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Identification of behaviour change techniques and engagement strategies to design a smartphone app to reduce alcohol consumption using a formal consensus method

Garnett, CV; Crane, D; West, R; Brown, J; Michie, S; (2015) Identification of behaviour change techniques and engagement strategies to design a smartphone app to reduce alcohol consumption using a formal consensus method. JMIR mHealth and uHealth , 3 (2) , Article e73. 10.2196/mhealth.3895. Green open access

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Abstract

Background: Digital interventions to reduce excessive alcohol consumption have the potential to have a broader reach and be more cost-effective than traditional brief interventions. However, there is not yet a strong evidence base on their ability to engage users or on their effectiveness. Objective: This study aimed to identify the behaviour change techniques (BCTs) and engagement strategies most worthy of further study by inclusion in a smartphone application (app) to reduce alcohol consumption, using formal expert consensus methods. Methods: The first phase of the study consisted of a Delphi exercise with three rounds. It was conducted with seven international experts in the field of alcohol and/or behaviour change. In the first round, experts identified BCTs most likely to be effective at reducing alcohol consumption and strategies most likely to engage users with an app; these were rated in the second round; and those rated as effective by at least four out of seven participants were ranked in the third round. The rankings were analysed using Kendall’s W coefficient of concordance, which indicates consensus between participants. The second phase consisted of a new, independent group of experts (n=43) ranking the BCTs that were identified in the first phase. The correlation between the rankings of the two groups was assessed using Spearman’s rank correlation coefficient. Results: Twelve BCTs were identified as likely to be effective. There was moderate agreement among the experts over their ranking (W=.465, χ2(11)=35.77, P<.001) and the BCTs receiving the highest mean rankings were self-monitoring, goal-setting, action planning, and feedback in relation to goals. There was a significant correlation between the ranking of the BCTs by the group of experts who identified them and a second independent group of experts (Spearman’s rho=.690, P=.01). Seventeen responses were generated for strategies likely to engage users. There was moderate agreement among experts on the ranking of these engagement strategies (W=.563, χ2(15)=59.16, P<.001) and those with the highest mean rankings were ease of use, design – aesthetic, feedback, function, design – ability to change design to suit own preferences, tailored information, and unique smartphone features. Conclusions: The BCTs with greatest potential to include in a smartphone app to reduce alcohol consumption were judged by experts to be self-monitoring, goal-setting, action planning, and feedback in relation to goals. The strategies most likely to engage users were ease of use, design, tailoring of design and information, and unique smartphone features.

Type: Article
Title: Identification of behaviour change techniques and engagement strategies to design a smartphone app to reduce alcohol consumption using a formal consensus method
Open access status: An open access version is available from UCL Discovery
DOI: 10.2196/mhealth.3895
Publisher version: http://dx.doi.org/10.2196/mhealth.3895
Additional information: © Claire Garnett, David Crane, Robert West, Jamie Brown, Susan Michie. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 29.06.2015. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR mhealth and uhealth, is properly cited. The complete bibliographic information, a link to the original publication on http://mhealth.jmir.org/, as well as this copyright and license information must be included.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
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
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Behavioural Science and Health
URI: https://discovery.ucl.ac.uk/id/eprint/1464205
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