Title : Beyond abstinence: Continuous behavioral intelligence for measuring recovery trajectories in addiction psychiatry
Abstract:
Background: Addiction treatment is commonly evaluated using episodic symptom scales, diagnostic status, and binary outcomes such as abstinence or relapse. These measures are clinically useful but can miss longitudinal changes in function, mood stability, relationships, motivation, and self-regulation that may determine whether recovery is durable.
Methods: We conducted a descriptive analysis of a longitudinal, naturalistic behavioral health dataset accumulated across approximately 5,400 individuals and 25,000 clinical encounters over 25 years. The dataset integrates structured symptom measures, repeated patient-reported measures of positive function and behavioral instability, medication and treatment changes, relapse events, and contemporaneous clinical narratives. Rather than analyzing encounters as isolated observations, these data were organized as within-person trajectories. This approach forms the basis of Continuous Behavioral Intelligence (CBI), implemented within the Epiphany360 platform.
Results: Longitudinal trajectories revealed clinically meaningful changes that were not always represented by single-visit symptom scores or abstinence status alone. In individual trajectories, improvement in positive function could emerge independently of immediate symptom resolution, while increasing behavioral instability sometimes accompanied or preceded deterioration, relapse, or treatment disruption. Conversely, stabilization could precede broader improvements in function and treatment engagement. Repeated measurement also helped distinguish transient fluctuations from sustained change and allowed patient-reported outcomes to be interpreted alongside treatment interventions and significant life events.
Conclusions: Addiction recovery may be better represented as a trajectory than as a sequence of isolated encounters. Combining repeated structured measures with contemporaneous narrative context provides a richer representation of treatment response, relapse, and functional recovery. Continuous Behavioral Intelligence offers a framework for converting routine clinical care into longitudinal real-world evidence that can support clinical decision-making and, with appropriate validation and governance, create a clinically meaningful data layer for future AI-enabled behavioral health systems.
