A state of mindfulness refers to a present-centered attentional awareness without judging. Being mindful seems to increase the ability to be flexible and adaptive in attention focus according to situational contingencies. The way mindfulness affects such attentional control is often measured with three different but interacting attentional networks of alerting (preparedness), orienting (selection of stimulus), and conflict detection (suppression of irrelevant stimuli). In the current study, the aim was to study the effects of dispositional mindfulness on these attention networks, and specifically the effects on the interactions between these attention networks. Fifty participants between 19 and 29 years old filled out the questionnaire Five Facet Mindfulness Questionnaire (FFMQ) and performed the revised version of the Attention Network Test (ANT-R). The five FFMQ facets of Describing, Non-Judgment, Orienting, Non-Reactivity, and Acting with Awareness were included as predictors in multiple linear regression analyses with the ANT-R scores of alerting, orienting, conflict detection, and the interaction scores of alerting by conflict detection and orienting by conflict detection as outcome variables, respectively. Higher dispositional mindfulness as measured with the five FFMQ facets predicted interaction scores (faster reaction times) of orienting by conflict detection, but none of the other ANT-R scores. It was specifically the FFMQ facets of Describing and non-judgment that predicted this lower interaction score of orienting by conflict detection. Our findings indicate that being mindful is associated with a more flexible and efficient orienting attention. It is associated with a higher ability to disengage from salient stimuli that is irrelevant to pursue goal-directed behavior (conflict detection).
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http://dx.doi.org/10.3389/fpsyg.2018.02359 | DOI Listing |
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFEur Heart J Digit Health
January 2025
Cardiology Department, Dr Balmis General University Hospital, Alicante Institute for Health and Biomedical Research (ISABIAL), C/Maestro Alonso s/n, Alicante 03010, Spain.
Aims: Evidence regarding the safety of early discharge following transcatheter aortic valve implantation (TAVI) is limited. The aim of this study was to evaluate the safety of very early (<24) and early discharge (24-48 h) as compared to standard discharge (>48 h), supported by the implementation of a voice-based virtual assistant using artificial intelligence (AI) and natural language processing.
Methods And Results: Single-arm prospective observational study that included consecutive patients who underwent TAVI in a tertiary hospital in 2023 and were discharged under an AI follow-up programme.
Background And Aims: Hematopoietic stem cell transplantation (HSCT) is a key therapeutic approach for pediatric patients with hematologic and non-hematologic disorders. However, post-transplant pulmonary complications remain a significant cause of morbidity and mortality. Pulmonary Function Tests (PFTs) are essential for the early detection of pulmonary dysfunction, yet their application in pediatric HSCT recipients has yielded inconsistent results.
View Article and Find Full Text PDFHealth Sci Rep
January 2025
Department of Research The Medical Research Circle (MedReC) Goma Democratic Republic of the Congo.
Background And Aim: Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy.
View Article and Find Full Text PDFBackground And Aims: In the current study, we aimed to identify the association between major and minor electrocardiographic abnormalities and cardiovascular risk factors.
Methods: We used the Tehran cohort study baseline data, an ongoing multidisciplinary, longitudinal study designed to identify cardiovascular disease risk factors in the adult population of Tehran. The electrocardiograms (ECGs) of 7630 Iranian adults aged 35 years and above were analyzed.
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