Research protocol for BootStRaP assessment phase: A nine-nation study on boosting societal adaptation and mental health in a rapidly digitalising, post-pandemic Europe.
Fineberg N, Brandtner A, et al. • Comprehensive psychiatry • 2026
BootStRaP is a five-year multinational research programme prospectively monitoring over 2500 schoolchildren aged 12-16 years across nine European countries over a 6-month period to determine risk factors associated with problematic usage of the internet (PUI) and its health economic impact, and to design and test preventative self-management interventions.
Key Findings
Methods
The BootStRaP programme recruited a sample of over 2500 schoolchildren aged 12-16 years across nine European countries for prospective longitudinal assessment of PUI risk factors.
Sample size exceeds 2500 schoolchildren
Age range is 12-16 years
Study spans nine European countries
The study is registered under ISRCTN59576080
This paper describes Cohort 1, the first phase of the project
Methods
Participants were prospectively monitored over a 6-month period using a dedicated smartphone application called BootstrApp.
The BootstrApp was used to measure internet use habits, health, and wellbeing
Assessment methods included standardised demographic and clinical questionnaires, ambulatory assessment techniques, cognitive testing, and passive digital monitoring
Young people were involved in the co-design of aspects of the protocol including the recruitment plan and elements of the app design
Methods
The assessment battery was designed to investigate specific individual, clinical, cognitive, and environmental risk determinants as defined a priori in an evidence-based logic model.
The logic model includes predisposing risk factors such as impulsivity and compulsivity
Affective and cognitive processes such as reward-related attentional biases are included
Executive functions such as inhibitory control are assessed
Components were chosen to investigate the interplay between these categories of risk determinants
Methods
Multimodal data collected in the study will be analysed using machine learning and structural equation modelling.
Machine learning approaches will be used to develop algorithms for predicting individuals at risk for PUI
Structural equation modelling is listed as a complementary analytical approach
Analyses are intended to identify actionable variables for application as interventions to be tested in the second phase of the project
Background
There is a scarcity of reliable evidence on the extent of PUI, who is most at risk, and how best to tackle it despite increasing global concern about harms associated with PUI affecting young people.
Various risk factors for PUI have been proposed but reliable evidence is scarce
The extent of the problem among young people is not well characterised
The question of who is most at risk of developing PUI and why remains unanswered by existing evidence
The BootStRaP programme is described as a response to this evidence gap
Background
The study aims to calculate the health economic cost and impact of PUI in young people across Europe.
Health economic impact assessment is one of four stated main aims of the study findings
The health economic component covers young people across the nine participating European countries
This is described as one contribution of the findings alongside risk prediction algorithms, identification of actionable variables, and validation of risk hypotheses
Fineberg N, Brandtner A, Löchner N, Kannen C, Smith M, Foster S, et al.. (2026). Research protocol for BootStRaP assessment phase: A nine-nation study on boosting societal adaptation and mental health in a rapidly digitalising, post-pandemic Europe.. Comprehensive psychiatry. https://doi.org/10.1016/j.comppsych.2025.152653