JMIR Publications

ABSTRACT

Background:

Mobile health (mHealth) apps have proven useful for people with Multiple Sclerosis (PwMS). Thus, easy-to-use digital solutions are now strongly required to assess and monitor cognitive impairment (CI), one of the most disturbing symptoms in MS, experienced by almost 43-70% of PwMS. In this view, we developed DIGICOG-MS® (DIGItal assessment of COGnitive impairment in Multiple Sclerosis), a smartphone and tablet-based mHealth app for self-assessment of CI in MS.

Objective:

This study aimed to test validity and usability of the novel mHealth app in a sample of PwMS.

Methods:

DIGICOG-MS® includes four digital tests assumed to evaluate the most affected cognitive domains in MS as visuospatial memory (VSM), verbal memory (VM), semantic fluency (SF) and information processing speed (IPS), taking inspiration from traditional paper-based tests known to assess the same cognitive functions, as 10/36 Spatial Recall Test, Rey Verbal Learning Test, Word List Generation, Symbol Digit Modalities Test. Participants were asked to complete both digital and traditional assessments in two separate sessions. Convergent validity was analysed using the Pearson correlation coefficient (r) to determine the strength of the association between digital and traditional tests. To test reliability of the app, the agreement between two repeated measurements was addressed with use of the intraclass correlation coefficients (ICC). System Usability Scale (SUS) and mHealth App Usability Questionnaire (MAUQ) were thus administered after the DIGICOG-MS® evaluation to test usability of the mHealth app.

Results:

The final sample consisted in ninety-two PwMS (female: 60), followed as outpatients at the AISM Rehabilitation Service of Genoa (Italy). They had a mean age of 51.38 (11.36) years, an education of 13.07 (2.74) years, a disease duration of 12.91 (9.51) years and a disability level as measured by the Expanded Disability Status Scale of 3.58 (1.75). Relapsing-remitting MS was most common (73.91%), followed by secondary progressive (16.30%) and primary progressive (9.78%) courses. Pearson correlation analyses indicated significantly strong correlations for VSM, VM, SF and IPS (all ps <.001), with r values ranging from 0.58 to 0.78 for all cognitive domains. Test-retest reliability of the mHealth app was excellent (ICCs > 0.90) for VM and IPS, and good for VSM and SF (ICCs > 0.80). Moreover, SUS score averaged 84.5 (13.34), and total score from MAUQ was 104.02 (17.69), suggesting that DIGICOG-MS® was highly usable and well appreciated by PwMS.

Conclusions:

Results indicated that tests from DIGICOG-MS® strongly correlated with traditional paper-based evaluation. Furthermore, PwMS positively evaluated DIGICOG-MS®, finding it highly usable. Since CI poses major limitations to PwMS, the current findings open new paths to deploy digital cognitive tests for MS and further support the use of such novel mHealth app for cognitive self-assessment in PwMS into clinical practice.