Pulmonary rehabilitation (PR) is a cost-effective intervention with well-known benefits to exercise capacity, symptoms and quality of life in patients with chronic respiratory diseases. Despite the compelling evidence of its benefits, PR implementation is still suboptimal, and maintenance of PR benefits is challenging. To overcome these pitfalls, there has been a growing interest in developing novel models for PR delivery. Digital health is a promising solution, as it has the potential to address some of the most reported barriers to PR uptake and adherence (such as accessibility issues), help maintain the positive results following a PR programme and promote patients' adherence to a more active lifestyle through physical activity (tele)coaching. Despite the accelerated use of digital health to deliver PR during the coronavirus disease 2019 pandemic, there are still several factors that contribute to the resistance to the adoption of digital health, such as the lack of evidence on its effectiveness, low acceptability by patients and healthcare professionals, concerns about implementation and maintenance costs, inequalities in access to the internet and technological devices, and data protection issues. Nevertheless, the trend towards reducing technology costs and the higher availability of digital devices, as well as the greater ease and simplicity of use of devices, enhance the opportunities for future development of digitally enabled PR interventions. This narrative review aims to examine the current evidence on the role of digital health in the context of PR, including strengths and weaknesses, and to determine possible threats and opportunities, as well as areas for future work.
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http://dx.doi.org/10.1183/23120541.00212-2022 | DOI Listing |
J Imaging Inform Med
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Department of Radiology, University of Pennsylvania Perelman School of Medicine, 3400 Spruce St., Philadelphia, PA, 19104, USA.
Integration of artificial intelligence (AI) into radiology practice can create opportunities to improve diagnostic accuracy, workflow efficiency, and patient outcomes. Integration demands the ability to seamlessly incorporate AI-derived measurements into radiology reports. Common data elements (CDEs) define standardized, interoperable units of information.
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Department of Movement Science, Institute of Sports Science, University of Klagenfurt, Klagenfurt, Austria.
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Division of Psychology and Mental Health, School of Health Sciences, Faculty of Biology, Medicine and Health, Manchester Academic Health Science Centre, The University of Manchester, Manchester, M13 9PL, UK.
There is increasing use of digital tools to monitor people with psychosis and schizophrenia remotely, but using this type of data is challenging. This systematic review aimed to summarise how studies processed and analysed data collected through digital devices. In total, 203 articles collecting passive data through smartphones or wearable devices, from participants with psychosis or schizophrenia were included in the review.
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January 2025
Department of Health Administration, Yonsei University Graduate School, Wonju, Republic of Korea.
This study is the first to examine the determinants of future anxiety in South Korea using the Social Ecological Model (SEM). It aimed to show that, beyond individual factors, mezzo- and macro-level aspects, particularly those related to housing, may influence anxiety. Utilizing 2018 data from the Korean Health Panel Survey, we employed a three-level multilevel analysis to investigate how these factors contribute to the perception of future anxiety among Koreans.
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Department of Pharmacy, Centre Hospitalier Universitaire de Toulouse, Toulouse, France
Purpose: More than 20% of prescription errors in hospitals are due to an incomplete medication history. Medication reconciliation is a solution to decrease unintentional discrepancies between medications taken at home and hospital prescriptions. It is a normalised clinical activity but it is time consuming.
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