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BMC Public Health

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Open Access 01-12-2016 | Study protocol

Evaluating the effectiveness of a smartphone app to reduce excessive alcohol consumption: protocol for a factorial randomised control trial

Authors: Claire Garnett, David Crane, Susan Michie, Robert West, Jamie Brown

Published in: BMC Public Health | Issue 1/2016

Abstract

Background

Excessive alcohol consumption is a leading cause of death and morbidity worldwide and interventions to help people reduce their consumption are needed. Interventions delivered by smartphone apps have the potential to help harmful and hazardous drinkers reduce their consumption of alcohol. However, there has been little evaluation of the effectiveness of existing smartphone interventions.

A systematic review, amongst other methodologies, identified promising modular content that could be delivered by an app: self-monitoring and feedback; action planning; normative feedback; cognitive bias re-training; and identity change. This protocol reports a factorial randomised controlled trial to assess the comparative potential of these five intervention modules to reduce excessive alcohol consumption.

Methods

A between-subject factorial randomised controlled trial. Hazardous and harmful drinkers aged 18 or over who are making a serious attempt to reduce their drinking will be randomised to one of 32 (2⁵) experimental conditions after downloading the ‘Drink Less’ app. Participants complete baseline measures on downloading the app and are contacted after 1-month with a follow-up questionnaire. The primary outcome measure is change in past week consumption of alcohol. Secondary outcome measures are change in AUDIT score, app usage data and usability ratings for the app. A factorial between-subjects ANOVA will be conducted to assess main and interactive effects of the five intervention modules for the primary and secondary outcome measures.

Discussion

This study will establish the extent to which the five intervention modules offered in this app can help reduce hazardous and harmful drinking. This is the first step in optimising and understanding what component parts of an app could help to reduce excessive alcohol consumption. The findings from this study will be used to inform the content of a future integrated treatment app and evaluated against a minimal control in a definitive randomised control trial with long-term outcomes.

Trial registration

ISRCTN40104069 Date of registration: 10/2/2016

Available only for authorised users

World Health Organization. Global status report on alcohol and health-2014. 2014.

Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012;380(9859):2224–60.CrossRefPubMedPubMedCentral

HM Government. The Government’s Alcohol Strategy. London; 2012. https://www.gov.uk/government/publications/alcohol-strategy.

Public Health England. Alcohol treatment in England 2012–13. 2013.

Kaner E, Dickinson H, Beyer F, Campbell F, Schlesinger C, Heather N, et al. Effectiveness of brief alcohol interventions in primary care populations (Review). Cochrane Libr. 2007;2.

Purshouse RC, Brennan A, Rafia R, Latimer NR, Archer RJ, Angus CR, et al. Modelling the cost-effectiveness of alcohol screening and brief interventions in primary care in England. Alcohol Alcohol. 2013;48(2):180–8. Available from: http://alcalc.oxfordjournals.org/content/48/2/180.short

Angus C, Latimer N, Preston L, Li J, Purshouse R. What are the Implications for Policy Makers? A Systematic Review of the Cost-Effectiveness of Screening and Brief Interventions for Alcohol Misuse in Primary Care. Front Psychiatry. 2014;5:114. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4150206&tool=pmcentrez&rendertype=abstract

Brown J, West R, Angus C, Beard E, Brennan A, Drummond C, et al. Comparison of brief interventions in primary care on smoking and excessive alcohol consumption: a population survey in England. Br J Gen Pract. 2016;66(642):e1–9. Available from: http://bjgp.org/content/66/642/e1.abstract

Heather N, Dallolio E, Hutchings D, Kaner E, White M. Implementing routine screening and brief alcohol intervention in primary health care: A Delphi survey of expert opinion. J Subst Use. 2004;9(2):68–85.CrossRef

10.

Taylor CB, Luce KH. Computer- and internet-based psychotherapy interventions. Curr Dir Psychol Sci. 2003;12(1):18–22.CrossRef

11.

Marlatt G. Harm reduction: Come as you are. Addict Behav. 1996;21(6):779–88.CrossRefPubMed

12.

