Background: There is significant variability in poststroke locomotor learning that is poorly understood and affects individual responses to rehabilitation interventions. Cognitive abilities relate to upper extremity motor learning in neurologically intact adults, but have not been studied in poststroke locomotor learning.
Objective: To understand the relationship between locomotor learning and retention and cognition after stroke.
Methods: Participants with chronic (>6 months) stroke participated in 3 testing sessions. During the first session, participants walked on a treadmill and learned a new walking pattern through visual feedback about their step length. During the second session, participants walked on a treadmill and 24-hour retention was assessed. Physical and cognitive tests, including the Fugl-Meyer-Lower Extremity (FM-LE), Fluid Cognition Composite Score (FCCS) from the NIH Toolbox -Cognition Battery, and Spatial Addition from the Wechsler Memory Scale-IV, were completed in the third session. Two sequential regression models were completed: one with learning and one with retention as the dependent variables. Age, physical impairment (ie, FM-LE), and cognitive measures (ie, FCCS and Spatial Addition) were the independent variables.
Results: Forty-nine and 34 participants were included in the learning and retention models, respectively. After accounting for age and FM-LE, cognitive measures explained a significant portion of variability in learning ( = 0.17, = .008; overall model = 0.31, = .002) and retention (Δ = 0.17, = .023; overall model = 0.44, = .002).
Conclusions: Cognitive abilities appear to be an important factor for understanding locomotor learning and retention after stroke. This has significant implications for incorporating locomotor learning principles into the development of personalized rehabilitation interventions after stroke.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122051 | PMC |
http://dx.doi.org/10.1177/15459683211001025 | DOI Listing |
PLoS Comput Biol
January 2025
Department of Computer Science, Colorado State University, Fort Collins, Colorado, United States of America.
Complex deep learning models trained on very large datasets have become key enabling tools for current research in natural language processing and computer vision. By providing pre-trained models that can be fine-tuned for specific applications, they enable researchers to create accurate models with minimal effort and computational resources. Large scale genomics deep learning models come in two flavors: the first are large language models of DNA sequences trained in a self-supervised fashion, similar to the corresponding natural language models; the second are supervised learning models that leverage large scale genomics datasets from ENCODE and other sources.
View Article and Find Full Text PDFAdv Skin Wound Care
January 2025
Tuba Sengul, PhD, RN, CWON, is Associate Professor, Koç University School of Nursing, Istanbul, Türkiye. Nurten Kaya, PhD, RN, is Professor, Health Sciences Faculty, Istanbul University-Cerrahpaşa, Istanbul.
Objective: To determine if an escape room game approach, which has emerged as a novel and engaging education tool, is an effective method to improve nursing students' knowledge of pressure injury (PI) prevention and attitudes toward the care of patients with a PI.
Methods: This study evaluated 33 university nursing students using a quasi-experimental pretest/posttest design. Students completed five questionnaires before the escape room experience and again 1 month after.
J Neurophysiol
January 2025
KU Leuven, Department of Movement Sciences, B-3000 Leuven, Belgium.
In motor adaptation, learning is thought to rely on a combination of several processes. Two of these are implicit learning (incidental updating of the movement due to sensory prediction error) and explicit learning (intentional adjustment to reduce target error). The explicit component is thought to be fast adapting, while the implicit one is slow.
View Article and Find Full Text PDFNursing
December 2024
At the York College of Pennsylvania, Jenna Davis is an assistant professor. She serves as the course coordinator for the Basic Principles course and teaches in the NCLEX support course. Carrie Pucino is an associate professor at York College of Pennsylvania. She has served as a leader in developing and improving the York College NCLEX Preparation Program, revising and teaching in the NCLEX support course, and providing one-to-one NCLEX coaching for high-risk students.
Purpose: To explore perceptions of student learning in undergraduate nursing students who repeat the fundamentals nursing course and simultaneously take a support course.
Methods: This qualitative descriptive design was conducted at one private liberal arts college. The study included interviews with six undergraduate baccalaureate nursing students repeating the fundamentals course and their perceptions following the repeated course.
Br J Nurs
January 2025
Registered Advanced Nurse Practitioner, Emergency Department, St James's Hospital, Dublin.
Clinical supervision is a valued learning tool for student nurses; however, there is a paucity of description around real-time experience of clinical supervision among qualified advanced nurse practitioners. Many qualified nurses claim delays in engaging with clinical supervision may be caused by staff shortages, time constraints, workload in busy clinical environments, or a reticence to engage in discussions that might reveal shortcomings in knowledge or practical skills. This article reviews a process of monthly clinical supervision that has been conducted among a group of qualified emergency department advanced nurse practitioners for 25 years.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!