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Attention-Deficit/Hyperactivity Disorder

Attention-Deficit/Hyperactivity Disorder

Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by persistent inattention, hyperactivity, and impulsivity. It affects approximately 5-8% of children globally, with around 60-70% of cases persisting into adulthood. ADHD has significant implications for educational attainment, social interactions, and occupational success.
Diagnostic Criteria and Symptoms
To diagnose ADHD, symptoms must manifest before age 12 and be evident across multiple settings....

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Machine learning on a smartphone-based CPT for ADHD prediction.

Núria Casals¹, Simon Larsson¹, Mikkel Hansen¹

¹Medical Department, Qbtech AB, Stockholm, Sweden.

Frontiers in Psychiatry

|November 24, 2025

Summary

This summary is machine-generated.

Smartphone sensors and Continuous Performance Tests (CPTs) can accurately diagnose Attention-Deficit/Hyperactivity Disorder (ADHD). Machine learning models integrating CPT, face, and motion data show high diagnostic performance, surpassing traditional methods.

Keywords:

ADHD AI CPT face tracking machine learning mobile motion sensor smartphone

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Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Using Brain Activation nir-HEG/Q-EEG and Execution Measures CPTs in a ADHD Assessment Protocol

Published on: April 1, 2018

Area of Science:

Neuroscience
Computer Science
Psychiatry

Background:

Continuous Performance Tests (CPTs) are standard for Attention-Deficit/Hyperactivity Disorder (ADHD) assessment.
Smartphone sensors offer novel behavioral monitoring for mental health.
Machine learning (ML) is emerging as a tool to enhance ADHD diagnosis.

Purpose of the Study:

To assess the feasibility of using smartphones for CPT-based ADHD evaluation.
To determine the utility of smartphone sensor data in predicting ADHD diagnosis.
To develop and validate an ML model for ADHD assessment using smartphone data.

Main Methods:

Utilized data from 952 neurotypical individuals and 292 unmedicated ADHD patients.
Developed an ML model trained on demographic, CPT, face, and motion sensor data.
Sequentially added feature groups to evaluate predictive performance for ADHD.

Main Results:

The best ML model achieved a sensitivity of 0.808 and a PR-AUC of 0.799.
Incorporating smartphone sensor data significantly improved diagnostic performance.
Performance did not vary significantly across age groups (6-60 years) or sex.

Conclusions:

Smartphone-based CPTs can accurately assess ADHD.
Integrating smartphone sensor data (face-tracking, motion) enhances diagnostic accuracy.
This approach shows potential to outperform traditional computer-based ADHD assessments.