Digital interventions for reducing alcohol consumption: do they work and if so, how and why?

By Dr. Claire Garnett, University College London

(This blog was first published by Alcohol Policy UK on 3rd April 2018 here)

Digital interventions are those delivered via computer technology, which includes smartphone apps, websites, computer programs, wearable devices, and telecommunications, that aim to promote behaviour change. Digital interventions for alcohol reduction have a number of advantages over the traditional face-to-face brief interventions such as: having a low cost per user, greater reach, can avoid the stigma associated with receiving help in person and are highly convenient in that they can be used as and when the individual wants. However, there is little evidence as to whether digital interventions are effective at reducing alcohol consumption.

A recent systematic review aimed to answer whether digital interventions are more effective at reducing alcohol consumption compared with no intervention controls. The review included 41 randomised controlled trials (RCTs) that evaluated the effectiveness of digital interventions for reducing hazardous or harmful alcohol consumption in people living in the community (the studies included people in workplaces, colleges or health clinics, and internet users). The primary outcome measure was the quantity of alcohol consumed in grams of alcohol per week (a UK unit is 8 grams). Participants were followed up between one month and one year later, and everyone was asked again how much they were drinking. The majority of digital interventions were web-based, though some trials involved a computer program and one used a smartphone app. The no-intervention control groups included assessment only, waiting list control groups and standard health-related information.

Do they work?

The 41 studies included in the review had 42 comparisons between a digital intervention and a no intervention control, and had 19,241 participants in total. The review showed that participants using a digital intervention drank 22.8 g alcohol weekly less than participants who received no or minimal interventions at end of follow up. This is equivalent to about 3 UK units. This evidence was classified as “moderate-quality” as the true effect is likely to be similar to this estimate of the effect size, though there is a possibility it is substantially different.

How do they work?

Behaviour change techniques (BCTs) are the “active ingredients” in an intervention and can be used to specify intervention content and, in turn, to assess how interventions work. Additional questions asked as part of this systematic review were:

1. What BCTs are used in digital alcohol interventions?
2. Are particular BCTs associated with the effectiveness of the digital alcohol intervention?

A median of nine BCTs were used in the digital interventions and the five most frequently used BCTs were: Feedback on drinking behaviour; Social comparison; Information about social and environmental consequences;Feedback on outcomes of behaviour; and Social support.

Three BCTs were significantly associated with the effectiveness of the digital intervention (i.e. the reduction in alcohol consumption). These were:

1. Behaviour substitution: Prompt substitution of the unwanted drinking behaviour with a wanted or neutral behaviour.
2. Problem solving:Analyse, or prompt the person to analyse, factors influencing the drinking behaviour and generate or select strategies that include overcoming barriers and/or increasing facilitators.
3. Credible source: Present verbal or visual communication from a credible source in favour of or against the behaviour.

Why do they work?

Behaviour change theories explain why a behaviour does or does not occur. Applying theory to the development and evaluation of interventions can increase their effectiveness and provide an understanding of why interventions do or do not work, which can inform future interventions. However, few interventions report this. This systematic review also answered the following questions:

1. What theories or models have been used in the development and/or evaluation of the intervention?
2. To what extent is theory use reported by the digital alcohol interventions?
3. Is there an association between intervention effectiveness and use of theory?

The most frequently mentioned theories or models in the included studies were Motivational Interviewing Theory, Transtheoretical Model and Social Norms Theory. Over half of the interventions made no mention of theory and about only 40% used theory to select or develop the intervention content. The limited reporting of theory use was unrelated to intervention effectiveness.

What this all means…

Digital interventions may lower alcohol consumption, with an average reduction of up to three (UK) units per week compared with control participants. This means that there is good evidence for the use of digital interventions to help people reduce their alcohol consumption. Although the changes are not quite as large as those seen in face to face interventions in primary care which have shown average reductions of closer to 5 units per week.

Anyone designing future digital alcohol interventions should consider that the BCTs of behaviour substitution, problem solving and credible source were associated with intervention effectiveness. For researchers, clearer selection, application, and reporting of theory use is needed to accurately assess how useful theory is in this field and to advance the field of behaviour change theories. Meanwhile, policy leads or practitioners may view this emerging evidence as tentative support for the role that digital alcohol interventions can play as part of the wider ‘intervention mix’ in helping drinkers reduce hazardous or harmful alcohol consumption.

Bio:

Dr Claire Garnett is a Research Associate in UCL’s Tobacco and Alcohol Research Group. Her research interests include health behaviours, behaviour change interventions, development and evaluation of digital interventions, and excessive alcohol consumption as a behaviour change target.