Early Detection of 5 Neurodevelopmental Disorders of Children and Prevention of Postnatal Depression With a Mobile Health App: Observational Cross-Sectional Study
Notice bibliographique
Résumé
BACKGROUND: Delay in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) is a major public health issue. In both cases, early intervention is crucial but too rarely implemented in practice. OBJECTIVE: Our goal was to determine if a dedicated mobile app can improve screening of 5 NDDs (autism spectrum disorder [ASD], language delay, dyspraxia, dyslexia, and attention-deficit/hyperactivity disorder [ADHD]) and reduce PND incidence. METHODS: We performed an observational, cross-sectional, data-based study in a population of young parents in France with at least 1 child aged <10 years at the time of inclusion and regularly using Malo, an "all-in-one" multidomain digital health record electronic patient-reported outcome (PRO) app for smartphones. We included the first 50,000 users matching the criteria and agreeing to participate between May 1, 2022, and February 8, 2024. Parents received periodic questionnaires assessing skills in neurodevelopment domains via the app. Mothers accessed a support program to prevent PND and were requested to answer regular PND questionnaires. When any PROs matched predefined criteria, an in-app recommendation was sent to book an appointment with a family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the incidence of PND detection after childbirth. One secondary outcome was the relevance of the NDD notification by consultation as assessed by health professionals. RESULTS: Among 55,618 children median age 4 months (IQR 9), 439 (0.8%) had at least 1 disorder for which consultation was critically necessary. The median ages of notification for probable ASD, language delay, dyspraxia, dyslexia, and ADHD were 32.5 (IQR 12.8), 16 (IQR 13), 36 (IQR 22.5), 80 (IQR 5), and 61 (IQR 15.5) months, respectively. The rate of probable ADHD, ASD, dyslexia, language delay, and dyspraxia in the population of children of the age included between the detection limits of each alert was 1.48%, 0.21%, 1.52%, 0.91%, and 0.37%, respectively. Sensitivity of alert notifications for suspected NDDs as assessed by the physicians was 78.6% and specificity was 98.2%. Among 8243 mothers who completed a PND questionnaire, highly probable PND was detected in 938 (11.4%), corresponding to a reduction of -31% versus our previous study without a support program. Suspected PND was detected a median 96 days (IQR 86) after childbirth. Among 130 users who filled in the satisfaction survey, 99.2% (129/130) found the app easy to use and 70% (91/130) reported that the app improved follow-up of their child. The app was rated 4.8/5 on Apple's App Store. CONCLUSIONS: Algorithm-based early alerts suggesting NDDs were highly specific with good sensitivity as assessed by real-life practitioners. Early detection of 5 NDDs and PNDs was efficient and led to a possible 31% reduction in PND incidence. TRIAL REGISTRATION: ClinicalTrials.gov NCT06301087; https://www.clinicaltrials.gov/study/NCT06301087.
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 | 0,004 |
| 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 | 0,000 |
| 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 ».