Passive remote monitoring using phone and wearable sensors to identify digital markers of ADHD (Preprint)
Authors: Sankesara Heet , Denyer Hayley , Sun Shaoxiong , Deng Qigang , Ranjan Yatharth , Conde Pauline , Rashid Zulqarnain , Asherson Philip , Bilbow Andrea , Groom Maddie , Hollis Chris , Dobson Richard JB , Folarin Amos , Kuntsi Jonna 
Published: November 2023

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. The field of mobile health (mHealth) offers a new approach to obtaining additional, passive, more objective and continuously measured behavioural data in the real world. Using our new ADHD Remote Technology (ART) system, based on active and passive remote monitoring using the open-source RADAR-base mHealth platform, we explore novel digital markers for their potential to distinguish between people with and without ADHD. Active monitoring involves the participant completing clinical symptom questionnaires on the RADAR-base smartphone Active App while the RADAR-base Passive App and wearable device collect sensor data in the background without additional burden to the participant.

OBJECTIVE

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

METHODS

As part of a remote monitoring pilot study using the ART system, 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) RADAR-base Active App derived data: mean and standard deviation of questionnaire notification-response-latency and of the time interval between questionnaires, ii) RADAR-base Passive App phone-derived data: 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, number of new apps added during the study period); and iii) wearable device (FitBit) daily number of steps taken while active on the phone. We applied Linear Mixed Models to examine differences between the groups for repeatedly measured variables over the study period. For non-longitudinal data, we used a t-test to assess group differences. In addition, we report effect sizes to convey the magnitude of differences found.

RESULTS

Group differences were significant for five of the ten variables. Compared to the participants without ADHD, the participants with ADHD were i) slower and more variable in their speed of responding to the notifications to complete the questionnaires; ii) had a higher standard deviation in the time interval between questionnaires; iii) had higher daily mean response time to social and communication app notifications; and iv) a greater change in ambient (background) light when they are actively using the smartphone. Moderate to high effect size (d > 0.6), though with non-significant p-values, were additionally observed for the mean of participants’ duration of intervals between questionnaires for the Active App questionnaires; daily standard deviation in responding to social and communication app notifications; and Fitbit step count while active on their smartphones. The groups did not differ in the duration for which participants were active on their smartphones and the number of new apps downloaded.

CONCLUSIONS

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

CLINICALTRIAL