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, Sujit Dey, Professor, Department of Electrical and Computer Engineering at the University of California in the US, said in a statement.
"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 said.
The study affirmed the importance of personalised data over generalised information as the former was more effective.
The team collected sleep, exercise and blood pressure data from eight patients over 90 days.
Using machine learning and the 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 research shows that using wireless wearables and other devices to collect and analyse personal data can help transition patients from reactive to continuous care," Dey said.
"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 noted.
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