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.

Item Type:	Refereed Article
Keywords:	multiple sclerosis, symptom measure, numeric rating scale, validity, responsiveness, MSSymS
Research Division:	Biomedical and Clinical Sciences
Research Group:	Neurosciences
Research Field:	Neurology and neuromuscular diseases
Objective Division:	Health
Objective Group:	Clinical health
Objective Field:	Clinical health not elsewhere classified
UTAS Author:	Zhang, Y (Miss Yan Zhang)
UTAS Author:	Taylor, BV (Professor Bruce Taylor)
UTAS Author:	Simpson Jr, S (Dr Steve Simpson JR)
UTAS Author:	Blizzard, L (Professor Leigh Blizzard)
UTAS Author:	Palmer, AJ (Professor Andrew Palmer)
UTAS Author:	van der Mei, I (Professor Ingrid van der Mei)
ID Code:	139525
Year Published:	2020
Web of Science® Times Cited:	15
Deposited By:	Menzies Institute for Medical Research
Deposited On:	2020-06-18
Last Modified:	2022-08-23
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