Vernon ML. A review of computer-based alcohol problem services designed for the general public. J Subst Abuse Treat. 2010;38(3):203–11. Available from: http://www.sciencedirect.com/science/article/pii/S0740547209001846

13.

Rooke S, Thorsteinsson E, Karpin A, Copeland J, Allsop D. Computer-delivered interventions for alcohol and tobacco use: a meta-analysis. Addiction. 2010;105(8):1381–90. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20528806.

14.

Riper H, Spek V, Boon B, Conijn B, Kramer J, Martin-Abello K, et al. Effectiveness of E-Self-help Interventions for Curbing Adult Problem Drinking: A Meta-analysis. J Med Internet Res. 2011;13(2):e42.CrossRefPubMedPubMedCentral

15.

White A, Kavanagh D, Stallman H, Klein B, Kay-Lambkin F, Proudfoot J, et al. Online alcohol interventions: a systematic review. J Med Internet Res. 2010;12(5):e62. Available from: http://www.jmir.org/2010/5/e62/

16.

Elliott JC, Carey KB, Bolles JR. Computer-based interventions for college drinking: a qualitative review. Addict Behav. 2008;33(8):994–1005. Available from: http://www.sciencedirect.com/science/article/pii/S030646030800083X

17.

Carey KB, Scott-Sheldon LAJ, Elliott JC, Bolles JR, Carey MP. Computer-delivered interventions to reduce college student drinking: a meta-analysis. Addiction. 2009;104(11):1807–19. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2763045&tool=pmcentrez&rendertype=abstract

18.

Khadjesari Z, Murray E, Hewitt C, Hartley S, Godfrey C. Can stand-alone computer-based interventions reduce alcohol consumption? A systematic review. Addiction. 2011;106(2):267–82. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21083832.

19.

Donoghue K, Patton R, Phillips T, Deluca P, Drummond C. The effectiveness of electronic screening and brief intervention for reducing levels of alcohol consumption: a systematic review and meta-analysis. J Med Internet Res. 2014;16(6):e142. Available from: http://www.jmir.org/2014/6/e142/

20.

Riper H, Blankers M, Hadiwijaya H, Cunningham J, Clarke S, Wiers R, et al. Effectiveness of guided and unguided low-intensity internet interventions for adult alcohol misuse: a meta-analysis. PLoS One Public Library of Science. 2014;9(6):e99912.CrossRef

21.

Kaner E, Beyer F, Brown J, Crane D, Garnett C, Hickman M, et al. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community-dwelling populations. Manuscript in preparation. 2016.

22.

Kaner EF, Beyer FR, Brown J, Crane D, Garnett C, Hickman M, Muirhead C, Redmore J, Michie S, de Vocht F. Personalised digital interventions for reducing hazardous and harmful alcohol consumption in community‐dwelling populations (Protocol). The Cochrane Library. 2015. Available from: http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD011479/abstract

23.

IMS Institute for Healthcare Informatics. Availability and profile of consumer mHealth apps. 2015.

24.

Research2Guidance. mHealth App Developer Economics 2015. 2015;(November):1–43.

25.

Bastawrous A, Armstrong MJ. Mobile health use in low- and high-income countries: an overview of the peer-reviewed literature. J R Soc Med SAGE Publications. 2013;106(4):130–42.CrossRef

26.

Azar KMJ, Lesser LI, Laing BY, Stephens J, Aurora MS, Burke LE, et al. Mobile applications for weight management: theory-based content analysis. Am J Prev Med Elsevier. 2013;45(5):583–9.CrossRef

27.

Cowan LT, Van Wagenen SA, Brown BA, Hedin RJ, Seino-Stephan Y, Hall PC, et al. Apps of steel: are exercise apps providing consumers with realistic expectations?: a content analysis of exercise apps for presence of behavior change theory. Health Educ Behav. 2013;40(2):133–9.CrossRefPubMed

28.

Abroms LC, Lee Westmaas J, Bontemps-Jones J, Ramani R, Mellerson J. A content analysis of popular smartphone apps for smoking cessation. Am J Prev Med Elsevier. 2013;45(6):732–6.CrossRef

29.

