Targeted therapy approach shows promise for chronic pain management
A new study finds that personalized psychological treatment focused on a patient's most influential mental barriers produces better outcomes than random approaches. The research suggests healthcare systems could improve pain management by first identifying which psychological factor matters most to each individual, then building treatment around that priority.
Originaltitel: Testing the network centrality hypothesis within process-based acceptance and commitment therapy – A single case experiment utilizing perceived causal networks
Idiographic network analysis, assessing associations between a system of variables of interest, also called nodes, is a proposed method for personalizing psychological treatment. The aim of this study was to test the centrality hypothesis that a treatment condition delivering interventions guided by the most central network node will yield better outcomes compared to a treatment condition delivering interventions guided by the least central node. We tested this using perceived causal networks (PECAN) and focusing on psychological inflexibility processes. Effects were examined in terms of pain interference, motivation, and pain intensity. We used a single case design with multiple baselines across six participants. Therapists were blind to treatment conditions. While participants were not blind to the responses that they provided for creating the PECAN, they were blind to the resulting network and the treatment conditions. Randomization was applied to baseline length and to whether the most central node or the least central node intervention came first. All participants had at least one outcome changing in beneficial directions in line with hypotheses. However, two participants also had one outcome each that changed in contradiction to the hypotheses. Adapting psychological treatment by matching interventions to the most central node in a perceived causal network looks promising. However, it is unlikely that this method will always be the best matching method. We need to keep exploring additional personalization methods and under which circumstances they are efficient. • We examined perceived causal network centrality as a treatment guiding principle • All participants demonstrated at least one outcome change in line with hypotheses • This guiding method does not seem universally beneficial in all circumstances