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.

Download full-text PDF

Source
http://dx.doi.org/10.1097/PHM.0000000000002706DOI Listing

Publication Analysis

Top Keywords

novel supervised
8
based intermittent
8
treadmill walking
8
moderate-intensity functional
8
functional training
8
training mift
8
walking performance
8
walking distance
8
gait speed
8
peak vo2
8

Similar Publications

Chronic coronary artery disease (CAD) remains a significant global healthcare burden. Current risk assessment methods have notable limitations in early detection and risk stratification. Hence, there is an urgent need for innovative biomarkers that facilitate the premature CAD diagnosis, ultimately leading to reduction in associated morbidity and mortality rates.

View Article and Find Full Text PDF

Hypertension is a critical risk factor and cause of mortality in cardiovascular diseases, and it remains a global public health issue. Therefore, understanding its mechanisms is essential for treating and preventing hypertension. Gene expression data is an important source for obtaining hypertension biomarkers.

View Article and Find Full Text PDF

Drug-Induced Liver Injury Associated With Emerging Cancer Therapies.

Liver Int

February 2025

Department of Clinical Pharmacology and Toxicology, University Hospital Zürich, University of Zürich, Zürich, Switzerland.

Targeted therapies and immunotherapies have shown great promise as best-in-class treatments for several cancers with respect to efficacy and safety. While liver test abnormalities are rather common in patients treated with kinase inhibitors or immunotherapy, events of severe hepatotoxicity in these patients are rare in comparison with those associated with chemotherapeutics. The underlying mechanisms and risk factors for severe hepatotoxicity with novel oncology therapies are not well understood, complicating the drug-induced liver injury (DILI) risk assessment in the preclinical and clinical phases of drug development.

View Article and Find Full Text PDF

Brain imaging data is one of the primary predictors for assessing the risk of Alzheimer's disease (AD). This study aims to extract image-based features associated with the possibly right-censored time-to-event outcomes and to improve predictive performance. While the functional proportional hazards model is well-studied in the literature, these studies often do not consider the existence of patients who have a very low risk and are approximately insusceptible to AD.

View Article and Find Full Text PDF

Enhanced Image Retrieval Using Multiscale Deep Feature Fusion in Supervised Hashing.

J Imaging

January 2025

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 PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!