Systematic review of context-aware digital behavior change interventions to improve health.

Autor:	Thomas Craig KJ; Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA., Morgan LC; Oncology, Imaging, and Life Sciences, IBM Watson Health, Cambridge, MA, USA., Chen CH; Computational Health Behavior and Decision Sciences, IBM Research, Yorktown Heights, NY, USA., Michie S; Centre for Behavior Change, University College London, London, UK., Fusco N; Oncology, Imaging, and Life Sciences, IBM Watson Health, Cambridge, MA, USA., Snowdon JL; Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA., Scheufele E; Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA., Gagliardi T; Center for AI, Research, and Evaluation, IBM Watson Health, Cambridge, MA, USA., Sill S; Oncology, Imaging, and Life Sciences, IBM Watson Health, Cambridge, MA, USA.
Jazyk:	angličtina
Zdroj:	Translational behavioral medicine [Transl Behav Med] 2021 May 25; Vol. 11 (5), pp. 1037-1048.
DOI:	10.1093/tbm/ibaa099
Abstrakt:	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. (© The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Behavioral Medicine.)
Databáze:	MEDLINE
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