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Background: Traditional telemedicine follow-up proves unsuitable for home continuous positive airway pressure (CPAP) therapy in children with obstructive sleep apnea syndrome (OSAS). Accompanying advancements in mobile internet, this study explores the feasibility and effectiveness of a mobile communication and remote monitoring system as a novel bidirectional telemedicine approach to enhance adherence to home CPAP in children with OSAS.
Methods: A prospective cohort utilizing bidirectional telemedicine follow-up from January to December 2022 (TM) was compared with a retrospective cohort receiving conventional phone follow-up from August 2018 to December 2021 (CP). Participants in TM group were subdivided into two groups based on the number of inquiries in the first week: a high-question group and a low-question group. The main endpoints included successful CPAP adaption and adherence at 2 months of follow-up.
Results: The TM group exhibited a significantly lower termination rate within 2 months compared to the CP group (1/24 vs. 6/22, p = 0.037). In the first week of home CPAP, the high-question group reported shorter average nightly usage and fewer days with usage of ≥4 h compared to the low-question group (5 h per night vs. 8.5 h per night, 4.5 days vs. 7 days, both p < 0.001). However, the high-question group showed significant improvement in adherence from the second week onward for the remainder of the study period.
Conclusions: Bidirectional telemedicine represents an effective and feasible method to improve adherence to home CPAP therapy in children with OSAS. Considering the costs, researchers recommend applying bidirectional telemedicine for at least 1 week to better enhance long-term adherence.
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http://dx.doi.org/10.1016/j.ijporl.2024.112047 | DOI Listing |
JMIR Form Res
September 2025
Department of Psychiatry, Cambridge Health Alliance, Cambridge, MA, United States.
Background: Measurement-based care (MBC), including remote MBC, is increasingly being considered or implemented for mental health treatment and outcomes monitoring in routine clinical care. However, little is known about the health equity implications in real-world practice or the impact on patient-provider relationships in lower-resource systems that offer mental health treatment for diverse patients.
Objective: This hypothesis-generating study examined the drivers of MBC implementation outcomes, the implications for health equity, and the impact of MBC on therapeutic alliance (TA).
Int J Surg
September 2025
Institute of Medical Robotics and Intelligent Systems, Tianjin University, Tianjin, China.
Background: A comprehensive understanding of surgical scenes by computers is a crucial foundation for achieving intelligent surgical assistance and autonomous decision-making. Surgical scene information encompasses coarse-grained data reflecting the overall process and fine-grained details showcasing specific operations. This study aims to construct a standardized, full-grained annotation dataset for laparoscopic radical nephrectomy and develop a deep learning framework for multi-hierarchical granularity integration, providing support for clinical intelligent applications.
View Article and Find Full Text PDFAnn Behav Med
January 2025
School of Kinesiology, University of British Columbia, Vancouver, BC V6T 1Z3, Canada.
Background: Engaging in physical activity can reduce the risk of multiple morbidities and premature mortality. Psychological stress can hinder the development and maintenance of physical activity behaviors. There is a dearth of research on how these two processes interact.
View Article and Find Full Text PDFSci Rep
August 2025
Department of Biomedical and Electronics Engineering, University of Bradford, Bradford, UK.
This work presents a machine learning (ML)-optimized dual-band wearable antenna designed specifically for biomedical applications in healthcare monitoring. Fabricated on a Rogers substrate of 40 × 41 mm, the antenna operates at 2.4 GHz and 5.
View Article and Find Full Text PDFBr J Ophthalmol
August 2025
Department of Ophthalmology, Ulm University Hospital, Ulm, Germany.
Background: In an ophthalmology emergency department, determining treatment urgency is crucial for patient safety and the efficient use of resources. The aim of this study was to use artificial intelligence to develop a neural network and evaluate its accuracy in predicting treatment urgency.
Methods: In a retrospective study, a medical history questionnaire consisting of a free-text section and checkbox questions was given to 1715 patients on arrival and the responses were used as input data.