Sankesara Heet, Denyer Hayley, Sun Shaoxiong, Deng Qigang, Ranjan Yatharth, Conde Pauline, Rashid Zulqarnain, Asherson Philip, Bilbow Andrea, Groom Madeleine J, Hollis Chris, Dobson Richard J B, Folarin Amos, Kuntsi Jonna
Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, United Kingdom, 44 2078365454.
Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
JMIR Form Res. 2025 Jan 29;9:e54531. doi: 10.2196/54531.
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 behavioral data. Using our new ADHD remote technology (ART) system, based on the Remote Assessment of Disease and Relapses (RADAR)-base platform, we explore novel digital markers for their potential to identify behavioral 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.
The main aim of this study was to investigate whether adults and adolescents with ADHD differ from individuals without ADHD on 10 digital signals that we hypothesize capture lapses in attention, restlessness, or impulsive behaviors.
We collected data over 10 weeks from 20 individuals with ADHD and 20 comparison participants without ADHD between the ages of 16 and 39 years. We focus on features derived from (1) Active App (mean and SD of questionnaire notification response latency and of the time interval between questionnaires), (2) Passive App (daily mean and SD of response time to social and communication app notifications, the SD in ambient light during phone use, total phone use time, and total number of new apps added), and (3) a wearable device (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.
Group differences were significant for 5 of the 10 variables. The participants with ADHD were (1) slower (P=.047, d=1.05) and more variable (P=.01, d=0.84) in their speed of responding to the notifications to complete the questionnaires, (2) had a higher SD in the time interval between questionnaires (P=.04, d=1.13), (3) had higher daily mean response time to social and communication app notifications (P=.03, d=0.7), and (4) had a greater change in ambient (background) light when they were actively using the smartphone (P=.008, d=0.86). Moderate to high effect sizes with nonsignificant P values were additionally observed for the mean of time intervals between questionnaires (P=.06, d=0.82), daily SD in responding to social and communication app notifications (P=.05, d=0.64), and steps taken while active on the phone (P=.09, d=0.61). The groups did not differ in the total phone use time (P=.11, d=0.54) and the number of new apps downloaded (P=.24, d=0.18).
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.
注意力缺陷多动障碍（ADHD）的症状及相关特征通常在诊所或研究实验室中由专业人员进行评估。移动健康提供了一种新方法，可获取额外的被动且连续测量的现实世界行为数据。利用我们基于疾病及复发远程评估（RADAR）平台的新型ADHD远程技术（ART）系统，我们探索新型数字标记，以确定其识别与ADHD相关行为模式的潜力。RADAR基础被动应用程序和可穿戴设备在后台收集传感器数据，而主动应用程序则让参与者完成临床症状问卷。
本研究的主要目的是调查患有ADHD的成年人和青少年在10种数字信号上是否与未患ADHD的个体存在差异，我们假设这些数字信号能捕捉注意力不集中、坐立不安或冲动行为。
我们在10周内收集了20名患有ADHD的个体以及20名年龄在16至39岁之间未患ADHD的对照参与者的数据。我们关注源自以下方面的特征：（1）主动应用程序（问卷通知响应延迟的均值和标准差以及问卷之间的时间间隔），（II）被动应用程序（对社交和通信应用程序通知的每日响应时间均值和标准差、手机使用期间环境光的标准差、总手机使用时间以及新增应用程序的总数），以及（3）可穿戴设备（Fitbit）（手机处于活跃状态时的每日步数）。分别采用线性混合模型和t检验来评估重复测量变量和时间汇总变量的组间差异。效应量（d）表示差异的大小。
10个变量中的5个存在显著的组间差异。患有ADHD的参与者（1）在完成问卷通知的响应速度上较慢（P = 0.047，d = 1.05）且变异性更大（P = 0.01，d = 0.84），（2）问卷之间的时间间隔标准差更高（P = 0.04，d = 1.13），（3）对社交和通信应用程序通知的每日平均响应时间更长（P = 0.03，d = 0.7），并且（4）在积极使用智能手机时环境（背景）光的变化更大（P = 0.008，d = 0.86）。问卷之间时间间隔的均值（P = 0.06，d = 0.82）、对社交和通信应用程序通知响应的每日标准差（P = 0.05，d = 0.64）以及手机活跃时的步数（P = 0.09，d = 0.61）还观察到中等至较大的效应量，但P值不显著。两组在总手机使用时间（P = 0.11，d = 0.54）和新下载应用程序的数量（P = 0.24，d = 0.18）上没有差异。
在对ADHD数字标记的全新探索中，我们识别出了坐立不安、注意力不连贯以及完成任务困难的候选数字信号。未来需要开展更大规模的研究来重复这些发现，并评估此类客观数字信号在追踪ADHD严重程度或预测结果方面的潜力。
