A Systematic Scoping Review of Fully Idiographic Network Analysis in Mental Health

Background: The network analysis (NA) approach has predominantly relied on cross-sectional data, to characterize the relationships between symptoms across individuals at a single time point. In contrast, fully idiographic network analysis (FINA) allows for a more personalized perspective by estimating symptom networks at the individual level using intensive data collection. The aim of this scoping review is to map current practices in FINA in mental health research, identify methodological trends and gaps, and offer recommendations to support future studies in planning, data collection, analysis, and reporting. Methods: We searched MEDLINE, PsycINFO, Scopus, and Web of Science for peer-reviewed journal articles (published until January 2025). The initial search identified 12,586 articles, of which 43 were included in the review. Information was extracted on study and sample characteristics, data collection methods, and data analytic techniques. Results: We observed high heterogeneity between the studies. Commonly employed data collection methods included experience sampling and ecological momentary assessment, and the FINA model most frequently employed was graphical vector auto-regressive. Most studies estimated both contemporaneous and temporal networks, and fewer than half shared their data following open science practices. Conclusions: FINA is a promising tool for mental health research, but future studies need to adopt greater scientific rigor. To support this goal, we provide a set of recommendations and a structured checklist to guide researchers in conducting FINA studies.