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

IF 1.4 Q3 HEALTH CARE SCIENCES & SERVICES JMIR Research Protocols Pub Date : 2024-10-30 DOI:10.2196/50378
Sakeina Howard-Wilson, Jack Ching, Sherri Gentile, Martin Ho, Alex Garcia, Didem Ayturk, Peter Lazar, Nova Hammerquist, David McManus, Bruce Barton, Steven Bird, John Moore, Apurv Soni
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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.

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多模式数字化行为改变干预对生活方式、心脏代谢生物标志物和医疗支出的影响:随机对照试验方案》。
背景:美国预防服务工作组建议医疗服务提供者为所有成年人提供个性化的健康行为干预,而不论其是否有心血管疾病风险。虽然有确凿证据支持旨在改善多种生活方式行为的特定疾病计划,但将这些干预措施应用于更广泛人群的方法尚未得到充分证实。数字行为改变干预(DBCIs)有望成为一种更具普遍性和可扩展性的方法,以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:我们旨在评估多模式 DBCI 在以下方面的效果:(1)自我报告的体育锻炼、营养、睡眠和正念行为;(2)心脏代谢生物标志物;以及(3)与慢性疾病相关的医疗支出:我们在美国一家学术医疗机构的员工中开展了一项为期 12 个月的双臂随机对照试验。干预组接受量表、智能手机应用程序、活动追踪器、健康行为建议视频库和按需健康指导。对照组则享受雇主提供的标准健康福利。研究的主要结果包括自我报告的生活方式行为、心脏代谢生物标志物和慢性病相关医疗支出的变化。我们通过基线调查和季度网络调查收集健康行为数据,通过基线和 12 个月的门诊测量生物标志物,并通过索赔数据集确定相关费用:共有 603 名参与者注册并随机分配到干预组(300 人,占 49.8%)和对照组(303 人,占 50.2%)。平均年龄为 46.7 岁(标准差 11.2),大多数参与者为女性(80.3%,人数=484)、白人(85.4%,人数=504)和非西班牙裔(90.7%,人数=547),研究臂之间的基线特征没有系统性差异。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):本研究是对未根据基础疾病选择参与者的 DBCIs 进行的规模最大、最全面的评估,以评估其对行为、心脏代谢生物标志物和医疗支出的影响:试验注册:ClinicalTrials.gov NCT04712383;https://clinicaltrials.gov/study/NCT04712383.International 注册报告标识符 (irrid):RR1-10.2196/50378。
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CiteScore
2.40
自引率
5.90%
发文量
414
审稿时长
12 weeks
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