Imagine a digital coach that understands how a person's unique biology reacts to their surroundings. For teens with autism, certain environmental triggers can flip a biological switch that leads to mental health struggles. This system maps those triggers and provides a personalized plan to stop the problem before it starts.
Adolescents with autism are 3-6 times more likely to develop mental health disorders, but there are no personalized, evidence-based tools to prevent this transition based on their unique biological and environmental triggers.
A Personalized Digital Nurse (PDN) mobile app and a quantitative EGM process model to identify and control environmental risk factors.
If you are an mHealth app developer dealing with high churn in specialized care apps — this project developed a Personalized Digital Nurse (PDN) mobile app that empowers users to self-control environmental risk factors. This provides a concrete biological basis for user engagement and health outcomes.
If you are a community care clinic dealing with the lack of evidence-based interventions for ASD adolescents — this project developed a service delivery model and selection criteria for the most appropriate interventions. This allows for a rapid adoption pathway into day-to-day clinical practice.
If you are a precision medicine firm dealing with the complexity of epigenetic triggers in mental health — this project developed a quantitative model of the Epigenetic-Genetic-Mental health (EGM) process chain. This provides a blueprint for designing biological-level personalized prevention tools.
Based on available project data, specific pricing or implementation costs are not disclosed.
Yes, the project objective states that the proposed solution could be adapted to prevent mental health disorders in the wider adolescent population.
Based on available project data, there is no specific information regarding IP or licensing terms.
The project defines a service delivery model and methods for a rapid adoption pathway to integrate the system into day-to-day clinical practice.
The project period runs from 2023-01-01 to 2025-12-31, suggesting the system is currently in development and testing.
The consortium is heavily industry-weighted with a 57% industry ratio, consisting of 4 industrial partners (including 2 SMEs) and 3 universities across 4 countries. This composition suggests a strong focus on commercial viability and practical application rather than purely academic research.
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