My main research topic is the study of psychosocial risk and protective factors that affect mental health during psychotherapy, during particularly difficult periods, and across the lifecourse. The primary aim of my research is to better understand the dynamic landscape of psychosocial factors – to leverage such knowledge for the development of tools that can help people to either stay mentally healthy or to regain mental health. In the below summaries you can read how my research has evolved over the years, what I am currently trying to work out, and you will find some info on my research ethics.
My PhD research
My doctoral research has focussed on three, rather theoretical, topics. Firstly, I have studied how we best can define and measure adversity, so that we eventually can better understand its consequences. Secondly, I have computed transdiagnostic mental health indices for both nosological and etiological purposes. Thirdly, I have estimated models that describe and visualize the complex relationships of a multitude of psychosocial factors. This research was conducted from a systemic angle, including predisposing factors (e.g. adversity), transdiagnostic mental health, intra-personal skills (e.g. self-esteem) as well as inter-personal resources (e.g. social or family support). 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 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. I was also 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.
My current research
Today, my work is still for large parts inspired by the same three research aims as described in the section of my PhD reserach, but has a slightly more clinical focus. At the moment I am trying to work out how we best can design personalized monitoring and outcome measures for psychotherapeutic interventions. More specifically, I am trying to work out how we can identify, assess, monitor and understand the complex dynamic interplay of symptoms, therapy goals, and quality-of-life over the course of the treatment process.
Despite my enthusiasm for a transdiagnostic concept for psychopathology, I am also passionate about diagnostic research. In particular, I am interested in researching internalizing problems, such as anxiety, obsessive-compulsive, (complicated) grief, trauma and stress-related disorders, as well as in researching sexual dysfunctions. Specifically, I am interested in identifying and studying factors that help explain the development and maintenance of those conditions.
Methods and stats
To achieve the above, I
- 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 statistical methods that can help us to better model and eventually better understand the complex nature of mental health problems, their assessment, prevention and their treatment.
I am very much in awe of the open 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 • internalizing disorders • adversity and risk factors • psychosocial (protective, promotive or resilience) factors • psychotherapy research • personalized feedback • personalized care • complex systems • psychological methods • lifecourse psychiatry • clinical psychology