ArticleInternal medicine (Tokyo, Japan)2023
To Use or Not to Use a Self-monitoring of Blood Glucose System? Real-world Flash Glucose Monitoring Patterns Using a Cluster Analysis of the FGM-Japan Study.
Article in Internal medicine (Tokyo, Japan), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- A machine learning approach to predicting severe diabetes distress in adults with type 1 diabetes mellitus.Journal of diabetes investigation · 2026Observational
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18 authors.
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Abstract
Objective This study investigated self-monitoring of blood glucose (SMBG) adherence and flash glucose monitoring patterns using a cluster analysis in Japanese type 1 diabetes (T1D) patients with intermittently scanned continuous glucose monitoring (isCGM). Methods We measured SMBG adherence and performed a data-driven cluster analysis using a hierarchical clustering in T1D patients from Japan using the FreeStyle Libre system. Clusters were based on three variables (testing glucose frequency and referred Libre data for hyperglycemia or hypoglycemia). Patients We enrolled 209 participants. Inclusion criteria were patients with T1D, duration of isCGM use ≥3 months, age ≥20 years old, and regular attendance at the collaborating center. Results The rate of good adherence to SMBG recommended by a doctor was 85.0%. We identified three clusters: cluster 1 (low SMBG test frequency but high reference to Libre data, 17.7%), cluster 2 (high SMBG test frequency but low reference to Libre data, 34.0%), and cluster 3 (high SMBG test frequency and high reference to Libra data, 48.3%). Compared with other clusters, individuals in cluster 1 were younger, those in cluster 2 had a shorter Libre duration, and individuals in cluster 3 had lower time-in-range, higher severe diabetic distress, and high intake of snacks and sweetened beverages. There were no marked differences in the incidence of diabetic complications and rate of wearing the Libre sensor among the clusters. Conclusion We stratified the patients into three subgroups with varied clinical characteristics and CGM metrics. This new substratification might help tailor diabetes management of patients with T1D using isCGM.
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