Feasibility and initial validation of 'HD-Mobile', a smartphone application for remote self-administration of performance-based cognitive measures in Huntington's disease.

ABSTRACT

OBJECTIVE: Smartphone-based cognitive assessment measures allow efficient, rapid, and convenient collection of cognitive datasets. Establishment of feasibility and validity is essential for the widespread use of this approach. We describe a novel smartphone application (HD-Mobile) that includes three performance-based cognitive tasks with four key outcome measures, for use with Huntington's disease (HD) samples. We describe known groups and concurrent validity, test-retest reliability, sensitivity, and feasibility properties of the tasks. METHODS: Forty-two HD CAG-expanded participants (20 manifest, 22 premanifest) and 28 healthy controls completed HD-Mobile cognitive tasks three times across an 8-day period, on days 1, 4, and 8. A subsample of participants had pen-and-paper cognitive task data available from their most recent assessment from their participation in a separate observational longitudinal study, Enroll-HD. RESULTS: Manifest-HD participants performed worse than healthy controls for three of four HD-Mobile cognitive measures, and worse than premanifest-HD participants for two of four measures. We found robust test-retest reliability for manifest-HD participants (ICC = 0.71-0.96) and with some exceptions, in premanifest-HD (ICC = 0.52-0.96) and healthy controls (0.54-0.96). Correlations between HD-Mobile and selected Enroll-HD cognitive tasks were mostly medium to strong (r = 0.36-0.68) as were correlations between HD-Mobile cognitive tasks and measures of expected disease progression and motor symptoms for the HD CAG-expanded participants (r = - 0.34 to - 0.54). CONCLUSIONS: Results indicated robust known-groups, test-retest, concurrent validity, and sensitivity of HD-Mobile cognitive tasks. The study demonstrates the feasibility and utility of HD-Mobile for conducting convenient, frequent, and potentially ongoing assessment of HD samples without the need for in-person assessment.