The Pace of Technologic Change Implications for Digital Health Behavior Intervention Research
The Pace of Technologic Change Implications for Digital Health Behavior Intervention Research
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DOI：
10.1016/j.amepre.2016.05.001
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发表时间：
2016-11-01
影响因子：
5.5
通讯作者：
Riley, William T.
中科院分区：
文献类型：
--
作者：
Patrick, Kevin;Hekler, Eric B.;Riley, William T.
本文讨论了支持数字干预的技术的快速变化；它们旨在解决的卫生问题的复杂性；以及科学方法的适应性，以适应这些技术可能带来的数据量、速度和多样性。信息、通信和计算技术现在是每个社会领域的一部分，基本上支持人类活动的各个方面。无处不在的计算，一个不到30年前提出的愿景，现在已经到来。与此同时，由于生活方式和与年龄有关的慢性疾病以及多种合并症的结合，存在着全球性的健康危机。计算密集型健康行为干预措施可能是减少这一危机后果的最有力方法之一，但卫生研究和实践需要新方法，需要证据来支持其广泛使用。挑战有很多，包括不愿放弃在自我报告数据时代出现的过时的健康行为理论和模型——以及更广泛的健康干预措施；预防、诊断和治疗的医学模式；科学方法建立在稀疏而昂贵的数据基础上。在证明新方法确实有效的过程中，也存在许多固有的挑战。潜在的解决方案可以在利用研究方法中找到，这些方法在其他领域，特别是工程领域已经被证明是成功的。可能需要一种更“敏捷的科学”，以简化各种方法，通过这些方法来显示卫生干预措施的要素是否有效，并更快地部署和迭代改进那些有效的方法。本文所讨论的问题以及本主题问题的论文还有很多工作要做。基于这些新模型和方法的干预措施是否与目前可用的干预措施同样有效，这仍然是一个悬而未决的问题。需要对这些新方法进行经济分析，因为与其他方法相比的净资产假设只是假设。以人为本的设计研究需要确保用户最终受益。最后，需要一个转化研究议程，因为现状可能会抵制改变。(C) 2016年由Elsevier Inc.代表《美国预防医学杂志》出版
This paper addresses the rapid pace of change in the technologies that support digital interventions; the complexity of the health problems they aim to address; and the adaptation of scientific methods to accommodate the volume, velocity, and variety of data and interventions possible from these technologies. Information, communication, and computing technologies are now part of every societal domain and support essentially every facet of human activity. Ubiquitous computing, a vision articulated fewer than 30 years ago, has now arrived. Simultaneously, there is a global crisis in health through the combination of lifestyle and age-related chronic disease and multiple comorbidities. Computationally intensive health behavior interventions may be one of the most powerful methods to reduce the consequences of this crisis, but new methods are needed for health research and practice, and evidence is needed to support their widespread use.The challenges are many, including a reluctance to abandon timeworn theories and models of health behavior-and health interventions more broadly-that emerged in an era of self-reported data; medical models of prevention, diagnosis, and treatment; and scientific methods grounded in sparse and expensive data. There are also many challenges inherent in demonstrating that newer approaches are, indeed, effective. Potential solutions may be found in leveraging methods of research that have been shown to be successful in other domains, particularly engineering. A more "agile science" may be needed that streamlines the methods through which elements of health interventions are shown to work or not, and to more rapidly deploy and iteratively improve those that do. There is much to do to advance the issues discussed in this paper, and the papers in this theme issue. It remains an open question whether interventions based in these new models and methods are, in fact, equally if not more efficacious as what is available currently. Economic analyses of these new approaches are needed because assumptions of net worth compared to other approaches are just that, assumptions. Human-centered design research is needed to ensure that users ultimately benefit. Finally, a translational research agenda will be needed, as the status quo will likely be resistant to change. (C) 2016 Published by Elsevier Inc. on behalf of American Journal of Preventive Medicine