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

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.