Harmonizing Big Data in Mental Health Research: A Proof-of-Principle in the R2D2-MH Consortium Using the International Classification of Functioning (ICF)

Abstract

Abstract Big data approaches in mental health research hold great promise to advance research and support for neurologically diverse populations. The Risk, Resilience and Developmental Diversity in Mental Health (R2D2-MH) project moves from risk-focused studies toward understanding and promoting resilience, and from a diagnosis-based approach to a developmental diversity approach that defines well-being across the lifespan. Here, we present a proof-of-principle in R2D2-MH demonstrating that the International Classification of Functioning (ICF) can facilitate content harmonization in mental health research to generate big data compatible with several contemporary approaches in psychiatry. Transforming data through the ICF classification system allowed data collected with a wide range of instruments across modalities and diverse populations to be harmonized under the widely accepted WHO framework. Data harmonized using the ICF provides novel opportunities for large-scale data analyses that may be more capable of capturing diversity, aligning with more transdiagnostic and neurodiversity-affirmative ways of understanding data.