COPD Exacerbation Modelling Using Unobtrusive Sensors - the TOLIFE Clinical Study A
COPD Exacerbation Modelling Study Using Daily-life Data From Unobtrusive Sensors - the TOLIFE Clinical Study A
About This Trial
This work is a multicentric prospective cohort study designed to improve chronic obstructive pulmonary disease (COPD) treatment and management. The study involves 150 patients diagnosed with COPD who are at risk of exacerbations. These patients are recruited from three tertiary hospitals in Spain, Germany, and Italy. The study will last 18 months, with a 12-month follow-up duration for each patient. The primary objective of this study is to develop and test Artificial Intelligence (AI)-based models that can predict moderate-to-severe COPD exacerbations early on. This will be done by analyzing daily-life data collected from unobtrusive sensors that monitor patients' psycho-physiological and environmental signals. By accurately predicting exacerbations, the study aims to support clinicians in providing more precise, optimized, and personalized treatment to COPD patients. A secondary objective is to train and test AI-based models to estimate the 12-month dynamics of health-related quality of life (HRQoL) in COPD patients. This will involve analyzing data related to the patients' functional exercise capacity, dyspnea (difficulty breathing), and health-related quality of life, as measured by the Clinical COPD Questionnaire (CCQ) score and the COPD Assessment Test (CAT) score.
Who May Be Eligible (Plain English)
Original Eligibility Criteria
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Treatments Being Tested
Variations in daily life activity signals detected by unobtrusive sensors
Patients will be equipped with unobtrusive devices for a duration of 12 months. Throughout this period, they will attend scheduled visits every three months following the baseline visit. These assessments will focus on determining the frequency of exacerbations, evaluating exercise capacity, measuring the severity of dyspnea, and assessing health-related quality of life. The data gathered from the sensors embedded in these unobtrusive devices will be instrumental in developing AI-based models. These models aim to accurately predict COPD exacerbations and effectively estimate the progression of the previously mentioned health outcome.