A program to reduce unhealthy alcohol use among college students through the use of a smartphone app.
Blueprints: Promising
Nicolas Bertholet, MD, MSc
Associate Professor, Head Physician
Department of Psychiatry
Lausanne University Hospital
Bugnon 23 A
1011 Lausanne
Switzerland
nicolas.bertholet@chuv.ch
Nicolas Bertholet, MD, MSc
University of Lausanne
Self-Monitoring Alcohol And health Risks Tool (SMAART) App is a brief six-module smartphone app intervention that gives feedback on self-reported alcohol consumption, estimates blood alcohol concentration, presents a self-monitoring tool with information on recommended limits, enables users to set goals, selects designated drivers through analysis of photos, and presents fact sheets.
The SMAART App is available in Switzerland and Canada for use in French and English.
Self-Monitoring Alcohol And health Risks Tool (SMAART) App is a brief six-module smartphone app intervention. The SMAART App is available in Switzerland and Canada for use in French and English.
Module 1
Personalized feedback - This module provides normative feedback and information on the health risks associated with the user's reported consumption. The reported alcohol consumption is compared to national (Switzerland) norms for people of the same age and sex with an emphasis on the percentage of people drinking less than the user. Additionally, the calorific content of the user's consumption is indicated and presented in terms that are equivalent to hamburgers.
Module 2
Blood alcohol content - This module estimates the blood alcohol content that users reach with a particular reported consumption and indicates the risks associated with different levels of blood alcohol content. The module also computes how long before the alcohol is eliminated.
Module 3
Self-monitoring - This module invites users to report their daily drinking and then their drinking patterns are presented graphically along with recommended drinking limits.
Module 4
Goal-setting - This module enables users to set themselves drinking limitations for one, two, seven, or 30 days. They are then invited to log their daily drinking. Users receive virtual badges when they drink at or below their self-determined drinking limits.
Module 5
Designated driver - This module allows users to take pictures of themselves and their friends. The app then randomly picks the picture of the designated sober driver.
Module 6
Fact sheets - This module presents fact sheets on alcohol and health (i.e., effects of alcohol on the human body, diseases caused by alcohol, acute and long-term effects of alcohol use on health, addiction), and available resources (treatment options and contacts).
Primary Evidence Base for Certification
Study 1
Bertholet et al. (2023) found that throughout the 12-month follow-up period, the intervention group relative to the control group reported significantly lower
Primary Evidence Base for Certification
The one study Blueprints has reviewed (Study 1) meets Blueprints evidentiary standards (specificity, evaluation quality, impact, dissemination readiness). The study was done by the developer.
Study 1
Bertholet et al. (2023) conducted a randomized controlled trial to examine 1,770 students attending four universities in Lausanne, Switzerland, who had reported unhealthy alcohol use. After random assignment to an intervention group or a control group, the students completed online assessments of alcohol use at three, six, and 12 months.
Study 1
Bertholet, N., Schmutz, E., Studer, J., Adam, A., Gmel, G., Cunningham, J. A., McNeely, J., & Daeppen, J.-B. (2023). Effect of a smartphone intervention as a secondary prevention for university students with unhealthy alcohol use: Randomized controlled trial. BMJ, 382: e073713. https://dx.doi.org/10.1136/bmj-2022-073713
Individual: Perceived risk of drug use
*
Risk/Protective Factor was significantly impacted by the program
Subgroup differences in program effects by race, ethnicity, or gender (coded in binary terms as male/female) or program effects for a sample of a specific racial, ethnic, or gender group:
Study 1 did not examine differences in program benefits by race, ethnicity, gender, or economic disadvantage.
Sample demographics including race, ethnicity, and gender for Blueprints-certified studies:
The Swiss sample averaged 22.4 years of age and included 54.1% women and 66.0% bachelor's degree students.
No training or materials are necessary as this is a fully self-guided, online, free-of-charge intervention that may be accessed using a smartphone app. The SMAART App is available in Switzerland and Canada in French and English.
Source: Washington State Institute for Public Policy
All benefit-cost ratios are the most recent estimates published by The Washington State Institute for Public Policy for Blueprint programs implemented in Washington State. These ratios are based on a) meta-analysis estimates of effect size and b) monetized benefits and calculated costs for programs as delivered in the State of Washington. Caution is recommended in applying these estimates of the benefit-cost ratio to any other state or local area. They are provided as an illustration of the benefit-cost ratio found in one specific state. When feasible, local costs and monetized benefits should be used to calculate expected local benefit-cost ratios. The formula for this calculation can be found on the WSIPP website.
No training or materials are necessary as this is a fully self-guided, online, free-of-charge intervention that may be accessed using a smartphone app. The SMAART App is available in Switzerland and Canada in French and English.
No information is available
No information is available
No information is available
No information is available
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No information is available
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No information is available
No information is available
Nicolas Bertholet, MD, MScAssociate Professor, Head PhysicianUniversity of LausanneDepartment of PsychiatryLausanne University Hospital1011 LausanneSwitzerlandnicolas.bertholet@chuv.ch
A program to reduce unhealthy alcohol use among college students through the use of a smartphone app.
