Smartphone Study Aims to Help Save Lives From Non-Communicable Illnesses
New Zealand scientists say they are conducting one of the world’s largest health studies by harnessing smartphones in a bid to save countless lives from non-communicable diseases (NCDs) such as strokes.
A neuroscience team from Auckland’s AUT University said they aimed to gather vast amounts of international data and vital epidemiological insights through a mobile app called Stroke Riskometer.
Findings from the study – “Reducing the International Burden of Stroke Using Mobile Technology” (RIBURST) – could significantly reduce the devastating impact of conditions such as stroke, diabetes, dementia and heart disease.
“This study has the potential to save countless lives and billions of dollars worldwide,” Xinhua news agency quoted programme leader Valery Feigin as saying in a statement on Friday.
“By delivering population-specific predictive algorithms plus preventative strategies tailored to different cultural and ethnic groups, we hope to dramatically reduce the burden these diseases place on people, families and health systems around the world.”
The Stroke Riskometer app, which enabled users to assess their individual stroke risk on a smartphone or tablet, was launched in October 2013.
It had recently been updated in multiple languages, and allowed users to participate in the study from the comfort of their homes.
With an estimated 1.75 billion smartphone users around the world, the potential scale of the research was immense and could eclipse the largest medical experiment in history, a polio vaccine study in the 1950s that involved up to two million participants and led to near eradication of the disease.
“Non-communicable diseases account for 66 percent of deaths worldwide and cause serious disability for millions of people. Current primary prevention strategies are simply not effective enough,” said Feigin.
The RIBURST data would enable the development of a predictive algorithm based on modern risk factors and allow for the generation of population-specific predictive algorithms.
“There’s a huge need for more accurate prediction, more effective prevention, and culturally and ethnically unique preventative strategies,” said Feigin.
“This study gives us a chance to achieve that.