs to be addressed to make sure that you are capturing accurate data through wearables and remote monitoring devices?

Mastrototaro: The FDA has guidance documents on how you must construct your clinical studies, and a lot of them are performed in a clinic environment with the person at rest, and this includes clinical trials for certain measures such as pulse oximetry or even heart rate. That is how they are designed. So, we will do the studies per FDA guidance, but in addition to that we are going to conduct some studies where we arrange for people to undergo certain activities, and we will monitor the type of data that we are getting, and we will look at our algorithms and their ability to extract the signal from the noise and then provide an accurate measure. This is not something that would be part of an FDA filing, but this helps to ensure you are capturing accurate data.

MTI: What did you feel was missing in the wearables market that you could address with Movano?

Mastrototaro: There was a real lack of solutions designed specifically for women and some of the unique health challenges women face as they age, whether it’s through the course of fertility or further on in time when they get to be premenopausal, menopausal or postmenopausal. Likewise, women suffer from high blood pressure and type 2 diabetes and other chronic conditions, but when we looked at the wearable market most of the products were designed initially for men. Over time companies have changed the colors or other aspects to make the devices look more feminine. But at the end of the day the size and the shape of the wearable—even what the wearables report—were designed for men but marketed to anybody and everybody.

We felt there was a real need to develop a product for women for the reasons stated above and because the healthcare industry, in general, has underserved women.

MTI: In addition to women being underserved in the healthcare market, there has been quite a bit of coverage recently about inaccuracies with pulse oximetry readings for patients with darker skin tones. What do developers need to be aware of when designing devices for all genders and skin tones?

Mastrototaro: One of the challenges with optical sensors is interrogating through different skin tones. And pulse oximeters as well as many of the noninvasive devices for glucose and blood pressure monitoring do use—or are looking at—optical approaches.

We have chosen to use an RF approach, which is not as susceptible to skin pigmentation challenges as the optical approaches, and that is one of the reasons we’re looking very heavily at the RF technology. That being said, there are certain measures you take, such as pulse oximetry and oxygen levels, where you need to use an optical sensor right now to do that. So, designing the system in a way where you can get the right intensity of optical transmission to pass through different skin tones is one of the things you need to try to do.

MTI: With wearables, we hear a lot about what the consumer needs in terms of ease of use and comfort. What do providers want from these devices?

Mastrototaro: Historically, providers have been a little bit wary of wearables. The fear is that people are going to come to them with their data and say, “Hey what’s going on with me? What’s right, what’s wrong?” and the clinician is going to be bombarded and burdened with a lot of data that may or may not be accurate.

In the diabetes space, providers very much wanted to have patient monitoring, where they could get summarized data from the past 30 to 60 days to see on average what was happening with their patient’s glucose control.

I would envision that if we can provide a summarized report from sensors that have medical-grade data on heart rate, respiration rate, sleep patterns and activity levels over the last 30 to 60 days, that could give the provider a pretty good feel for what’s been going on with this person’s health. That is what we want to get into.

You cannot have a solution that’s going to take minutes for someone to review because they won’t do it and it doesn’t work within the current healthcare system. So, we’re really looking to provide a solution that summarizes the important points very easily and that can be interpreted and understood very quickly, so the clinician can have a meaningful conversation with the patient during their visit.

MTI: How does RF noninvasively measure blood pressure?

Mastrototaro: We have seen with our RF technology that when we’re interrogating an artery within the wrist, we can see pulse pressure waveforms, which basically mimic the beat of the heart. We can see where there is an initial spike and then a hump, and these are associated with the systole and the diastole of blood pressure. Of course, from that repetitive pattern we can clearly determine the heart rate to measure heart rate variability as well.

MTI: As consumer interest in wearables continues to grow and the healthcare industry continues to seek better remote patient monitoring devices, do you think we will see a convergence of these technologies?

Mastrototaro: There is a convergence of consumer products and medical device products happening, and we see ourselves at that intersection. From the medical device aspect you expect quality, reliability and accuracy of data. From the consumer side you expect a cost effective, aesthetically pleasing, easy to use, simple app experience.

What we are trying to do is bring health care more to the masses. We are looking at various models to make this very affordable to people, and that includes looking at a pure subscription model where they may get the hardware for free.

For many years, we have seen increases in Type 2 diabetes and hypertension. We don’t want to just provide numbers. We want to provider insight to consumers. If people are just looking at their heart rate and blood pressure, it can create more stress because they don’t know what they’re looking at—is this number good or bad? Our intent is to reduce stress and provide peace of mind. So one thing that is very important to us is to not just to take those measures, but to distill all this data down to what it means to that person, so they know how their activities of daily living are affecting their health.