Digital technology has highlighted a new pregnancy risk. Data collected from a wearable device reveals that reduced sleep and activity in pregnancy is linked to premature birth risk.
The information has been assessed by Stanford University, looking at deviations from normal sleep and activity in pregnancy. The findings show that the two factors are connected, leading to a risk for premature delivery,.
In the study, the researchers collected data from devices worn by more than 1,000 women throughout pregnancy. With a machine learning algorithm, the scientists sifted through participants’ activity information to detect fine-grained changes in sleep and physical activity patterns.
Through the use of an artificial intelligence algorithm, it is possible to build can build a ‘clock’ of physical activity and sleep during pregnancy, and to tell how far along a patient’s pregnancy is.
This shows that normal pregnancy is characterized by progressive changes in sleep and physical activity as the pregnancy advances. A concern arises when patients do not follow that clock.
This means that when patients’ sleep and activity levels do not change on a typical trajectory, this becomes a warning sign for premature birth.
The researchers found that as pregnancies progress, sleep typically became more disrupted, and women became less physically active. Here some women’s sleep and activity patterns changed on an accelerated timeline relative to how far along they were in their pregnancies. These individuals were more likely to deliver early.
Premature birth is defined when a baby is born three or more weeks early. This affects 10.5 percent of births in the U.S. Premature newborns can suffer many medical complications, including diseases of the eyes, lungs, brain and digestive system. Prematurity is the leading cause of death for children under age 5 around the world.
The risk factors for premature delivery include greater levels of inflammation in the pregnant person, specific immune-system changes,
Based on the new findings, if researchers can identify sleep and activity patterns that lower prematurity risk, they can design interventions to help expectant mothers adopt better sleep and exercise habits, a potentially low-risk way of reducing preterm births.
For the research, the Stanford Medicine team collaborated with scientists at Washington University in St. Louis, who collected the sleep and physical activity data from 1,083 pregnant women treated there. More than half of the cohort (706 participants) were Black. In the United States, the rate of premature birth is about 50 percent higher in Black women than in white women.
The participants wore actigraphy devices similar to smartwatches to collect once-a-minute measurements of physical activity and light exposure starting in the first trimester of pregnancy and continuing until their babies were born. The researchers also had data from participants’ electronic medical records on gestational age, or how far along each pregnancy was; maternal medical conditions such as high blood pressure, diabetes, heart disease and depression; pregnancy complications such as preeclampsia and infections; and information about the birth, including duration of the pregnancy, the baby’s birth weight and newborn medical complications.
With the movement and light exposure data, the research team developed a machine learning model of activity and sleep during pregnancy. The model shows that patterns of sleep and physical activity change over the course of pregnancy, which generally is associated with more sleep disruption and less physical activity as pregnancy progresses.
The researchers noted how strongly deviations from the normal pattern of sleep and physical activity could predict preterm birth. If the machine-learning model classified a woman as sleeping better and being more physically active than usual for her stage of pregnancy, this was linked with a 48 percent reduction in risk for preterm delivery.
Conversely, if the model classified a woman as sleeping worse and being less physically active than usual for her stage of pregnancy, her risk for preterm delivery was 44 percent higher than for pregnant women with typical sleep and activity patterns.
Further work is needed to understand their implications for preventing prematurity.
The research appears in the journal npj Digital Medicine, titled “Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.”
