Related Experiment Video
Updated: Jan 1, 2026

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Validation of 0-10 MS symptom scores in the Australian multiple sclerosis longitudinal study
Yan Zhang1, Bruce V Taylor1, Steve Simpson2
1Menzies Institute for Medical Research, University of Tasmania, Hobart, Australia.
Background:
Multiple sclerosis (MS) symptom measurements often use multiple-item scales per symptom, creating a high burden when multiple symptoms are assessed. We aimed to examine the validity, stability and responsiveness of single-item 0-10 numeric rating MS Symptom Scores (MSSymS).
Methods:
The study included 1,985 participants from the Australian Multiple Sclerosis Longitudinal Study. Thirteen MS symptoms were assessed using the MSSymS, of which we were able to validate six (walking difficulties, fatigue, pain, feelings of anxiety, feelings of depression and vision problems). Comparison measures included Patient Determined Disease Steps (PDDS), Fatigue Severity Scale (FSS), Hospital Anxiety and Depression Scale (HADS), and Assessment of Quality of Life (AQoL). We used spearman rank correlation for concurrent validity, linear regression for predictive validity, intra-class correlations for stability, and percentage change for responsiveness.
Results:
We found high correlations between walking difficulties and PDDS (r = 0.82), pain and AQoL-pain (r = 0.77), fatigue and FSS (r = 0.72); moderate correlations between feelings of anxiety and HADS-Anxiety (r = 0.68), feelings of depression and HADS-Depression (r = 0.63); and low correlation between vision and AQoL-vision (r = 0.43) For predictive validity, the graphs with quality of life were largely overlapping and the R2 of the regression lines were generally similar. The stability and responsiveness of the MSSymS were adequate.
Conclusion:
The six assessed symptoms of the MSSymS performed equally well compared to validated comparison measures in terms of concurrent and predictive validity, temporal stability and responsiveness.
More Related Videos
10:46A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
06:49A Quick Phenotypic Neurological Scoring System for Evaluating Disease Progression in the SOD1-G93A Mouse Model of ALS
Published on: October 6, 2015