Murray Elizabeth, Hekler Eric B, Andersson Gerhard, Collins Linda M, Doherty Aiden, Hollis Chris, Rivera Daniel E, West Robert, Wyatt Jeremy C
Research Department of Primary Care and Population Health, University College London, London, United Kingdom.
Designing Health Lab, School of Nutrition and Health Promotion, Arizona State University, Phoenix, Arizona.
Am J Prev Med. 2016 Nov;51(5):843-851. doi: 10.1016/j.amepre.2016.06.008.
Digital health interventions have enormous potential as scalable tools to improve health and healthcare delivery by improving effectiveness, efficiency, accessibility, safety, and personalization. Achieving these improvements requires a cumulative knowledge base to inform development and deployment of digital health interventions. However, evaluations of digital health interventions present special challenges. This paper aims to examine these challenges and outline an evaluation strategy in terms of the research questions needed to appraise such interventions. As they are at the intersection of biomedical, behavioral, computing, and engineering research, methods drawn from all of these disciplines are required. Relevant research questions include defining the problem and the likely benefit of the digital health intervention, which in turn requires establishing the likely reach and uptake of the intervention, the causal model describing how the intervention will achieve its intended benefit, key components, and how they interact with one another, and estimating overall benefit in terms of effectiveness, cost effectiveness, and harms. Although RCTs are important for evaluation of effectiveness and cost effectiveness, they are best undertaken only when: (1) the intervention and its delivery package are stable; (2) these can be implemented with high fidelity; and (3) there is a reasonable likelihood that the overall benefits will be clinically meaningful (improved outcomes or equivalent outcomes at lower cost). Broadening the portfolio of research questions and evaluation methods will help with developing the necessary knowledge base to inform decisions on policy, practice, and research.
数字健康干预作为可扩展工具，在通过提高有效性、效率、可及性、安全性和个性化来改善健康及医疗服务提供方面具有巨大潜力。要实现这些改善需要一个累积的知识库来为数字健康干预的开发和部署提供信息。然而，对数字健康干预的评估存在特殊挑战。本文旨在审视这些挑战，并根据评估此类干预所需的研究问题概述一种评估策略。由于它们处于生物医学、行为学、计算机科学和工程研究的交叉点，需要从所有这些学科中汲取方法。相关研究问题包括界定数字健康干预的问题及可能的益处，这反过来需要确定干预可能的覆盖范围和接受程度、描述干预如何实现其预期益处的因果模型、关键组成部分及其相互作用方式，以及从有效性、成本效益和危害方面估计总体益处。虽然随机对照试验对于评估有效性和成本效益很重要，但仅在以下情况下进行才最为合适：（1）干预及其实施包稳定；（2）能够高保真地实施；（3）总体益处具有合理的临床意义可能性（以更低成本改善结局或取得等效结局）。拓宽研究问题和评估方法的范围将有助于建立必要的知识库，为政策、实践和研究决策提供信息。
