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Examining the Potential of Person-centered Temporal Network Models to Individualize Type 1 Diabetes Care
Elizabeth Pyatak1, Raymond Hernandez2,3, Jeffrey S Gonzalez4,5
1Chan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. beth.pyatak@chan.usc.edu.
International Journal of Behavioral Medicine
|August 14, 2026
Summary
Temporal network models reveal personalized connections between glucose levels and daily experiences in type 1 diabetes (T1D). These patterns link glucose variability to well-being and functioning, suggesting tailored care approaches.
Area of Science:
- Endocrinology and Metabolism
- Psychology and Behavioral Medicine
- Data Science and Network Analysis
Background:
- Type 1 diabetes (T1D) management involves complex interactions between physiological and psychological factors.
- Understanding individualized dynamics between glucose levels and daily experiences is crucial for effective T1D care.
- Current approaches may not fully capture the intricate, person-specific relationships influencing diabetes outcomes.
Purpose of the Study:
- To characterize dynamic, individualized relationships among glucose, physical symptoms, emotions, and functioning in adults with T1D.
- To employ person-centered temporal network models to analyze these interconnections.
- To assess whether network features correlate with retrospective well-being and functioning.
Main Methods:
- Utilized data from 158 adults with T1D undergoing 14-day continuous glucose monitoring (CGM).
- Conducted ecological momentary assessments (EMAs) of experiential variables.
- Applied the Group Iterative Multiple Model Estimation (GIMME) algorithm to construct temporal networks for each participant, quantifying dynamic interconnections.
Main Results:
- GIMME models demonstrated feasibility, with high convergence and model fit rates.
- Individual networks revealed significant heterogeneity in glucose-experiential variable associations.
- Higher "glucose-in" density (experiential variables predicting glucose) was linked to increased depressive symptoms, perceived stress, anxiety, negative affect, and poorer lifestyle balance.
Conclusions:
- Temporal network models successfully identified individualized patterns linking glucose with daily experiences in T1D.
- Network features are associated with retrospective well-being and functioning, highlighting their clinical relevance.
- These findings support the potential of network-informed approaches for tailoring T1D management strategies.
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