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通过优化反应和定制参与度来改善糖尿病治疗不依从性的强化学习(REINFORCE):一项实用随机试验的研究方案

REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial.

作者信息

Lauffenburger Julie C, Yom-Tov Elad, Keller Punam A, McDonnell Marie E, Bessette Lily G, Fontanet Constance P, Sears Ellen S, Kim Erin, Hanken Kaitlin, Buckley J Joseph, Barlev Renee A, Haff Nancy, Choudhry Niteesh K

机构信息

Center for Healthcare Delivery Sciences, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA

Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.

出版信息

BMJ Open. 2021 Dec 3;11(12):e052091. doi: 10.1136/bmjopen-2021-052091.

Abstract

INTRODUCTION

Achieving optimal diabetes control requires several daily self-management behaviours, especially adherence to medication. Evidence supports the use of text messages to support adherence, but there remains much opportunity to improve their effectiveness. One key limitation is that message content has been generic. By contrast, reinforcement learning is a machine learning method that can be used to identify individuals' patterns of responsiveness by observing their response to cues and then optimising them accordingly. Despite its demonstrated benefits outside of healthcare, its application to tailoring communication for patients has received limited attention. The objective of this trial is to test the impact of a reinforcement learning-based text messaging programme on adherence to medication for patients with type 2 diabetes.

METHODS AND ANALYSIS

In the REinforcement learning to Improve Non-adherence For diabetes treatments by Optimising Response and Customising Engagement (REINFORCE) trial, we are randomising 60 patients with suboptimal diabetes control treated with oral diabetes medications to receive a reinforcement learning intervention or control. Subjects in both arms will receive electronic pill bottles to use, and those in the intervention arm will receive up to daily text messages. The messages will be individually adapted using a reinforcement learning prediction algorithm based on daily adherence measurements from the pill bottles. The trial's primary outcome is average adherence to medication over the 6-month follow-up period. Secondary outcomes include diabetes control, measured by glycated haemoglobin A1c, and self-reported adherence. In sum, the REINFORCE trial will evaluate the effect of personalising the framing of text messages for patients to support medication adherence and provide insight into how this could be adapted at scale to improve other self-management interventions.

ETHICS AND DISSEMINATION

This study was approved by the Mass General Brigham Institutional Review Board (IRB) (USA). Findings will be disseminated through peer-reviewed journals, clinicaltrials.gov reporting and conferences.

TRIAL REGISTRATION NUMBER

Clinicaltrials.gov (NCT04473326).

摘要

引言

实现最佳糖尿病控制需要多种日常自我管理行为,尤其是坚持用药。有证据支持使用短信来促进坚持用药,但仍有很大的机会提高其有效性。一个关键限制是短信内容一直比较通用。相比之下,强化学习是一种机器学习方法,可通过观察个体对提示的反应来识别其反应模式,然后据此进行优化。尽管它在医疗保健之外已显示出益处,但其在为患者量身定制沟通方面的应用受到的关注有限。本试验的目的是测试基于强化学习的短信计划对2型糖尿病患者用药依从性的影响。

方法与分析

在“通过优化反应和定制参与来改善糖尿病治疗不依从性的强化学习”(REINFORCE)试验中,我们将60名口服降糖药治疗但糖尿病控制不佳的患者随机分为接受强化学习干预组或对照组。两组受试者都将使用电子药瓶,干预组的受试者将每天收到短信。这些短信将使用基于药瓶每日依从性测量的强化学习预测算法进行个性化调整。试验的主要结局是6个月随访期内的平均用药依从性。次要结局包括通过糖化血红蛋白A1c测量的糖尿病控制情况以及自我报告的依从性。总之,REINFORCE试验将评估为患者定制短信框架以支持用药依从性的效果,并深入了解如何大规模应用此方法来改善其他自我管理干预措施。

伦理与传播

本研究已获得美国马萨诸塞州综合医院布莱根分院机构审查委员会(IRB)的批准。研究结果将通过同行评审期刊、ClinicalTrials.gov报告和会议进行传播。

试验注册号

ClinicalTrials.gov(NCT04473326)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c16e/8647547/9d2cc0913c8a/bmjopen-2021-052091f01.jpg

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