Expected Outcome:

This topic aims at supporting activities that are enabling or contributing to one or several expected impacts of destination “Tackling diseases and reducing disease burden”. To that end, proposals under this topic should aim to deliver results that are directed at, tailored towards and contributing to all the following expected outcomes:

• Researchers and healthcare professionals have an improved understanding of the neuro-biological and cognitive/behavioural evidence base on the correlation and impact of digital technologies on mental health, including brain development.
• Policymakers and digital technology and content developers are provided with a robust evidence base on the impact (positive or negative) of digital technologies on mental health in children and young adults[1].
• Policymakers, digital technology developers, and educational institutions amongst others make use (e.g. developing guidelines) of the evidence base and widely implement the newly developed interventions aimed at promoting children and young adults’ mental health while mitigating any negative impacts of digital technology use.
• Children, young adults, families, guardians, educators, and carers have access to the newly developed interventions designed to prevent harm and promote the positive use of digital technologies.
• Children and young adults are empowered and develop resilience, including digital literacy, enabling them to engage in a healthy and positive way with digital technologies.

Scope:

Already before the COVID-19 pandemic, 1 in 6 people in the EU suffered from mental health issues. The economic costs of it are estimated at 4% of the Gross Domestic Product (GDP)[2] and since then these figures worsened[3] in particular among vulnerable groups such as children and adolescents or those at risk of discrimination. Digital technologies have the potential to enhance mental health for instance by providing access to information, support networks and therapy services[4]. However, there are indications that the excessive or misguided use of digital technologies, particularly among children and young adults, can negatively affect mental health and exacerbate mental disorders. There is an urgent need for more robust data to foster a safer, responsible and healthier use of digital technologies among children and young adults, prioritising the protection of their mental health.

Therefore, proposals should aim at generating robust scientific evidence on the impact of digital technologies, as well as developing and testing context-specific digital interventions that promote the positive and responsible use of them to improve mental health, avoiding the development or exacerbation of mental disorders. These innovative digital interventions should leverage multi-source data (e.g. sleep patterns, heart rate, stress levels, screen-time analytics, social media use, biological data, clinical data), and could include the use of Artificial Intelligence (AI). When handling data and indicators, sex and gender identity-disaggregated data should be collected and analysed, incorporating intersectional factors where feasible.

The applicants should address all the following aspects:

• Generate the neuro-biological and cognitive/behavioural evidence base on the correlation and impact of digital technologies on mental health, including brain development (both positive and negative).
• Develop and test innovative digital interventions aiming for example at: counteracting addictive design patterns (e.g. on social media and gaming platforms), gaining insights into risk patterns and enabling early risk detection (e.g. detecting early warning signs of mental disorders or digital addiction), redirecting users towards healthy use and positive engagement with digital technologies, and/or reducing exposure to harmful content.
• Assess the changes in behaviour in children and young adults of the newly developed interventions, aiming at fostering their resilience and promoting responsible use and healthy digital habits.

The topic is open to address any mental disorder[5] caused or aggravated by the use of digital technologies such as addiction, self-harm behaviour, increased anxiety or decreased self-esteem, sleeping-disorders, post-traumatic stress disorders.

Cohort studies and clinical studies are in the scope for this topic. Applicants envisaging to include longitudinal cohort studies are invited to indicate a sustainability plan on how those cohorts are maintained over an extended period beyond the end period of the project for a long-term follow-up. They should make use of existing cohorts data when available. Applicants are welcome to consider recruiting participants transnationally and from diverse settings in the clinical study design to ensure generalizability of findings. In addition, it should be detailed in the proposal how the proposed intervention(s) could be scaled-up and transferred to other settings. Applicants should also consider the inclusion of end-users in the codesign of the interventions, for example for the young age groups, this includes the involvement of families, carers, educators. Applicants should access and make best-use of already existing European Research Infrastructures relevant for brain-research (e.g. EBRAINS[6], Euro-BioImaging[7]).

All projects funded under this topic should liaise with relevant European projects on mental health[8] and the future co-funded European Partnership for Brain Health[9]. They are also encouraged to explore potential synergies with projects to be funded under the EU4Health Work Programme 2026 related to the harmful effects of using digital technologies on the mental health of children and young adults.

The participation of start-ups and/or micro, small and medium-sized enterprises (SMEs)[10] is encouraged with the aim to strengthen their scientific and technological basis and valorise their innovations and to advance commercial exploitation.

