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Mobile health use in low- and high-income countries: an overview of the peer-reviewed literature.移动医疗在低收入和高收入国家的使用:同行评议文献综述。
J R Soc Med. 2013 Apr;106(4):130-42. doi: 10.1177/0141076812472620.
2
Glucose meters with built-in automated bolus calculator: gadget or real value for insulin-treated diabetic patients?带内置自动胰岛素剂量计算器的血糖仪:胰岛素治疗的糖尿病患者的小工具还是真正的价值?
Diabetes Ther. 2013 Jun;4(1):1-11. doi: 10.1007/s13300-012-0017-4. Epub 2012 Dec 19.
3
There's an app for that: content analysis of paid health and fitness apps.有一款针对此的应用程序:付费健康与健身应用程序的内容分析。
J Med Internet Res. 2012 May 14;14(3):e72. doi: 10.2196/jmir.1977.
4
Evidence, preferences, recommendations--finding the right balance in patient care.证据、偏好、建议——在患者护理中找到恰当的平衡。
N Engl J Med. 2012 May 3;366(18):1653-5. doi: 10.1056/NEJMp1201535.
5
Guidelines for optimal bolus calculator settings in adults.成人最佳推注计算器设置指南。
J Diabetes Sci Technol. 2011 Jan 1;5(1):129-35. doi: 10.1177/193229681100500118.
6
Standards of medical care in diabetes--2011.《糖尿病医疗护理标准——2011 年》
Diabetes Care. 2011 Jan;34 Suppl 1(Suppl 1):S11-61. doi: 10.2337/dc11-S011.
7
Decision aids for people facing health treatment or screening decisions.为面临医疗治疗或筛查决策的人群提供的决策辅助工具。
Cochrane Database Syst Rev. 2009 Jul 8(3):CD001431. doi: 10.1002/14651858.CD001431.pub2.
8
Insulin pump therapy: guidelines for successful outcomes.胰岛素泵治疗:成功治疗的指南
Diabetes Educ. 2009 Mar-Apr;35 Suppl 2:29S-41S; quiz 28S, 42S-43S. doi: 10.1177/0145721709333493.
9
The next step in guideline development: incorporating patient preferences.指南制定的下一步:纳入患者偏好。
JAMA. 2008 Jul 23;300(4):436-8. doi: 10.1001/jama.300.4.436.
10
A shared treatment decision-making approach between patients with chronic conditions and their clinicians: the case of diabetes.慢性病患者与其临床医生之间的共同治疗决策方法:以糖尿病为例。
Health Expect. 2006 Mar;9(1):25-36. doi: 10.1111/j.1369-7625.2006.00359.x.

iDECIDE:一款使用基于证据的方程式来考虑患者偏好的胰岛素剂量计算移动应用程序。

iDECIDE: A Mobile Application for Insulin Dosing Using an Evidence Based Equation to Account for Patient Preferences.

作者信息

Lloyd Buffy, Groat Danielle, Cook Curtiss B, Kaufman David, Grando Adela

机构信息

Arizona State University Department of Biomedical Informatics, AZ.

出版信息

Stud Health Technol Inform. 2015;216:93-7.

PMID:26262017
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4717835/
Abstract

Diabetes is a complex disease affecting 29.1 million (9.3%) US citizens [1]. It is a chronic illness that needs continual medical care and ongoing patient self-management, education, and support [2]. There is no cure for diabetes, requiring patients to conduct frequent self-monitoring of blood glucose and dosing of insulin in many cases. Evidence has shown that patients are more adherent to their diabetes management plan when they incorporate personal lifestyle choices [3]. To address the challenge of empowering patients to better manage their diabetes, we have developed a novel mobile application prototype, iDECIDE, that refines rapid-acting insulin dose calculations by incorporating two important patient variables in addition to carbohydrates consumed that are not a part of standard insulin dose calculation algorithms: exercise and alcohol intake [4, 5]. A retrospective analysis for the calibration and evaluation of iDECIDE is underway by comparing recommendations made by the application against dosing recommendations made by insulin pumps.

摘要

糖尿病是一种复杂的疾病,影响着2910万(9.3%)美国公民[1]。它是一种慢性病,需要持续的医疗护理以及患者持续的自我管理、教育和支持[2]。糖尿病无法治愈,在许多情况下,患者需要频繁进行血糖自我监测并注射胰岛素。有证据表明,当患者将个人生活方式选择纳入其中时,他们会更严格地遵守糖尿病管理计划[3]。为应对帮助患者更好地管理糖尿病这一挑战,我们开发了一款新型移动应用程序原型iDECIDE,除了消耗的碳水化合物外,该应用程序还纳入了两个重要的患者变量(这两个变量并非标准胰岛素剂量计算算法的一部分):运动和酒精摄入量,从而优化速效胰岛素剂量计算[4,5]。目前正在通过将该应用程序给出的建议与胰岛素泵给出的给药建议进行比较,对iDECIDE进行校准和评估的回顾性分析。