Attention Deficit/Hyperactivity Disorder (ADHD) is the most prevalent psychiatric disorder in children, yet behavioral problem assessment methods have their limitations. Performance-based lab tasks are time-intensive and lack ecological validity, while rating scales are affected by mono-informant bias and interpersonal discrimination. Wearable sensor technology offers a promising alternative by capturing a large volume of objective behavioral data. This poster explores whether temporal data on adolescents' intensity of movement, as measured by Fitbit devices, correlates with report-based ADHD symptoms.

Utilizing data from the Adolescent Brain Cognitive Development (ABCD) Study, a large, US population-based sample of 6,257 children aged 11-12 years was analyzed. Participants wore Fitbit devices for three weeks, yielding at least 600 daily minutes of daytime data. Activity levels were measured in Metabolic Equivalent Tasks (METs), with both mean activity intensity and day-to-day activity variability analyzed. Attentional and externalizing problems were assessed using the Child Behavior Checklist (CBCL) and the Brief Problem Monitor (BPM), incorporating reports from parents, teachers, and youths.

Bivariate associations and linear regression models were employed to evaluate the relationships between sensor activity measures and ADHD symptoms, adjusting for sex and site effects. Findings reveal that while traditional measures focus on average activity levels, actigraphy data offers unique insights into within-child variability in activity and its association with ADHD symptoms. Variability in activity intensity was particularly informative, highlighting the potential of actigraphy to capture impulsive and inattentive behaviors in everyday settings.

Actigraphy could potentially improve diagnostic accuracy and address disparities in ADHD diagnoses among historically underdiagnosed groups, including Black youth and girls. However, the inclusion of both in-school and out-of-school daytime hours is a possible limitation, as the impact of after-school activities on the associations is unclear. Future research should explore how contextual factors, such as climate, sleep, and neighborhood characteristics, influence the relationship between activity levels and ADHD symptoms. Additionally, the longitudinal nature of the ABCD study could allow for insights into whether actigraphy data can serve as a predictive tool for ADHD diagnoses in later adolescence or early adulthood, potentially advancing remote symptom monitoring and scalable screening methods for clinicians.