Background: Traditional cardiac rehabilitation (CR) is effective in improving physical performance and prognosis after myocardial infarction (MI). Anyway, it is not consistently recommended to older adults, and its attendance rate is low. Previous studies suggested that alternative, early and tailored exercise interventions are feasible and effective in improving physical performance in older MI patients. Anyway, the demonstration that they are associated also with a significant reduction of hard endpoints is lacking.
Aim: To describe rationale and design of the "Physical activity Intervention in Elderly patients with myocardial Infarction" (PIpELINe) trial.
Methods: The PIpELINe trial is a prospective, randomized, multicentre study with a blinded adjudicated evaluation of the outcomes. Patients aged ≥ 65 years, admitted to hospital for MI and with a low physical performance one month after discharge, as defined as short physical performance battery (SPPB) value between 4 and 9, will be randomized to a multi-domain lifestyle intervention (including dietary counselling, strict management of cardiovascular and metabolic risk factors, and exercise training) or health education. The primary endpoint is the one-year occurrence of the composite of cardiovascular death or re-hospitalization for cardiovascular causes.
Results: The recruitment started in March 2020. The estimated sample size is 456 patients. The conclusion of the enrolment is planned for mid-2023. The primary endpoint analysis will be available for the end of 2024.
Conclusions: The PIpELINe trial will show if a multi-domain lifestyle intervention is able to reduce adverse events in older patients with reduced physical performance after hospitalization for MI.
Trial Registration: ClinicalTrials.gov NCT04183465.
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http://dx.doi.org/10.1007/s40520-023-02389-9 | DOI Listing |
Proc Natl Acad Sci U S A
January 2025
Department of Mechanical and Aerospace Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada.
Limiting climate change to targets enshrined in the Paris Agreement will require both deep decarbonization of the energy system and the deployment of carbon dioxide removal at potentially large scale (gigatons of annual removal). Nations are pursuing direct air capture to compensate for inertia in the expansion of low-carbon energy systems, decarbonize hard-to-abate sectors, and address legacy emissions. Global assessments of this technology have failed to integrate factors that affect net capture and removal cost, including ambient conditions like temperature and humidity, as well as emission factors of electricity and natural gas systems.
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January 2025
Institute of Medical Teaching and Medical Education Research, University Hospital Würzburg, Würzburg, Germany.
Background: Objective structured clinical examinations (OSCEs) are a widely recognized and accepted method to assess clinical competencies but are often resource-intensive.
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Methods: Fifth-year medical students participated in an OSCE consisting of 10 stations.
PLoS One
January 2025
Department of Nursing and Physiotherapy, Faculty of Medicine and Health Sciences, University of Alcalá, Alcalá de Henares, Spain.
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January 2025
National Key Laboratory of Human Factors Engineering, China Astronaut Research and Training Centre, Beijing, China.
Objective: Attention forms the foundation for the formation of situation awareness. Low situation awareness can lead to driving performance decline, which can be dangerous in driving. The goal of this study is to investigate how different types of pre-takeover tasks, involving cognitive, visual and physical resources engagement, as well as individual attentional function, affect driver's attention restoration in conditionally automated driving.
View Article and Find Full Text PDFSci Adv
January 2025
Institute of Materials Research and Engineering (IMRE), Agency for Science Technology and Research (A*STAR), 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634, Republic of Singapore.
Combining physics with computational models is increasingly recognized for enhancing the performance and energy efficiency in neural networks. Physical reservoir computing uses material dynamics of physical substrates for temporal data processing. Despite the ease of training, building an efficient reservoir remains challenging.
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