ArticlePrevention science : the official journal of the Society for Prevention Research2023
Integrating Intensive Longitudinal Data (ILD) to Inform the Development of Dynamic Theories of Behavior Change and Intervention Design: a Case Study of Scientific and Practical Considerations.
Article in Prevention science : the official journal of the Society for Prevention Research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Polygenic risk meets early trauma: toward a neuropsychodevelopmental model of schizophrenia.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Measuring human behavior through controlled perturbations: a framework for reconstructing behavioral systems.Frontiers in psychology · 2026Article
- A Comparison of In-Person and Telehealth Personalized Exercise Programs for Cancer Survivors: A Secondary Data Analysis.Cancers · 2025Article
- A Continuous-Time Dynamic Factor Model for Intensive Longitudinal Data Arising from Mobile Health Studies.Psychometrika · 2025Article
- Correlates of nicotine patch adherence in daily life.Drug and alcohol dependence · 2025Article
- The theoretical impact of AI-based quality evaluation of short-video health information on public cognition and treatment adherence: a case study of denosumab combined with PD-1/PD-L1 therapy for lung cancer bone metastasis.Frontiers in public health · 2025Article
- A latent variable approach to jointly modeling longitudinal and cumulative event data using a weighted two-stage method.Statistics in medicine · 2024Article
- Advancing Intervention and Prevention Research for Behavioral Health Problems Through Data Synthesis.Prevention science : the official journal of the Society for Prevention Research · 2023Article
- Retrospective Psychometrics and Effect Heterogeneity in Integrated Data Analysis: Commentary on the Special Issue.Prevention science : the official journal of the Society for Prevention Research · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The increasing sophistication of mobile and sensing technology has enabled the collection of intensive longitudinal data (ILD) concerning dynamic changes in an individual's state and context. ILD can be used to develop dynamic theories of behavior change which, in turn, can be used to provide a conceptual framework for the development of just-in-time adaptive interventions (JITAIs) that leverage advances in mobile and sensing technology to determine when and how to intervene. As such, JITAIs hold tremendous potential in addressing major public health concerns such as cigarette smoking, which can recur and arise unexpectedly. In tandem, a growing number of studies have utilized multiple methods to collect data on a particular dynamic construct of interest from the same individual. This approach holds promise in providing investigators with a significantly more detailed view of how a behavior change processes unfold within the same individual than ever before. However, nuanced challenges relating to coarse data, noisy data, and incoherence among data sources are introduced. In this manuscript, we use a mobile health (mHealth) study on smokers motivated to quit (Break Free; R01MD010362) to illustrate these challenges. Practical approaches to integrate multiple data sources are discussed within the greater scientific context of developing dynamic theories of behavior change and JITAIs.
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