Knowledge for Improving Indoor Air Quality and Health: Follow up of 200 High-risk Chronic Respiratory Patients During 24 Months.

The study is structured as a comprehensive two-phase approach. From January to December 2024 (Phase I) the protocol focuses on the assessment, and refinement, of the four core components of the study: i) Enhanced lung function testing, ii) IAQ home monitoring, iii) Advanced digital support to innovative clinical processes, and iv) Predictive modelling for early detection and management of exacerbations. The main outcome at the end of Phase I is the design of an innovative integrated care service aiming at enhanced management of exacerbations and reduction of unplanned hospitalizations in high-risk patients.

From January to December 2025 (Phase II), the protocol aims to refine the novel clinical process, including the four core components alluded to above, as well as to evaluate the potential for healthcare value generation and scalability/transferability of the new integrated care service.

PHASE I (2024):

1. Enhanced Lung Function Testing: Adoption of oscillometry (forced oscillation technique) to measure respiratory system resistance and reactance, as a complementary tool of forced spirometry, exploring its potential for patients' monitoring and management of exacerbations.
2. Continuous Monitoring of IAQ at patients' homes: Assessment of advanced monitoring systems in patients' homes to continuously track air quality parameters, enabling the identification of environmental triggers linked to respiratory exacerbations.
3. Advanced Digital Support to innovative clinical processes with a two-fold aim: i) patient's empowerment for self-management of his/her condition, and ii) enhancing the role of the nurse case manager for early detection and management of exacerbations promoting share care agreements between the patient and the reference doctor (primary care physician and/or specialist). To this end, adoption of an Adaptive Case Management (ACM) Approach constitutes a key element.
4. Predictive Modeling: Development, and refinement, of machine learning-based modelling for early detection and management exacerbations. Key input data in the modelling approach will be: i) Clinical information (symptoms, Patient Reported Outcome Measures - PROMs), ii) Lung function testing, and iii) Patient's self-capturing physiological data through wrist sensors (health rate, heart rate variability and physical activity). Moreover, the impact of IAQ monitoring in the modelling will be explored.

The implementation, and refinement, of the four components alluded to above, as well as the design of the novel integrated care service, will be done with active engagement of patients, healthcare professionals, and other stakeholders in a co-design process using the Plan-Do-Study-Act (PDSA) methodology. Two PDSA cycles, with a six-month duration each, will be undertaken during 2024.