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.”
New Tech Can Predict Breast Cancer Years in Advance
Every two minutes, a woman is diagnosed with breast cancer. The question is, can those at high risk be identified early enough to survive?
New AI software has predicted risk equally well in both white and black women for the first time.
The current standard practice in medicine is not exactly precise. It relies on age, family history of cancer, and breast density, among other factors, to determine risk. But these factors do not always tell the whole story, leaving many women to slip through the cracks. In addition, a racial gap persists in breast cancer treatment and survival. African-American women are 42 percent more likely to die from the disease despite relatively equal rates of diagnosis.
But now those grim statistics could be changing. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory have developed a deep learning model that can more accurately predict a patient's breast cancer risk compared to established clinical guidelines – and it has predicted risk equally well in both white and black women for the first time.
The Lowdown
Study results published in Radiology described how the AI software read mammogram images from more than 60,000 patients at Massachusetts General Hospital to identify subtle differences in breast tissue that pointed to potential risk factors, even in their earliest stages. The team accessed the patients' actual diagnoses and determined that the AI model was able to correctly place 31 percent of all cancer patients in the highest-risk category of developing breast cancer within five years of the examination, compared to just 18 percent for existing models.
"Each image has hundreds of thousands of pixels identifying something that may not necessarily be detected by the human eye," said MIT professor Regina Barzilay, one of the study's lead authors. "We all have limited visual capacities so it seems some machines trained on hundreds of thousands of images with a known outcome can capture correlations the human eye might not notice."
Barzilay, a breast cancer survivor herself, had abnormal tissue patterns on mammograms in 2012 and 2013, but wasn't diagnosed until after a 2014 image reading, illustrating the limitations of human processing alone.
MIT professor Regina Barzilay, a lead author on the new study and a breast cancer survivor herself.
(Courtesy MIT)
Next up: The MIT team is looking at training the model to detect other cancers and health risks. Barzilay recalls how a cardiologist told her during a conference that women with heart diseases had a different pattern of calcification on their mammograms, demonstrating how already existing images can be used to extract other pieces of information about a person's health status.
Integration of the AI model in standard care could help doctors better tailor screening and prevention programs based on actual instead of perceived risk. Patients who might register as higher risk by current guidelines could be identified as lower risk, helping resolve conflicting opinions about how early and how often women should receive mammograms.
Open Questions: While the results were promising, it's unknown how well the model will work on a larger scale, as the study looked at data from just one institution and used mammograms supplied by just one hospital. Some risk factor information was also unavailable for certain patients during the study, leaving researchers unable to fully compare the AI model's performance to that of the traditional standard.
One incentive to wider implementation and study, however, is the bonus that no new hardware is required to use the AI model. With other institutions now showing interest, this software could lead to earlier routine detection and treatment of breast cancer — resulting in more lives saved.
Sarah Mancoll was 22 years old when she noticed a bald spot on the back of her head. A dermatologist confirmed that it was alopecia aerata, an autoimmune disorder that causes hair loss.
Of 213 new drugs approved from 2003 to 2012, only five percent included any data from pregnant women.
She successfully treated the condition with corticosteroid shots for nearly 10 years. Then Mancoll and her husband began thinking about starting a family. Would the shots be safe for her while pregnant? For the fetus? What about breastfeeding?
Mancoll consulted her primary care physician, her dermatologist, even a pediatrician. Without clinical data, no one could give her a definitive answer, so she stopped treatment to be "on the safe side." By the time her son was born, she'd lost at least half her hair. She returned to her Washington, D.C., public policy job two months later entirely bald—and without either eyebrows or eyelashes.
After having two more children in quick succession, Mancoll recently resumed the shots but didn't forget her experience. Today, she is an advocate for including more pregnant and lactating women in clinical studies so they can have more information about therapies than she did.
"I live a very privileged life, and I'll do just fine with or without hair, but it's not just about me," Mancoll said. "It's about a huge population of women who are being disenfranchised…They're invisible."
About 4 million women give birth each year in the United States, and many face medical conditions, from hypertension and diabetes to psychiatric disorders. A 2011 study showed that most women reported taking at least one medication while pregnant between 1976 and 2008. But for decades, pregnant and lactating women have been largely excluded from clinical drug studies that rigorously test medications for safety and effectiveness.
An estimated 98 percent of government-approved drug treatments between 2000 and 2010 had insufficient data to determine risk to the fetus, and close to 75 percent had no human pregnancy data at all. All told, of 213 new pharmaceuticals approved from 2003 to 2012, only five percent included any data from pregnant women.
