TY - GEN
T1 - Continuous glucose monitoring and personalized nutrition in type 2 diabetes-a scoping review
AU - Hoque, Bushra
AU - Al-Mesaifri, Asmaa
AU - Suleiman, Sara
AU - Tariq, Zain Ul Abideen
AU - Househ, Mowafa
N1 - Publisher Copyright:
© 2026 The Authors.
PY - 2026/6/29
Y1 - 2026/6/29
N2 - Continuous Glucose Monitoring (CGM) is increasingly applied to personalized nutrition in Type 2 Diabetes (T2D), yet evidence is scattered. This scoping review mapped CGM-based nutrition interventions, classified models, summarized outcomes, and identified gaps. Following JBI and PRISMA-ScR guidelines, five databases and Google Scholar were searched (2020-2025) for studies of adults with T2D using real-time or intermittently scanned CGM to guide diet. Forty-five studies were included, mostly randomized trials, with additional pilot and observational designs. Interventions included CGM-guided nutrition, AI-enabled prediction, CGM-AI hybrid/digital-twin models, and telehealth coaching. Measured outcomes focused on HbA1c, Time in Range, and weight, while behavioral, cardiovascular, and microbiome measures were rarely assessed. Overall, CGM-enabled nutrition shows promise but remains methodologically inconsistent, with gaps in participant reporting, outcome diversity, and AI-driven approaches. Larger, long-term studies are needed to advance precision nutrition in diabetes care using continuous glucose monitoring.
AB - Continuous Glucose Monitoring (CGM) is increasingly applied to personalized nutrition in Type 2 Diabetes (T2D), yet evidence is scattered. This scoping review mapped CGM-based nutrition interventions, classified models, summarized outcomes, and identified gaps. Following JBI and PRISMA-ScR guidelines, five databases and Google Scholar were searched (2020-2025) for studies of adults with T2D using real-time or intermittently scanned CGM to guide diet. Forty-five studies were included, mostly randomized trials, with additional pilot and observational designs. Interventions included CGM-guided nutrition, AI-enabled prediction, CGM-AI hybrid/digital-twin models, and telehealth coaching. Measured outcomes focused on HbA1c, Time in Range, and weight, while behavioral, cardiovascular, and microbiome measures were rarely assessed. Overall, CGM-enabled nutrition shows promise but remains methodologically inconsistent, with gaps in participant reporting, outcome diversity, and AI-driven approaches. Larger, long-term studies are needed to advance precision nutrition in diabetes care using continuous glucose monitoring.
KW - Continuous glucose monitoring
KW - diabetes
KW - dietary interventions
KW - personalized nutrition
UR - https://www.scopus.com/pages/publications/105045088586
U2 - 10.3233/SHTI260847
DO - 10.3233/SHTI260847
M3 - Conference contribution
C2 - 42394009
AN - SCOPUS:105045088586
T3 - Studies in Health Technology and Informatics
SP - 281
EP - 285
BT - Health Sciences Informatics Leads and Empowers the Digital Health Transformation - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026
A2 - Mantas, John
A2 - Hasman, Arie
A2 - Gallos, Parisis
A2 - Haux, Reinhold
A2 - Karitis, Konstantinos
PB - IOS Press BV
T2 - 24th International Conference on Informatics, Management, and Technology in Healthcare, ICIMTH 2026
Y2 - 3 July 2026 through 5 July 2026
ER -