Information technology and interventions for unhealthy alcohol use

Transcription

1 CAMH, Toronto, Information technology and interventions for unhealthy alcohol use N.Bertholet, MD, MSc Associate Physician, Privat Docent, Senior Lecturer Alcohol Treatment Center, Lausanne University Hospital, Switzerland

2 For individuals 15+ y.o. in Switzerland: 10l of ethanol per year 22gr of ethanol (about two drinks) per day, on average

3 Introduction Addressing unhealthy alcohol use:

4 Population based secondary prevention approaches Individualized approaches (brief motivational interventions)

5 Introduction Despite recommendations to deliver screening and brief interventions for unhealthy alcohol use in primary care, implementation has been challenging Electronic intervention could help overcome some of the barriers Limited cost No need to train providers Less burden on providers ( more time to focus on patients with more severe problems) Greater reach (can be used 24/7, no geographical restrictions) Could be more easily implemented and have a higher consistency (Noell and Glasgow 1999).

6 Introduction Young adults Significant morbidity and mortality attributable to unhealthy alcohol use (Switzerland: 25% of deaths in y.o. males attributable to alcohol) High prevalence of internet access: >90% go online daily or almost daily adequate target population for electronic interventions

7 Evidence Efficacy is present but effects are modest More evidence is needed among young individuals in general population samples 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: PATTON R., DELUCA P., KANER E., NEWBURY-BIRCH D., PHILLIPS T., DRUMMOND C. Alcohol screening and brief intervention for adolescents: the how, what and where of reducing alcohol consumption and related harm among young people, Alcohol Alcohol 2014: 49: 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, Journal of medical Internet research 2014: 16: e142. MOREIRA M. T., SMITH L. A., FOXCROFT D. Social norms interventions to reduce alcohol misuse in university or college students, Cochrane Database Syst Rev 2009: 3 TANSIL, K. A., M. B. ESSER, P. SANDHU, J. A. REYNOLDS, R. W. ELDER, R. S. WILLIAMSON, S. K. CHATTOPADHYAY, M. K. BOHM, R. D. BREWER, L. R. MCKNIGHT-EILY, D. W. HUNGERFORD, T. L. TOOMEY, R. W. HINGSON, J. E. FIELDING AND F. Community Preventive Services Task (2016). "Alcohol Electronic Screening and Brief Intervention: A Community Guide Systematic Review." Am J Prev Med 51(5):

8 Bertholet, N., J. A. Cunningham, M. Faouzi, J. Gaume, G. Gmel, B. Burnand and J. B. Daeppen (2015). "Internet-based brief intervention for young men with unhealthy alcohol use: a randomized controlled trial in a general population sample." Addiction 110(11): Bertholet, N., J. A. Cunningham, M. Faouzi, J. Gaume, G. Gmel, B. Burnand and J. B. Daeppen (2015). "Internet-Based Brief Intervention to Prevent Unhealthy Alcohol Use among Young Men: A Randomized Controlled Trial." PLoS One 10(12): e

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10 Switzerland: 26 cantons, 4 national languages Switzerland has a mandatory army recruitment process (age 19) Unique opportunity to access the entire population at a given age

11 COHORT STUDY ON SUBSTANCE USE RISK FACTORS C-SURF recruited participants at 3/6 army recruitment centers: Lausanne (VD), Windisch (AG), Mels (SG) (covering 21/26 cantons) Between August 2010 and July 2011, 13,245 men were invited to participate in C-SURF C-SURF had no a priori exclusion criterion virtually all Swiss 19-year-old men were eligible for enrollment

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13 Sample We proposed to test the impact of an internet-based brief intervention targeting alcohol use among young men The internet study took advantage of C-SURF From June 2012 to February 2013, C-SURF participants were invited by to participate in the brief intervention trial, regardless of their drinking status

14 Methods Baseline assessment classification: unhealthy vs low risk use Unhealthy use defined as: drinking >14 drinks per week OR at least one episode of binge drinking (6 or more drinks per occasion) per month OR Alcohol Use Disorders Identification Test (AUDIT) score >=8 Low risk use defined as: absence of unhealthy use Randomization: automated, no experimenter involvement (embedded in the website code). The concealment of allocation was total Follow-up assessments at 1 and 6 months after randomization Entire study was done electronically

15 Methods Primary outcomes and 6 months): Weekly alcohol use (mean number of drinks per week) Prevalence of binge drinking

16 SECONDARY PREVENTION Census of young Swiss men (n=13245) C-SURF participants (n=5990) Individuals invited to participate (n= 4365) Individuals completing the baseline assessment (n=1633) 37.4% Did not access the website (n=2278) or declined participation (n=724) Unhealthy alcohol use (n=737) 45.1% No unhealthy alcohol use (n=896) 54.9% Random allocation Control (n=370) BL Intervention (n=367) Control (n=338) 1mo Intervention (n=340) Control (n=329) 6mo Intervention (n=338)

17 Intervention Intervention: based on the intervention Control: baseline assessment only

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23 Methods The intervention impact was assessed using: A random-effects negative binomial regression model (drinks/week) A random-effects logit regression model (binge drinking prevalence) Both models specified the subject as random effects and treatment and time as fixed effects. A treatment x time interaction was included to display the effect of the intervention over time. Analyses were adjusted for age, linguistic region and AUDIT score ITT analyses Missing data at 1 or 6 months were replaced with the last observation carried forward

