Objective: HAPpEN aims to implement and evaluate a holistic general practitioner-centered, interdisciplinary obesity management strategy in rural Germany, focusing on feasibility, health outcomes, and economic benefits.
Methods: HAPpEN is a 12-month, pragmatic single-arm, multicenter trial, informed by a formative survey, and initiated in April 2023 with 98 obese participants (body mass index, BMI ≥ 30 kg/m) in Kulmbach, Germany. The program integrates nutritional counseling, physical activity, and behavior change techniques, including smartphone-based self-monitoring.
Individuals living in rural areas report poorer health outcomes, including obesity, compared to individuals living in urban areas. Amongst others, this is due to restricted access to opportunities for healthy eating and physical activity. Interventions are urgently needed to address this gap.
View Article and Find Full Text PDFObjective: Automatic segmentation and detection of vestibular schwannoma (VS) in MRI by deep learning is an upcoming topic. However, deep learning faces generalization challenges due to tumor variability even though measurements and segmentation of VS are essential for growth monitoring and treatment planning. Therefore, we introduce a novel model combining two Convolutional Neural Network (CNN) models for the detection of VS by deep learning aiming to improve performance of automatic segmentation.
View Article and Find Full Text PDFLimited evidence exists that serves to guide the field of practice and research pertaining to the long-term issues and needs of adults with spina bifida. Understanding the lived experience of adults with spina bifida has lagged behind considerably resulting in limited evidence-based guidance for individuals with spina bifida and their families and the health care professionals who provide services to this population. Given the paucity of knowledge of the lived experience as it pertains to adulthood, this scoping review was undertaken.
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