Identifying Digital Markers of Attention-Deficit/Hyperactivity Disorder (ADHD) in a Remote Monitoring Setting: Prospective Observational Study
Notice bibliographique
Résumé
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 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. Objective: 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. Methods: 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. Results: 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). 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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».