Design, implement, and evaluate a digital wellbeing intervention for university students

Thesis Abstract

Increasing mental health concerns and variable wellbeing among university students threaten academic success and long-term adaptation, yet access to timely, scalable support remains uneven across campuses. This study addresses the gap by designing, implementing, and evaluating a digital wellbeing intervention tailored to higher education environments, integrating psychoeducational content, self-monitoring, peer support, and brief clinician-guided modules to foster resilience, stress management, and healthy study practices. The aim is to determine whether a structured digital intervention can improve wellbeing indicators, reduce perceived stress, and enhance academic engagement compared with a waitlist control. Specific objectives are (1) to develop a theory-informed digital wellbeing program grounded in the Job Demands-Resources (JD-R) model and Self-Determination Theory (SDT); (2) to evaluate feasibility, acceptability, and user engagement (retention, completion rates, and module usage); (3) to assess changes in wellbeing outcomes, including perceived stress, depressive and anxiety symptoms, sleep quality, and vitality, using validated measures; (4) to examine effects on academic engagement, motivation, and self-regulated learning strategies; and (5) to identify mediators (e.g., coping skills, social connectedness) and moderators (e.g., baseline distress, gender, program exposure) of intervention effects. The study adopts a mixed-methods, parallel-group randomized controlled design. A total of 320 undergraduate and postgraduate students from three universities will be recruited through campus communications and student services. Participants will be randomly assigned (11) to receive the digital intervention immediately or to a 6-week waitlist control, with assessments at baseline, post-intervention (6 weeks), and 3-month follow-up. The intervention comprises four modules delivered over six weeks (a) psychoeducation on stress and wellbeing, (b) cognitive-behavioral stress management techniques, (c) sleep hygiene and routines, and (d) social connectedness and peer-support features, complemented by automated self-monitoring and optional brief tele-mentoring sessions. Instrumentation includes the Warwick-Edinburgh Mental Wellbeing Scale, Perceived Stress Scale, Hospital Anxiety and Depression Scale, Pittsburgh Sleep Quality Index, Utrecht Work Engagement Scale, and the Self-Regulated Learning Interview Schedule. Validity and reliability of instruments are ensured through established translations where needed and prior usage in student populations. Quantitative analyses will be conducted using intention-to-treat principles. Primary outcomes will be analyzed with mixed-effects repeated-measures ANOVA to detect group-by-time interactions, supplemented by growth-curve modeling to capture trajectories. Secondary outcomes will be examined using multivariate regression controlling for baseline covariates. Mediation analyses will utilize PROCESS macro and structural equation modeling to test indirect pathways through coping skills and social connectedness. Moderator analyses will explore differential effects by baseline distress, gender, and degree level. Qualitative data from optional participant interviews (n ? 30) will be analyzed thematically to provide depth on user experience, acceptability, and contextual factors influencing engagement, with coding conducted by two independent researchers and consensus discussed in a debriefing session. It is anticipated that the digital wellbeing intervention will yield statistically significant, clinically meaningful improvements in perceived stress, anxiety/depression symptoms, sleep quality, and vitality relative to controls at post-intervention and maintenance at follow-up. Secondary gains are expected in academic engagement and self-regulated learning, partially mediated by enhanced coping skills and social connectedness. The study contributes to knowledge by providing robust evidence on the feasibility and effectiveness of scalable digital wellbeing solutions for diverse student populations, integrating theoretical frameworks from the JD-R model and SDT with contemporary digital health design principles. It also offers practical guidance on implementation, including module content, engagement strategies, data privacy considerations, and integration with university mental health services. Policy implications include informing university wellbeing strategies, resource allocation for digital interventions, and the potential for adaptation across tertiary education settings. Potential limitations include self-selection bias, reliance on self-reported outcomes, and differential digital literacy. Recommendations emphasize iterative refinement based on user feedback, broader demographic sampling in future research, integration with campus support structures, and exploration of personalization features to optimize impact across varied student contexts.