AI-Driven Personalized Diabetes Management Platform Clinical Study Results

1,452 patients with type 2 diabetes were enrolled in a 12-month multicenter study between April 2022 and April 2023. The dataset contains the study results document evaluating an AI-powered platform that generates dynamic self-management plans. The author is Reziwanguli Amuti, and the document was last updated in May 2026.

Use Cases

• Analyze the effectiveness of AI-driven personalized care plans based on reported glycated hemoglobin (HbA1c) and fasting plasma glucose (FPG) outcomes.
• Compare intervention and control group results based on metrics like Self-Monitoring of Blood Glucose (SMBG) frequency and hypoglycemia incidence.
• Study the relationship between personalized management and secondary outcomes such as Body Mass Index (BMI) and Urine Albumin-to-Creatinine Ratio (UACR).
• Evaluate remission rates in type 2 diabetes based on the reported co-primary outcome data.

Strengths

• Study includes 1,452 enrolled patients, providing a substantial cohort size.
• Results report specific numerical outcomes, including HbA1c reduction (1.92% vs. 1.31%) and FPG reduction (3.23 vs. 2.31 mmol/L).
• Document is licensed under CC-BY-4.0, allowing for open sharing and reuse.

Limitations

• The dataset is a 33.2 KB DOCX file containing a study document, not a structured data table; column-level documentation is absent.
• Row count is unknown, which may limit suitability assessment for quantitative analysis.
• The description does not specify the geographic scope of the multicenter study.

Provenance

Source

figshare

Collection Method

Data originates from a 12-month, multicenter, prospective, open-label, quasi-experimental cohort study.

Time Range

Patient enrollment occurred between April 2022 and April 2023, with a 12-month study period.

Freshness

Last updated 2026-05-07 05:44:50; freshness should be verified.