Systematic review of context-aware digital behavior change interventions to improve health.
Systematic review of context-aware digital behavior change interventions to improve health.
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DOI：
10.1093/tbm/ibaa099
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发表时间：
2021-05-25
影响因子：
3.6
通讯作者：
Sill S
中科院分区：
文献类型：
--
作者：
Thomas Craig KJ;Morgan LC;Chen CH;Michie S;Fusco N;Snowdon JL;Scheufele E;Gagliardi T;Sill S
健康危险行为是发病率、与慢性病相关的过早死亡和不断上升的医疗费用的主要贡献者。然而，传统的改变健康行为的干预措施往往效果不大，适用性和规模有限。为了更好地支持整个护理连续体的健康改善目标，正在利用结合各种智能技术的新方法来创建更个性化的数字行为改变干预(DBCI)。这项研究的目的是确定提供个性化干预以改善健康的情景感知DBCI。从多个数据库和人工搜索对已发表的文献(2013-2020年)进行了系统的审查。所有纳入的DBCI都是情景感知的自动化数字健康技术，用户输入、活动或位置影响干预。纳入的研究涉及明确的健康行为和报告的行为改变结果数据。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。其中包括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。最常采用的行为改变技术是反馈和监控、塑造知识、联想、目标和规划的组合。用于以背景感知的自动化方式应用这些技术的技术包括需要不同程度地访问数据的分析和人工智能(例如，机器学习和符号推理)方法。研究表明，在体力活动、饮食行为、服药依从性和防晒实践方面都有所改善。情境感知DBCI有效地支持用户的行为改变，从而改善用户的健康行为。数字健康技术可以有效地接触到日常生活背景下的个人，以改善健康行为。
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. Digital health technologies can effectively reach individuals within the context of their daily lives to improve health behaviors.
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影响因子：
7.4
作者：
Duan YP;Liang W;Guo L;Wienert J;Si GY;Lippke S
通讯作者：
Lippke S
影响因子：
6.1
作者：
Block, G.;Azar, K. M. J.;Romanelli, R. J.;Block, T. J.;Palaniappan, L. P.;Dolginsky, M.;Block, C. H.
通讯作者：
Block, C. H.
影响因子：
10.9
作者：
Buller, David B.;Berwick, Marianne;Liu, Xia
通讯作者：
Liu, Xia
影响因子：
4.1
作者：
Boerema, Simone;van Velsen, Lex;Hermens, Hermie
通讯作者：
Hermens, Hermie
B-Mobile-一项基于智能手机的干预措施，以减少超重/肥胖个体的久坐时间：受试者内试验。
影响因子：
3.7
作者：
Bond DS;Thomas JG;Raynor HA;Moon J;Sieling J;Trautvetter J;Leblond T;Wing RR
通讯作者：
Wing RR