Early Detection of Neurodevelopmental Disorders of Toddlers and Postnatal Depression by Mobile Health App: Observational Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Delays in the diagnosis of neurodevelopmental disorders (NDDs) in toddlers and postnatal depression (PND) in mothers are major public health issues. In both cases, early intervention is crucial. OBJECTIVE: We aimed to assess if a mobile app named Malo can reduce delay in the recognition of NDD and PND. METHODS: We performed an observational, cross-sectional, data-based study in a population of young parents with a minimum of 1 child under 3 years of age at the time of inclusion and using Malo on a regular basis. We included the first 4000 users matching the criteria and agreeing to participate between November 11, 2021, and January 14, 2022. Parents received monthly questionnaires via the app, assessing skills on sociability, hearing, vision, motricity, language of their infants, and possible autism spectrum disorder. Mothers were also requested to answer regular questionnaires regarding PND, from 4-28 weeks after childbirth. When any patient-reported outcomes matched predefined criteria, an in-app notification was sent to the user, recommending the booking of an appointment with their family physician or pediatrician. The main outcomes were the median age of the infant at the time of notification for possible NDD and the median time of PND notifications after childbirth. One secondary outcome was the relevance of the NDD notification for a consultation as assessed by the physicians. RESULTS: Among 4242 children assessed by 5309 questionnaires, 613 (14.5%) had at least 1 disorder requiring a consultation. The median age of notification for possible autism spectrum, vision, audition, socialization, language, or motor disorders was 11, 9, 17, 12, 22, and 4 months, respectively. The sensitivity of the alert notifications of suspected NDDs as assessed by the physicians was 100%, and the specificity was 73.5%. Among 907 mothers who completed a PND questionnaire, highly probable PND was detected in 151 (16.6%) mothers, and the median time of detection was 8-12 weeks. CONCLUSIONS: The algorithm-based alert suggesting NDD was highly sensitive with good specificity as assessed by real-life practitioners. The app was also efficient in the early detection of PND. Our results suggest that the regular use of this multidomain familial smartphone app would permit the early detection of NDD and PND. TRIAL REGISTRATION: ClinicalTrials.gov NCT04958174; https://clinicaltrials.gov/ct2/show/NCT04958174.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".