Wolf JA, Moreau JF, Akilov O, Patton T, English JC, Ho J, et al. Diagnostic inaccuracy of smartphone applications for melanoma detection. JAMA dermatology. 2013;149(4):422–6.CrossRefPubMedPubMedCentral

30.

Chomutare T, Fernandez-Luque L, Arsand E, Hartvigsen G. Features of mobile diabetes applications: review of the literature and analysis of current applications compared against evidence-based guidelines. J Med Internet Res. 2011;13(3):e65.CrossRefPubMedPubMedCentral

31.

Mobasheri MH, Johnston M, King D, Leff D, Thiruchelvam P, Darzi A. Smartphone breast applications - What’s the evidence? The Breast. 2014;23(5):683–9.CrossRefPubMed

32.

Liu F, Kong X, Cao J, Chen S, Li C, Huang J, et al. Mobile Phone Intervention and Weight Loss Among Overweight and Obese Adults: A Meta-Analysis of Randomized Controlled Trials. Am J Epidemiol. 2015;181(5):337–48.CrossRefPubMedPubMedCentral

33.

Lyzwinski LN. A systematic review and meta-analysis of mobile devices and weight loss with an Intervention Content Analysis. J Pers Med. 2014;4(3)311–85.CrossRefPubMedPubMedCentral

34.

Derbyshire E, Dancey D. Smartphone Medical Applications for Women’s Health: What Is the Evidence-Base and Feedback? Int J Telemed Appl. 2013;2013:9.

35.

Muntaner A, Vidal-Conti J, Palou P. Increasing physical activity through mobile device interventions: A systematic review. Health informatics journal. 2015 Feb 3:1460458214567004.

36.

Hamine S, Gerth-Guyette E, Faulx D, Green BB, Ginsburg AS. Impact of mHealth Chronic Disease Management on Treatment Adherence and Patient Outcomes: A Systematic Review. J Med Internet Res. 2015;17(2):e52.CrossRefPubMedPubMedCentral

37.

Karasouli E, Adams A. Assessing the Evidence for e-Resources for Mental Health Self-Management: A Systematic Literature Review. JMIR Mental Health. 2014;1(1):e3.CrossRefPubMedPubMedCentral

38.

Glynn LG, Hayes PS, Casey M, Glynn F, Alvarez-Iglesias A, Newell J, et al. Effectiveness of a smartphone application to promote physical activity in primary care: The SMART MOVE randomised controlled trial. Br J Gen Pract. 2014;64(624):384–91.CrossRef

39.

Smith JJ, Morgan PJ, Plotnikoff RC, Dally KA, Salmon J, Okely AD, et al. Smart-phone obesity prevention trial for adolescent boys in low-income communities: the ATLAS RCT. Pediatrics. 2014;134(3):e723–31.CrossRefPubMed

40.

Watts S, Mackenzie A, Thomas C, Griskaitis A, Mewton L, Williams A, et al. CBT for depression: a pilot RCT comparing mobile phone vs. computer. BMC Psychiatry. 2013;13(1):49.CrossRefPubMedPubMedCentral

41.

Kauer SD, Reid SC, Crooke AHD, Khor A, Hearps SJC, Jorm AF, et al. Self-monitoring Using Mobile Phones in the Early Stages of Adolescent Depression: Randomized Controlled Trial. J Med Internet Res. 2012;14(3):e67.CrossRefPubMedPubMedCentral

42.

Quinn CC, Clough SS, Minor JM, Lender D, Okafor MC, Gruber-Baldini A. WellDoc mobile diabetes management randomized controlled trial: change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technol Ther. 2008;10(3):160–8.CrossRefPubMed

43.

Gajecki M, Berman AH, Sinadinovic K, Rosendahl I, Andersson C. Mobile phone brief intervention applications for risky alcohol use among university students: a randomized controlled study. Addict Sci Clin Pract. 2014;9(1):11. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4091647&tool=pmcentrez&rendertype=abstract

44.

