Scripps Study: COVID Impacts Lasting Average Of 2-3 Months
Speaker 1: 00:00 Can your smart watch or Fitbit detect COVID-19 before symptoms appear. And can those devices tell you how your body is recovering emerging data from an ongoing study with scripts research, translational Institute says it's quite possible. Scientists are using activity trackers to look at the physiological and behavioral changes that happen just before symptoms appear and extend months after the infection is gone. Jennifer Raden is an epidemiologist with the digital medicine division at Scripps, and she is leading. What's called the detect study a Dr. Raden welcome. Thank you for having me. You are finding common technology like smartwatches to be very helpful and understanding how a COVID 19 infection impacts the body. What have you discovered from people who are wearing these activity trackers? Speaker 2: 00:49 Yeah, so activity trackers can really give us a better view into each individual's unique, normal resting heart rate activity, and sleep patterns when they're healthy. And so when someone comes down with a viral infection, such as COVID, we can identify, um, changes compared to each user's unique, normal to better understand how they are responding to an infection in our current study that just came out. We looked at people's recovery patterns. Um, so how long it took them to go back to their baselines for resting heart rate activity and sleep. And what we found was that on average, it took, um, participants who came down with COVID about two to three months to return to their baseline resting heart rate. Speaker 1: 01:37 And how do these new findings compare to the current understanding of the impacts of COVID on the body? Speaker 2: 01:43 Yeah. So this is the first time that we've really been able to collect continuous longitudinal objective data from purchase of Pence, to be able to understand what their healthy normal was prior to infection, and then really, uh, follow them continuously over time to really see how their body responded to the infection and how long it's taking to recover. And so this gives further evidence that long COVID is, um, really impacting many individuals who come down with COVID the detection, the Speaker 1: 02:16 Study previously found that, that these devices may also be able to tell if there's an infection before symptoms show up. How so? Speaker 2: 02:23 Yeah. So, um, when people come down with a viral illness, they may start to see a subtle small change in the resting heart rate, um, even before fever onset or different symptom onset. And so sensors may actually give us an early warning that something is impacting someone's health and that maybe an individual needs to stay at home or be more aware of, of their health in any symptoms that they may develop in the next few days. So Speaker 1: 02:52 The study is ongoing and we'll look at the impact of vaccines. What are you hoping to find out there? So Speaker 2: 02:58 They're, we're hoping to better understand the physiological and behavioral response to vaccines as well. Um, to compare Pfizer versus Madrona and also compare users who had, um, uh, COVID infection prior to getting their vaccines, how that may impact their, um, physiological response to receiving the vaccine. Uh, we also want to look at different age groups in different gender, um, look at gender as well to see if there's any differences there in response. And ultimately with that study, we, um, hope to in the future create, um, collect biomarkers so we can compare the physiological response to perhaps immune response. So we think that this can be maybe in the future a way to understand whether an individual has now did, uh, uh, immune response to, with the vaccine. Speaker 1: 03:50 Yeah. What other ways might the information in this study be useful? Speaker 2: 03:53 Yeah, so we are currently partnering with the Rockefeller foundation to increase enrollment on specifically in the San Diego county. And our goal is to create an early warning system where we can identify hotspots of viral illness, infection in different communities faster than traditional, um, viral illness detection. So, um, typically, um, surveillance for viral illnesses, um, relies on par or for relies on people seeking care from their healthcare provider and their healthcare provider, then reporting the number of people they see each week that meet a certain case definition, as well as those who, um, receive testing for COVID and flu. And that system, the traditional system is actually pretty delayed. It takes about one to three weeks before those data are collected and reported. And so we're hoping that with our wearable data, that we can provide an earlier warning in an earlier detection of, um, local outbreak outbreaks that may occur from new strains of COVID, um, or other viral infections, such as flu epidemics Speaker 1: 05:02 And Dr. Raden, how can people sign up for this study? Yeah, Speaker 2: 05:05 They can go to our research app, which is called my data helps, or you can go to our website, um, detect study.org, and that will direct you to our research app and participants, um, can download our app and then they go through an e-consent process where they learn about our study, and then after that they can share, um, their device data. So we are device agnostic. We can pull in any wearable device that connects to apple health kit or Google fit, and then participants can share with us, um, any symptoms. They may develop vaccination status, any, um, COVID tests or flu tests they may receive. And, um, this allows us to compare both the sensor data to what participants are experiencing. I've been Speaker 1: 05:53 Speaking with Jennifer Raiden and epidemiologist with the digital medicine division at scripts research translational Institute, who is leading the detect study. Dr. Raden thanks so much for joining us. Thank you.
