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人工智能与人工教练在糖尿病预防中的效果比较:一项随机对照试验的研究方案。

Effectiveness of artificial intelligence vs. human coaching in diabetes prevention: a study protocol for a randomized controlled trial.

机构信息

Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.

出版信息

Trials. 2024 May 16;25(1):325. doi: 10.1186/s13063-024-08177-8.

Abstract

BACKGROUND

Prediabetes is a highly prevalent condition that heralds an increased risk of progression to type 2 diabetes, along with associated microvascular and macrovascular complications. The Diabetes Prevention Program (DPP) is an established effective intervention for diabetes prevention. However, participation in this 12-month lifestyle change program has historically been low. Digital DPPs have emerged as a scalable alternative, accessible asynchronously and recognized by the Centers for Disease Control and Prevention (CDC). Yet, most digital programs still incorporate human coaching, potentially limiting scalability. Furthermore, existing effectiveness results of digital DPPs are primarily derived from per protocol, longitudinal non-randomized studies, or comparisons to control groups that do not represent the standard of care DPP. The potential of an AI-powered DPP as an alternative to the DPP is yet to be investigated. We propose a randomized controlled trial (RCT) to directly compare these two approaches.

METHODS

This open-label, multicenter, non-inferiority RCT will compare the effectiveness of a fully automated AI-powered digital DPP (ai-DPP) with a standard of care human coach-based DPP (h-DPP). A total of 368 participants with elevated body mass index (BMI) and prediabetes will be randomized equally to the ai-DPP (smartphone app and Bluetooth-enabled body weight scale) or h-DPP (referral to a CDC recognized DPP). The primary endpoint, assessed at 12 months, is the achievement of the CDC's benchmark for type 2 diabetes risk reduction, defined as any of the following: at least 5% weight loss, at least 4% weight loss and at least 150 min per week on average of physical activity, or at least a 0.2-point reduction in hemoglobin A1C. Physical activity will be objectively measured using serial actigraphy at baseline and at 1-month intervals throughout the trial. Secondary endpoints, evaluated at 6 and 12 months, will include changes in A1C, weight, physical activity measures, program engagement, and cost-effectiveness. Participants include adults aged 18-75 years with laboratory confirmed prediabetes, a BMI of ≥ 25 kg/m (≥ 23 kg/m for Asians), English proficiency, and smartphone users. This U.S. study is conducted at Johns Hopkins Medicine in Baltimore, MD, and Reading Hospital (Tower Health) in Reading, PA.

DISCUSSION

Prediabetes is a significant public health issue, necessitating scalable interventions for the millions affected. Our pragmatic clinical trial is unique in directly comparing a fully automated AI-powered approach without direct human coach interaction. If proven effective, it could be a scalable, cost-effective strategy. This trial will offer vital insights into both AI and human coach-based behavioral change strategies in real-world clinical settings.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05056376. Registered on September 24, 2021, https://clinicaltrials.gov/study/NCT05056376.

摘要

背景

糖尿病前期是一种高发疾病,预示着患 2 型糖尿病的风险增加,同时还伴有微血管和大血管并发症。糖尿病预防计划(DPP)是一种经过验证的有效预防糖尿病的干预措施。然而,参与这个为期 12 个月的生活方式改变计划的人数历来较低。数字 DPP 已经成为一种可扩展的替代方案,可以异步访问,并得到疾病控制与预防中心(CDC)的认可。然而,大多数数字计划仍然包含人工教练,这可能限制了可扩展性。此外,数字 DPP 的现有有效性结果主要来自于方案设计的、纵向的非随机研究,或与不代表 DPP 标准护理的对照组进行比较。人工智能驱动的 DPP 作为 DPP 的替代方案的潜力尚未得到研究。我们提出了一项随机对照试验(RCT),以直接比较这两种方法。

方法

这是一项开放标签、多中心、非劣效性 RCT,将比较完全自动化的人工智能驱动的数字 DPP(ai-DPP)与基于标准护理的人工教练的 DPP(h-DPP)的有效性。共有 368 名体重指数(BMI)升高且患有糖尿病前期的参与者将被平均随机分为 ai-DPP(智能手机应用程序和支持蓝牙的体重秤)或 h-DPP(转诊至 CDC 认可的 DPP)。主要终点是在 12 个月时达到 CDC 降低 2 型糖尿病风险的基准,定义为以下任何一项:至少 5%的体重减轻,至少 4%的体重减轻和至少 150 分钟/周的平均体力活动,或血红蛋白 A1C 至少降低 0.2 个点。体力活动将通过基线和整个试验期间每 1 个月的连续活动记录仪进行客观测量。次要终点在 6 个月和 12 个月评估,包括 A1C、体重、体力活动测量、项目参与度和成本效益的变化。参与者包括年龄在 18-75 岁之间的成年人,实验室确诊为糖尿病前期,BMI≥25kg/m(亚洲人为≥23kg/m),精通英语,并且使用智能手机。这项美国研究在马里兰州巴尔的摩的约翰霍普金斯医学中心和宾夕法尼亚州雷丁的雷丁医院(Tower Health)进行。

讨论

糖尿病前期是一个重大的公共卫生问题,需要针对数百万受影响的人采取可扩展的干预措施。我们的实用临床试验是独一无二的,因为它直接比较了一种完全自动化的人工智能驱动的方法,而不需要直接的人工教练互动。如果被证明有效,它可能是一种可扩展的、具有成本效益的策略。该试验将为人工智能和基于人工教练的行为改变策略在真实临床环境中的有效性提供重要见解。

试验注册

ClinicalTrials.gov NCT05056376。注册于 2021 年 9 月 24 日,https://clinicaltrials.gov/study/NCT05056376。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e523/11100129/1bee10e5d001/13063_2024_8177_Fig1_HTML.jpg

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