Developing contextually specific interventions to reduce alcohol consumption amongst increasing and higher risk drinkers within the Drink Less App
Lead Research Organisation:
University College London
Department Name: Behavioural Science and Health
Abstract
We aim to improve the effectiveness of the health app, Drink Less, by making it more tailored to how people actually drink. Drink Less was designed by researchers to help heavier drinkers reduce their alcohol consumption. We will develop two new parts of the app. These will provide more personalised support to people by using information about their specific drinking contexts, which could include where someone drinks or who they drink with.
The contexts in which people drink and their reasons for drinking are highly variable and previous research suggests drinkers do not think about their alcohol consumption in terms of a weekly total, but as individual drinking occasions that play different roles in their daily lives. It is likely that some drinking occasions are more open to change than others, and that strategies for cutting down will be more effective for some types of occasions. As such, personalising intervention strategies to the contexts in which individuals drink may be more effective than providing the same support to everyone who uses the app.
The app currently contains a drinking diary where people can record what they drink each day. We will modify this diary so that people can add the contexts in which they drink. We will compare two approaches to collecting this information, to determine the most effective and acceptable. Two groups of participants will use two different versions of the app, which will be identical apart from the drinking diary. Participants will be randomly allocated to either select all of the contextual characteristics that apply to their drinking occasion (e.g. they drank in a pub, and they drank with friends), or will choose from a list of pre-specified occasion types (e.g. a 'big night out', 'quiet night in'). We will then ask participants to rate the app in terms of how easy it is to use. We will use these ratings, alongside engagement data (e.g. how much the apps were used) and interviews with some participants to decide which version is best.
The next step will be to adapt two of the existing intervention components, Self-Monitoring & Feedback and Action Planning, so that they make use of this contextual information. The Self-Monitoring & Feedback component currently lets people view how much they have drunk and what impact this has on their mood and sleep. This component will be developed so that it also provides information on the drinking context (e.g. it could inform people that when they drink more heavily they tend to be in a particular location or with particular people). The Action Planning component is where people set themselves goals to reduce their consumption. The contextual information here will be used to help people set more specific goals which may be more effective. For example, someone who records drinking heavily in the pub with friends may be prompted to set a goal to avoid drinking in rounds or to order soft drinks between alcoholic ones. Whereas someone who records drinking more heavily at home may set goals to reduce the amount of alcohol they buy in the supermarket or to measure how much alcohol they are pouring. These will then be rated by users and behaviour change experts in interviews and focus groups.
The final part of this project will be to refine the two contextualised intervention components to make sure that they are clear, appropriate, in line with theory and technically sound. This will be done by interviews with potential app users, researchers and the app developer. We will also do some work evaluating how acceptable the newly developed intervention components are to users, as it is important that users feel comfortable with using the app.
The contexts in which people drink and their reasons for drinking are highly variable and previous research suggests drinkers do not think about their alcohol consumption in terms of a weekly total, but as individual drinking occasions that play different roles in their daily lives. It is likely that some drinking occasions are more open to change than others, and that strategies for cutting down will be more effective for some types of occasions. As such, personalising intervention strategies to the contexts in which individuals drink may be more effective than providing the same support to everyone who uses the app.
The app currently contains a drinking diary where people can record what they drink each day. We will modify this diary so that people can add the contexts in which they drink. We will compare two approaches to collecting this information, to determine the most effective and acceptable. Two groups of participants will use two different versions of the app, which will be identical apart from the drinking diary. Participants will be randomly allocated to either select all of the contextual characteristics that apply to their drinking occasion (e.g. they drank in a pub, and they drank with friends), or will choose from a list of pre-specified occasion types (e.g. a 'big night out', 'quiet night in'). We will then ask participants to rate the app in terms of how easy it is to use. We will use these ratings, alongside engagement data (e.g. how much the apps were used) and interviews with some participants to decide which version is best.
