Automatic prediction of chronic obstructive pulmonary disease exacerbations through home telemonitoring of symptoms

Chronic Obstructive Pulmonary Disease (COPD) is a widespread progressive disease of the lung with a remarkable socio-economic impact on patients and health systems. Early detection of exacerbations could reduce the adverse effects on patients health and cut down costs burdened on patients with COPD. A group of 16 patients were telemonitored at home by use of a novel electronic daily symptoms questionnaire during a 6-months field trial. Recorded data were used to train and validate a Probabilistic Neural Network (PNN) classifier in order to enable the automatic prediction of exacerbations. The proposed system was capable of predicting COPD exacerbations early with a margin of 4.8±1.8 days (average ± SD). Detection accuracy was 80.5% (33 out of 41 exacerbations were early detected); 78.8% (26 out of 33) of theses detected events were reported exacerbation and 87.5% (7 out of 8) were unreported episodes. The proposed questionnaire and the designed automatic classifier may support the early detection of COPD exacerbations of benefit to physicians as well as patients.

Physical activity programme

A series of five online courses that comprehensively explore physical activity and the related role of physiotherapy / physical therapy