Graph-Based Modeling of Behavioral Transitions for Digital Health Applications: Trajectory Analysis Framework

Abstract Background Digital health applications generate rich behavioral data; yet, how users transition between behavioral states remains poorly understood. Existing approaches show limited capacity to capture and represent the evolving and nuanced nature of real-world behavioral dynamics, which are essential for informing personalized behavior change interventions. Objective This study aimed to understand how users’ behaviors evolve within digital health applications by developing a data-driven approach that captures transition dynamics across behavioral features. We aimed to validate modeled transitions against observed user data, analyze transition pathways, and derive interpretable insights. Methods We analyzed 32 weeks of data from 36,574 users from a population health program run by the Health Promotion Board (HPB) in Singapore. We developed a graph-based behavioral trajectory model (GraphBeTraM ) that models behavioral transitions as shortest paths through user similarity graphs. By aggregating paths over time, the model captures users’ progression toward target behavioral states and quantifies the direction, magnitude, and timing of change. We validated modeled transitions against real-world data by comparing (1) transition matrices and (2) feature-level pathways, using Spearman correlation, Fisher z-transformed means, and cosine distance. We focused on high-variance features and used heatmaps to visualize patterns of change. Finally, characterized transition pathways between behavioral states and distinguished generalizable and context-specific features. Results We observed strong alignment between observed and modeled transition matrices with strong correlation (Spearman ρ=0.82; P <.001) and low cosine distance (0.05), suggesting that real-world behavioral transitions can be effectively represented through shortest paths with GraphBeTraM. Feature-level pathway validation showed consistent patterns of change across both personalized and real-world pathways. Physical activity features like weekly moderate to vigorous physical activity (MVPA) emerged as stable, generalizable signals of change at the population level, with strong mean correlation (ρ_ personalized =0.97; ρ_ real-world =0.79) and low mean cosine distance (cosine_ personalized =0.24; cosine_ real-world =0.41) across both personalized and real-world transitions. In contrast, features related to personal preferences, including time-related activity patterns (eg, proportion of weekday to weekend MVPA), purchase preferences for healthy foods and drinks, and engagement indicators (eg, last contact with the program or app), exhibited context-specific relevance and reflected change at an individual level. An in-depth transition analysis from an active state with prolonged sedentary periods to an active state with healthy eating habits revealed interpretable patterns in onset, magnitude, and rate of change. Specifically, features such as preferences for healthy food and drink purchases emerged later in the trajectory but then showed sharper and faster transitions once these behaviors began to shift. These insights highlight how GraphBeTraM can guide nudging strategies in alignment with natural behavior dynamics, including both stable and later-onset patterns. Conclusions GraphBeTraM provides an interpretable framework for modeling behavioral transitions in digital health applications and captures key dynamics that support personalized intervention design.