Thomas Craig, KJ;
Morgan, LC;
Chen, C-H;
Michie, S;
Fusco, N;
Snowdon, JL;
Scheufele, E;
... Sill, S; + view all
(2020)
Systematic review of context-aware digital behavior change interventions to improve health.
Translational Behavioral Medicine
10.1093/tbm/ibaa099.
(In press).
Preview |
Text
ibaa099.pdf - Published Version Download (1MB) | Preview |
Abstract
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.
| Type: | Article |
|---|---|
| Title: | Systematic review of context-aware digital behavior change interventions to improve health |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/tbm/ibaa099 |
| Publisher version: | http://dx.doi.org/10.1093/tbm/ibaa099 |
| Language: | English |
| Additional information: | © The Author(s) 2020. Published by Oxford University Press on behalf of the Society of Behavioral Medicine. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
| Keywords: | Artificial intelligence, Digital behavior change interventions, Internet of Things, Machine learning, mHealth |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10114023 |
Archive Staff Only
 |
View Item |
