High-intensity interval training (HIIT) is an emerging exercise strategy and is considered to be a recipe for health promotion. This study aimed to systematically identify collaboration networks, track research trends, highlight current hotspots, and predict future frontiers in HIIT and its applications in health promotion since the start of the new century. Relevant original publications were obtained from the Science Citation Index Expanded of the Web of Science Core Collection (WoSCC) database between 2001 and 2020. CiteSpace and VOSviewer software were used to perform bibliometric visualization and comparative analysis of involved indexes that included countries, institutions, journals, authors, references, and keywords. A total of 572 papers were included, and the trend of annual publications showed a remarkable growth. The United States and the University of Exeter were the most productive country and institutions, respectively, with 107 and 18 publications, respectively. took the lead in the number of published articles, and ranked first in the cocitation counts. Barker AR and Gibala MJ were considered as the most productive and the most highly-cited authors. "Health risks," "adolescent," and "aging" are the three noteworthy topics during the evolution of HIIT-health promotion (HIIT-HP) research. The current research hotspots of HIIT and its practices in the health promotion domain lies in "metabolic diseases," "cardiovascular diseases," "neurological diseases," and "musculoskeletal diseases." The authors summarize that "prevention and rehabilitation," "micro and molecular level," and "cognition and mental health" are becoming frontiers and focus on the health topics related to HIIT in the upcoming years, which are worthy of further exploration.
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http://dx.doi.org/10.3389/fpubh.2021.697633 | DOI Listing |
Med Phys
January 2025
Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.
Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.
Sci Rep
January 2025
Zhejiang Gongshang University Hangzhou College of Commerce, Hangzhou, Zhejiang, China.
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January 2025
School of Economics and Management, University of Cyprus, 2109, Aglantzia, Nicosia, Cyprus.
Analyzing the habits of exercisers is crucial for developing targeted interventions that can effectively promote long-term physical activity behavior. While much of existing literature has focused on individual-level factors, there is a growing recognition of the importance of examining how broader determinants impact physical activity. In this study, we analyze large-scale human mobility data from over 20 million individuals to investigate how visits to various locations, such as cafes and restaurants, influence visits to fitness centers.
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January 2025
Harbin Normal University, Harbin, 150025, China.
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January 2025
Federal University of Bahia, Institute of Computing, Salvador, 40170-110, Brazil.
Multiple Myeloma (MM) is a cytogenetically heterogeneous clonal plasma cell proliferative disease whose diagnosis is supported by analyses on histological slides of bone marrow aspirate. In summary, experts use a labor-intensive methodology to compute the ratio between plasma cells and non-plasma cells. Therefore, the key aspect of the methodology is identifying these cells, which relies on the experts' attention and experience.
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