We are thrilled to welcome Noelia to the PHAE Lab family!

Noelia brings an outstanding background in Biochemistry and Translational Neuropsychopharmacology, complemented by specialized training in Pharmacogenetics. As Subcoordinator of the Pharmacogenetics Platform at ISABIAL and PhD candidate at UMH, she is actively contributing to landmark projects including PREVESTATGx, MORPHEO, and OPIC. Her expertise in pharmacogenetic-guided therapy, AI-based prediction of opioid use disorder, and sex/gender differences will be a tremendous asset to our team. Welcome aboard, Noelia!

Our systematic review examining how process evaluations are conducted in public health campaigns targeting substance use and physical activity has just been published in Health Education Research. The review highlights key gaps in methodological transparency and theoretical integration, recommending the adoption of standardised assessment tools to improve the quality and real-world applicability of future campaign evaluations.

🔗 Read the full article: https://doi.org/10.1093/her/cyag008 

Thomas was invited to speak on Trentino TV (Italy) about risk and protective factors in the context of doping, addressing how athletes are often left alone in their choices and how many of them face stigmatisation. He also had the opportunity to present the PHAE Lab during the interview. The video is in Italian with English subtitles.

Thomas discussed our research on AlicantíTV radio, focusing on the study published in Addictive Behaviors titled "Predictive modelling links exercise dependence to associated psychological and behavioral risk factors". This research marks an initial advancement in creating quantitative risk assessments for Exercise Dependence by examining multidimensional factors and determining how each dimension influences overall risk using a transparent and regulated machine learning methodology (January 27, 2026).

Thomas was interviewed by some radio channels regarding the study published in Addictive Behaviors, entitled "Predictive modelling links exercise dependence to associated psychological and behavioral risk factors".  This work represents a first step toward developing quantitative risk profiles for Exercise Dependence by considering multidimensional constructs and investigating the contribution of each dimension to the final risk through a controlled and interpretable machine learning predictive approach  (January 16 and 23, 2026).

Several Spanish media outlets have highlighted our study have highlighted our study published in Addictive Behaviors, entitled "Predictive modelling links exercise dependence to associated psychological and behavioral risk factors".  Scientific evidence supports physical exercise as one of the primary recommendations for maintaining good health. However, when practiced compulsively and uncontrollably, it can become problematic: exercise addiction. This international study, led by the head of our laboratory, has identified perfectionism and competitive sports practice as two of the factors most strongly associated with the risk of developing this behavior (January 12, 2026).