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Updated: Aug 12, 2025
The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool
Published on: June 30, 2014
Disease severity classification using passively collected smartphone-based keystroke dynamics within multiple
Aleide Hoeijmakers1, Giovanni Licitra2, Kim Meijer1
1Neurocast B.V., Amsterdam, The Netherlands.
Abstract:
Multiple Sclerosis (MS) is a progressive demyelinating disease of the central nervous system characterised by a wide range of motor and non-motor symptoms. The level of disability of people with MS (pwMS) is based on a wide range of clinical measures, though their frequency of evaluation and inaccuracies coming from objective and self-reported evaluations limits these assessments. Alternatively, remote health monitoring through devices can offer a cost-efficient solution to gather more reliable, objective measures continuously. Measuring smartphone keyboard interactions is a promising tool since typing and, thus, keystroke dynamics are likely influenced by symptoms that pwMS can experience. Therefore, this paper aims to investigate whether keyboard interactions gathered on a person's smartphone can provide insight into the clinical status of pwMS leveraging machine learning techniques. In total, 24 Healthy Controls (HC) and 102 pwMS were followed for one year. Next to continuous data generated via smartphone interactions, clinical outcome measures were collected and used as targets to train four independent multivariate binary classification pipelines in discerning pwMS versus HC and estimating the level of disease severity, manual dexterity and cognitive capabilities. The final models yielded an AUC-ROC in the hold-out set above 0.7, with the highest performance obtained in estimating the level of fine motor skills (AUC-ROC=0.753). These findings show that keyboard interactions combined with machine learning techniques can be used as an unobtrusive monitoring tool to estimate various levels of clinical disability in pwMS from daily activities and with a high frequency of sampling without increasing patient burden.
Insights
Smartphone typing patterns can help monitor Multiple Sclerosis (MS) progression. Machine learning analyzes keystroke dynamics to assess disability, offering a frequent, unobtrusive method for people with MS (pwMS).
Area of Science:
- Neurology
- Biomedical Engineering
- Data Science
Background:
- Multiple Sclerosis (MS) is a central nervous system disorder causing varied motor and non-motor symptoms.
- Current disability assessments for people with MS (pwMS) are limited by infrequent evaluations and potential inaccuracies.
- Remote health monitoring offers a cost-effective approach for continuous, objective data collection.
Purpose of the Study:
- To investigate if smartphone keyboard interactions can provide insights into the clinical status of pwMS.
- To leverage machine learning to analyze keystroke dynamics for assessing MS-related disability.
- To explore an unobtrusive method for frequent monitoring of pwMS.
Main Methods:
- Collected one year of smartphone interaction data from 24 Healthy Controls (HC) and 102 pwMS.
- Utilized machine learning to train four classification pipelines.
- Used clinical outcome measures as targets for model training and validation.
Main Results:
- Models achieved an AUC-ROC above 0.7 in the hold-out set.
- The highest performance was in estimating fine motor skills (AUC-ROC=0.753).
- Keyboard interaction data successfully discerned pwMS from HC and estimated clinical status.
Conclusions:
- Smartphone keyboard interactions, analyzed with machine learning, can serve as an effective tool for monitoring MS clinical disability.
- This method allows for high-frequency, unobtrusive data collection without increasing patient burden.
- Findings support the use of digital biomarkers for remote health monitoring in neurological conditions.
