The Fatigue Scale for Motor and Cognitive Functions (FSMC): validation of a new instrument to assess multiple sclerosis-related fatigue

Mult Scler. 2009 Dec;15(12):1509-17. doi: 10.1177/1352458509348519. Epub 2009 Dec 7.


Fatigue symptoms are reported by a majority of patients with multiple sclerosis (MS). Reliable assessment, however, is a demanding issue as the symptoms are experienced subjectively and as objective assessment strategies are missing. The objective of this study was to develop and validate a new tool, the Fatigue Scale for Motor and Cognitive Functions (FSMC), for the assessment of MS-related cognitive and motor fatigue. A total of 309 MS patients and 147 healthy controls were included into the validation study. The FSMC was tested against several external criteria (e.g. cognition, motivation, personality and other fatigue scales). The item-analysis and validation procedure showed that the FSMC is highly sensitive and specific in detecting fatigued MS patients, that both subscales significantly differentiated between patients and controls (p < 0.01), and that internal consistency (Cronbach's alpha alpha > 0.91) as well as test-retest reliability (r > 0.80) were high. Cut-off values were determined to classify patients as mildly, moderately or severely fatigued. In conclusion, the FSMC is a new scale that has undergone validation based on a large sample of patients and that provides differential quantification and graduation of cognitive and motor fatigue.

Publication types

  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Adult
  • Case-Control Studies
  • Cognition*
  • Disability Evaluation*
  • Discriminant Analysis
  • Fatigue / complications
  • Fatigue / diagnosis*
  • Fatigue / physiopathology
  • Fatigue / psychology
  • Female
  • Humans
  • Male
  • Middle Aged
  • Motor Activity*
  • Multiple Sclerosis / complications*
  • Multiple Sclerosis / physiopathology
  • Multiple Sclerosis / psychology
  • Neuropsychological Tests*
  • Predictive Value of Tests
  • Principal Component Analysis
  • ROC Curve
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Severity of Illness Index