Skip to main content
universidade lusófona

NETS: Loneliness Prevention and Counteractions: AI-based Monitoring Tool and Behavioural Changes Protocols for Youngsters towards a More Resilient European Community 

A. Project Name

NETS: Loneliness Prevention and Counteractions: AI-based Monitoring Tool and Behavioural Changes Protocols for Youngsters towards a More Resilient European Community 

B. Submitted to the call/funding institution

Call: [HORIZON-CL2-2024-TRANSFORMATIONS-01] — [Policy recommendations from socio-economic impacts of loneliness in Europe]/ UniSMART (UNIS) 

C. Principal Investigator

Sónia Vladimira Correia/ CIDEFES

D. Timeline 

Submission: 2024-02-07  

E. Partners

Università degli studi di Padova (UNIPD)

Universitat de Girona (UDG)

Aristotelio Panepistimio Thessalonikis (AUTH)

Universiteit Van Tilburg (TiU)


Universidade Lusófona (LUSOFONA)

Polo Europeo della Conoscenza (POLO)

Scuola Universitaria Professionale della Svizzera Italiana (SUPSI)


Sónia V. Correia

G. Summary 


NETS proposal has the goal of promoting the development of a more resilient European society through behavioural change. Specifically, the aim is to prevent, counter and manage loneliness among youths in order to provide policy advice that can strengthen the future generations of EU citizens. This will be achieved through a series of activities that tap onto different strategies including: (i) pooling of existing socio-economic data with data on loneliness; (ii) intervention assessment in selected countries; (iii) development of monitoring tool to ensure effective long-term action; (iv) development of a network interested in monitoring, preventing and counteracting loneliness in the EU population; (v) dissemination of information, training and teaching. 

Implementation: What activities are you going to implement?

Baseline survey design and delivery among schools.

Based on the information gathered in WP2, the objectives pertain to the development, data collection, and data analyses for the baseline survey (qualitative and quantitative). This survey will be instrumental to get a baseline reference for the prevalence of loneliness in the specific target, to understand which factors should be considered, to measure the changes in the prevalence of loneliness between pre- and post-intervention phases. Finally, it will provide data to train the AI-based monitoring tool. 
Development of qualitative measures for Qualitative Baseline survey

The quantitative survey will be developed starting from both the information gathered in WP1 and the qualitative data emerging from the previous task. The quantitative survey will: i) allow to quantify the prevalence of loneliness in each school across the three countries and to measure the drivers that lead to experience loneliness in the specific tested population; ii) will provide data for training the AI-based monitoring tool; (iii) rely on open-ended questions to use for extracting psychological insights associated with specific text analysis indexes (e.g., emotional valence, analytic thinking). The survey will include the measurement of socio-economic information about the students and their families and other relevant emerging WP2 information.

Development of quantitative measures for Quantitative Baseline survey

The interviews and the variables to investigate are set, it will collect the data. Students from schools in each of the three countries involved in the pilot test will be interviewed. Data will be collected only on a subsample of students to explore what is their experience of loneliness and whether (or how) they perceive a stigma associated with this experience. Interviews will be carried out until a convergence of topics covered by different students will be found. Then it will start their analysis to extract the relative dimensions associated with loneliness. KPI: 40 interviews in each country (20 middle school students and 20 high school students).

Holistic data analyses to highlight risk factors, drivers and trends among youngsters

In this task, thanks to all previous task contributions (qualitative and quantitative data gathered with survey) and WP1 overview on socio-economic data, it will then evaluate and valorise the nature of loneliness in terms of risk factors, trends and drivers of change. The method will be an assessment of correlations and regression analyses 
Outline the Stigma facets associated with Loneliness state

The baseline survey will also investigate the student’s perception of the loneliness stigma. Therefore, it will be possible to assess the relationship between the perception of the stigma and the students level of loneliness, further considering the role played by psychological, socio-economic, and demographic factors. This task will be also connected to a proper experimental test that will be carried out to determine different communication strategies on loneliness and stigma (WP9).

Results: What project results and other outcomes do you expect your project to have?

  • percentage change in loneliness between baseline and follow-up measures;
  • difference in loneliness between control and experimental groups after the intervention (follow-up) versus before it (baseline);
  • effect size of the differences between control and experimental groups at each stage of the pilot test;
  • effect size of the relationships between risk factors and loneliness in the existing data;
  • predicting capabilities of the AI-based tool in term of precision and recall of the information;
  • usability of the AI-based tool for ensuring its use from non-technical personnel.