This narrative review summarizes current evidence and clinical experience regarding telemonitoring across major respiratory diseases and care settings, including chronic obstructive pulmonary disease (COPD), asthma, interstitial lung diseases, obstructive sleep apnea, as well as non-invasive ventilation and pulmonary rehabilitation programmes. Advances in connectivity, artificial intelligence (AI), and wearable devices are facilitating the early detection of clinical deterioration, personalized interventions, and improved self-management, thereby optimizing the use of healthcare resources. Strong evidence supports the benefits of telemonitoring in COPD, particularly in reducing exacerbations and hospital admissions, whereas results are more heterogeneous in asthma and emerging conditions such as interstitial lung diseases. Telemonitoring systems leverage AI-driven analytical frameworks and interoperable digital platforms to process and interpret large volumes of patient data, enabling both automated responses and targeted human interventions. Key challenges include ensuring patient engagement, addressing digital literacy and inequities in access, safeguarding data privacy, and integrating digital solutions into standard care and reimbursement frameworks. The COVID-19 pandemic accelerated the adoption of telemonitoring, confirming its feasibility and acceptability, but also revealed persistent gaps in long-term cost-effectiveness and implementation strategies. Future directions should focus on integrating telemonitoring with AI-supported, coordinated clinical decision-making, enhancing system interoperability, and above all, prioritizing equitable access to digital care. Telemonitoring is poised to become a central component of respiratory patient management, although its large-scale implementation will require overcoming existing technical, ethical, and organizational barriers to fully realize its clinical potential.
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© Clarivate Analytics, Journal Citation Reports 2025
SRJ is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and qualitative measure of the journal's impact.
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