Smart technology could halve asthma patients’ risk of suffering attacks and being admitted to hospital, preventing deaths, University of Auckland research has found.
During a decade-long study involving almost 15,000 patients, University of Auckland school of pharmacy senior clinical research fellow Dr Amy Chan, alongside researchers from University College London and Queen Mary University of London examined 50 years of research on asthma care.
They determined smart technologies, including automated text messages and electronic prompts, made a significant difference in asthma control.
Chan said about 80 Kiwis died from “highly preventable” asthma attacks each year and smart technology could work to stop those unnecessary deaths.
About 80 Kiwis die from asthma attacks each year. (File photo)
“Digital technologies that aim to improve medication-taking can increase people taking their medication in way it has been prescribed by 15% and improve asthma control and quality of life,” she said.
“Most people with asthma are hospitalised because of poor control, by having regular medication taking, it will reduce the risk.”
The smart technologies send reminders to asthma sufferers through text messages or electronic adherence monitors attached to the inhaler, prompting them to take the medication.
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University of Auckland senior clinical research fellow Dr Amy Chan led the study alongside London researchers.
With one in eight Kiwi adults and one in seven children taking medication for asthma, the technologies were “life-changing” for many, she said.
“Not only does medication taking everyday make a difference, it can save lives. The key message to digital technologies, they need to integrate it, because it does work and helps with control.”
Chan was also researching and developing an app that would operate as a risk prediction tool for asthma attacks.
“Asthma attacks are still the main cause of loss of life from asthma and loss of quality of life.
“At the moment, we don’t have any good tools that can predict when someone will have an attack.”
The tool would identify changes in the body before an attack, like increased heart rate, that people could not pick up by themselves.
It was thought that information could possibly identify the risks up to a week before an attack.