The UPIC cohort study establishes a comprehensive nationwide prospective dataset combining surveys, behavioural tests, registers, and smartphone sensors to assess mental health trajectories among 2000 adolescents and young adults aged 15-29 years in Sweden over 2 years.
Key Findings
Background
Rates of adolescents and young adults reporting poor mental health have increased, with anxiety and depression being the most common symptoms.
The study protocol explicitly identifies rising rates of poor mental health among adolescents and young adults (AYAs) as the primary motivation for the cohort
Symptoms of anxiety and depression are identified as most common among this population
The processes underlying these trends are described as 'remaining unclear'
This gap in understanding is cited as justification for the comprehensive multi-source data collection approach
Methods
The UPIC cohort targets recruitment of 2000 adolescents and young adults aged 15-29 years from the general population in Sweden.
Recruitment uses an initial random sample invited by post, followed by recruitment through digital channels
The age range of 15-29 years reflects the defined adolescent and young adult (AYA) population
Participants are drawn from the general population, not a clinical sample
Representatives from the target population contributed to the study's development
Methods
Participants are followed for 2 years under one of three distinct data collection protocols.
Data are collected across all protocols through surveys using a study-specific mobile app and digital behavioural tests
Methods
The study integrates data from multiple sources including surveys, behavioural tests, national health registers, smartphone sensors, and blood biomarkers.
Demographic and clinical information including psychiatric diagnoses and prescribed medications are obtained through national health registers
Smartphone sensor data are collected to provide insights into phone usage, sleep patterns, and mobility
Blood samples are collected from a subset of participants via home-based testing to enable biomarker analysis
The multi-source approach is designed to enable analyses of prevalence, risk and protective factors, and predictive modelling of mental health trajectories
Methods
Machine learning, including deep learning techniques, is planned for application to develop predictive algorithms for mental health outcomes.
The predictive algorithms are intended to 'inform early identification and prevention' of mental health problems
Machine learning approaches are applied 'when applicable' to the collected dataset
The dataset is designed to enable 'predictive modelling of mental health trajectories'
The combination of sensor data, behavioural tests, and register data is intended to support these computational approaches
Methods
The study includes formal evaluation of feasibility as a primary outcome alongside mental health assessments.
Feasibility outcomes include recruitment and retention rates
Acceptability of study procedures and measures is explicitly assessed
Feasibility data are intended 'to inform future studies'
The study is described as evaluating 'the feasibility of such longitudinal data collection among AYAs'
Methods
The study complies with GDPR and has received ethical approval from the Swedish Ethical Review Authority.
The study involves collection of sensitive health data including psychiatric diagnoses and prescribed medications from national registers
Smartphone sensor data collection, which includes mobility and phone usage patterns, is conducted under GDPR compliance
Blood sample collection is conducted via home-based testing from a subset of participants
Findings are planned for dissemination through scientific publications, conferences, and to the public and relevant organisations
What This Means
This paper describes the design of a large Swedish research study called the UPIC cohort, which aims to understand why mental health problems — especially anxiety and depression — have been increasing among teenagers and young adults. The study plans to follow 2,000 people aged 15 to 29 years for two years, collecting information from multiple sources at the same time: questionnaires completed through a smartphone app, computer-based thinking and behaviour tests, official health records (including any psychiatric diagnoses or medications), data from phone sensors tracking sleep and movement, and blood samples taken at home for some participants. Different groups of participants will be assessed at different frequencies, with some completing brief daily or weekly check-ins in addition to longer annual surveys.
This research suggests that combining all of these data sources together — rather than relying on surveys alone — could give a much richer picture of what factors protect or put young people at risk for mental health problems over time. The researchers also plan to use artificial intelligence and machine learning tools to identify patterns in the data that might help predict who is likely to develop mental health difficulties, with the long-term goal of improving early detection and prevention. Young people from the target age group were involved in shaping the study design.
Because this is a study protocol paper rather than a results paper, no findings about mental health outcomes have yet been reported — this paper describes what the researchers plan to do and how they will do it. A key goal is also to test whether this kind of complex, multi-source data collection is practical and acceptable to young people, since that information will help design future large-scale studies. The study has been approved by Swedish ethical authorities and follows European data protection regulations.
Hovén E, Frick A, Skalkidou A, Papadopoulos F, Ekselius L, Gingnell M, et al.. (2026). The 'UPIC' cohort: a nationwide prospective study of mental health among adolescents and young adults in Sweden - a study protocol.. BMJ open. https://doi.org/10.1136/bmjopen-2026-120432