The next step will be to adapt two of the existing intervention components, Self-Monitoring & Feedback and Action Planning, so that they make use of this contextual information. The Self-Monitoring & Feedback component currently lets people view how much they have drunk and what impact this has on their mood and sleep. This component will be developed so that it also provides information on the drinking context (e.g. it could inform people that when they drink more heavily they tend to be in a particular location or with particular people). The Action Planning component is where people set themselves goals to reduce their consumption. The contextual information here will be used to help people set more specific goals which may be more effective. For example, someone who records drinking heavily in the pub with friends may be prompted to set a goal to avoid drinking in rounds or to order soft drinks between alcoholic ones. Whereas someone who records drinking more heavily at home may set goals to reduce the amount of alcohol they buy in the supermarket or to measure how much alcohol they are pouring. These will then be rated by users and behaviour change experts in interviews and focus groups.
The final part of this project will be to refine the two contextualised intervention components to make sure that they are clear, appropriate, in line with theory and technically sound. This will be done by interviews with potential app users, researchers and the app developer. We will also do some work evaluating how acceptable the newly developed intervention components are to users, as it is important that users feel comfortable with using the app.
Technical Summary
Emerging research increasingly highlights the need to view alcohol consumption as an occasion-level phenomenon, rather than a weekly number of drinks. We will adapt two intervention components of an existing app, Drink Less, so that they incorporate information about the context of an individual's drinking occasions (e.g. location, company etc.) to provide targeted support in cutting down.
In Stage 1, two approaches to collecting contextual data in the Drink Less drinking diary will be compared. 50 new app users will use one of two versions of the app. In version 1 separate characteristics of drinking occasions will be recorded (e.g. location, company) whilst in version 2 predominant occasion types in the UK (e.g. 'big night out') will be listed so that people can select the occasion type most similar to theirs. After two weeks of use, participants will evaluate the usability of the app via a survey and interviews with a subset of participants. This, alongside engagement data will be used to determine which method will be taken forwards to intervention development.
In Stage 2 we will co-produce with end-users and key stakeholders two adapted intervention components, Self-Monitoring & Feedback and Action Planning. These interventions will be targeted at individual drinking contexts as measured in Stage 1. Contextually specific messaging will be developed with reference to behaviour change theories such as COM-B and Michie et al's. Behaviour Change Technique Taxonomy and through consultation with experts and PPI groups. Focus groups will then be held with potential users who will evaluate the messaging. This will determine the final design of the two contextualised drinking components.
In Stage 3 to refine the developed interventions, we will conduct interviews with users, an app developer, and experts in mHealth in order to ensure the developed interventions are technically and theoretically sound, helpful, visually appealing and acceptable to users.
In Stage 1, two approaches to collecting contextual data in the Drink Less drinking diary will be compared. 50 new app users will use one of two versions of the app. In version 1 separate characteristics of drinking occasions will be recorded (e.g. location, company) whilst in version 2 predominant occasion types in the UK (e.g. 'big night out') will be listed so that people can select the occasion type most similar to theirs. After two weeks of use, participants will evaluate the usability of the app via a survey and interviews with a subset of participants. This, alongside engagement data will be used to determine which method will be taken forwards to intervention development.
In Stage 2 we will co-produce with end-users and key stakeholders two adapted intervention components, Self-Monitoring & Feedback and Action Planning. These interventions will be targeted at individual drinking contexts as measured in Stage 1. Contextually specific messaging will be developed with reference to behaviour change theories such as COM-B and Michie et al's. Behaviour Change Technique Taxonomy and through consultation with experts and PPI groups. Focus groups will then be held with potential users who will evaluate the messaging. This will determine the final design of the two contextualised drinking components.
In Stage 3 to refine the developed interventions, we will conduct interviews with users, an app developer, and experts in mHealth in order to ensure the developed interventions are technically and theoretically sound, helpful, visually appealing and acceptable to users.