I study psychosocial factors and facets that affect mental health during psychotherapy, during particularly difficult life-periods, and across the lifecourse. The primary aim of my research is to better understand the dynamic landscape of factors and facets that – in addition to psychopathological symptoms – contribute to (or maybe even constitute) „mental health“. Such knowledge can inform a comprehensive, valid, reliable and personally-relevant assessment of mental health, which in turn can be leveraged for screening, monitoring and intervention purposes. In the below summaries you can read how my research has evolved over the years, and you will find some info on my research ethics.
My PhD research
My doctoral research focussed on three topics rooted in a resilience framework, and had a strong theoretical and psychometric emphasis. Firstly, I have computed transdiagnostic mental health indices for both nosological and etiological purposes. Secondly, I have studied how we best can define and measure adversity, so that we eventually can better understand its mental health consequences. Thirdly, I have estimated models that describe and visualize the complex relationships of a multitude of psychosocial factors that are associated with mental health. Furthermore, I have investigated which psychosocial protective factors seem to be particularly effective and whether psychosocial factors change over the course of life or in response to stress. In sum, the major objective of my doctoral research was to shed light on how psychosocial factors operate, to inform translational research and thereby aid screening, prevention and treatment efforts.
My Postdoc research
During my first postdoc my research has become more translational. For example, I was involved in running a time-series study, in which we monitored mental health, psychosocial protective and risk factors of Cambridge University students during the Covid-19 pandemic, on a daily basis. The purpose of this study was twofold. Firstly, we aimed to identify which factors have a particularly positive or negative impact on mental health. Secondly, we aimed to find out which form of personalized feedback – i.e. feedback that sheds light on changes in mental health and offers recommendations for psychosocial resources and interventions – seems particularly helpful. I was also involved in evaluating data from the UK-wide IAPT psychotherapy services (Improving Access to Psychological Therapies), where we aimed to identify predictors that describe psychological recovery and therapy outcome.
My current research
Today, my work is still for large parts inspired by similar research aims as described above, but has a more applied angle. By bringing together theoretical, psychometric, and clinically-focused research, I currently aim to examine a) which components – in addition to psychopathological symptoms – are particularly relevant for a meaningful ‚mental health‘ assessment, b) how these components can be captured with monitoring and outcome tools, and c) how we can best add personalized components to monitoring and outcome tools, to enable the most comprehensive and personally-relevant assessment possible.
Despite my enthusiasm for a transdiagnostic concept of psychopathology, I am also passionate about diagnostic research. In particular, I am interested in researching internalizing problems, such as trauma and stress-related disorders, (complicated) grief, and anxiety. Specifically, I am interested in identifying and studying factors that help explain the development and maintenance of those conditions.
Methods I use
To achieve the above, I
- conduct literature-based, qualitative and quantitative studies,
- use cross-sectional, panel, and time-series data – spanning the life course – and
- apply data reduction methods (e.g. exploratory/confirmatory factor models), classification methods (e.g. factor mixture models, latent class analysis), methods to analyse longitudinal patterns in data (e.g. structural equation models, path models with and without moderation and mediation effects, latent change models), methods to analyse complex systems/ the interactions of a multitude of variables (e.g. network analysis), prediction methods (e.g. machine learning models), methods to analyse time-series (e.g. mixed-effects models, dynamic network models such as multi-level vector-autoregression models), methods to treat and/or compensate for missing data (e.g. multiple imputation models), and methods to compare models (e.g. permutation tests, models with invariance constraints).
Along those lines, I am always keen to learn new psychological methods that can help us to better capture, model and understand the complex nature of mental health problems, their assessment, prevention and their treatment.
I am very much in awe of the open science and repro science movement, and I believe that research should advance knowledge, not individual careers. I also (perhaps naively) dream of a kind, supportive and collaborative science community. Kindness matters.
transdiagnostic mental health • adversity and risk factors • psychosocial (protective, promotive or resilience) factors • personalized care • psychotherapy research • internalizing disorders • complex systems • psychological methods • lifecourse psychiatry • clinical psychology