Michie S, Whittington C, Hamoudi Z, Zarnani F, Tober G, West R. Identification of behaviour change techniques to reduce excessive alcohol consumption. Addiction. 2012;107 (8):1431–40. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22340523.

45.

Garnett C, Crane D, West R, Brown J, Michie S. Identification of Behavior Change Techniques and Engagement Strategies to Design a Smartphone App to Reduce Alcohol Consumption Using a Formal Consensus Method. JMIR mHealth uHealth. 2015;3(2):e73. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4526967&tool=pmcentrez&rendertype=abstract

46.

Crane D, Garnett C, Brown J, West R, Michie S. Behavior change techniques in popular alcohol reduction apps: content analysis. J Med Internet Res. 2015;17(5):e118. Available from: http://www.jmir.org/2015/5/e118/.

47.

National Institute for Health and Care Excellence. Behaviour change: individual approaches. NICE. 2014. Available from: http://www.nice.org.uk/guidance/ph49/chapter/about-this-guidance

48.

Carver CS, Scheier MF. Control theory: a useful conceptual framework for personality-social, clinical, and health psychology. Psychol Bull. 1982;92:111–35.CrossRefPubMed

49.

Baker RC, Kirschenbaum DS. Weight control during the holidays: highly consistent self-monitoring as a potentially useful coping mechanism. Health Psychol. 1998;17:367–70.CrossRefPubMed

50.

Sperduto WA, Thompson HS, O'Brien RM. The effect of target behavior monitoring on weight loss and completion rate in a behavior modification program for weight reduction. Addictive behaviors. 1986;11(3):337-40

51.

Burke LE, Wang J, Sevick MA. Self-monitoring in weight loss: a systematic review of the literature. J Am Diet Assoc. 2011;111(1):92–102. Available from: http://www.sciencedirect.com/science/article/pii/S0002822310016445

52.

Guerci B, Drouin P, Grange V, Bougneres P, Fontaine P, Kerlan V, Passa P, Thivolet C, Vialettes B, Charbonnel B, ASIA Group. Self-monitoring of blood glucose significantly improves metabolic control in patients with type 2 diabetes mellitus: the Auto-Surveillance Intervention Active (ASIA) study. Diabetes & metabolism. 2003;29(6):587-94.

53.

Dunlap G, Clarke S, Jackson M, Wright S, Ramos E, Brinson S. Self-monitoring of classroom behaviors with students exhibiting emotional and behavioral challenges. School Psychology Quarterly. 1995;10(2):165.

54.

Todd AW, Horner RH, Sugai G. Self-Monitoring and Self-Recruited Praise Effects on Problem Behavior, Academic Engagement, and Work Completion in a Typical Classroom. Journal of Positive Behavior Interventions. 1999;1(2):66-122

55.

Michie S, Abraham C, Whittington C, McAteer J, Gupta S. Effective techniques in healthy eating and physical activity interventions: a meta-regression. Health Psychol. 2009;28(6):690–701. Available from: http://www.ncbi.nlm.nih.gov/pubmed/19916637.

56.

Dombrowski SU, Sniehotta FF, Avenell A, Johnston M, MacLennan G, Araújo-Soares V. Identifying active ingredients in complex behavioural interventions for obese adults with obesity-related co-morbidities or additional risk factors for co-morbidities: a systematic review. Health Psychol Rev. Taylor & Francis Group; 2012;6(1):7–32. Available from: http://www.tandfonline.com/doi/abs/10.1080/17437199.2010.513298#.VZqAo-tSb8E

57.

Ivers N, Jamtvedt G, Flottorp S, Young JM, Odgaard-Jensen J, French SD, et al. Audit and feedback: effects on professional practice and healthcare outcomes. Cochrane database Syst Rev. 2012;6(6):CD000259.

58.

Gaume J, McCambridge J, Bertholet N, Daeppen J-B. Mechanisms of action of brief alcohol interventions remain largely unknown - a narrative review. Front Psychiatry. 2014;5:108. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4143721&tool=pmcentrez&rendertype=abstract

59.

Crane D, Garnett C, Brown J, Kaner E, Beyer F, Muirhead C, et al. Behaviour change techniques used in digital interventions to reduce excessive alcohol consumption: a meta-regression. Manuscript in preparation. 2016.

