Excessive alcohol use contributes significantly to physical and psychological illness, injury and death, and a wide array of social harm in all age groups. A proven strategy for reducing excessive alcohol consumption levels is to offer a brief conversation-based intervention in primary care settings, but more recent technological innovations have enabled people to interact directly via computer, mobile device or smartphone with digital interventions designed to address problem alcohol consumption.
To assess the effectiveness and cost-effectiveness of digital interventions for reducing hazardous and harmful alcohol consumption, alcohol-related problems, or both, in people living in the community, specifically: (i) Are digital interventions more effective and cost-effective than no intervention (or minimal input) controls? (ii) Are digital interventions at least equally effective as face-to-face brief alcohol interventions? (iii) What are the effective component behaviour change techniques (BCTs) of such interventions and their mechanisms of action? (iv) What theories or models have been used in the development and/or evaluation of the intervention? Secondary objectives were (i) to assess whether outcomes differ between trials where the digital intervention targets participants attending health, social care, education or other community-based settings and those where it is offered remotely via the internet or mobile phone platforms; (ii) to specify interventions according to their mode of delivery (e.g. functionality features) and assess the impact of mode of delivery on outcomes.
We searched CENTRAL, MEDLINE, PsycINFO, CINAHL, ERIC, HTA and Web of Knowledge databases; ClinicalTrials.com and WHO ICTRP trials registers and relevant websites to April 2017. We also checked the reference lists of included trials and relevant systematic reviews.
We included randomised controlled trials (RCTs) that evaluated the effectiveness of digital interventions compared with no intervention or with face-to-face interventions for reducing hazardous or harmful alcohol consumption in people living in the community and reported a measure of alcohol consumption.
Data collection and analysis
We used standard methodological procedures expected by The Cochrane Collaboration.
We included 57 studies which randomised a total of 34,390 participants. The main sources of bias were from attrition and participant blinding (36% and 21% of studies respectively, high risk of bias). Forty one studies (42 comparisons, 19,241 participants) provided data for the primary meta-analysis, which demonstrated that participants using a digital intervention drank approximately 23 g alcohol weekly (95% CI 15 to 30) (about 3 UK units) less than participants who received no or minimal interventions at end of follow up (moderate-quality evidence).
Fifteen studies (16 comparisons, 10,862 participants) demonstrated that participants who engaged with digital interventions had less than one drinking day per month fewer than no intervention controls (moderate-quality evidence), 15 studies (3587 participants) showed about one binge drinking session less per month in the intervention group compared to no intervention controls (moderate-quality evidence), and in 15 studies (9791 participants) intervention participants drank one unit per occasion less than no intervention control participants (moderate-quality evidence).
Only five small studies (390 participants) compared digital and face-to-face interventions. There was no difference in alcohol consumption at end of follow up (MD 0.52 g/week, 95% CI -24.59 to 25.63; low-quality evidence). Thus, digital alcohol interventions produced broadly similar outcomes in these studies. No studies reported whether any adverse effects resulted from the interventions.
A median of nine BCTs were used in experimental arms (range = 1 to 22). ‘B’ is an estimate of effect (MD in quantity of drinking, expressed in g/week) per unit increase in the BCT, and is a way to report whether individual BCTs are linked to the effect of the intervention. The BCTs of goal setting (B -43.94, 95% CI -78.59 to -9.30), problem solving (B -48.03, 95% CI -77.79 to -18.27), information about antecedents (B -74.20, 95% CI -117.72 to -30.68), behaviour substitution (B -123.71, 95% CI -184.63 to -62.80) and credible source (B -39.89, 95% CI -72.66 to -7.11) were significantly associated with reduced alcohol consumption in unadjusted models. In a multivariable model that included BCTs with B > 23 in the unadjusted model, the BCTs of behaviour substitution (B -95.12, 95% CI -162.90 to -27.34), problem solving (B -45.92, 95% CI -90.97 to -0.87), and credible source (B -32.09, 95% CI -60.64 to -3.55) were associated with reduced alcohol consumption.
