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Updated: Jun 6, 2025

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

Smartphone tests quantify lower extremities dysfunction in multiple sclerosis.

Kimberly Jin¹, Peter Kosa¹, Bibiana Bielekova¹

¹Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States.

Frontiers in Neurology

|December 2, 2024

Summary

This summary is machine-generated.

Smartphone tests can reliably measure lower extremity motor function in patients with neurological diseases. This technology aids in remote patient monitoring and assessment, improving care for chronic conditions.

Keywords:

foot tapping gait analysis motor function multiple sclerosis neurological examination neurology smartphone app telehealth

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Last Updated: Jun 6, 2025

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

Published on: June 30, 2014

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

Published on: March 14, 2017

Home-Based Monitor for Gait and Activity Analysis

Home-Based Monitor for Gait and Activity Analysis

Published on: August 8, 2019

Area of Science:

Neurology
Biomedical Engineering
Digital Health

Background:

Growing neurologist shortages and an aging population strain chronic neurological disease care.
Telemedicine offers potential but faces challenges in monitoring neurological disability.
Smartphone technology presents an opportunity for remote, self-administered neurological assessments.

Purpose of the Study:

To evaluate the Neurological Functional Test Suite (NeuFun-TS) smartphone tests for quantifying lower extremity motor function.
To assess the correlation of NeuFun-TS digital biomarkers with traditional neurological assessments and MRI outcomes in multiple sclerosis (MS) patients.

Main Methods:

108 MS patients underwent neurological exams, brain imaging, and completed NeuFun-TS smartphone tests.
Digital biomarkers from NeuFun-TS were correlated with disability scales and MRI findings.
Machine learning models were developed to predict physical disability using digital biomarkers.

Main Results:

NeuFun-TS biomarkers showed strong correlations with gait and lower extremity function outcomes (Spearman ρ > 0.8).
Significant correlations were found with global disability measures (e.g., EDSS, ρ 0.46-0.65) and imaging outcomes (e.g., medulla atrophy, ρ ~ 0.4).
Machine learning models combining reliable digital features outperformed individual biomarkers in predicting physical disability.

Conclusions:

NeuFun-TS tests generate reliable digital biomarkers for lower extremity motor functions.
Smartphone-based neurological assessments offer a viable tool for remote monitoring and disability quantification.