24 Results Baseline characteristics of participants n=737 Age (1.13) Number of drinks/week, mean(sd) 9.82 (7.86) Binge drinking, n (%) 626 (84.9%) AUDIT score, mean (SD) (4.15) AUDIT score >=10, n (%) 383 (52.0%) Follow-up rate: 92% at 1 month, 91% at 6 months

25 Results: number of drinks/week Adjusted mean difference [95%CI], baseline to 6 months Intervention [-2.42; -0.76] control [-1.30; 0.35]

26 Results: prevalence of binge Adjusted mean difference [95%CI], baseline to 6 months Intervention -15.5%[-21.4; -9.6] control -13.4%[-19.4; -7.6]

27 Conclusions Participants who received the intervention reported 10% less drinking at 6 months compared to participants who did not receive the intervention. No intervention effect on binge drinking prevalence

28 Cohort study: allows to assess potential long term effects At the most recent cohort assessment (2017, mean of 47.2 months after baseline), n=626 (85% of the sample) Number of drinks per week Baseline 6mo 47mo Intervention 10.1 (7.9) 8.4 (8.3) 11.1 (14.8) Control 9.5 (7.8) 9.2 (8.8) 11.2 (15.0) Binge drinking Baseline 6mo 47mo Intervention 85.6% 70.0% 63.8% Control 84.3% 70.8% 64.9%

29 Internet-based interventions: limitations Effects are modest Effectiveness trials tend to shown smaller effects of interventions than efficacy trials (McCambridge, Bendtsen 2013, Kypri, Vater 2014) Possible ways to increase intervention effects: Increase exposure to the intervention Single shot intervention multiple interactions

30 One possible option: smartphone The penetration of smartphones has been rapid and important 2016 Pew Research Center survey: Close to 70% of people in Europe, Canada and the US own a smartphone Proportion of the population owning a smartphone is especially high for years old (94% in Canada ) Smartphone is a highly prevalent tool that may be used to disseminate interventions, especially among young individuals (

31 One possible option: smartphone Pilot study in Switzerland and Canada is smartphone an acceptable medium to deliver interventions? is multiple use happening? (and is it associated with less drinking?) is use happening in contexts «outside of reach» of other information technology-based interventions? Bertholet, N., J. B. Daeppen, J. McNeely, V. Kushnir and J. A. Cunningham (2017). "Smartphone application for unhealthy alcohol use: a pilot study." Subst Abus:

32 We developed a smartphone application (Alcooquizz) with 5 modules: Modules Personalized feedback Self-monitoring tool Designated driver Blood alcohol content calculator Information

33 Methods 130 adults with unhealthy alcohol use recruited in Switzerland (n=70) and Canada (n=60) Recruitment by digital media ads Participants were offered to use the application Follow-up was at 3 months

34 Results FU rate: 86.2% Women, n (%) 62 (47.7%) Age, mean (SD) 32.8 (10.0) Swiss resident, n (%) 70 (53.8%) Canadian resident, n (%) 60 (46.2%) AUDIT score, mean (SD) 12.8 (6.8) BL (n=130) FU (n=112) BL-FU difference Number of alcoholic drinks/week, mean (SD) 15.0 (16.5) 10.9 (10.5) p= Monthly binge drinking, n(%) 124 (95.4%) 72 (64.3%) p<0.0001

35 Frequency of use 23% never used the application 13% used it once 64% used it more than once

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38 Users perception median (IQR) scores, 1-10 VAS, 10=most positive rating did you appreciate the module did you find the module useful Personal Feedback 6 (5; 8) 6 (5; 8) Self monitoring (diary) 6 (5; 7) 7 (5; 8) Designated driver 7 (6; 8) 7 (6; 8) BAC calculator 7 (5; 8) 7 (4; 8) Information 8 (7; 9) 7 (6; 9) 42 participants (37.5%) indicated they shared the application with a friend

39 Context of use (multiple responses were possible) Used the app Alone 76.3% With friends 36.9% With relatives 10.0% With colleagues 6.8% Used the app At a party 33.7% During a drinking episode 19.7% After a drinking episode 32.1% Before a drinking episode 24.5% Other context 26.1%

40 Associations between application use and drinking Number of drinks per week in the past 3 months*: IRR 95% CI p Weekly alcohol use at follow-up, for participants who used app 2+ versus 0-1 times Binge drinking in the past 3 months**: OR 95% CI p Binge drinking at follow up, among participants who used app 2+ versus 0-1 times *negative binomial regression model ** logistic regression model adjusted for baseline value and gender

41 Conclusion A smartphone application for unhealthy alcohol use appears acceptable (but there is room for improvement) Without prompting, its use is infrequent Use in contexts «outside of reach» of computer-based interventions is reported Those who used the application 2 times or more reported less weekly drinking than those who did not use the app or used it once Efficacy of the application should be tested in a randomized trial taking into account strategies to make its use more frequent

42 In the waiting room Currently testing screening for tobacco, alcohol, illegal drugs, prescription drugs, physical activity

43 The internet study was funded by the Swiss National Science Foundation (grant _135538/1, PI: N Bertholet) The study website was developed in part with funds from a grant from the Department of community medicine and health (to N Bertholet) C-SURF funded by the Swiss National Science Foundation (33CSCO , PI: G Gmel) Smartphone pilot study was funded by the Swiss Foundation for Alcohol Research (grant #226) All studies were approved by the Ethics Committee for Clinical Research of the Lausanne University Medical School

44 Thank you!