The most frequently mentioned theories or models in the included studies were Motivational Interviewing Theory (7/20), Transtheoretical Model (6/20) and Social Norms Theory (6/20). Over half of the interventions (n = 21, 51%) made no mention of theory. Only two studies used theory to select participants or tailor the intervention. There was no evidence of an association between reporting theory use and intervention effectiveness.
There is moderate-quality evidence that digital interventions may lower alcohol consumption, with an average reduction of up to three (UK) standard drinks per week compared to control participants. Substantial heterogeneity and risk of performance and publication bias may mean the reduction was lower. Low-quality evidence from fewer studies suggested there may be little or no difference in impact on alcohol consumption between digital and face-to-face interventions.
The BCTs of behaviour substitution, problem solving and credible source were associated with the effectiveness of digital interventions to reduce alcohol consumption and warrant further investigation in an experimental context.
Reporting of theory use was very limited and often unclear when present. Over half of the interventions made no reference to any theories. Limited reporting of theory use was unrelated to heterogeneity in intervention effectiveness.
Plain language summary
Does personalised advice via computer or mobile devices reduce heavy drinking?
We aimed to find out if personalised advice to reduce heavy drinking provided using a computer or mobile device is better than nothing or printed information. We also compared advice provided using a computer or mobile device to advice given in a face-to-face conversation. The main outcome was how much alcohol people drank.
Heavy drinking causes over 60 diseases, as well as many accidents, injuries and early deaths each year. Brief advice or counselling, delivered by doctors or nurses, can help people reduce their drinking by around 4 to 5 units a week. In the UK, this is around two pints (1.13 L) of beer or half a bottle of wine (375 mL) each week. However, people may be embarrassed by talking about alcohol.
Current to March 2017.
The studies included people in workplaces, colleges or health clinics and internet users. Everyone typed information about their drinking into a computer or mobile device – which then gave half the people advice about how much they drank and the effect this has on health. This group also received suggestions about how to cut down on drinking. The other group could sometimes read general health information. Between one month and one year later, everyone was asked to confirm how much they were drinking. Drinking levels in both groups were compared to each other at these time points.
Study funding sources
Many (56%) studies were funded by government or research foundation funds. Some (11%) were funded by personal awards such as PhD fellowships. The rest did not report sources of funding.
We included 57 studies comparing the drinking of people getting advice about alcohol from computers or mobile devices with those who did not after one to 12 months. Of these, 41 studies (42 comparisons, 19,241 participants) focused on the actual amounts that people reported drinking each week. Most people reported drinking less if they received advice about alcohol from a computer or mobile device compared to people who did not get this advice.
Evidence shows that the amount of alcohol people cut down may be about 1.5 pints (800 mL) of beer or a third of a bottle of wine (250 mL) each week. Other measures supported the effectiveness of digital alcohol interventions, although the size of the effect tended to be smaller than for overall alcohol consumption. Positive differences in measures of drinking were seen at 1, 6 and 12 months after the advice.
There was not enough information to help us decide if advice was better from computers, telephones or the internet to reduce risky drinking. We do not know which pieces of advice were the most important to help people reduce problem drinking. However, advice from trusted people such as doctors seemed helpful, as did recommendations that people think about specific ways they could overcome problems that might prevent them from drinking less and suggestions about things to do instead of drinking. We included five studies which compared the drinking of people who got advice from computers or mobile devices with advice from face-to-face conversations with doctors or nurses; there may be little or no difference between these to reduce heavy drinking.
No studies reported whether any harm came from the interventions.
Personalised advice using computers or mobile devices may help people reduce heavy drinking better than doing nothing or providing only general health information. Personalised advice through computers or mobile devices may make little or no difference to reduce drinking compared to face-to-face conversation.
Quality of the evidence
Evidence was moderate-to-low quality.