Digital Homework Support Program for Children and Adolescents With Attention-Deficit/Hyperactivity Disorder: Protocol for a Randomized Controlled Trial
Notice bibliographique
Résumé
BACKGROUND: Attention-deficit/hyperactivity disorder (ADHD) affects 4% to 5% of the general population. Homework sessions are frequent conflictual moments characterized by increased anxiety in children and stress in their parents, contributing to a lower family quality of life. Children with ADHD experience more severe homework problems than typically developing peers. Poor academic performance in individuals with ADHD is partly attributed to challenging homework. Psychoeducational and school-based approaches are time-consuming and not fully accessible to professionals. Digital tools, such as smartphone and tablet apps, might offer an interesting alternative. We present our digital homework support program for children and adolescents, known as "Programme d'Aide Numérique aux Devoirs pour Enfant avec TDA-H" (PANDAH), along with the study protocol of our ongoing randomized controlled trial. OBJECTIVE: This study aims to test PANDAH's efficacy in improving homework performance and family quality of life. METHODS: Individuals aged 9-16 years with an ADHD diagnosis and no comorbid psychiatric disorders are included. This is a multicenter study involving 9 reference centers for ADHD in France. The study comprises (1) a 3-month period with a randomized controlled trial design, where participants are divided into 2 parallel groups (group 1: care as usual or waiting list; group 2: PANDAH app), followed by (2) an extension period of 3 months (months 3-6), during which all participants will have access to the app. This second phase serves as a crucial incentive for patients initially randomly assigned to group 1. Assessments will be conducted at baseline, month 3, and month 6 for each patient by trained psychologists. The primary end point will be the global Homework Performance Questionnaire (HPQ), Parent version score at 6 months. The main analysis will adhere to the "intent-to-treat principle" (all patient data will be analyzed according to their initial group determined by randomization). We expect (1) HPQ score improvement in individuals using the app during the first 3-month period compared to individuals not using the app; (2) greater HPQ score improvement for individuals using the app for 6 months compared to those using the app for 3 months only; and (3) adherence to the PANDAH program, measured with in-app metrics. RESULTS: Recruitment began in January 2024, and the trial is ongoing. CONCLUSIONS: This study contributes to the digital transformation of health care. The use of smartphone apps in self-care and self-management is a societal phenomenon, and its implementation in the field of psychiatry is of particular interest. The app might serve as both valuable support for patients and an opportunity for parents to distance themselves from conflict-laden homework sessions. Since the market for smartphone apps in the health care and well-being sector is primarily industry driven, it is crucial to have an academic conception and evaluation of such digital tools. TRIAL REGISTRATION: ClinicalTrials.gov NCT04857788; https://clinicaltrials.gov/ct2/show/NCT04857788. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/44553.
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 | 0,022 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,005 | 0,003 |
| Méta-épidémiologie (sens large) | 0,013 | 0,006 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,095 | 0,012 |
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 ».