Mental health disorders like depression and anxiety are common and have consequences on all aspects of health. Mental health disorders like depression have a complex aetiology comprised of genetic and environmental risk factors, but importantly these risk factors predict different longitudinal profiles of depression (i.e., those with persistent depressive symptoms across adolescence compared to those who experience early adult onset depressive symptoms compared to those who experience low symptoms; Kwong et al., 2019 - JAMA Network Open). We have also shown that these depression profiles are associated with poorer downstream consequences like lower educational attainment and NEET status (Lopez-Lopez et al., 2019).

However, these studies are subject to measurement error and potential biases in recall which can weaken evidence. In order to identify the mechanisms underpinning poorer mental health and design appropriate interventions and preventions, it is important to understand the processes of mental health in granular detail. Ecological momentary assessment (EMA) provides one alternative design to address these biases and provides in depth information on mental health in real time via multiple assessments per day or per week over a given period of time. Crucially, EMA of mental health can be paired with other forms of data collection such as sleep, exercise and diet to examine relationships between mental health and wellbeing.

The purpose of this project is to conduct a small scale feasibility study to examine how EMA studies could be collected in ALSPAC and in particular, if they can be collected in individuals from different depression profiles. The results from this study would provide initial results that would inform how to run a large scale study in subsequent work.