60.

Williams SL, French DP. What are the most effective intervention techniques for changing physical activity self-efficacy and physical activity behaviour--and are they the same? Health Educ Res. 2011;26(2):308–22.CrossRefPubMed

61.

Handley M, MacGregor K, Schillinger D, Sharifi C, Wong S, Bodenheimer T. Using Action Plans to Help Primary Care Patients Adopt Healthy Behaviors: A Descriptive Study. J Am Board Fam Med. 2006;19(3):224–31.CrossRefPubMed

62.

Palfai T. Automatic processes in self-regulation: Implications for alcohol interventions. Cogn Behav Pract. 2004;11(2):190–201.CrossRef

63.

Armitage CJ. Effectiveness of experimenter-provided and self-generated implementation intentions to reduce alcohol consumption in a sample of the general population: a randomized exploratory trial. Health Psychol. 2009;28(5):545–53.CrossRefPubMed

64.

Hagger MS, Lonsdale A, Koka A, Hein V, Pasi H, Lintunen T, et al. An intervention to reduce alcohol consumption in undergraduate students using implementation intentions and mental simulations: a cross-national study. Int J Behav Med. 2012;19(1):82–96.CrossRefPubMed

65.

Gollwitzer PM. Implementation intentions: strong effects of simple plans. American psychologist. 1999;54(7):493–503.CrossRef

66.

Orbeil S, Hodgldns S, Sheeran P. Implementation intentions and the theory of planned behavior. Personality and Social Psychology Bulletin. 1997;23(9):945–54.CrossRef

67.

Milne S, Orbell S, Sheeran P. Combining motivational and volitional interventions to promote exercise participation: Protection motivation theory and implementation intentions. British journal of health psychology. 2002;7(2):163–84.CrossRefPubMed

68.

Collins SE, Carey KB, Sliwinski MJ. Mailed personalized normative feedback as a brief intervention for at-risk college drinkers. J Stud Alcohol. 2002;63(5):559–67. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12380852.

69.

Cunningham J, Neighbors C, Wild TC, Humphreys K. Normative misperceptions about alcohol use in a general population sample of problem drinkers from a large metropolitan city. Alcohol Alcohol. 2012;47(1):63–6. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3243438&tool=pmcentrez&rendertype=abstract

70.

Cunningham J a, Wild TC, Bondy SJ, Lin E. Impact of normative feedback on problem drinkers: a small-area population study. J Stud Alcohol. 2001;62(2):228–33. Available from: http://www.ncbi.nlm.nih.gov/pubmed/11327189.

71.

Kypri K, Langley JD. Perceived social norms and their relation to university student drinking. J Stud Alcohol. 2003;64(6):829–34. Available from: http://www.ncbi.nlm.nih.gov/pubmed/14743946.

72.

Neighbors C, Larimer ME, Lewis M a. Targeting misperceptions of descriptive drinking norms: efficacy of a computer-delivered personalized normative feedback intervention. J Consult Clin Psychol. 2004;72(3):434–47. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15279527.

73.

Wild TC. Personal drinking and sociocultural drinking norms: a representative population study. J Stud Alcohol. 2002;63(4):469–75. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12160106.

74.

Garnett C, Crane D, West R, Michie S, Brown J, Winstock A. Normative misperceptions about alcohol use in the general population of drinkers: A cross-sectional survey. Addict Behav. 2015;42:203–6. Available from: http://www.sciencedirect.com/science/article/pii/S0306460314003827

75.

Baer JS, Stacy a, Larimer M. Biases in the perception of drinking norms among college students. J Stud Alcohol. 1991;52(6):580–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/1758185.

76.

Neal DJ, Carey KB. Developing discrepancy within self-regulation theory: Use of personalized normative feedback and personal strivings with heavy-drinking college students. Addict Behav. 2004;29(2):281–97. Available from: http://linkinghub.elsevier.com/retrieve/pii/S0306460303001175

77.

