多模态数字行为改变干预对生活方式行为、心血管代谢生物标志物和医疗支出的疗效：一项随机对照试验的方案。

Efficacy of a Multimodal Digital Behavior Change Intervention on Lifestyle Behavior, Cardiometabolic Biomarkers, and Medical Expenditure: Protocol for a Randomized Controlled Trial.

机构信息

Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States.

Google LLC, Mountain View, CA, United States.

出版信息

JMIR Res Protoc. 2024 Oct 30;13:e50378. doi: 10.2196/50378.

DOI:10.2196/50378

PMID:39475852

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11561444/

Abstract

BACKGROUND

The US Preventive Services Task Force recommends providers offer individualized healthy behavior interventions for all adults, independent of their risk of cardiovascular disease. While strong evidence exists to support disease-specific programs designed to improve multiple lifestyle behaviors, approaches to adapting these interventions for a broader population are not well established. Digital behavior change interventions (DBCIs) hold promise as a more generalizable and scalable approach to overcome the resource and time limitations that traditional behavioral intervention programs face, especially within an occupational setting.

OBJECTIVE

We aimed to evaluate the efficacy of a multimodal DBCI on (1) self-reported behaviors of physical activity, nutrition, sleep, and mindfulness; (2) cardiometabolic biomarkers; and (3) chronic disease-related medical expenditure.

METHODS

We conducted a 2-arm randomized controlled trial for 12 months among employees of an academic health care facility in the United States. The intervention arm received a scale, a smartphone app, an activity tracker, a video library for healthy behavior recommendations, and an on-demand health coach. The control arm received standard employer-provided health and wellness benefits. The primary outcomes of the study included changes in self-reported lifestyle behaviors, cardiometabolic biomarkers, and chronic disease-related medical expenditure. We collected health behavior data via baseline and quarterly web-based surveys, biometric measures via clinic visits at baseline and 12 months, and identified relevant costs through claims datasets.

RESULTS

A total of 603 participants were enrolled and randomized to the intervention (n=300, 49.8%) and control arms (n=303, 50.2%). The average age was 46.7 (SD 11.2) years, and the majority of participants were female (80.3%, n=484), White (85.4%, n=504), and non-Hispanic (90.7%, n=547), with no systematic differences in baseline characteristics observed between the study arms. We observed retention rates of 86.1% (n=519) for completing the final survey and 77.9% (n=490) for attending the exit visit.

CONCLUSIONS

This study represents the largest and most comprehensive evaluation of DBCIs among participants who were not selected based on their underlying condition to assess its impact on behavior, cardiometabolic biomarkers, and medical expenditure.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04712383; https://clinicaltrials.gov/study/NCT04712383.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/50378.

摘要

背景

美国预防服务工作组建议为所有成年人提供个性化的健康行为干预，无论其心血管疾病风险如何。虽然有强有力的证据支持设计用于改善多种生活方式行为的特定于疾病的计划，但为更广泛的人群调整这些干预措施的方法尚未得到很好的建立。数字行为改变干预（DBCI）作为一种更具普遍性和可扩展性的方法，有望克服传统行为干预计划所面临的资源和时间限制，尤其是在职业环境中。

目的

我们旨在评估一种多模式 DBCI 对（1）身体活动、营养、睡眠和正念的自我报告行为；（2）心血管代谢生物标志物；和（3）与慢性病相关的医疗支出的影响。

方法

我们在美国一家学术医疗保健机构的员工中进行了为期 12 个月的 2 臂随机对照试验。干预组接受量表、智能手机应用程序、活动追踪器、健康行为推荐视频库和按需健康教练。对照组接受标准雇主提供的健康和健康福利。该研究的主要结局包括自我报告的生活方式行为、心血管代谢生物标志物和与慢性病相关的医疗支出的变化。我们通过基线和每季度的网络调查收集健康行为数据，通过基线和 12 个月的诊所就诊收集生物标志物测量值，并通过索赔数据集确定相关成本。

结果

共有 603 名参与者入组并随机分配至干预组（n=300，49.8%）和对照组（n=303，50.2%）。平均年龄为 46.7（SD 11.2）岁，大多数参与者为女性（80.3%，n=484）、白人（85.4%，n=504）和非西班牙裔（90.7%，n=547），研究组之间没有观察到基线特征的系统差异。我们观察到完成最终调查的保留率为 86.1%（n=519），参加退出访问的保留率为 77.9%（n=490）。