Lavefjord, A., Sundström, F.T.A., Preihs, L., Hammar, A., Forslund, S., Clason van de Leur, J., Scholten, S., Buhrman, M., McCracken, L.M. (2026), Examining the utility of process-focused data driven psychological networks for individualizing psychological treatment in chronic pain-A single case experiment testing the centrality hypothesis. Frontiers in Psychology,17. DOI: 10.3389/fpsyg.2026.1809958.
Introduction
Idiographic network analysis, where associations between multiple nodes are estimated, can potentially guide choice of interventions in psychological treatment. In this single case experiment using ecological momentary assessment data to estimate continuous time network models, we aimed to test the so-called centrality hypothesis. We did so by comparing effects of interventions guided by the most central node to those guided by the least central node. We used the composite level psychological inflexibility processes lack of openness, lack of awareness, and lack of engagement, alongside an interference outcome, as network nodes. Effects on pain interference, motivation, and pain intensity were examined.
Method
We employed a multiple baseline design across six participants. Therapists and participants were blinded to participants' treatment conditions. Baseline length and order of treatment phases were randomized.
Results
Four participants had an overall treatment effect on pain interference, but it was generally not possible to discern that one particular phase was more beneficial than another. For three participants, the picture was somewhat clearer, indicating one of the treatment phases as more beneficial, although the results for these participants were not consistently in line with hypotheses. Retrospectively examining other potential guidance methods for these three participants, we saw a potential in discrete time contemporaneous network models.
Discussion
Current results are not in line with previous assumptions or research on idiographic network models for treatment personalization, although the previous research in this area is scarce. Future research should investigate alternative network models or estimation choices to determine the potential utility of data driven idiographic networks.