Lewis MA, Neighbors C. Social norms approaches using descriptive drinking norms education: a review of the research on personalized normative feedback. J Am Coll Health. Routledge; 2006;54(4):213–8. Available from: http://dx.doi.org/10.3200/JACH.54.4.213-218

78.

Elster J. Social norms and economic theory. The Journal of Economic Perspectives. 1989;3(4):99-117. Available from: http://www.jstor.org/stable/1942912?__redirected.

79.

Miller DT, McFarland C. When social comparison goes awry: The case of pluralistic ignorance. Social comparison: Contemporary theory and research. 1991. p. 287–313.

80.

Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nature Publishing Group; Nat Neurosci. 2005;8(11):1458–63. Available from: http://dx.doi.org/10.1038/nn1584

81.

Strack F, Deutsch R. Reflective and impulsive determinants of social behavior. Pers Soc Psychol Rev. 2004;8(3):220–47. Available from: http://psr.sagepub.com/content/8/3/220.short

82.

Wiers RW, Bartholow BD, van den Wildenberg E, Thush C, Engels RCME, Sher KJ, et al. Automatic and controlled processes and the development of addictive behaviors in adolescents: a review and a model. Pharmacol Biochem Behav. 2007;86(2):263–83. Available from: http://www.sciencedirect.com/science/article/pii/S0091305706003236

83.

Bowley C, Faricy C, Hegarty B, Johnstone SJ, Smith JL, Kelly PJ, Rushby JA. The effects of inhibitory control training on alcohol consumption, implicit alcohol-related cognitions and brain electrical activity. International Journal of Psychophysiology. 2013;89(3):342-8 Available from: http://www.ncbi.nlm.nih.gov/pubmed/23623953.

84.

Reich RR, Below MC, Goldman MS. Explicit and implicit measures of expectancy and related alcohol cognitions: a meta-analytic comparison. NIH Public Access. Psychol Addict Behav. 2010;24(1):13–25. Available from: http://europepmc.org/articles/PMC2845325/?report=abstract

85.

Rooke SE, Hine DW, Thorsteinsson EB. Implicit cognition and substance use: a meta-analysis. Addict Behav. 2008;33(10):1314–28. Available from: http://www.sciencedirect.com/science/article/pii/S0306460308001664

86.

Thush C, Wiers RW, Moerbeek M, Ames SL, Grenard JL, Sussman S, et al. Influence of motivational interviewing on explicit and implicit alcohol-related cognition and alcohol use in at-risk adolescents. Psychol Addict Behav. 2009;23(1):146–51. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=3140345&tool=pmcentrez&rendertype=abstract

87.

Wiers RW, van de Luitgaarden J, van den Wildenberg E, Smulders FTY. Challenging implicit and explicit alcohol-related cognitions in young heavy drinkers. Addiction. 2005;100(6):806–19. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15918811.

88.

Field M, Duka T, Eastwood B, Child R, Santarcangelo M, Gayton M. Experimental manipulation of attentional biases in heavy drinkers: do the effects generalise? Psychopharmacology. 2007;192(4):593–608. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17361393.

89.

Schoenmakers TM, de Bruin M, Lux IFM, Goertz AG, Van Kerkhof DH a T, Wiers RW. Clinical effectiveness of attentional bias modification training in abstinent alcoholic patients. Drug Alcohol Depend. 2010;109(1–3):30–6. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20064698.

90.

Eberl C, Wiers RW, Pawelczack S, Rinck M, Becker ES, Lindenmeyer J. Approach bias modification in alcohol dependence: do clinical effects replicate and for whom does it work best? Developmental cognitive neuroscience. 2013;4:38-51. Available from: http://www.ncbi.nlm.nih.gov/pubmed/23218805.

91.

Wiers RW, Eberl C, Rinck M, Becker ES, Lindenmeyer J. Retraining automatic action tendencies changes alcoholic patients’ approach bias for alcohol and improves treatment outcome. Psychol Sci. 2011;22(4):490–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/21389338.

92.

Wiers RW, Rinck M, Kordts R, Houben K, Strack F. Retraining automatic action-tendencies to approach alcohol in hazardous drinkers. Addiction. 2010;105(2):279–87. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20078486.