College students with unhealthy alcohol use.
Subgroup differences in program effects by race, ethnicity, or gender (coded in binary terms as male/female) or program effects for a sample of a specific racial, ethnic, or gender group:
Study 1 did not examine differences in program benefits by race, ethnicity, gender, or economic disadvantage.
Sample demographics including race, ethnicity, and gender for Blueprints-certified studies:
The Swiss sample averaged 22.4 years of age and included 54.1% women and 66.0% bachelor's degree students.
Lack of information and understanding about alcohol use and normative drinking patterns
Individual: Perceived risk of drug use
*Risk/Protective Factor was significantly impacted by the program
Self-Monitoring Alcohol And health Risks Tool (SMAART) App is a brief six-module smartphone app intervention that gives feedback on self-reported alcohol consumption, estimates blood alcohol concentration, presents a self-monitoring tool with information on recommended limits, enables users to set goals, selects designated drivers through analysis of photos, and presents fact sheets.
The SMAART App is available in Switzerland and Canada for use in French and English.
Self-Monitoring Alcohol And health Risks Tool (SMAART) App is a brief six-module smartphone app intervention. The SMAART App is available in Switzerland and Canada for use in French and English.
Module 1
Personalized feedback - This module provides normative feedback and information on the health risks associated with the user's reported consumption. The reported alcohol consumption is compared to national (Switzerland) norms for people of the same age and sex with an emphasis on the percentage of people drinking less than the user. Additionally, the calorific content of the user's consumption is indicated and presented in terms that are equivalent to hamburgers.
Module 2
Blood alcohol content - This module estimates the blood alcohol content that users reach with a particular reported consumption and indicates the risks associated with different levels of blood alcohol content. The module also computes how long before the alcohol is eliminated.
Module 3
Self-monitoring - This module invites users to report their daily drinking and then their drinking patterns are presented graphically along with recommended drinking limits.
Module 4
Goal-setting - This module enables users to set themselves drinking limitations for one, two, seven, or 30 days. They are then invited to log their daily drinking. Users receive virtual badges when they drink at or below their self-determined drinking limits.
Module 5
Designated driver - This module allows users to take pictures of themselves and their friends. The app then randomly picks the picture of the designated sober driver.
Module 6
Fact sheets - This module presents fact sheets on alcohol and health (i.e., effects of alcohol on the human body, diseases caused by alcohol, acute and long-term effects of alcohol use on health, addiction), and available resources (treatment options and contacts).
The theoretical bases for the smartphone app involve norms perception and risk perception. Perceived norms are a strong predictor of alcohol use. Research has shown that college students have high levels of misperception (difference between one's actual behavior and what one believes is true of others). Providing information on actual norms encourages individuals to reduce their levels of alcohol use, moving closer to population norms. Likewise, acquiring a more accurate perception of risk is expected to lead to decreases in drinking.
Primary Evidence Base for Certification
The one study Blueprints has reviewed (Study 1) meets Blueprints evidentiary standards (specificity, evaluation quality, impact, dissemination readiness). The study was done by the developer.
Study 1
Bertholet et al. (2023) conducted a randomized controlled trial to examine 1,770 students attending four universities in Lausanne, Switzerland, who had reported unhealthy alcohol use. After random assignment to an intervention group or a control group, the students completed online assessments of alcohol use at three, six, and 12 months.
Primary Evidence Base for Certification
Study 1
Bertholet et al. (2023) found that throughout the 12-month follow-up period, the intervention group relative to the control group reported significantly lower drinks per week, heavy drinking days, and maximum number of drinks.
Primary Evidence Base for Certification
Study 1
Bertholet et al. (2023) found that throughout the 12-month follow-up period, the intervention group relative to the control group reported significantly lower
One study meets Blueprints standards for high-quality methods with strong evidence of program impact (i.e., "certified" by Blueprints): Study 1 (Bertholet et al., 2023). The study took place at four universities in Lausanne, Switzerland and compared the intervention group to an attention-procedure control group.
Study 1 (Bertholet et al., 2023) was pre-registered (ISRCTN 10007691).
Blueprints: Promising
Nicolas Bertholet, MD, MSc
Associate Professor, Head Physician
Department of Psychiatry
Lausanne University Hospital
Bugnon 23 A
1011 Lausanne
Switzerland
nicolas.bertholet@chuv.ch
Certified
Bertholet, N., Schmutz, E., Studer, J., Adam, A., Gmel, G., Cunningham, J. A., McNeely, J., & Daeppen, J.-B. (2023). Effect of a smartphone intervention as a secondary prevention for university students with unhealthy alcohol use: Randomized controlled trial. BMJ, 382: e073713. https://dx.doi.org/10.1136/bmj-2022-073713
Summary
Bertholet et al. (2023) conducted a randomized controlled trial to examine 1,770 students attending four universities in Lausanne, Switzerland, who had reported unhealthy alcohol use. After random assignment to an intervention group or a control group, the students completed online assessments of alcohol use at three, six, and 12 months.
