Objective Assessment of Attention Deficit Hyperactivity Disorder with QbMobile: A Smartphone Application for Clinical Use

TL;DR

QbMobile, a smartphone app, objectively assesses ADHD symptoms by measuring behavior, showing significant differences between ADHD and normative groups.

Contribution

QbMobile is introduced as a smartphone-based tool for objective ADHD assessment, demonstrating clinical differentiation and potential for streamlined diagnosis.

Findings

ADHD participants had significantly higher Total Scores (mean 83.0) compared to normative individuals (mean 48.9).

QbMobile showed sensitivity of 0.86 and specificity of 0.75 in distinguishing ADHD from normative groups.

Abstract

Digital mental health interventions such as web or mobile applications have become more prominent in the last years to improve the clinical assessment and workflow in mental health disorders while also being potentially more accessible than laptops. QbMobile is a software application that provides objective assessments of hyperactivity, impulsivity, and inattention to aid in the clinical evaluation of attention deficit hyperactivity disorder (ADHD). The purpose was to examine whether QbMobile could objectively quantify symptoms and reveal significant clinical differences between an ADHD population and a normative population.

Data were acquired from two low-intervention/observational studies (conducted in Europe and US), involving participants aged 6 to 60 years. The application (QbMobile) was configured on the smartphone (iPhone) with embedded instructions to ensure a consistent experience. Participants were seated at a desk in a stabilized chair and instructed to hold the smartphone with both hands and to tap the screen whenever an infrequent target stimulus appeared, while withholding a response to non-target stimuli. Concurrently, to measure activity, the camera of the smartphone captured the physical activity of the participant as well as the movements from holding the device. Approximately 20% of the complete dataset for each study was combined as a pooled dataset for a model validation of output parameters from QbMobile. A Total Score between 0 and 100 was calculated, where lower scores indicate a lower likelihood of ADHD symptoms and higher scores indicate a higher likelihood.

The ADHD cohort (n=63) demonstrated a higher mean Total Score of 83.0 (Standard deviation=17.5) compared to 48.9 (Standard deviation=18.8) in the normative population (n=354), a difference that was statistically significant (p<0.001). Significant domain-specific differences in SD-scores (movement pattern, activity, impulsivity, inattention) were identified between the ADHD cohort and the normative comparison group (p<0.001). A sensitivity of 0.86 and specificity of 0.75 were seen overall, though a low specificity was found in children, which was likely due to a smaller sample size and high activity levels in younger children in general.

This investigation demonstrates that QbMobile can generate objective symptom measurements that distinguish clinically relevant differences between individuals with ADHD and a normative population. A smartphone application of quantified behavioral psychometric testing of the core symptoms could streamline a faster diagnostic evaluation and treatment titration in the ADHD clinical workflow. The authors are employed by the manufacturer of QbMobile, which is discussed in this manuscript. This affiliation is disclosed to ensure transparency and does not affect the objectivity or scientific integrity of the work presented.

QbMobile demonstrated the ability to differentiate between ADHD and normative cohorts, indicating its potential as an accessible and objective tool for clinical assessment and treatment evaluation. Future studies should be conducted to further validate the effectiveness of QbMobile as an aid tool in the clinical assessment of ADHD and further explore its use in various populations.

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