The 'UPIC' cohort: a nationwide prospective study of mental health among adolescents and young adults in Sweden - a study protocol.
Rates of adolescents and young adults (AYAs) reporting poor mental health have increased, with symptoms of anxiety and depression being most common. Yet, the processes underlying these trends remain unclear. This cohort study aims to establish a comprehensive dataset combining data from multiple sources (surveys, behavioural tests, registers and smartphone sensors) to assess mental health, including mental ill-health and well-being. The dataset will enable analyses of prevalence, risk and protective factors and predictive modelling of mental health trajectories. The study also evaluates the feasibility of such longitudinal data collection among AYAs.
This study recruits 2000 AYAs aged 15-29 years from the general population in Sweden using an initial random sample invited by post, followed by recruitment through digital channels. Participants are followed for 2 years under one of three protocols: annual assessments, bi-annual assessments and annual assessments complemented by repeated ecological momentary assessments. Across protocols, data are collected through surveys using a study-specific mobile app and digital behavioural tests. Demographic and clinical information, including psychiatric diagnoses and prescribed medications, are obtained through national health registers. Smartphone sensor data are collected for insights into phone usage, sleep patterns and mobility. Blood samples are collected from a subset via home-based testing to enable biomarker analysis. When applicable, machine learning, including deep learning techniques, is applied to develop predictive algorithms for mental health outcomes, with the potential to inform early identification and prevention. Feasibility outcomes include recruitment and retention rates and the acceptability of study procedures and measures, assessed to inform future studies.
Representatives from the target population have contributed to the study's development. The study complies with General Data Protection Regulation (GDPR) and has been approved by the Swedish Ethical Review Authority. Findings will be disseminated through scientific publications, conferences and to the public and relevant organisations.
This study recruits 2000 AYAs aged 15-29 years from the general population in Sweden using an initial random sample invited by post, followed by recruitment through digital channels. Participants are followed for 2 years under one of three protocols: annual assessments, bi-annual assessments and annual assessments complemented by repeated ecological momentary assessments. Across protocols, data are collected through surveys using a study-specific mobile app and digital behavioural tests. Demographic and clinical information, including psychiatric diagnoses and prescribed medications, are obtained through national health registers. Smartphone sensor data are collected for insights into phone usage, sleep patterns and mobility. Blood samples are collected from a subset via home-based testing to enable biomarker analysis. When applicable, machine learning, including deep learning techniques, is applied to develop predictive algorithms for mental health outcomes, with the potential to inform early identification and prevention. Feasibility outcomes include recruitment and retention rates and the acceptability of study procedures and measures, assessed to inform future studies.
Representatives from the target population have contributed to the study's development. The study complies with General Data Protection Regulation (GDPR) and has been approved by the Swedish Ethical Review Authority. Findings will be disseminated through scientific publications, conferences and to the public and relevant organisations.
Authors
Hovén Hovén, Frick Frick, Skalkidou Skalkidou, Papadopoulos Papadopoulos, Ekselius Ekselius, Gingnell Gingnell, Karlsson Karlsson, Brocki Brocki, Ljungvall Ljungvall, Lindqvist Lindqvist, Eklund Eklund, Bergquist Bergquist, Eklund Eklund, Nilses Nilses, Hallsén Hallsén, Åsenlöf Åsenlöf
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