93.

Houben K, Havermans RC, Nederkoorn C, Jansen A. Beer à no-go: learning to stop responding to alcohol cues reduces alcohol intake via reduced affective associations rather than increased response inhibition. Addiction. 2012;107(7):1280–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22296168

94.

Houben K, Nederkoorn C, Wiers RW, Jansen A. Resisting temptation: decreasing alcohol-related affect and drinking behavior by training response inhibition. Drug Alcohol Depend. 2011;116 (1–3): 132–6. Available from: http://www.sciencedirect.com/science/article/pii/S0376871611000329.

95.

Schoenmakers T, Wiers RW, Jones BT, Bruce G, Jansen ATM. Attentional re-training decreases attentional bias in heavy drinkers without generalization. Addiction. 2007;102(3):399–405. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17298647.

96.

Piacentini MG, Banister EN. Getting hammered? …students coping with alcohol. J Consum Behav. 2006;5:145–56.CrossRef

97.

Tajfel H, Turner JC. The Social Identity Theory of Intergroup Behavior. The psychology of intergroup behaviour. Chicago: Nelson Hall; 1986. p. 7–22.

98.

Oyserman D, Fryberg SA, Yoder N. Identity-based motivation and health. J Pers Soc Psychol. 2007;93(6):1011–27. Available from: http://www.ncbi.nlm.nih.gov/pubmed/18072851.

99.

Kearney MH, O’Sullivan J. Identity shifts as turning points in health behavior change. West J Nurs Res. 2003;25(2):134–52. Available from: http://www.ncbi.nlm.nih.gov/pubmed/12666640.

100.

West R, Brown J. Theory of Addiction. John Wiley & Sons; 2013.

101.

Michie S, Johnston M, Abraham C, Lawton R, Parker D, Walker A. Making psychological theory useful for implementing evidence based practice: a consensus approach. Qual Saf Health Care. 2005;14(1):26–33. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=1743963&tool=pmcentrez&rendertype=abstract

102.

West R, Walia A, Hyder N, Shahab L, Michie S. Behavior change techniques used by the English Stop Smoking Services and their associations with short-term quit outcomes. Nicotine Tob Res. 2010;12(7):742–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/20478957.

103.

Tombor I, Shahab L, Brown J, West R. Positive smoker identity as a barrier to quitting smoking: findings from a national survey of smokers in England. Drug Alcohol Depend. 2013;133(2):740–5. Available from: http://www.sciencedirect.com/science/article/pii/S037687161300358X

104.

Tombor I, Shahab L, Herbec A, Neale J, Michie S, West R. Smoker identity and its potential role in young adults’ smoking behavior: A meta-ethnography. Health Psychology. 2015;34(10):992.

105.

Collins LM, Murphy SA, Strecher V. The multiphase optimization strategy (MOST) and the sequential multiple assignment randomized trial (SMART): new methods for more potent eHealth interventions. Am J Prev Med. 2007;32(5 Suppl):S112–8. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2062525&tool=pmcentrez&rendertype=abstract

106.

Collins LM, Trail JB, Kugler KC, Baker TB, Piper ME, Mermelstein RJ. Evaluating individual intervention components: making decisions based on the results of a factorial screening experiment. Transl Behav Med. 2014;4(3):238–51. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4167900&tool=pmcentrez&rendertype=abstract

107.

OpenSignal. Android Fragmentation Visualized. 2015. Available from: http://opensignal.com/reports/2015/08/android-fragmentation/

108.

Localytics. App Retention Increasing: iPhone Ahead of Android. 2012. Available from: http://info.localytics.com/blog/app-user-loyalty-increasing-ios-beats-android.

109.

Kunz FM, French MT, Bazargan-Hejazi S. Cost-effectiveness analysis of a brief intervention delivered to problem drinkers presenting at an inner-city hospital emergency department. J Stud Alcohol. 2004;65(3):363–70. Available from: http://www.ncbi.nlm.nih.gov/pubmed/15222593

110.

