Internet of things-based pulmonary rehabilitation for moderate-to-severe chronic obstructive pulmonary disease: a...

Pulmonary rehabilitation (PR) constitutes a cornerstone of non-pharmacological management in chronic obstructive pulmonary disease (COPD); however, its clinical effectiveness is frequently attenuated by suboptimal patient adherence and the absence of individualized, continuous monitoring within the home setting. Internet of Things (IoT) technology offers a promising solution to these persistent challenges; however, comprehensive closed-loop management models that fully integrate IoT capabilities remain underexplored. This prospective, non-randomized controlled intervention study will enroll 588 patients with moderate-to-severe stable COPD (GOLD grades 2-4) from a single institution. Participants will be assigned to either conventional PR (Group A) or IoT-assisted PR (Group B) based on initial preference and technology readiness. Group B will receive IoT-enabled devices and a smartphone app to support home-based PR, including daily respiratory muscle training and structured aerobic/resistance exercise (3-5 sessions/week). Group A will receive standard PR education and guidance. Follow-ups will occur at weeks 4, 12, 26, and 52. The primary outcome is the 12-month rate of moderate-to-severe AECOPD. Secondary outcomes include pulmonary function, exercise capacity, HRQoL, right cardiac function, pneumonia incidence, all-cause mortality, and adherence. Analyses will be performed on an intention-to-treat basis. This study will evaluate the efficacy of an IoT-based pulmonary rehabilitation management model in reducing AECOPD-related readmissions and improving exercise tolerance, quality of life, and dyspnea in patients with moderate-to-severe COPD, while examining its feasibility and safety in a real-world setting. The findings may support the development of a technology-enabled continuous care model for chronic respiratory diseases, facilitating the transition from hospital-centric to patient-centric care and advancing the application of smart nursing and remote health management. The study protocol has been registered with the Chinese Clinical Trial Registry (ChiCTR2500106412).