no public description

"Preventable asthma attacks cause significant morbidity, mortality and a massive economic burden. The key reason for this is the failure to identify early warning signs - while to patients it may seem like an attack is sudden, warning signs actually appear days or weeks before the attack happens. However, by the time the patients perceive the symptoms, its often too late and they have to be rushed to the emergency. The extent to which this need is unmet is evident in the statistics from National Review of Asthma Deaths (NRAD), according to which of 1300 people in UK who die each year because of asthma, about 45% die even before they reach the hospital. Even a few hours of early warning could make a difference between life and death. We are developing an automated, real time and personalised monitoring system to predict and prevent asthma attacks at home."

In the UK, every 10 seconds someone experiences a potentially life threatening Asthma attack, every 8 minutes someone is hospitalised and every 8 hours someone dies due to Asthma. These attacks cause significant morbidity and disruption to daily activities, besides burdening the NHS hundreds of millions in emergency healthcare costs. In the National Review of Asthma Deaths, it was found that patients often detect worsening symptoms late and the delay in prompt treatment contributed to many preventable deaths. Moreover, in the community, it is impossible for clinicians to assess the effectiveness of therapy for patients. While regular self-monitoring (using a symptom diary or breathing tests) is part of clinical guidance and shown to reduce asthma severity and risk of attacks, most patients report difficulty in adhering to manual monitoring every day. Passive physiological monitoring of asthma symptoms at home could flag early worsening and thus an asthma attack. We would like to develop, refine and validate an innovative monitoring technology that requires minimal patient interaction (thereby passive monitoring), and detects clinically useful measures, which can improve patient health by detecting worsening early.