Objective: This study aimed to analyze the effect of a novel supervised exercise therapy (SET) program based on intermittent treadmill walking and circuit-based moderate-intensity functional training (MIFT) on walking performance and HRQoL in PAD patients.
Design: All participants underwent a 12-week SET that involved 15 to 30 minutes of treadmill walking followed by a 15-minute moderate-intensity functional training (MIFT) continued by 12-week of follow-up. Maximum walking distance (MWD), pain-free walking distance (PFWD), gait speed and estimated peak oxygen uptake (peak VO2) were calculated through the 6-minute walk test (6-MWT) and HRQoL through the Short Form-36 (SF-36) and the Vascular Quality of Life Questionnaire-6 (VascuQol-6).
Results: There were statistically significant differences (p < 0.05) between baseline and post-intervention for walking performance outcomes [MWD (MD: 88.53 m), PFWD (MD: 62.89 m), gait speed (MD: 0.24 m·s-1) and peak VO2 (MD: 2.04 ml·kg-1·min-1)] and for HRQoL [physical functioning in SF-36 (MD: 6.93 points) and VascuQol-6 (MD: 1.46 points)]; while no differences were found between baseline and 12-week follow-up.
Conclusion: Results seem to show that 12-week of novel SET based on intermittent walking and MIFT induced significant clinical improvements in key functional variables of PAD while cessation of exercise leads to significant negative clinical changes in subsequent weeks of follow-up.
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http://dx.doi.org/10.1097/PHM.0000000000002706 | DOI Listing |
Medicine (Baltimore)
January 2025
Cardiovascular Research Center, Shiraz University of Medical Sciences, Shiraz, Iran.
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February 2025
Department of Statistics and Actuarial Science, Simon Fraser University, Burnaby, British Columbia, Canada.
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View Article and Find Full Text PDFJ Imaging
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RCAM Laboratory, Telecommunications Department, Sidi Bel Abbes University, Sidi Bel Abbes 22000, Algeria.
In recent years, deep-network-based hashing has gained prominence in image retrieval for its ability to generate compact and efficient binary representations. However, most existing methods predominantly focus on high-level semantic features extracted from the final layers of networks, often neglecting structural details that are crucial for capturing spatial relationships within images. Achieving a balance between preserving structural information and maximizing retrieval accuracy is the key to effective image hashing and retrieval.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!