Related Experiment Video
Updated: Feb 14, 2026
11:35
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
58.8K
Predictive Models of Cognitive Fatigue in Multiple Sclerosis.
Jason A Berard1,2, Andra M Smith1,2,3, Lisa A S Walker1,2,3,4
1University of Ottawa, School of Psychology, Ottawa, Canada.
Summary
Poor sleep quality significantly predicts cognitive fatigue in multiple sclerosis (MS) patients. Addressing sleep issues may alleviate cognitive fatigue, though other factors also contribute.
Area of Science:
- Neuroscience
- Clinical Psychology
Background:
- Cognitive fatigue (CF) is a decline in performance due to sustained mental effort.
- Understanding factors influencing CF is crucial for managing conditions like multiple sclerosis (MS).
Purpose of the Study:
- To investigate the relationships between disease severity, fatigue, depression, and sleep quality in predicting cognitive fatigue in MS.
- To assess four theoretical models explaining these interrelationships.
Main Methods:
- Fifty-eight individuals with MS participated.
- Cognitive fatigue was assessed using the Paced Auditory Serial Addition Test (PASAT).
- Path analysis was employed to evaluate predictive models.
Main Results:
- Cognitive fatigue showed significant correlations with depression and sleep quality.
- Sleep quality was the strongest independent predictor of cognitive fatigue, explaining 17.3% of the variance.
- Depression was the primary predictor of both sleep quality and fatigue.
Conclusions:
- Sleep quality is a key treatable factor for cognitive fatigue in MS.
- While significant, sleep quality alone does not account for all CF variance, suggesting other unassessed factors.
- Further research with larger samples is recommended.
Related Concept Videos
01:21Fatigue
865
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
865
01:38Cognitive Dissonance
37.5K
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
37.5K
02:27Predicting Molecular Geometry
46.1K
VSEPR Theory for Determination of Electron Pair Geometries
46.1K
01:24Muscle Recovery and Fatigue
4.3K
Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
4.3K
01:22Fatigue Strength of Concrete
580
Fatigue, in the context of materials science and engineering, refers to the weakening or failure of a material caused by repeatedly applied loads, even if these loads are below the strength limit of the material. Fatigue strength in concrete is a critical property that influences its durability and longevity. Concrete can fail in two ways due to fatigue. Static fatigue or creep rupture occurs under a constant load or one that increases slowly. The other failure mode is due to cyclical or...
580
01:03Prediction Intervals
3.4K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.
3.4K