Assessing the Predictive Validity of the TUG, Timed up and Go Test, and a Falls Screening Questionnaire to Determine the Risk of Falling in People with Multiple Sclerosis
Multiple Sclerosis is a chronic disease with a high falls incidence of greater than 60% (Gunn et al, 2013). Therefore, it is essential to identify potential fallers early and initiate appropriate falls prevention interventions. As of yet there is no reliable, quick, easy to use falls assessment tool validated for people with MS.
The objective of this analysis is to examine the difference between fallers and non-fallers for the timed up and go test (TUG) with and without cognitive challenges, and the Symbol Digit Modalities Test (SDMT).
Consecutive patients with MS attending the Neurology service in a tertiary hospital were recruited. Data collected included the EDSS score (disability), SDMT score (cognitive impairment), time since diagnosis, type of MS and walking aid(s) used. Consenting participants completed a questionnaire of falls risk factors, TUG and SDMT. Falls were prospectively recorded for 3 months using falls diaries.
Mean age (N=75) was 54 and 67% were female. The majority of the group had secondary progressive MS (53%) and 75% used a walking aid. There was a total of 539 falls recorded over the 3 month period from 38 participants. 58% of the fallers were multiple fallers. There was a statistically significant difference (Mann-Whitney U test) in the TUG scores between fallers 12.1(7.6 IQR) and non-fallers 11.3(5.4 IQR) (p=0.041) but not in the Tug cognitive scores 13.7(10.7 IQR) for fallers and 13.2(6.3 IQR) for non-fallers. There was no statistically significant difference between fallers and non-fallers in SDMT scores with a mean difference of 2.2, 95% CI (-7.5, 3.2), p=0.425. (Independent Samples T Test)
Initial analysis suggests that the TUG may be able to differentiate between fallers and non-fallers but not the TUG cognitive or SDMT. Data collection is ongoing and further analysis of a larger sample will be carried out. A simple screening questionnaire will also be evaluated in this study, along with combinations of disability, symptoms and walking mobility in order to develop a fall risk algorithm and thus prompt more focused and timely interventions.