An automated structured education intervention based on a smartphone app in Chinese patients with type 1 diabetes: a pro
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An automated structured education intervention based on a smartphone app in Chinese patients with type 1 diabetes: a protocol for a single-blinded randomized controlled trial Fansu Huang1,2†, Xinyin Wu3†, Yuting Xie2, Fang Liu2,4, Juan Li2, Xia Li2*
and Zhiguang Zhou2
Abstract Background: Although evidence had demonstrated the effectiveness of smartphone apps in diabetes care, the majority of apps had been developed for type 2 diabetes mellitus (T2DM) patients and targeted at populations outside of China. The effects of applying a smartphone app with structured education on glycemic control in type 1 diabetes mellitus (T1DM) are unclear. A digital, culturally tailored structured education program was developed in a smartphone app (Yi tang yun qiao) to provide an automated, individualized education program aimed at improving self-management skills in patients with T1DM in China. This trial aims to investigate the effectiveness of this smartphone app among Chinese T1DM patients. Methods and analysis: This single-blinded, 24-week, parallel-group randomized controlled trial of a smartphone app versus routine care will be conducted in Changsha, China. We plan to recruit 138 patients with T1DM who will be randomly allocated into the intervention group (automated, individualized education through an app) or routine care group. The intervention will last for 24 weeks. The primary outcome will be the change in glycated hemoglobin (HbA1c) from baseline to week 24. The secondary outcomes will include time in range, fasting blood glucose, levels of serum triglycerides and cholesterol, blood pressure, body mass index, quality of life, diabetes selfcare activities, diabetes self-efficacy, depression, anxiety, and patient satisfaction. Adverse events will be formally documented. Data analysis will be conducted using the intention-to-treat principle with appropriate univariate and multivariate methods. Missing data will be imputed with a multiple imputation method under the “missing at random” assumption. (Continued on next page)
* Correspondence: [email protected] † Fansu Huang and Xinyin Wu are joint first authors. 2 National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not
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