To identify patterns of physical activity (PA) participation, exercise preference, and barriers of stage 2-3 prostate cancer survivors across cancer trajectories based on selected demographic and medical variables. The current study is a descriptive cross-sectional study which included data from a total of 111 prostate cancer survivors, at Shinchon Severance Hospital, Seoul, Korea. The survey includes PA levels before and after prostate cancer diagnosis, exercise barriers, and preferences. Moderate- to vigorous-intensity PA levels were significantly lower after cancer diagnosis (vigorous PA:41.9 ± 123.1 min/week vs. 4.6 ± 29.8 min/week, < 0.001; moderate PA: 159.9 ± 240.0 min/week vs. 56.8 ± 129.7 min/week, < .001) compared to their PA level before cancer diagnosis. Perceived exercise barriers were distinctly different according to participants' age and time since surgery. The two most prevalent exercise barriers among prostate cancer survivors <65 years were lack of time (28.6%) and poor health (26.5%), whereas the exercise barriers for prostate cancer survivors aged ≥65 years were lack of exercise facilities (21.4%) and lack of exercise information (17.9%). Furthermore, within 6 months after surgery, prostate cancer survivors perceived poor health (29.5%) and pain at the surgery site (29.5%) to be the two most prevalent exercise barriers. 6 months after surgery, prostate cancer survivors perceived lack of time (21.3%) and poor health (14.8%) to be the two most prevalent exercise barriers. Walking, pelvic floor and Kegel exercises were three most preferred exercises among prostate cancer survivors in our study, which uniquely differ according to time since surgery. This study showed significant reduction in PA levels among prostate cancer survivors and their perceived exercise barriers were distinct according to their age and time since surgery. Therefore, PA and exercise recommendation should be specific to their personal characteristics such as age and time since surgery.
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http://dx.doi.org/10.1080/13557858.2019.1634184 | DOI Listing |
PLoS One
January 2025
Department of Public Health, Policy and Systems, University of Liverpool, Liverpool, United Kingdom.
Introduction: Undiagnosed chronic disease has serious health consequences, and variation in rates of underdiagnosis between populations can contribute to health inequalities. We aimed to estimate the level of undiagnosed disease of 11 common conditions and its variation across sociodemographic characteristics and regions in England.
Methods: We used linked primary care, hospital and mortality data on approximately 1.
Ann Nucl Med
January 2025
Department of Biomedical Sciences, Humanitas University, Milan, Italy.
The purpose of this systematic review was to evaluate the role of PSMA PET/CT in intermediate-risk prostate cancer (PCa) patients, to determine whether it could help improve treatment strategy and prognostic stratification. A systematic literature search up to May 2024 was conducted in the PubMed, Embase and Scopus databases. Articles with mixed risk patient populations, review articles, editorials, letters, comments, or case reports were excluded.
View Article and Find Full Text PDFJ Neurooncol
January 2025
Department of Neurosurgery, Allegheny Health Network, Neuroscience Institute, Pittsburgh, PA, United States.
Langenbecks Arch Surg
January 2025
Department for the Promotion of Medical Device Innovation, National Cancer Center Hospital East, 6-5-1, Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan.
Purpose: Assessing surgical skills is vital for training surgeons, but creating objective, automated evaluation systems is challenging, especially in robotic surgery. Surgical procedures generally involve dissection and exposure (D/E), and their duration and proportion can be used for skill assessment. This study aimed to develop an AI model to acquire D/E parameters in robot-assisted radical prostatectomy (RARP) and verify if these parameters could distinguish between novice and expert surgeons.
View Article and Find Full Text PDFInsights Imaging
January 2025
Department of Radiology, the Second Affiliated Hospital of Dalian Medical University, Dalian, 116023, China.
Objective: To evaluate the feasibility of utilizing artificial intelligence (AI)-predicted biparametric MRI (bpMRI) image features for predicting the aggressiveness of prostate cancer (PCa).
Materials And Methods: A total of 878 PCa patients from 4 hospitals were retrospectively collected, all of whom had pathological results after radical prostatectomy (RP). A pre-trained AI algorithm was used to select suspected PCa lesions and extract lesion features for model development.
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