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Mobile Health App (DIGICOG-MS) for Self-Assessment of Cognitive Impairment in People With Multiple Sclerosis:
Jessica Podda1, Andrea Tacchino1, Michela Ponzio1
1Scientific Research Area, Italian Multiple Sclerosis Foundation, Genoa, Italy.
Background:
Mobile health (mHealth) apps have proven useful for people with multiple sclerosis (MS). Thus, easy-to-use digital solutions are now strongly required to assess and monitor cognitive impairment, one of the most disturbing symptoms in MS that is experienced by almost 43% to 70% of people with MS. Therefore, we developed DIGICOG-MS (Digital assessment of Cognitive Impairment in Multiple Sclerosis), a smartphone- and tablet-based mHealth app to self-assess cognitive impairment in MS.
Objective:
This study aimed to test the validity and usability of the novel mHealth app with a sample of people with MS.
Methods:
DIGICOG-MS includes 4 digital tests assumed to evaluate the most affected cognitive domains in MS (visuospatial memory [VSM], verbal memory [VM], semantic fluency [SF], and information processing speed [IPS]) and inspired by traditional paper-based tests that assess the same cognitive functions (10/36 Spatial Recall Test, Rey Auditory Verbal Learning Test, Word List Generation, Symbol Digit Modalities Test). Participants were asked to complete both digital and traditional assessments in 2 separate sessions. Convergent validity was analyzed using the Pearson correlation coefficient to determine the strength of the associations between digital and traditional tests. To test the app's reliability, the agreement between 2 repeated measurements was assessed using intraclass correlation coefficients (ICCs). Usability of DIGICOG-MS was evaluated using the System Usability Scale (SUS) and mHealth App Usability Questionnaire (MAUQ) administered at the conclusion of the digital session.
Results:
The final sample consisted of 92 people with MS (60 women) followed as outpatients at the Italian Multiple Sclerosis Society (AISM) Rehabilitation Service of Genoa (Italy). They had a mean age of 51.38 (SD 11.36) years, education duration of 13.07 (SD 2.74) years, disease duration of 12.91 (SD 9.51) years, and a disability level (Expanded Disability Status Scale) of 3.58 (SD 1.75). Relapsing-remitting MS was most common (68/92, 74%), followed by secondary progressive (15/92, 16%) and primary progressive (9/92, 10%) courses. Pearson correlation analyses indicated significantly strong correlations for VSM, VM, SF, and IPS (all P<.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, the SUS score averaged 84.5 (SD 13.34), and the mean total MAUQ score was 104.02 (SD 17.69), suggesting that DIGICOG-MS was highly usable and well appreciated.
Conclusions:
The DIGICOG-MS tests were strongly correlated with traditional paper-based evaluations. Furthermore, people with MS positively evaluated DIGICOG-MS, finding it highly usable. Since cognitive impairment poses major limitations for people with MS, these findings open new paths to deploy digital cognitive tests for MS and further support the use of a novel mHealth app for cognitive self-assessment by people with MS in clinical practice.