Dienes Z. Using Bayes to get the most out of non-significant results. Front Psychol. 2014;5:781. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=4114196&tool=pmcentrez&rendertype=abstract

111.

Apptamin. App Store Optimization (ASO): App Name And Keywords. [cited 2016 Jan 14]. Available from: http://www.apptamin.com/blog/app-store-optimization-aso-app-name-and-keywords/.

112.

Kissmetrics. App Store Optimization – A Crucial Piece of the Mobile App Marketing Puzzle. [cited 2016 Jan 14]. Available from: https://blog.kissmetrics.com/app-store-optimization

113.

Shah Z. The Definitive Guide to App Store Optimization (ASO). [cited 2016 Jan 14]. Available from: http://www.searchenginejournal.com/definitive-guide-app-store-optimization-aso/78719/

114.

Cowly Owl. The importance of App Store reviews. [cited 2016 Jan 14]. Available from: http://www.cowlyowl.com/blog/app-store-reviews

115.

Microsoft UK Developers. App ratings and reviews - how important are they?. [cited 2016 Jan 14]. Available from: http://www.microsoft.com/en-gb/developers/articles/week02jun14/app-ratings-and-reviews-how-important-are-they.

116.

Babor T, Higgins J, Saunders J, Monteiro M. The alcohol use disorders identification test. 2nd ed. Guidelines for use in primary care. World Health Organisation; 2001.

117.

Kaner E, Bland M, Cassidy P, Coulton S, Dale V, Deluca P, et al. Effectiveness of screening and brief alcohol intervention in primary care (SIPS trial): pragmatic cluster randomised controlled trial. BMJ. 2013;346:e8501. Available from: http://www.bmj.com/content/346/bmj.e8501.abstract

118.

Brown J, Michie S, Geraghty AWA, Yardley L, Gardner B, Shahab L, et al. Internet-based intervention for smoking cessation (StopAdvisor) in people with low and high socioeconomic status: a randomised controlled trial. Lancet Respir Med. 2014;2(12):997–1006. Available from: http://www.sciencedirect.com/science/article/pii/S221326001470195X

119.

Voogt CV, Kleinjan M, Poelen EAP, Lemmers LACJ, Engels RCME. The effectiveness of a web-based brief alcohol intervention in reducing heavy drinking among adolescents aged 15 to 20 years with a low educational background: a two-arm parallel group cluster randomized controlled trial. BMC Public Health. 2013;13(1):694.CrossRefPubMedPubMedCentral

120.

Schulz DN, Candel MJ, Kremers SPJ, Reinwand DA, Jander A, De Vries H. Effects of a web-based tailored intervention to reduce alcohol consumption in adults: Randomized controlled trial. J Med Internet Res. 2013;15(9):e206.

121.

Schunk DH. Self-Regulation through Goal Setting. ERIC Clearinghouse on Counseling and Student Service, University of North Carolina at Greensboro. 2001.

122.

Locke EA, Latham GP. A theory of goal setting & task performance. New Jersey: Prentice-Hall; 1989.

123.

Moskowitz GB, Grant H. The Psychology of Goals. New York: Guilford Press; 2009.

124.

Collins LM, Baker TB, Mermelstein RJ, Piper ME, Jorenby DE, Smith SS, et al. The multiphase optimization strategy for engineering effective tobacco use interventions. Ann Behav Med. 2011;41(2):208–26.CrossRefPubMedPubMedCentral

Title: Evaluating the effectiveness of a smartphone app to reduce excessive alcohol consumption: protocol for a factorial randomised control trial
Authors: Claire Garnett
David Crane
Susan Michie
Robert West
Jamie Brown
Publication date: 01-12-2016
Publisher: BioMed Central
Published in: BMC Public Health / Issue 1/2016
Electronic ISSN: 1471-2458
DOI: https://doi.org/10.1186/s12889-016-3140-8

At a glance: The STEP trials

Springer Medicine

Evaluating the effectiveness of a smartphone app to reduce excessive alcohol consumption: protocol for a factorial randomised control trial

Abstract

Background

Methods

Discussion

Trial registration

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Discussion

Trial registration

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