AI system uses wearable device data to derive BP

Reuters file photo for representation.

Scientists -- including one of Indian origin -- have developed an artificial intelligence system that uses data collected by wearable devices to predict an individual's blood pressure and provide personalised recommendations to lower it.

This is the first work investigating daily blood pressure prediction and its relationship to health behaviour data collected by wearables, according to the researchers from the University of California (UC) San Diego in the US.

When doctors tell their patients to make a lot of significant lifestyle changes -- exercise more, sleep better, lower their salt intake etc -- it can be overwhelming, and compliance is not very high, said Sujit Dey, from UC San Diego.

"What if we could pinpoint the one health behaviour that most impacts an individual's blood pressure, and have them focus on that one goal, instead," Dey asked.

Researchers collected sleep, exercise and blood pressure data from eight patients over 90 days using a wireless blood pressure monitor.

Using machine learning and this data from existing wearable devices, they developed an algorithm to predict the users' blood pressure and show which particular health behaviours affected it most.

This study affirmed the importance of personalised data over generalised information. While many health databases add large amounts of patient data into one model, considering all patients together to make health suggestions, the personalised information in this study was more effective.

For example, one subject's blood pressure was most affected by the number of minutes they were sedentary throughout the day.

Changing that one factor had a significant impact, lowering their average systolic blood pressure by 15.4 percent and their diastolic blood pressure by 14.2 percent in one week.

For another subject, the time they went to bed was the most important factor in lowering their blood pressure based on their historical data.

When this subject went to bed a total of 58 minutes earlier over the week prior, they experienced a 3.6 per cent drop systolic blood pressure and 6.6 per cent decrease in their average diastolic blood pressure from the previous week.

"This research shows that using wireless wearables and other devices to collect and analyse personal data can help transition patients from reactive to continuous care," said Dey.

"Instead of saying 'My blood pressure is high therefore I'll go to the doctor to get medicine,' giving patients and doctors access to this type of system can allow them to manage their symptoms on a continuous basis," he said.

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AI system uses wearable device data to derive BP

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