Blood glucose trajectories in diabetes patients receiving CAPDCA model of personalised patient education: A cluster randomised controlled trial

医学 2型糖尿病 星团(航天器) 糖尿病 整群随机对照试验 随机对照试验 干预(咨询) 健康教育 物理疗法 内科学 公共卫生 内分泌学 护理部 计算机科学 程序设计语言
作者
Jie Li,Yuangeng Liu,Wei Xing,Yue Jiang
出处
期刊:Diabetes, Obesity and Metabolism [Wiley]
卷期号:27 (12): 7189-7197
标识
DOI:10.1111/dom.70119
摘要

Abstract Objective The CAPDCA (Collection, Assessment, Plan, Do, Check, Aggrandisement) Model is a structured and individualised health education framework designed for dynamic adjustment and continuous improvement. This study evaluated its efficacy in diabetes management. Methods A cluster randomised controlled trial was conducted across 6 community health centres, involving 178 patients with type 2 diabetes. The intervention group ( n = 90) received CAPDCA model education, while the control group ( n = 88) received traditional education. The intervention spanned 18 months, with HbA1c collected at baseline and study end. Blood glucose was collected at each follow‐up. Analysis used Group‐Based Trajectory Model (GBTM). Results Compared with the control group, the intervention group showed: lower HbA1c ( t = 6.356, p < 0.01) and greater HbA1c reduction ( t = −6.117, p < 0.01). GBTM revealed distinct glucose trajectories: FBG had two trajectories (Steady descent group and rebound group). The 2 h‐PG had three trajectories (High BG—high descent group, Medium BG—low descent group, and low BG—high descent group). All trajectories demonstrated that blood glucose levels reached clinically target ranges post‐intervention. Baseline HbA1c influenced FBG trajectories, while baseline HbA1c and medication adherence influenced 2 h‐PG trajectories. Age, gender, education, and disease duration showed no significant association with trajectories. Conclusions The CAPDCA model can effectively improve the control of HbA1c. The analysis of influencing factors of different trajectories suggested that the model was suitable for patients with different ages, genders, education levels, and disease duration. Further studies would be needed in exploring the application to various diseases and integrating the CAPDCA model with technologies such as artificial intelligence.
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