Commentary on Serre et al. : Demonstrating the next era of addiction science

Persistent challenges to accurately conceptualizing and effectively treating substance use disorders (SUDs) and behavioral addictions have motivated researchers to adopt increasingly sophisticated methodologies. Among these methods, Ecological Momentary Assessments (EMA) [1-4]—real-time data collected in individuals’ daily lives—combined with cutting-edge network analyses, such as multi-level vector autoregression models and group iterative multiple model estimation (GIMME) [5-7], offer much potential to improve our understanding of substance use and other addictive behaviors. Serre et al.’s [1] recent study exemplifies how these methods may reshape addiction research by capturing and modeling the dynamic, moment-to-moment fluctuations in key variables (e.g., craving and self-efficacy), producing rich and highly precise information on potential treatment targets. Combining EMA as a data collection design choice with network analyses as an analytical choice, this study highlights the value of conceptualizing addictions as a personalized, fluid process requiring individualized medicine—paving the way for the next frontier of addiction research and treatments. We note two key reasons why this study’s design and analytical choice articulate this combination’s potential and impact [8].

Bryant M. Stone: Conceptualization (equal); writing—original draft (lead); writing-review & editing (equal). Johannes Thrul: Conceptualization (equal); writing-review & editing (equal).

All authors certify that they have no affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.