Proposals should adhere to the FAIR[11] data principles, adopt wherever relevant, data standards and data sharing/access good practices, and apply good practices for GDPR[12] compliant personal data protection.

The topic requires the effective contribution of social sciences and humanities (SSH) disciplines and the involvement of SSH experts, institutions as well as the inclusion of relevant SSH expertise, in order to produce meaningful and significant effects enhancing the societal impact of the related research activities. The support and involvement of citizens and civil society should be considered.

Applicants should provide details of their clinical studies[13] in the dedicated annex using the template provided in the submission system. As proposals under this topic are expected to include clinical studies, the use of the template is strongly encouraged.

AI Based Application Success Predictor

1️⃣ Strong, Mission-Aligned Impact (Most Important Across EC Calls)

The EC is impact-driven: proposals must show how the project will:

Solve a major European or global societal challenge

Deliver measurable, lasting benefits for EU citizens

Produce outputs that can be used by policymakers, industry, or society

Align with Horizon Europe missions, priorities, and strategic agendas

Predictor: Clear, quantifiable, EU-level impact → strongest scoring factor.

2️⃣ Clear, Ambitious, but Achievable Objectives

Successful proposals show:

2–4 well-defined objectives linked to the Work Programme call text

Clearly articulated research questions or innovation goals

Logical, realistic expected outcomes and deliverables

Feasible scientific and technical approaches

Predictor: Balanced ambition + feasibility.

3️⃣ Excellent, Cutting-Edge Science or Innovation

For RIA/IA/CSA or ERC-level grants, reviewers expect:

High novelty and innovation

Strong grounding in current state-of-the-art

Clear advancement beyond existing approaches

Solid theoretical or experimental foundations

Robust methodological design

Predictor: Scientific excellence is essential for competitive scoring.

4️⃣ Strong Consortium with Complementary Expertise

EC proposals are consortium-driven (except ERC/EIC Accelerator).

High-scoring consortia:

Cover all needed competencies (science, industry, policy, ethics, dissemination)

Include SMEs, industry partners, NGOs, and public bodies when relevant

Are geographically diverse across EU Member States and Associated Countries

Demonstrate strong leadership and communication structures

Predictor: Well-constructed consortium with clear roles.

5️⃣ Clear Pathway From Outputs → Outcomes → Impact

Evaluators look for a credible trajectory showing:

How research leads to specific outputs (data, tools, prototypes)

How outputs lead to uptake or use

How use produces societal, economic, scientific, or policy impact

Strong Key Performance Indicators (KPIs) and impact metrics

Predictor: Clearly mapped impact pathway.

6️⃣ Strong Implementation Plan (Work Packages, Deliverables, Gantt Chart)

Winning proposals have:

Well-designed Work Packages (WPs) with clear scope and responsibilities

Interdependencies identified and risk-mitigation strategies

Detailed milestones and deliverables

Feasible budget aligned with tasks

Strong project management plan

Predictor: High implementation quality boosts the “Excellence” and “Implementation” scores.

7️⃣ Policy Relevance and Contribution to EU Strategies

Especially critical for health, climate, digital, and social calls.

Proposals score higher when they link to:

EU Cancer Mission

EU Green Deal

Digital Europe strategy

EU Biodiversity Strategy

EU Health Union & One Health

Open Science & FAIR data mandates

Predictor: Clear alignment with EU policies.

8️⃣ Strong Stakeholder & Citizen Engagement (Especially in Social & Health Missions)

EC values inclusivity:

Patient groups

Civil society organizations

Public sector bodies

Regulatory agencies

Citizen science components

Stakeholder letters of intent or commitment strengthen credibility.

Predictor: Engagement adds impact and relevance.

9️⃣ Robust Data Management, Open Science, and Ethics

Mandatory components include:

FAIR Data Management Plan

Open access publications

Ethics self-assessment

GDPR compliance

Data security, governance, and ethical approvals

Animal-use reduction and justification (if applicable)

Predictor: Clear compliance with ethical and data obligations.

10️⃣ Well-Justified Budget and Resource Allocation

Budget must be:

Proportional to tasks

Transparent and reasonable

Efficiently distributed among partners

Free from padding or unjustified costs

Predictor: Realistic budgets improve Implementation scores.

🚫 COMMON PITFALLS THAT LEAD TO EC GRANT REJECTION