SADIE: a longitudinal Survey on Anxiety, Depression, Internalising and Externalising symptoms in Italian university students – a study protocol
Andrea Marchi, Nicola Meda, Susanna Pardini, Irene Slongo, Francesco Visioli, Alexandru Banescu, Pasquale Caponnetto, Francesco Casadei, Marco Colizzi, Pietro Costanza, Alessandro Cuomo, Giorgia Dalla Libera, Pasquale De Fazio, Andrea Fagiolini, Ada Farinato, David Carlo Gagliano, Alessandra Galmonte, Carla Gramaglia, Sara Guidotti, Riccardo Lenti, Mirko Manchia, Giuseppe Maiello, Marco Menchetti, Alessio Maria Monteleone, Palmiero Monteleone, Andrea Pozza, Carlo Pruneti, Ciro Rizzaro, Rita Roncone, Benedetta Rigotti, Anita Zanatta, Fabio Sambataro, Caterina NovaraIntroduction
University students experience escalating academic, social and financial stress, resulting in high prevalence of anxiety (18%–40%), depression (12–35%) and burnout (20–30%). The COVID-19 pandemic intensified these issues, but most studies report symptom remission post-lockdown, highlighting the need for long-term prospective data on symptom trajectories and risk factors. The Survey on Anxiety, Depression, Internalising and Externalising symptoms (SADIE) study aims to (1) estimate the prevalence and incidence of anxiety, depression, burnout and suicidal ideation in Italian university students over 36 months and (2) identify modifiable psychological and behavioural predictors—such as multidimensional perfectionism, academic stress and substance use—associated with adverse symptom trajectories.
Methods and analyses
We will recruit 5000 undergraduates and postgraduates (aged ≥18 years, fluent in Italian or English) from 14 Italian universities. Participants will complete online assessments via REDCap at baseline, 6-month, 18-month and 36-month follow-ups. Measures include demographic survey, Maslach Burnout Inventory-2, Beck Depression Inventory–II, Generalized Anxiety Disorder–7, WHO-5 Well-Being Index, Alcohol Use Disorders Identification Test-3, Cigarette Dependence Scale-5, Columbia Suicide Severity Rating Scale and Hewitt and Flett Multidimensional Perfectionism Scale. Mixed-effects models (R/lme4) and growth-mixture models will be used to evaluate symptom trajectories, while logistic regression will identify predictors of adverse outcomes. Sensitivity analyses will address potential self-selection and attrition bias.
Ethics and dissemination
The study has received approval from the Ethics Committee of the University of Padova (Protocol No. 235-d). Participants will provide electronic informed consent and may withdraw at any time. Risk management includes automated alerts and links to local mental health services for high-risk responses. Study findings will be disseminated through peer-reviewed publications, conference presentations and summary reports to participating institutions; deidentified data may be made available on reasonable request in line with institutional and data protection regulations.