But recent developments suggest that could be changing. Amid widespread concerns about increased maternal mortality rates, women's health advocates, physicians, and researchers are sensing and encouraging a cultural shift toward protecting women through responsible research instead of from research.
"The question is not whether to do research with pregnant women, but how," Anne Drapkin Lyerly, professor and associate director of the Center for Bioethics at the University of North Carolina at Chapel Hill, wrote last year in an op-ed. "These advances are essential. It is well past time—and it is morally imperative—for research to benefit pregnant women."
"In excluding pregnant women from drug trials to protect them from experimentation, we subject them to uncontrolled experimentation."
To that end, the American College of Obstetricians and Gynecologists' Committee on Ethics acknowledged that research trials need to be better designed so they don't "inappropriately constrain the reproductive choices of study participants or unnecessarily exclude pregnant women." A federal task force also called for significantly expanded research and the removal of regulatory barriers that make it difficult for pregnant and lactating women to participate in research.
Several months ago, a government change to a regulation known as the Common Rule took effect, removing pregnant women as a "vulnerable population" in need of special protections -- a designation that had made it more difficult to enroll them in clinical drug studies. And just last week, the U.S. Food and Drug Administration (FDA) issued new draft guidances for industry on when and how to include pregnant and lactating women in clinical trials.
Inclusion is better than the absence of data on their treatment, said Catherine Spong, former chair of the federal task force.
"It's a paradox," said Spong, professor of obstetrics and gynecology and chief of maternal fetal medicine at University of Texas Southwestern Medical Center. "There is a desire to protect women and fetuses from harm, which is translated to a reluctance to include them in research. By excluding them, the evidence for their care is limited."
Jacqueline Wolf, a professor of the history of medicine at Ohio University, agreed.
"In excluding pregnant women from drug trials to protect them from experimentation, we subject them to uncontrolled experimentation," she said. "We give them the medication without doing any research, and that's dangerous."
Women, of course, don't stop getting sick or having chronic medical conditions just because they are pregnant or breastfeeding, and conditions during pregnancy can affect a baby's health later in life. Evidence-based data is important for other reasons, too.
Pregnancy can dramatically change a woman's physiology, affecting how drugs act on her body and how her body acts or reacts to drugs. For instance, pregnant bodies can more quickly clear out medications such as glyburide, used during diabetes in pregnancy to stabilize high blood-sugar levels, which can be toxic to the fetus and harmful to women. That means a regular dose of the drug may not be enough to control blood sugar and prevent poor outcomes.
Pregnant patients also may be reluctant to take needed drugs for underlying conditions (and doctors may be hesitant to prescribe them), which in turn can cause more harm to the woman and fetus than had they been treated. For example, women who have severe asthma attacks while pregnant are at a higher risk of having low-birthweight babies, and pregnant women with uncontrolled diabetes in early pregnancy have more than four times the risk of birth defects.
Current clinical trials involving pregnant women are assessing treatments for obstructive sleep apnea, postpartum hemorrhage, lupus, and diabetes.
For Kate O'Brien, taking medication during her pregnancy was a matter of life and death. A freelance video producer who lives in New Jersey, O'Brien was diagnosed with tuberculosis in 2015 after she became pregnant with her second child, a boy. Even as she signed hospital consent forms, she had no idea if the treatment would harm him.
"It's a really awful experience," said O'Brien, who now is active with We are TB, an advocacy and support network. "All they had to tell me about the medication was just that women have been taking it for a really long time all over the world. That was the best they could do."
More and more doctors, researchers and women's health organizations and advocates are calling that unacceptable.
By indicating that filling current knowledge gaps is "a critical public health need," the FDA is signaling its support for advancing research with pregnant women, said Lyerly, also co-founder of the Second Wave Initiative, which promotes fair representation of the health interests of pregnant women in biomedical research and policies. "It's a very important shift."
Research with pregnant women can be done ethically, Lyerly said, whether by systematically collecting data from those already taking medications or enrolling pregnant women in studies of drugs or vaccines in development.
Current clinical trials involving pregnant women are assessing treatments for obstructive sleep apnea, postpartum hemorrhage, lupus, and diabetes. Notable trials in development target malaria and HIV prevention in pregnancy.
"It clearly is doable to do this research, and test trials are important to provide evidence for treatment," Spong said. "If we don't have that evidence, we aren't making the best educated decisions for women."