REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial.
REinforcement learning to improve non-adherence for diabetes treatments by Optimising Response and Customising Engagement (REINFORCE): study protocol of a pragmatic randomised trial.
复制标题
通过优化反应和自定义参与度（增强）：务实随机试验的研究方案来提高糖尿病治疗的不遵守研究的强化学习。
DOI：
10.1136/bmjopen-2021-052091
复制
发表时间：
2021-12-03
期刊：
影响因子：
2.9
通讯作者：
Choudhry NK
中科院分区：
文献类型：
--
作者：
Lauffenburger JC;Yom-Tov E;Keller PA;McDonnell ME;Bessette LG;Fontanet CP;Sears ES;Kim E;Hanken K;Buckley JJ;Barlev RA;Haff N;Choudhry NK
实现最佳的糖尿病控制需要几个日常自我管理行为，特别是坚持服药。证据支持使用短信来支持遵守，但仍然有很多机会来提高其有效性。一个关键的限制是消息内容是通用的。相比之下，强化学习是一种机器学习方法，可以通过观察个体对线索的反应来识别个体的反应模式，然后相应地优化它们。尽管它在医疗保健之外的好处已经得到证明，但它在为患者定制通信方面的应用却受到了有限的关注。这项试验的目的是测试基于强化学习的短信程序对2型糖尿病患者药物依从性的影响。在通过优化反应和定制参与（REINFORCE）试验改善非依从性的强化学习中，我们随机选择了60名口服糖尿病药物治疗的糖尿病控制不佳的患者接受强化学习干预或控制。两组受试者都将收到电子药瓶使用，干预组受试者将每天收到短信。这些消息将使用强化学习预测算法进行单独调整，该算法基于药瓶的每日依从性测量。试验的主要结果是在6个月的随访期内平均坚持服药。次要结局包括糖尿病控制（通过糖化血红蛋白A1c测量）和自我报告的依从性。总之，REINFORCE试验将评估个性化短信框架对患者支持药物依从性的影响，并深入了解如何大规模调整以改善其他自我管理干预措施。本研究获得了Mass General Brigham机构审查委员会（IRB）（美国）的批准。研究结果将通过同行评审期刊、clinicaltrials.gov报告和会议传播。Clinicaltrials.gov（NCT 04473326）。
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. 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. 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. Clinicaltrials.gov (NCT04473326).
登录
查看更多内容
DOI：
10.1037/a0040388
发表时间：
2016-10
期刊：
The American psychologist
影响因子：
--
作者：
Gonzalez JS;Tanenbaum ML;Commissariat PV
通讯作者：
Commissariat PV
影响因子：
2.9
作者：
Aguilera, Adrian;Figueroa, Caroline A.;Lyles, Courtney R.
通讯作者：
Lyles, Courtney R.
影响因子：
6.2
作者：
Arora, Sanjay;Peters, Anne L.;Menchine, Michael
通讯作者：
Menchine, Michael
DOI：
10.1093/jamia/ocy160
发表时间：
2019-03-01
影响因子：
6.4
作者：
Gonul, Suat;Namli, Tuncay;Cosar, Ahmet
通讯作者：
Cosar, Ahmet
影响因子：
2.2
作者：
Delahanty, Linda M.;Chang, Yuchiao;Wexler, Deborah J.
通讯作者：
Wexler, Deborah J.