WP3 – Intervention
1. To develop candidate interventions
2. To test the effectiveness of digital (preventative) interventions
3. To assess the cost effectiveness of digital interventions
In WP3, two candidate preventative lifestyle interventions (A for affect regulation and B for executive inhibitory control) will be developed on grounds of the current evidence and theory-based knowledge as well as informed by data of the first phase (ML). These interventions will be tested under randomised conditions in a pilot-study against a control condition (educational material) and evaluated based on a 3-month follow-up. Based on results of a second step of ML of assessed variables (as per WP2) to determine outcome predictors, the final intervention will be tailored to individuals based on an algorithm that predicts likelihood of responding and tested in a RCT. In this part of the study, interventions will be randomly assigned to condition 1, [Intervention A or B tailored by algorithm], 2 [Intervention A or B randomly allocated] or 3 [the control condition]. In both studies, the interventions will be stratified in intensity according to level of risk (low risk or high risk). Outcomes will be tested in a 6-months follow-up. Results will then be analysed to test the efficacy as well cost-effectiveness of the interventions (for a detailed description of all activities, please see Annex 2,3).
Task 3.1 Development of candidate interventions (M21-M27)
Lead partner and participants UKSH, ELTE, UULM, JGU Mainz, UNIGIB, IDC, MON, UPORTO
• Perform an updated review of the literature about preventative intervention for PUI.
• Search for existing app-based interventions for PUI to guide study design.
• Developing the content of the intervention A, B, control, in co-creation with WP4 stakeholder groups.
• Stratifying the intensity of the interventions (low-risk and high-risk)
• Coordinating with programming of the app (WP 2) with UULM
• Pilot testing (WP 4) and resolving bugs with UULM, IDC and WP4 stakeholder groups
Task 3.2 Effectiveness of digital (preven- tative) interventions (M27-M48)
Lead partner and participants UoH, UNIGIB, UZH, UKSH, UPORTO, IDC, JGU Mainz, FIBHGM, ELTE, LSMU, VUA, CHUM
• Conducting a pilot trial on two interventions developed for improving either A affect regulation or B inhibitory executive control, stratified according to individual level of risk (high versus low) based on algorithms developed in
WP2, compared to control condition (educational material).
• Conducting a 3-month follow-up to evaluate efficacy outcomes (measured as per WP2) with input from WP4 stakeholder groups
• Outcome analysis, to be led by IDC
• Developing a new set of algorithms to predict outcomes to A versus B (using ML techniques) with IDC
• Including algorithms assessing risk and response predictors in the interventions
• Conducting a Randomized control trial, [Intervention A or B tailored by algorithm], vs. [Intervention A or B randomly allocated] vs 3 [the control condition]. A and B will be stratified in intensity according to WP2 risk algorithms.
• Conducting a 6 months follow-up to evaluate efficacy oucomes (measured as per WP2) with input from WP4 stakeholder groups
• Outcome analysis, to be led by IDC with support from UoH.
Task 3.3 Cost effectiveness of digital interventions (M39-M48)
Lead partner (bold) and participants CAM, USKH
• We will assess the burden of PUI (see WP2) and the effectiveness of our screening and intervention to model effectiveness, utility and health gains after our main intervention (WP3).
• Costs will reflect the total costs of screening and health care services use for participants in the study. Costs will be assessed from a societal perspective and expressed in 2023 Euro adjusting for inflation.
• Costs of screening and intervention services, together with indirect costs and services outside of the study will be included, and parental/caregiver time and travel costs associated with services use will be incorporated. We also include measures of productivity losses e.g., school attendance and performance, modelled as outcomes of interest.
• Because of the large number of study sites, unit micro-costs will be developed for each study site to quantify service costs across participating countries and a discrete event model experiment will be included to assess scalability to other countries not included in our study sites.
• Using the discrete event framework we will assess possible implementation barriers across EU countries, including possible roll out bottlenecks that will be included to assess scalability.
• We will also include unit costs from nationwide sources by the number of services recorded in study records for CBT, intervention, and medication management visits to observe possible dispersion of the micro-costs distribution and to plan for a probabilistic sensitivity analysis (PSA).
• With the main outcomes stemming from this work, we will inform the Policy Toolkit and policy initiatives (see WP5), aiming at proposing easily scalable outputs that are cost effective across EU countries and that maximize impact and wellbeing in our participants.