Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA.
Oncology, Imaging, and Life Sciences, IBM Watson Health, Cambridge, MA, USA.
Transl Behav Med. 2021 May 25;11(5):1037-1048. doi: 10.1093/tbm/ibaa099.
Health risk behaviors are leading contributors to morbidity, premature mortality associated with chronic diseases, and escalating health costs. However, traditional interventions to change health behaviors often have modest effects, and limited applicability and scale. To better support health improvement goals across the care continuum, new approaches incorporating various smart technologies are being utilized to create more individualized digital behavior change interventions (DBCIs). The purpose of this study is to identify context-aware DBCIs that provide individualized interventions to improve health. A systematic review of published literature (2013-2020) was conducted from multiple databases and manual searches. All included DBCIs were context-aware, automated digital health technologies, whereby user input, activity, or location influenced the intervention. Included studies addressed explicit health behaviors and reported data of behavior change outcomes. Data extracted from studies included study design, type of intervention, including its functions and technologies used, behavior change techniques, and target health behavior and outcomes data. Thirty-three articles were included, comprising mobile health (mHealth) applications, Internet of Things wearables/sensors, and internet-based web applications. The most frequently adopted behavior change techniques were in the groupings of feedback and monitoring, shaping knowledge, associations, and goals and planning. Technologies used to apply these in a context-aware, automated fashion included analytic and artificial intelligence (e.g., machine learning and symbolic reasoning) methods requiring various degrees of access to data. Studies demonstrated improvements in physical activity, dietary behaviors, medication adherence, and sun protection practices. Context-aware DBCIs effectively supported behavior change to improve users' health behaviors.
健康风险行为是导致发病和与慢性病相关的过早死亡以及不断攀升的医疗成本的主要因素。然而，传统的改变健康行为的干预措施往往效果有限，适用性和规模有限。为了更好地支持整个医疗保健连续体中的健康改善目标，正在利用新的方法来整合各种智能技术，以创建更具个性化的数字行为改变干预措施 (DBCIs)。本研究旨在确定提供个性化干预措施以改善健康的情境感知 DBCIs。对来自多个数据库和手动搜索的已发表文献进行了系统综述（2013-2020 年）。所有纳入的 DBCIs 都是情境感知的自动化数字健康技术，用户的输入、活动或位置会影响干预措施。纳入的研究都涉及明确的健康行为，并报告了行为改变结果的数据。从研究中提取的数据包括研究设计、干预类型，包括其功能和使用的技术、行为改变技术以及目标健康行为和结果数据。共纳入 33 篇文章，包括移动健康 (mHealth) 应用程序、物联网可穿戴设备/传感器和基于互联网的网络应用程序。采用最多的行为改变技术是在反馈和监测、塑造知识、关联和目标以及计划分组中。用于以情境感知、自动化方式应用这些技术的技术包括需要不同程度数据访问的分析和人工智能（例如，机器学习和符号推理）方法。研究表明，这些技术可以改善身体活动、饮食行为、药物依从性和防晒措施。情境感知 DBCIs 有效地支持行为改变，以改善用户的健康行为。