Bertholet et al. (2023) found that throughout the 12-month follow-up period, the intervention group relative to the control group reported significantly lower
Study 1
Evaluation Methodology
Design:
Recruitment: The sample came from four participating universities located in the greater Lausanne, Switzerland, area. Students attending the universities were recruited through official communications, student association websites and social media, information screens, hallway posters, and emails. Eligibility criteria were student status at recruitment, aged 18 years or older, a positive result from screening for unhealthy alcohol use, ownership of a smartphone, and willingness to complete the follow-up questionnaires. Over three days in April 2021, 3,714 students completed anonymous screening, 2,694 (72.5%) screened positive for unhealthy alcohol use, 2,364 (87.8%) were eligible to participate, and 1,770 (74.9%) completed the baseline assessment and were included in the study.
Assignment: The 1,770 participants who completed the baseline assessment were randomly assigned by independent researchers to two conditions using a 1:1 allocation ratio in blocks of 10. The intervention group (n = 884) was given access to the app, while the control group (n = 886) participated in an attention control procedure in which participants received a personal code by email and a request to log on to a specific webpage and enter the code to unlock a coupon.
Assessments/Attrition: Assessments occurred at baseline and three, six, and 12 months after assignment. Overall follow-up rates were 96.4% at three months, 95.9% at six months, and 93.8% at 12 months. However, one measure of academic performance at 12 months had to be obtained from a mailed questionnaire and had a 77.7% completion rate.
Sample:
The sample averaged 22.4 years of age and included 54.1% women and 66.0% bachelor's degree students. The baseline mean for the number of drinks per week was 8.59 and the baseline mean number of heavy drinking days over the past 30 days was 3.53.
Measures:
All assessments were conducted online, and all measures were self-reported. Due to a configuration problem in the online questionnaire, the 12-month academic performance measure was collected separately via email. The primary and secondary outcomes were prespecified. The primary outcome was the mean number of standard drinks per week over the past 30 days, as assessed using a validated quantity per frequency measure. The secondary outcome was the number of heavy drinking days (i.e., days with ≥ 5 drinks for men and ≥ 4 drinks for women) over the past 30 days. Additional outcomes were the maximum number of drinks on any day over the past 30 days; alcohol-related adverse consequences (measured using the short inventory of problems); and academic performance (measured using the question: "How do you rate your performance in comparison with your fellow students?").
Analysis:
The analysis used generalized linear mixed models that controlled for the baseline outcome. The models tested for intervention effects over all three follow-up time points combined and for each time point separately. Models used either linear regression or a Poisson or negative binomial regression for count outcomes (e.g., drinks per week). The authors stated, "To account for the data's nested structure, two random effects were put into the model: one random intercept for the recruitment site and another for participants nested within that recruitment site." (p. 5).
Missing Data Method: Missing data were handled using multiple imputation (details in the supplementary materials). In the imputation model, "the participants were set as a grouping factor." (p. 4). Condition, sex, recruitment site, time, age, and all outcomes except for the outcome to be imputed were set as fixed effects. Fifty datasets were imputed, with ten iterations performed for each imputed dataset.
Intent-to-Treat: All participants, including those in the intervention group who did not download the app, were included in the analysis.
Outcomes
Implementation Fidelity:
Among the intervention group, 83.5% downloaded the app; among the control group, 95.5% completed the corresponding procedure. Intervention participants who downloaded the app used a mean of 2.0 of its modules and used the app a mean of 21.2 times.
Baseline Equivalence:
Table 2 lists the condition means for 10 measures (three sociodemographics, two moderators, five outcomes). The table does not present significance tests or standardized mean differences, but Blueprints calculations using the means and standard deviations show few significant differences and small standardized mean differences (d < .12 for all comparisons).
Differential Attrition:
Based on the overall attrition rate of 6.2% and the difference in attrition rates between conditions of 0.4% (6.4% - 6.0%) at 12-month follow-up, the study meets both the What Works Clearinghouse cautious and optimistic standards. In addition, the use of multiple imputation may moderate any potential differential attrition bias.
Posttest:
For the primary outcome, the intervention significantly reduced the number of standard drinks per week averaged across all three follow-up assessments. The effects did not differ significantly between the three assessments, and the effect at 12 months was significant.
For the secondary outcome, the intervention significantly reduced the number of heavy drinking days averaged across all three follow-up assessments. The effects did not differ significantly between the three assessments, but the effect at 12 months was only marginally significant.
For the other three outcomes, one showed an intervention benefit. The intervention significantly reduced the maximum number of drinks consumed on one occasion averaged across all three follow-up assessments. The effects did not differ significantly between the three assessments, but the effects at 6 months and 12 months were not significant.
The intervention had no significant main effect on consequences related to alcohol or academic performance.
Subgroup analyses showed intervention effects of similar magnitudes among students reporting and not reporting a potential alcohol use disorder at baseline.
Long-Term
Because the program app can be reused on an ongoing basis and usage ranged from zero to 403 times, the 12-month follow-up does not meet the Blueprints criteria for long-term.