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评估参与数字健康干预对随机试验中患者结局的影响。

Estimating the impact of engagement with digital health interventions on patient outcomes in randomized trials.

机构信息

Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

Center for Health Behavior and Health Education, Vanderbilt University Medical Center, Nashville, Tennessee, USA.

出版信息

J Am Med Inform Assoc. 2021 Dec 28;29(1):128-136. doi: 10.1093/jamia/ocab254.

Abstract

OBJECTIVE

Guidance is needed on studying engagement and treatment effects in digital health interventions, including levels required for benefit. We evaluated multiple analytic approaches for understanding the association between engagement and clinical outcomes.

MATERIALS AND METHODS

We defined engagement as intervention participants' response rate to interactive text messages, and considered moderation, standard regression, mediation, and a modified instrumental variable (IV) analysis to investigate the relationship between engagement and clinical outcomes. We applied each approach to two randomized controlled trials featuring text message content in the intervention: REACH (Rapid Encouragement/Education and Communications for Health), which targeted diabetes, and VERB (Vanderbilt Emergency Room Bundle), which targeted hypertension.

RESULTS

In REACH, the treatment effect on hemoglobin A1c was estimated to be -0.73% (95% CI: [-1.29, -0.21]; P = 0.008), and in VERB, the treatment effect on systolic blood pressure was estimated to be -10.1 mmHg (95% CI: [-17.7, -2.8]; P = 0.007). Only the IV analyses suggested an effect of engagement on outcomes; the difference in treatment effects between engagers and non-engagers was -0.29% to -0.51% in the REACH study and -1.08 to -3.25 mmHg in the VERB study.

DISCUSSION

Standard regression and mediation have less power than a modified IV analysis, but the IV approach requires specification of assumptions. This is the first review of the strengths and limitations of various approaches to evaluating the impact of engagement on outcomes.

CONCLUSIONS

Understanding the role of engagement in digital health interventions can help reveal when and how these interventions achieve desired outcomes.

摘要

目的

需要指导如何研究数字健康干预措施中的参与度和治疗效果,包括产生益处所需的水平。我们评估了多种分析方法,以了解参与度与临床结果之间的关联。

材料和方法

我们将参与度定义为干预参与者对互动文本消息的回复率,并考虑了调节、标准回归、中介和改良的工具变量(IV)分析,以研究参与度与临床结果之间的关系。我们将每种方法应用于两个具有干预措施中短信内容的随机对照试验:针对糖尿病的 REACH(快速鼓励/教育和通信促进健康)和针对高血压的 VERB(范德比尔特急诊室捆绑)。

结果

在 REACH 中,血红蛋白 A1c 的治疗效果估计为-0.73%(95%CI:[-1.29,-0.21];P=0.008),而在 VERB 中,收缩压的治疗效果估计为-10.1mmHg(95%CI:[-17.7,-2.8];P=0.007)。只有 IV 分析表明参与度对结果有影响;在 REACH 研究中,参与度高的患者和低的患者之间的治疗效果差异为-0.29%至-0.51%,在 VERB 研究中为-1.08 至-3.25mmHg。

讨论

标准回归和中介的效力低于改良的 IV 分析,但 IV 方法需要指定假设。这是首次对评估参与度对结果影响的各种方法的优势和局限性进行的综述。

结论

了解参与度在数字健康干预措施中的作用可以帮助揭示这些干预措施何时以及如何达到预期的结果。

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