Identifying Digital Markers of ADHD in a Remote Monitoring Setting: Prospective Observational Study

Background: The symptoms and associated characteristics of attention deficit hyperactivity disorder (ADHD) are typically assessed in person at a clinic or in a research lab. Mobile health offers a new approach to obtaining additional passively and continuously measured real-world behavioural data. Using our new ADHD Remote Technology (ART) system, based on the RADAR-base platform, we explore novel digital markers for their potential to identify behavioural patterns associated with ADHD. The RADAR-base Passive App and wearable device collect sensor data in the background, while the Active App involves participants completing clinical symptom questionnaires.

Objective: The main aim of this study was to investigate whether adults and adolescents with ADHD differ from individuals without ADHD on ten digital signals that we hypothesise capture lapses in attention, restlessness or impulsive behaviours.

Methods: We collected data over ten weeks from 20 individuals with ADHD and 20 comparison participants without ADHD between the ages of 16 and 39. We focus on features derived from i) Active App: mean and standard deviation of questionnaire notification-response-latency and of the time interval between questionnaires, ii) Passive App: daily mean and standard deviation of response time to social and communication app notifications, the standard deviation in ambient light during phone use, total phone use time, total number of new apps added, and iii) wearable (Fitbit): daily steps taken while active on the phone. Linear mixed models and t-tests were employed to assess the group differences for repeatedly measured and time-aggregated variables, respectively. Effect sizes (d) convey the magnitude of differences.

Results: Group differences were significant for five of the ten variables. The participants with ADHD were i) slower (P=0.047, d=1.05) and more variable (P=0.010, d=0.84) in their speed of responding to the notifications to complete the questionnaires; ii) had a higher standard deviation in the time interval between questionnaires (P=0.043, d=1.13); iii) had higher daily mean response time to social and communication app notifications (P=0.030, d=0.7); and iv) a greater change in ambient (background) light when they are actively using the smartphone (P=0.008, d=0.86). Moderate to high effect sizes with non-significant p-values were additionally observed for the mean of time intervals between questionnaires (P=0.059, d=0.82), daily standard deviation in responding to social and communication app notifications (P=0.050, d=0.64), and steps taken while active on the phone (P=0.095, d=0.61). The groups did not differ in the total phone use time (P=0.110, d=0.54) and the number of new apps downloaded (P=0.236, d=0.18).

Conclusions: In a novel exploration of digital markers of ADHD, we identified candidate digital signals of restlessness, inconsistent attention and difficulties completing tasks. Larger future studies are needed to replicate these findings and to assess the potential of such objective digital signals for tracking ADHD severity or predicting outcomes.