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Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations

Overview of attention for article published in Cochrane database of systematic reviews, September 2017
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  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (95th percentile)

Mentioned by

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5 news outlets
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108 tweeters
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10 Facebook pages

Citations

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84 Dimensions

Readers on

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393 Mendeley
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Title
Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations
Published in
Cochrane database of systematic reviews, September 2017
DOI 10.1002/14651858.cd011479.pub2
Pubmed ID
Authors

Eileen FS Kaner, Fiona R Beyer, Claire Garnett, David Crane, Jamie Brown, Colin Muirhead, James Redmore, Amy O'Donnell, James J Newham, Frank de Vocht, Matthew Hickman, Heather Brown, Gregory Maniatopoulos, Susan Michie

Abstract

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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 108 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 393 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Unknown 392 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 66 17%
Researcher 58 15%
Student > Ph. D. Student 56 14%
Student > Bachelor 46 12%
Other 18 5%
Other 62 16%
Unknown 87 22%
Readers by discipline Count As %
Medicine and Dentistry 96 24%
Psychology 63 16%
Nursing and Health Professions 45 11%
Social Sciences 34 9%
Computer Science 10 3%
Other 37 9%
Unknown 108 27%

Attention Score in Context

This research output has an Altmetric Attention Score of 111. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 May 2020.
All research outputs
#183,831
of 15,622,016 outputs
Outputs from Cochrane database of systematic reviews
#380
of 11,224 outputs
Outputs of similar age
#6,464
of 277,780 outputs
Outputs of similar age from Cochrane database of systematic reviews
#10
of 241 outputs
Altmetric has tracked 15,622,016 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 11,224 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.3. This one has done particularly well, scoring higher than 96% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 277,780 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 241 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 95% of its contemporaries.