Aim Older inpatients have reduced physical function and walking ability with a higher risk of falls after being discharged home. Gait variability can assess ambulation and is strongly related to the risk of falls. However, the clinical factors affecting gait variability in inpatients have not been identified. The purpose of this study was to investigate the predictive factors affecting gait variability in older inpatients. Methods A total of 42 older orthopedic inpatients with fractures of the hip, spine, and other segments and 18 healthy volunteers as the control group were enrolled in this study. Inpatients wore tri-axial accelerometers for a 10m walk before discharge. Gait variability was assessed by the coefficient of variation (CV) based on five consecutive stride times. Clinical assessment of muscle strength, joint mobility, balance, pain, and activities of daily living were also evaluated. Results The CV in inpatients was higher than that in healthy elderly. Quadriceps muscle strength, ankle dorsiflexion range of motion, and balance described the CV. When model 2 (adjusted R = 0.473) was compared with model 1 (adjusted R = 0.293), the quadriceps muscle strength and ankle dorsiflexion range of motion had a major effect on CV, while balance had not a greater influence than these two factors when compared with model 3 (adjusted R = 0.537). Conclusions Poor knee extension strength, balance, and restriction of ankle dorsiflexion mobility have influenced gait variability in older inpatients.
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http://dx.doi.org/10.7759/cureus.71733 | DOI Listing |
Diabetol Int
January 2025
Department of Plastic and Reconstructive Surgery and Maxillofacial Surgery, Kurume University School of Medicine, Kurume, Japan.
Aim: Patients with diabetes are frequently complicated with diabetic foot ulcers (DFUs) which are vulnerable to recurrence after healing. We retrospectively surveyed the recurrence of foot ulcer and related factors in Japanese patients with DFUs.
Subjects And Methods: Forty-two feet of 39 patients were initially recruited in this study.
Comput Biol Med
January 2025
Khalifa University, Abu Dhabi, United Arab Emirates. Electronic address:
Musculoskeletal modeling based on inverse dynamics provides a cost-effective non-invasive means for calculating intersegmental joint reaction forces and moments, solely relying on kinematic data, easily obtained from smart wearables. On the other hand, the accuracy and precision of such models strongly hinge upon the selected scaling methodology tailored to subject-specific data. This study investigates the impact of upper body mass distribution on internal and external kinetics computed using a comprehensive musculoskeletal model during level walking in both normal weight and obese individuals.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Medicine, Surgery and Dentistry, Center for Neurodegenerative Diseases (CEMAND), University of Salerno, Fisciano, Italy.
Subtle gait and cognitive dysfunction are common in Parkinson's disease (PD), even before most evident clinical manifestations. Such alterations can be assumed as hypothetical phenotypical and prognostic/progression markers. To compare spatiotemporal gait parameters in PD patients with three cognitive status: cognitively intact (PD-noCI), with subjective cognitive impairment (PD-SCI) and with mild cognitive impairment (PD-MCI) in order to detect subclinical gait differences.
View Article and Find Full Text PDFAlzheimers Dement
January 2025
Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Ontario, Canada.
Introduction: We aimed to compare gait between individuals with Alzheimer's disease (AD), dementia with Lewy bodies (DLB), and cognitively unimpaired (CU) individuals and to evaluate the association between gait and regional amyloid beta (Aβ) burden in AD and DLB.
Methods: We included 420 participants (70 AD, 70 DLB, 280 CU) in the Mayo Clinic Study of Aging (MCSA). Gait was assessed using a pressure-sensor walkway.
Alzheimers Res Ther
January 2025
Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany.
Background: Alzheimer's disease (AD) is a progressive neurodegenerative disorder affecting millions worldwide, leading to cognitive and functional decline. Early detection and intervention are crucial for enhancing the quality of life of patients and their families. Remote Monitoring Technologies (RMTs) offer a promising solution for early detection by tracking changes in behavioral and cognitive functions, such as memory, language, and problem-solving skills.
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