Aims
Utilising a large pre-existing sample of longitudinal data from individuals with a diagnosed with an eating disorder, this study aims to build a model of treatment personalisation for identifying individual-level intervention targets. The preliminary efficacy, acceptability and feasibility of the model will also be assessed in a small pilot study. By integrating perspectives from clinical psychology, nutrition, data science, and complex systems theory, the research aims to move beyond one-size-fits-all approaches toward novel, mechanistic-focused interventions that address the complex biopsychosocial factors maintaining eating disorders. This is an innovative, mechanistic-focused research project that will advance the field beyond current averages-based treatments towards novel interventions responsive to the complex system of biopsychosocial factors involved in the development and maintenance of disordered eating.

Background
Despite the existence of evidence-based treatments for eating disorders, remission remains low and relapse high, partly due to the dominant one-size-fits all approach. Given high symptom heterogeneity is typical in eating disorders, it is not surprising that outcomes for first-line treatments like cognitive behavioural therapy have stalled for decades. Personalised, data-driven care is urgently needed to improve recovery rates. This study brings together, for the first time, different perspectives from clinical psychology, nutrition, data science and complex systems theory.