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The RODI mHealth app Insight: Machine-Learning-Driven Identification of Digital Indicators for Neurodegenerative

Panagiota Giannopoulou¹, Aristidis G Vrahatis¹, Mary-Angela Papalaskari²

¹Bioinformatics and Human Electrophysiology Laboratory, Department of Informatics, Ionian University, 49100 Corfu, Greece.

Healthcare (Basel, Switzerland)

|November 24, 2023

Summary

This summary is machine-generated.

Early detection of neurocognitive disorders (NCDs) is vital. The RODI mobile health app effectively uses visual working memory tasks to identify NCDs, outperforming other cognitive tests.

Keywords:

digital indicators mHealth apps machine learning neurodegenerative disorders

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Area of Science:

Neuroscience
Digital Health
Gerontology

Background:

Neurocognitive Disorders (NCDs) represent a significant global health challenge.
Early detection of NCDs is critical for effective treatment and management.
Mobile health (mHealth) applications offer a promising approach for cognitive assessment.

Purpose of the Study:

To develop and validate the RODI mHealth app for detecting NCDs.
To identify key cognitive tasks within the app that serve as digital indicators for NCDs.
To compare the performance of individuals with NCDs and healthy controls using the app.

Main Methods:

A study involving 182 participants (NCD patients and healthy controls) was conducted using the RODI mHealth app.
Machine learning algorithms were employed to analyze performance data and identify significant features.
An ensemble strategy using feature importance from three classification algorithms was utilized to prioritize tasks.

Main Results:

Tasks assessing visual working memory demonstrated the highest significance in differentiating between individuals with and without NCDs.
Cognitive processes such as mental calculations, executive working memory, and recall showed less influence in NCD detection via the app.
The study identified specific digital indicators within the RODI app crucial for NCD identification.

Conclusions:

The RODI mHealth app, particularly its visual working memory tasks, is a valuable tool for the early detection of NCDs.
This research provides a framework for developing future mHealth applications for cognitive disorder screening.
Optimizing mHealth app design based on task significance can enhance the accuracy of digital biomarker detection for neurological conditions.