Smart insulin dosage software based on trend arrows: an effective method for controlling hyperglycemia in hospitalized diabetic patients in non-endocrine departments (a randomized controlled trial)

Artificial intelligence and real-time continuous glucose monitoring (rtCGM) are important inventions in the history of diabetes. The aim of this study was to compare the effectiveness of two methods of glycemic control (smart insulin dosage software and traditional physician’s experience). 86 patients with type 2 diabetes (T2DM) who received multiple daily insulin injections were randomized 1:1 into the smart insulin group (App group) and the traditional physician group (Control group). The former calculates the insulin doses based on an application (which is equipped with trend arrows that reflect the magnitude and direction of glucose changes), while the latter calculates the insulin doses based on physician’s experience. During the study, all participants underwent rtCGM and capillary blood glucose (CBG) testing. The study duration was 5 to 7 days. We compared glucose profile of patients in both group after the intervention. Patients in the App group had lower mean CBG (mmol/L) before dinner (10.07 (8.70, 11.60) vs 11.42 (9.89, 14.86), P = 0.006) and at bedtime (9.93 (8.83, 11.33) vs 10.71 (9.62, 12.72), P = 0.039) compared to the control group. The results of rtCGM showed that patients in the App group had lower mean glucose (mmol/L) (9.01(8.04, 9.70) vs10.45 (8.78, 12.27), P = 0.003), higher glucose management index (7.81% (7.09%, 8.59%) vs 7.19%(6.77%, 7.49%), P = 0.003), higher time in range (64.0% (51.0%, 73.0%) vs 47.0% (29.5%, 67.0%), P = 0.001) and less time above of range (35.0% (23.0%, 47.0%) vs 52.0% (29.5.0%, 70.0%), P = 0.003). No significant difference was observed in time below of range (1.0% (0%, 2.0) % vs 0% (0%, 2.5%), P = 0.469). There was no difference in the incidence of hypoglycemia between the two groups of patients (14.0% vs 20.9%, P = 0.394). Among T2DM receiving insulin treatment, the smart insulin dosage software with trend arrows has more advantage on controlling hyperglycemia than the physician’s experience. Further research with larger samples and longer durations is needed to support our findings. The clinical trial registration number was NCT05389839.

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Publication Details

Journal
Endocrine
Published
2026-02-16
DOI
https://doi.org/10.1007/s12020-025-04509-z
Primary Topic
Diabetes Management and Research
Type
article
Field-Weighted Citation Impact
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article

Smart insulin dosage software based on trend arrows: an effective method for controlling hyperglycemia in hospitalized diabetic patients in non-endocrine departments (a randomized controlled trial)

Yaling Yang, Chenwei Wu, Xiaolong Zhao, Xia Yu et al.
Endocrine
Diabetes Management and Research
article

Smart insulin dosage software based on trend arrows: an effective method for controlling hyperglycemia in hospitalized diabetic patients in non-endocrine departments (a randomized controlled trial)

Yaling Yang, Chenwei Wu, Xiaolong Zhao, Xia Yu, Jia Liu, Chunhong Wang, Qin Zhu, Lihua Zhou, Xinyue Xu, Duoduo Qu, Xinwei Liao
article en

Abstract

Artificial intelligence and real-time continuous glucose monitoring (rtCGM) are important inventions in the history of diabetes. The aim of this study was to compare the effectiveness of two methods of glycemic control (smart insulin dosage software and traditional physician’s experience). 86 patients with type 2 diabetes (T2DM) who received multiple daily insulin injections were randomized 1:1 into the smart insulin group (App group) and the traditional physician group (Control group). The former calculates the insulin doses based on an application (which is equipped with trend arrows that reflect the magnitude and direction of glucose changes), while the latter calculates the insulin doses based on physician’s experience. During the study, all participants underwent rtCGM and capillary blood glucose (CBG) testing. The study duration was 5 to 7 days. We compared glucose profile of patients in both group after the intervention. Patients in the App group had lower mean CBG (mmol/L) before dinner (10.07 (8.70, 11.60) vs 11.42 (9.89, 14.86), P = 0.006) and at bedtime (9.93 (8.83, 11.33) vs 10.71 (9.62, 12.72), P = 0.039) compared to the control group. The results of rtCGM showed that patients in the App group had lower mean glucose (mmol/L) (9.01(8.04, 9.70) vs10.45 (8.78, 12.27), P = 0.003), higher glucose management index (7.81% (7.09%, 8.59%) vs 7.19%(6.77%, 7.49%), P = 0.003), higher time in range (64.0% (51.0%, 73.0%) vs 47.0% (29.5%, 67.0%), P = 0.001) and less time above of range (35.0% (23.0%, 47.0%) vs 52.0% (29.5.0%, 70.0%), P = 0.003). No significant difference was observed in time below of range (1.0% (0%, 2.0) % vs 0% (0%, 2.5%), P = 0.469). There was no difference in the incidence of hypoglycemia between the two groups of patients (14.0% vs 20.9%, P = 0.394). Among T2DM receiving insulin treatment, the smart insulin dosage software with trend arrows has more advantage on controlling hyperglycemia than the physician’s experience. Further research with larger samples and longer durations is needed to support our findings. The clinical trial registration number was NCT05389839.

EndocrineVol. 91(1)
Shanghai Public Health Clinical Center (CN), Northeastern University (CN)
Good health and well-being
Openalex Percentile: Top 56%
Diabetes Management and Research
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