Internet of Things-Based Home Respiratory Muscle Training for Patients with Chronic Obstructive Pulmonary Disease: A Randomized Clinical Trial.

Purpose: Whether Internet of Things (IoT)-based home respiratory muscle training (RMT) benefits patients with comorbid chronic obstructive pulmonary disease (COPD) remains unclear. Therefore, this study aims to evaluate the effectiveness of IoT-based home RMT for patients with COPD. Patients and Methods: Seventy-eight patients with stable COPD were randomly divided into two groups. The control group received routine health education, while the intervention group received IoT-based home RMT (30 inspiratory muscle training [IMT] and 30 expiratory muscle training [EMT] in different respiratory cycles twice daily for 12 consecutive weeks). Assessments took place pre-intervention and 12 weeks post-intervention, including lung function tests, respiratory muscle strength tests, the mMRC dyspnea scale, CAT questionnaires, the HAMA scale, and 6-month COPD-related readmission after intervention. Results: Seventy-four patients with COPD were analyzed (intervention group = 38, control group = 36), and the mean age and FEV1 of the patients were 68.65 ± 7.40 years, 1.21 ± 0.54 L. Compared to those of the control population, the intervention group exhibited higher FEV1/FVC (48.23 ± 10.97 vs 54.32 ± 10.31, p = 0.016), MIP (41.72 ± 7.70 vs 47.82 ± 10.99, p = 0.008), and MEP (42.94 ± 7.85 vs 50.29 ± 15.74, p = 0.013); lower mMRC (2.00 [2.00-3.00] vs 1.50 [1.00-2.00], p < 0.001), CAT (17.00 [12.00-21.75] vs 11.00 [9.00-13.25], p < 0.001), and HAMA (7.00 [5.00-9.00] vs 2.00 [1.00-3.00], p < 0.001) scores; and a lower incidence rate of 6-month readmission (22% vs 5%, p = 0.033). Conclusion: Compared with no intervention, IoT-based home RMT may be a more beneficial intervention for patients with COPD.

Depression and anxiety symptom network structure among patients with coronary heart disease and association with quality of life: protocol for a multicentre cross-sectional and prospective longitudinal study.

BACKGROUND: Anxiety and depression are critical mental health problems among persons with coronary heart disease (CHD). The range of symptoms is an important stressor for adverse cardiovascular events, and these symptoms can be involved in various ways during the course of CHD. However, the characteristics and mechanisms of comorbidity between the two mental states from the viewpoint of symptom interactions in patients with CHD remain unclear. Therefore, we aim to apply a symptom-oriented approach to identify core and bridge symptoms between anxiety and depression in a population with CHD and to identify differences in network structure over time and symptomatic link profiles. METHODS AND ANALYSIS: We designed a multicentre, cross-sectional, longitudinal study of anxiety and depression symptoms among patients with CHD. We will evaluate degrees of symptoms using the Generalized Anxiety Disorder Scale, the Patient Health Questionnaire and the WHO Quality of Life-Brief version. Patients will be followed up for 1, 3 and 6 months after baseline measurements. We will analyse and interpret network structures using R software and its packages. The primary outcomes of interest will include centrality, bridge connections, estimates, differences in network structures and profiles of changes over time. The secondary outcome measures will be the stability and accuracy of the network. By combining cross-sectional and longitudinal analyses, this study should elucidate the central and potential causative pathways among anxiety and depression symptom networks as well as their temporal stability in patients with CHD. ETHICS AND DISSEMINATION: The project conforms to the ethical principles enshrined in the Declaration of Helsinki (2013 amendment) and all local ethical guidelines. The ethics committee at the University of South China approved the study (Approval ID: 2023-USC-HL-414). The findings will be published and presented at conferences for widespread dissemination. TRIAL REGISTRATION NUMBER: ChiCTR2300075813.