Adolescents and young adults (AYA) often face mental health challenges and are heavily influenced by technology. Digital health interventions (DHIs), leveraging smartphone data and artificial intelligence, offer immense potential for personalized and accessible mental health support. However, ethical guidelines for DHI research fail to address AYA’s unique developmental and technological needs and leave crucial ethical questions unanswered. This gap creates risks of either over- or under-protecting AYA in DHI research, slowing progress and causing harm. This Perspective examines ethical gaps in DHI research for AYA, focusing on three critical domains: challenges of passive data collection and artificial intelligence, consent practices, and risks of exacerbating inequities. We propose an agenda for ethical guidance based on bioethical principles autonomy, respect for persons, beneficence and justice, developed through participatory research with AYA, particularly marginalized groups. We discuss methodologies to achieve this agenda, ensuring ethical, youth-focused and equitable DHI research for the mental health of AYA.

This study reviews existing cone penetration test (CPT) correlations for the prediction of the small strain shear modulus (Gmax). Its performance is investigated through application to an open-source offshore ground investigation dataset from the Netherlands. The research evaluates correlations involving various parameters such as cone tip and corrected cone tip resistance, net corrected cone resistance, sleeve friction, pore water pressure, and vertical effective stress. Results indicate that correlations are highly site-specific, often requiring recalibration to account for local conditions. Existing sand correlations provide more accurate predictions but tend to underpredict Gmax, while clay-specific correlations tend to overpredict Gmax, particularly in stiffer offshore clays. The study also highlights the need for further exploration of parameter normalisation to refine these correlations. This work represents an additional step in the understanding of CPT-based Gmax predictions and their associated uncertainties, emphasizing the importance of developing robust, site-specific correlations for optimal offshore wind foundation design.