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Monitoring and Predicting Health Status in Neurological Patients: The ALAMEDA Data Collection Protocol.

Alexandru Sorici1, Lidia Băjenaru1, Irina Georgiana Mocanu1

  • 1AI-MAS Laboratory, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania.

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|October 13, 2023
PubMed
Summary
This summary is machine-generated.

This study uses AI to analyze data from wearable devices and patient-reported outcomes (PROs) to classify the health status of Parkinson's disease, multiple sclerosis, and stroke patients. The novel approach enhances digital health monitoring for neurological disorders.

Keywords:
MSPDmood estimationpatient reported outcomesquantitative motor analysissleep analysisstrokewearables

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Area of Science:

  • Digital Health
  • Artificial Intelligence in Medicine
  • Neurology

Background:

  • Neurological disorders like Parkinson's disease (PD), multiple sclerosis (MS), and stroke pose significant burdens on patients and healthcare systems.
  • Digital transformation in healthcare offers potential solutions for managing these complex conditions.
  • The ALAMEDA project aims to leverage technology for improved patient care in these neurological conditions.

Purpose of the Study:

  • To explore the predictive power of combined wearable device data and patient-reported outcomes (PROs) for classifying the health status of patients with PD, MS, and stroke (PMSS).
  • To introduce a novel, AI-first approach for analyzing this integrated data stream.
  • To assess the feasibility and effectiveness of a comprehensive data collection strategy for long-term neurological patient monitoring.

Main Methods:

  • Continuous collection of PRO data via mobile applications over one year.
  • Integration of data from minimally invasive wearable devices (IMU sensors, insoles, sleep mattress) at study milestones.
  • Development of AI-first analysis methods to evaluate prediction capabilities across various data setups and medical targets.

Main Results:

  • A novel combination of wearable sensors and smartphone applications was successfully implemented for comprehensive outcome analysis (motor, sleep, emotional, quality-of-life).
  • AI-first analysis methods demonstrated the potential to uncover predictive insights from longitudinal and cross-sectional data.
  • The mobile application and data collection schedule proved effective in maintaining patient engagement and study compliance.

Conclusions:

  • The integration of wearable technology and PROs, analyzed with AI, offers a powerful tool for classifying the health status of neurological patients.
  • This approach supports the digital transformation of healthcare for Parkinson's disease, multiple sclerosis, and stroke.
  • The ALAMEDA project's methodology demonstrates feasibility and effectiveness in enhancing patient monitoring and engagement.