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Updated: Mar 31, 2026
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Evaluating more naturalistic outcome measures: A 1-year smartphone study in multiple sclerosis
Riley Bove1, Charles C White1, Gavin Giovannoni1
1Program in Translational Neuropsychiatric Genomics (R.B., C.C.W., B.G., M.L., S.P., A.R., H.W., P.L.D.J.), Ann Romney Center for Neurologic Diseases, and the Partners Multiple Sclerosis Center, Department of Neurology, Brigham and Women's Hospital, Brookline, MA; Harvard Medical School (R.B., B.G., S.P., H.W., P.L.D.G.), Boston, MA; Blizard Institute (G.G.) and Royal Holloway (D.L.), University College London, London, UK; Vertex Pharmaceuticals Incorporated (V.G., A.L., S.R., R.R., M.B.), Boston MA; Woo Sports (J.H.), Boston, MA; McGovern Institute Neurotechnology Program (C.J.), MIT, Cambridge, MA; and Biogen-Idec (J.P., J.R.), Cambridge, MA.
This study shows smartphones can collect frequent, real-world data for multiple sclerosis (MS) patients, aiding in monitoring chronic neurologic disorders and identifying new insights like learning curves.
Area of Science:
- Neurology
- Digital Health
- Patient Monitoring
Background:
- Multiple sclerosis (MS) is a chronic neurologic disorder requiring continuous monitoring.
- Traditional monitoring methods often lack real-world data and high-frequency assessments.
- Smartphones offer novel approaches for in-home, naturalistic patient monitoring.
Purpose of the Study:
- To illustrate novel smartphone-based monitoring approaches for patients with chronic neurologic disorders.
- To assess the feasibility and barriers of deploying a smartphone platform for data collection in MS patients.
- To explore passive and active data gathering at high frequency in an unstructured setting.
Main Methods:
- A cohort of individuals with and without MS (aged 18-55) participated.
- Participants used smartphones with custom applications for 19 tests over 1 year.
- Data collected included performance metrics and patient-reported outcomes (PROs).
Main Results:
- Smartphone data captured fluctuations in MS outcomes (e.g., fatigue) correlated with environmental factors.
- Active and passive data collection methods (e.g., Ishihara test) demonstrated utility.
- Spline analysis identified person-specific learning curves in neuropsychological testing.
- Averaging repeated measures provided robust correlations between outcome measures.
Conclusions:
- Smartphone platforms are feasible for collecting high-frequency, naturalistic data in patients with MS.
- Barriers to deployment were identified, suggesting areas for improvement.
- This approach may enable large-scale studies for MS and other neurologic diseases.
