Smartwatches can track COVID-19 symptoms, study finds
If a COVID-19 infection develops, a wearable device may eventually be able to clue you in. A study at the University of Michigan found that a smartwatch can monitor how symptoms progress.
The study evaluated the effects of COVID-19 with various factors derived from heart-rate data. This method also could be employed to detect other diseases, such as influenza and the common cold, at home or when medical resources are limited, such as during a pandemic or in developing countries.
Tracking students and medical interns across the country, the University of Michigan researchers found that new signals embedded in heart rate indicated when individuals were infected with COVID-19 and how ill they became.
For instance, they discovered that individuals with COVID-19 experienced an increase in heart rate per step after the onset of their symptoms. Meanwhile, people who reported a cough as one of their COVID-19 symptoms had a much more elevated heart rate per step than those without a cough.
“We previously developed a variety of algorithms to analyze data from wearable devices. So, when the COVID-19 pandemic hit, it was only natural to apply some of these algorithms to see if we can get a better understanding of disease progression,” says Caleb Mayer, a doctoral student in mathematics at the University of Michigan and a co-first author of the study.
People may not internally sense COVID-19’s direct impact on the heart, but “heart rate is a vital sign that gives a picture of overall health," says Daniel Forger, a University of Michigan professor.
Millions of people are tracking their heart rate through wearable devices. This information is already generating a tremendous amount of data for researchers to analyze, says co-author Daniel Forger, professor of mathematics and research professor of computational medicine and bioinformatics at the University of Michigan.
“Heart rate is affected by many different physiological signals,” Forger explains. “For instance, if your lungs aren’t functioning properly, your heart may need to beat faster to meet metabolic demands. Your heart rate has a natural daily rhythm governed by internal biological clocks.” While people may not internally sense COVID-19’s direct impact on the heart, he adds that “heart rate is a vital sign that gives a picture of overall health.”
Among the total of 2,164 participants who enrolled in the student study, 72 undergraduate and graduate students contracted COVID-19, providing wearable data from 50 days before symptom onset to 14 days after. The researchers also analyzed this type of data for 43 medical interns from the Intern Health Study by the Michigan Neuroscience Institute and 29 individuals (who are not affiliated with the university) from the publicly available dataset.
Participants could wear the device on either wrist. They also documented their COVID-19 symptoms, such as fever, shortness of breath, cough, runny nose, vomiting, diarrhea, body aches, loss of taste, loss of smell, and sore throat.
Experts not involved in the study found the research to be productive. “This work is pioneering and reveals exciting new insights into the many important ways that we can derive clinically significant information about disease progression from consumer-grade wearable devices,” says Lisa A. Marsch, director of the Center for Technology and Behavioral Health and a professor in the Geisel School of Medicine at Dartmouth College. “Heart-rate data are among the highest-quality data that can be obtained via wearables.”
Beyond the heart, she adds, “Wearable devices are providing novel insights into individuals’ physiology and behavior in many health domains.” In particular, “this study beautifully illustrates how digital-health methodologies can markedly enhance our understanding of differences in individuals’ experience with disease and health.”
Previous studies had demonstrated that COVID-19 affects cardiovascular functions. Capitalizing on this knowledge, the University of Michigan endeavor took “a giant step forward,” says Gisele Oda, a researcher at the Institute of Biosciences at the University of Sao Paulo in Brazil and an expert in chronobiology—the science of biological rhythms. She commends the researchers for developing a complex algorithm that “could extract useful information beyond the established knowledge that heart rate increases and becomes more irregular in COVID patients.”
Wearable devices open the possibility of obtaining large-scale, long, continuous, and real-time heart-rate data on people performing everyday activities or while sleeping. “Importantly, the conceptual basis of this algorithm put circadian rhythms at the center stage,” Oda says, referring to the physical, mental, and behavioral changes that follow a 24-hour cycle. “What we knew before was often based on short-time heart rate measured at any time of day,” she adds, while noting that heart rate varies between day and night and also changes with activity.
However, without comparison to a control group of people having the common flu, it is difficult to determine if the heart-rate signals are unique to COVID-19 or also occur with other illnesses, says John Torous, an assistant professor of psychiatry at Harvard Medical School who has researched wearable devices. In addition, he points to recent data showing that many wearables, which work by beaming light through the skin, may be less accurate in people with darker skin due to variations in light absorption.
While the results sound interesting, they lack the level of conclusive evidence that would be needed to transform how physicians care for patients. “But it is a good step in learning more about what these wearables can tell us,” says Torous, who is also director of digital psychiatry at Beth Israel Deaconess Medical Center, a Harvard affiliate, in Boston. A follow-up step would entail replicating the results in a different pool of people to “help us realize the full value of this work.”
It is important to note that this research was conducted in university settings during the early phases of the pandemic, with remote learning in full swing amid strict isolation and quarantine mandates in effect. The findings demonstrate that physiological monitoring can be performed using consumer-grade wearable sensors, allowing research to continue without in-person contact, says Sung Won Choi, a professor of pediatrics at the University of Michigan who is principal investigator of the student study.
“The worldwide COVID-19 pandemic interrupted a lot of activities that relied on face-to-face interactions, including clinical research,” Choi says. “Mobile technology proved to be tremendously beneficial during that time, because it allowed us to collect detailed physiological data from research participants remotely over an entire semester.” In fact, the researchers did not have any in-person contact with the students involved in the study. “Everything was done virtually," Choi explains. "Importantly, their willingness to participate in research and share data during this historical time, combined with the capacity of secure cloud storage and novel mathematical analytics, enabled our research teams to identify unique patterns in heart-rate data associated with COVID-19.”
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.