Aims: Recent studies showed that exercise-based cardiac rehabilitation (ECR) programmes are often not personalized to individual patient characteristics according to latest recommendations. This study investigates whether a computerized decision support (CDS) system based on latest recommendations and guidelines can improve personalization of ECR prescriptions. Pseudo-randomized intervention study.
Methods And Results: Among participating Dutch cardiac rehabilitation centres, ECR programme characteristics of consecutive patients were recorded during 1 year. CDS was used during a randomly assigned 4-month period within this year. Primary outcome was concordance to latest recommendations in three phases (before, during, and after CDS) for 12 ECR programme characteristics. Secondary outcome was variation in training characteristics. We recruited ten Dutch CR centres and enrolled 2258 patients to the study. Overall concordance of ECR prescriptions was 59.9% in Phase 1, 62.1% in Phase 2 (P = 0.82), and 59.9% in Phase 3 (P = 0.56). Concordance varied from 0.0% to 99.9% for the 12 ECR characteristics. There was significant between-centre variation for most training characteristics in Phases 1 and 2. In Phase 3, there was only a significant variation for aerobic and resistance training intensity (P = 0.01), aerobic training volume (P < 0.01), and the number of strengthening exercises but no longer for the other characteristics. Aerobic training volume was often below recommended (28.2%) and declined during the study.
Conclusion: CDS did not substantially improve concordance with ECR prescriptions. As aerobic training volume was often lower than recommended and reduced during the study, a lack of institutional resources might be an important barrier in personalizing ECR prescriptions.
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http://dx.doi.org/10.1093/eurjpc/zwaa066 | DOI Listing |
Zhonghua Nei Ke Za Zhi
January 2025
Atrial fibrillation (AF) has emerged as a major global cardiovascular disease in the 21st century. In China, there are greater than 12 million patients with AF, and its incidence continues to rise. AF affects patients' quality of life and significantly increases the risks of mortality, stroke, heart failure, cognitive impairment, and dementia.
View Article and Find Full Text PDFStrahlenther Onkol
January 2025
Department of Radiation Oncology, University Hospital Düsseldorf, Düsseldorf, Germany.
Purpose: The aim of this review is to give an overview of the results of prospective and retrospective studies using allogenic reconstruction and postmastectomy radiotherapy (PMRT) in breast cancer and to make recommendations regarding this interdisciplinary approach.
Materials And Methods: A PubMed search was conducted to extract relevant articles from 2000 to 2024. The search was performed using the following terms: (breast cancer) AND (reconstruction OR implant OR expander) AND (radiotherapy OR radiation).
Background: Recent advances in automatic face recognition have increased the risk that de-identified research imaging data could be re-identified from face imagery in brain scans.
Method: An ADNI committee of independent imaging experts evaluated 11 published techniques for face-deidentification ("de-facing") and selected four algorithms (FSL-UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de-faced images (confirming negligible side effects on analyses).
Background: The Finnish Geriatric Intervention Study to Prevent Cognitive Impairment and Disability (FINGER) multimodal lifestyle intervention yielded cognitive and other health benefits in older adults at risk of cognitive decline. The two-year multinational randomized controlled LETHE trial evaluates the feasibility of a digitally supported, adapted FINGER intervention among at-risk older adults. Technology is used to complement in-person activities, for the intervention delivery, personalize recommendations, and collect digital biomarkers.
View Article and Find Full Text PDFBackground: Recent advances in automatic face recognition have increased the risk that de-identified research imaging data could be re-identified from face imagery in brain scans.
Method: An ADNI committee of independent imaging experts evaluated 11 published techniques for face-deidentification ("de-facing") and selected four algorithms (FSL-UK Biobank, HCP/XNAT, mri_reface, and BIC) for formal testing using 183 longitudinal scans of 61 racially and ethnically diverse ADNI participants, evaluated by their facial feature removal on 3D rendered surfaces (confirming sufficient privacy protection) and by comparing measurements from ADNI routine image analyses on unmodified vs. de-faced images (confirming negligible side effects on analyses).
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