Background: The burgeoning mental health issues among emerging adults (ages 19 to 25) worldwide has fuelled concerns about youths' widespread experiences of emotional distress, specifically concerning anxiety and depression, and coping mechanisms. Digital peer support intervention has demonstrated varying degrees of clinical effectiveness for positive health and psychological outcomes across different developmental stages. The onset of the COVID-19 pandemic has resulted in prolonged isolation and reduced social connections, and emerging literature is amounting to the import of digital peer emotional disclosure and support for young people psychological well-being. However, much remains to be explored regarding implementation and clinical effectiveness-how best to conduct digital peer intervention in support of youths' psychological well-being, and the associated mechanism of change.

Objective: This protocol delineates a randomized controlled trial for evaluating the implementation and clinical effectiveness of Assecet, a digital peer support intervention for youth mental well-being, which comprises two components. The digital peer support training curriculum aims to equip youth befrienders (i.e., peers who provide support) with knowledge and skills in harnessing four active ingredients of youth mental well-being-specifically, Mattering, selfhood, compassion, and mindfulness, in providing effective peer support for seekers (peers who seek support). The Acceset intervention that involves three components-digital biomarkers of psychological well-being, peer emotional disclosure process and community engagement.

Methods: Participants will consist of 100 students ages 19 to 25 from the National University of Singapore to engage with the Acceset platform. At the time of recruitment, individuals will be screened for inclusion and exclusion criteria based on self-reporting. The qualified participants (seekers) will be randomly allocated into two arms. Arm 1 (n = 50) seekers will engage with the Acceset platform for a period of 3 weeks, together with befrienders (n = 30) and moderators (n = 30). Arm 2 (n = 50) a control group will be placed on a waitlist for Acceset intervention. These individuals will be age and gender matched with the intervention group (i.e., arm 1). Their mental well-being, as well as their help seeking behavior, at the same time points and via the same questionnaire battery will be compared with those in arm 1. Both seekers and befrienders will be monitored using a questionnaire battery listed (including help seeking behaviors beyond the Acceset platform) at 4 time points: baseline (before the intervention), 3 weeks (the end of the intervention), 6 weeks and 9 weeks (to measure carry over effects). The implementation outcomes will be adoption and fidelity evaluation of the digital peer support training curriculum, the feasibility and acceptability of Acceset in providing continuous access to online peer support throughout the duration of the seekers' engagement. The clinical outcomes will include Mattering, self-hood, compassion, mindfulness, psychological well-being scores for befrienders and seekers, and additionally perceived social support for seekers. The mechanism of change linking befrienders' support and seekers' mental well-being will be assessed using latent growth curve modelling.

Data Collection, Management and Analyses: We conducted power analysis to ascertain the adequate sample size needed for the randomized controlled trial, specifically in comparing the intervention and control groups on different clinical outcomes, so that the study has sufficient power to detect valid effects. The study sample size of n = 50 for intervention and n = 50 for control is needed because of multiple dependent variables measuring clinical outcomes included in the study and the testing of implementation outcomes (i.e., feasibility, utility). In dealing with missing data, we will first determine if missing data is missing completely at random (MCAR). If missing data is not systematic, as indicated by the non-significant result from Little's Missing Completely at Random Test, this study will handle missing data using full information maximum likelihood imputation.

Data will be analyzed and interpreted through three means. First, independent sample t-tests will be conducted whenever we need to compare intervention vs. control group on the different clinical outcomes in this study. Second, to elucidate the content of emotional disclosure on the Acceset platform, particularly in relation to the four active ingredients of psychological well-being (i.e., Mattering, self-hood, compassion and mindfulness), we will perform topic modelling using Latent Dirichlet Allocation (LDA) analyses with R to filter the huge amount of data available and drill down relevant themes and topics. LDA is an advanced statistical method that increases precision and accuracy in coding as compared to the exclusive use of qualitative approach /analyses. Third, to compare the mechanism of change , we will use Latent Growth Curve Modelling (LCM) to examine the trajectories of Acceset intervention in predicting the change in clinical outcomes experienced by youths. LCM elucidates whether an increasing trend in befrienders' digital peer support predicts increasing trends in youths' sense of Mattering, self-hood, compassion, mindfulness, and psychological well-being (i.e., clinical outcomes).