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Related Experiment Video

Updated: Jun 26, 2026

Measuring the Motor Aspect of Cancer-Related Fatigue using a Handheld Dynamometer

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Published on: February 20, 2020

Longitudinal correlates of fatigue in multiple sclerosis.

E Patrick¹, C Christodoulou, L B Krupp

¹Department of Neurology, State University of New York at Stony Brook, New York 11794-8121, USA.

Multiple Sclerosis (Houndmills, Basingstoke, England)

|February 3, 2009

Summary

Predictors of fatigue in multiple sclerosis (MS) include baseline fatigue, pain, and mood. These factors significantly influence fatigue levels over time, aiding in patient management.

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Area of Science:

Neurology
Clinical Research
Patient Outcomes

Background:

Fatigue is the most prevalent symptom in multiple sclerosis (MS).
Understanding fatigue predictors in MS remains a significant clinical challenge.
The New York State Multiple Sclerosis Consortium (NYSMSC) database provides extensive longitudinal data for MS research.

Purpose of the Study:

To identify predictors of longitudinal changes in fatigue.
To examine the influence of pain, mood, and neurological impairment on MS fatigue.
To analyze these predictors across different multiple sclerosis subtypes.

Main Methods:

Utilized baseline and 1-year follow-up data from 2768 patients in the NYMSC database.
Assessed fatigability, pain, depressive symptoms, MS subtype, and Expanded Disability Status Scale (EDSS).
Employed correlational and multiple regression analyses to determine fatigue correlates and predictors.

Main Results:

Baseline fatigue, pain, and depression explained 34.6% of the variance in 1-year follow-up fatigue.
Fatigue levels were lower in relapsing-remitting MS compared to other subtypes.
Consistent correlations were observed between fatigue, depressive symptoms, pain severity, and EDSS at baseline and follow-up.

Conclusions:

Baseline fatigue, pain, mood, and EDSS are significant predictors of 1-year fatigue outcomes.
These key symptoms demonstrate inter-correlations at baseline, follow-up, and in change scores.
Identifying fatigue predictors can significantly improve patient management strategies in MS.