Evidence map›Paper›PMID 42595947›Full record

ArticleInternational journal of behavioral medicine2026

Examining the Potential of Person-centered Temporal Network Models to Individualize Type 1 Diabetes Care.

Elizabeth Pyatak, Raymond Hernandez, Jeffrey S Gonzalez, Loree Pham, Anya Khurana, Stefan Schneider

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Article in International journal of behavioral medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Elizabeth PyatakChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA. beth.pyatak@chan.usc.edu.ORCID https://orcid.org/0000-0003-3280-8912
Raymond HernandezChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Jeffrey S GonzalezFerkauf Graduate School of Psychology, Yeshiva University, New York, NY, USA.
Loree PhamChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Anya KhuranaChan Division of Occupational Science and Occupational Therapy, University of Southern California, Los Angeles, CA, USA.
Stefan SchneiderCenter for Social and Economic Research, University of Southern California, Los Angeles, CA, USA.

Funding

Function and Emotion in Everyday Life with Type 1 Diabetes: FEEL-T1DR01DK121298 · NIDDK · UNIVERSITY OF SOUTHERN CALIFORNIA · PI GONZALEZ, JEFFREY S, PYATAK, ELIZABETH F · 2019 to 2022
$3.2M
NIDDK NIH HHS R01 DK121298
6 · The paper itself

Abstract

purposeWe aimed to characterize dynamic, individualized relationships among glucose, physical symptoms, emotions, and functioning (e.g., experiential variables) in adults with type 1 diabetes (T1D) using person-centered temporal network models, and assess whether network features relate to retrospective well-being and functioning.

methodsWe analyzed data from 158 adults with T1D who completed 14 days of blinded continuous glucose monitoring (CGM) and ecological momentary assessments of experiential variables. For each participant, we estimated a temporal network using the Group Iterative Multiple Model Estimation (GIMME) algorithm, which quantifies dynamic interconnections of variables within individuals. We assessed the variability of models across participants, and examined whether interconnections among variables (network density) were associated with retrospective measures of well-being and functioning.

resultsGIMME models converged for 96.3% of participants, and showed excellent model fit for 77.2% of participants, establishing feasibility. Individual networks had a median of 20.5 (IQR 16-32.75) connections, of which 5 (IQR 3-8) were between glucose and experiential variables. Networks showed marked heterogeneity in the presence, direction, and strength of associations between variables. Individuals whose glucose was strongly predicted by experiential variables (i.e., those whose networks had higher "glucose-in" density), experienced greater depressive symptoms (β = 0.17, p = 0.03), perceived stress (β = 0.24, p < 0.001), anxiety symptoms (β = 0.19, p = 0.02), and negative affect (β = 0.16, p = 0.05) and poorer lifestyle/occupational balance (β = -0.16, p = 0.04).

conclusionsGIMME models identify individualized patterns linking glucose with daily experiences, which may help generate hypotheses relevant to tailoring care. Network features are associated with retrospective measures of well-being and functioning, supporting the potential of network-informed approaches to T1D care.

Indexed as

Continuous glucose monitoringDigital healthEcological momentary assessmentNetwork analysisPsychosocial functioningType 1 Diabetes

Identifiers

PMID42595947

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