Intensive Longitudinal Data for Behavioral and Digital Health Innovation

The rapid growth of wearable and mobile sensing technologies has enabled the continuous collection of highly granular behavioral data over time. These intensively longitudinal data streams provide unprecedented insight into the dynamic nature of health-related behaviors and their interaction with environmental and contextual factors. Despite this promise, behavioral scientists face challenges in fully harnessing this data to generate actionable knowledge.The goal of this Research Topic is to catalyze progress in behavioral and digital health research by showcasing how intensive longitudinal data can reveal dynamic, context-sensitive, and person-specific insights into health behavior. This collection aims to bridge methodological innovation with applied science by offering tangible strategies for analyzing and interpreting high-frequency data from wearables, mobile sensors, and other sources. Suitable themes for manuscripts include (but are not limited to):• Analytical strategies for modeling behavior dynamics, such as transitions, rhythms, or bursts in movement or physiological signals,• Use of intensively longitudinal data to better understand behavior change processes (e.g., habit formation, relapse, adherence),• Cross-domain applications of intensive data (e.g., integrating physical activity, sleep, mood, or social interaction data),• Methods for detecting person-specific patterns or tailoring interventions using individual-level data streams,• Applications of intensively longitudinal data to characterize intraindividual variability in health-related behaviors,• Approaches for linking behavioral time series data with environmental or contextual covariates (e.g., geolocation, EMA, weather),• Methodological tutorials or walkthroughs that help demystify analytic techniques for high-frequency behavioral data,• Comparative analyses of analytic methods (e.g., functional data analysis, machine learning, time-series modeling) applied to shared datasets,• Ethical, privacy, and feasibility considerations in collecting and using intensively longitudinal data in behavioral health.All article types accepted by Frontiers in Digital Health are welcome, including Original Research, Methods, Reviews, and Technology Reports. We especially encourage contributions that combine real-world applications with methodological transparency and accessibility. Topic editors: Jason Fanning, Vincent Berardi, Masha Remskar Source: https://www.frontiersin.org/research-topics/72372/intensive-longitudinal-data-for-behavioral-and-digital-health-innovation