http://informahealthcare.com/ada ISSN: 0095-2990 (print), 1097-9891 (electronic) Am J Drug Alcohol Abuse, 2015; 41(2): 183–187 ! 2015 Informa Healthcare USA, Inc. DOI: 10.3109/00952990.2014.991022
ORIGINAL ARTICLE
The efficacy of computerized alcohol intervention tailored to drinking motives among college students: a quasi-experimental pilot study Natale Canale, MSC, Alessio Vieno, PhD, Massimo Santinello, BA, Francesca Chieco, MD, and Stefano Andriolo, HSD Department of Developmental and Social Psychology, University of Padova, Padova, Italy
Abstract
Keywords
Background: Although motivational processes may influence the intervention effects and help prevention programmes identify students at great risk for alcohol-related problems, no computerized alcohol intervention has yet to be tailored to drinking motives. Objective: To describe the development and initial pilot testing of a computer-delivered intervention tailored to drinking motives, to prevent alcohol abuse and its adverse consequences among university students in general and among baseline hazardous drinkers specifically. Methods: 124 college students attending a public university in northeastern Italy participated in this study in October of 2012 (89.2% female- mean age ¼ 21.64–34% baseline hazardous drinkers). Two classes (one undergraduate, one graduate) were assigned to one of two conditions: intervention and control group. Both groups received profile-specific feedback and then the intervention group received profile-specific online training for 4 weeks. This profile was based on their risk type (high-low) and drinking motives (enhancement-social-conformity-coping). Results: Controlling for corresponding baseline alcohol measures, analyses showed a significant interaction between intervention condition and hazardous drinkers at baseline. For hazardous drinkers at baseline, the alcohol intervention results showed a significant decrease in frequency and quantity of alcohol use at follow-up, while no difference was observed between intervention conditions for non-hazardous drinkers at baseline. Conclusions: The results suggest that hazardous drinkers (college students) who completed the specific training and received personalized feedback seemed to do better on frequency and quantity of alcohol use than hazardous drinkers (college students) who received only personalized feedback. These results seem to provide support for a larger trial of the intervention and for more appropriate evaluations.
Alcohol abuse, college students, computer-delivered intervention, drinking motives, prevention
Introduction Research shows that alcohol consumption peaks during the transition from high school to college (1). Findings from national surveys suggest that almost half of college students (44.7%) report heavy episodic (i.e. ‘‘binge’’) drinking in the last month (2). College students were more likely to sustain accidental injury and engage in crime, sexual violence, and suicide than their same aged peers who were not enrolled in college (2). One recent response to the problem of heavy drinking in college students is the development of computer-delivered interventions (CDIs) such as electronic-screening and brief intervention (e-SBI). At the very least, e-SBI involves screening individuals for excessive drinking and delivering a brief intervention, which provides personalized feedback about the risks and consequences of excessive drinking. The Address correspondence to Alessio Vieno, Department of Developmental and Social Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy, Tel: +39 (0) 49 82 76493. Fax. +39 (0) 49 82 76547. E-mail: [email protected]
History Received 22 August 2014 Revised 10 November 2014 Accepted 18 November 2014 Published online 20 February 2015
CDIs that span across multiple sessions are more effective than when compressed in a single-session feedback intervention (3); moreover, a higher degree of interactivity is associated with higher effect sizes (4). Computer-based personalized feedback has included different types of information such as drinking norms, costs, and consequences (5,6) but has not considered drinking motives (7,8). Considering that alcohol drinking motives are an important proximal factor for drinking behavior (9), online interventions tailored to specific drinking motives may be a novel variant of such interventions by providing students with a summary of their motivations to drink. The Motivational Model of Alcohol Use assumes that people display a certain behavior to achieve expected or desired effects (10). Identifying the specific needs for alcohol may help people develop more adaptive ways to meet these needs. For example, ‘‘personality-related’’ interventions were associated with both reductions in alcoholrelated problems and changes in drinking motivation; thus, it is possible that the mechanism by which these interventions work is to alter drinking motivations, which in turn result in behavioral change (11). Finally, motives may also be able to
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aid prevention programmes in identifying students at greater risk for alcohol-related problems (e.g. indicated prevention) (12). Therefore, the purpose of the current study was to deliver a theoretically-based and empirically supported prevention program form via email to Italian college students enrolled in two lecture courses and evaluate the preliminary efficacy in reducing risky alcohol use at a one-month follow-up. The ‘‘Which type of drinker are you?’’ online prevention program was developed together with a panel of prevention experts that implemented evidence-based substance abuse prevention strategies (mainly cognitive and behavioural). The current e-SBI includes the aforementioned different types of information (e.g. drinking profile, didactic information, practical cost, consequences) and in addition, alters its message/information on the drinking motives of the students identified depending on their responses in an online questionnaire (more detail available on Disperati et al. (13)). Since the effectiveness of the intervention is moderated by individual differences such as alcohol-related negative consequences (6), a secondary objective was to examine whether specific training would be differentially effective for hazardous drinkers who had experienced more heavy drinking episodes at baseline (before intervention).
Methods Participants Participants were students in two courses (one undergraduate, one graduate), randomly selected from psychology courses in which the professor administered the consent for the study. The research assistants attended classes to explain the research opportunity and how to access the website. All students in the undergraduate course were assigned to the experimental arm; all students in the graduate course were assigned to the control arm. This first assignment was determined because of that fact that, in order to test all online activities, we needed a great number of participants in the intervention condition (undergraduate courses are generally more frequently attended by students than graduate courses). In addition, we considered that undergraduate psychology students, who generally have a lower familiarity with the range of methods used to investigate psychological questions than graduate students, would have a lower likelihood of anticipating the various elements in conducting action research (e.g. aim, methodologies). A total of 131 college students took part on a voluntary basis. Due to seven participants being absent from the post-test, the data reported here concerned 124 college students (22 male and 102 female) with a mean age of 21.64 years (SD ¼ 2.58). Hazardous drinking students were identified as those who reported two or more heavy drinking episodes in the past month (6). The design was quasi-experimental (without randomization) and the two classes were assigned to two conditions: intervention (n ¼ 94, in undergraduate course) and control (n ¼ 30, in graduate course). Randomization was not considered possible under the circumstances of this study as it would interfere with ongoing relationships with students, and also be highly likely to suffer contamination. Although the intervention was completed individually on a computer outside of class, students probably knew one another well enough to
Am J Drug Alcohol Abuse, 2015; 41(2): 183–187
share information (e.g. students in the control condition could know the online activities which characterized students in the intervention condition). The intention was to recruit students naturally in their usual place of work (classes), and to deliver interventions within routine conditions. All were involved in the pretest and delayed post-test research design. Every student at the university had an official personal university e-mail address, with all official mail delivered to this address. The e-SBI was performed by the individual students on personal computers at a location of their preference. Measures All measures were completed during the computerized assessment at baseline/follow-up (one month later). Single items assessing age and gender were used to describe the sample and compare groups at baseline. Measures used to generate drinking outcome variables Heavy drinking episodes in the past month were measured through one item. Participants were asked to consider the frequency (number of occasions) of drinking five or more drinks on one occasion. The frequency and quantity of alcohol use was assessed with the Alcohol Use Disorders Identification Test (AUDIT) (14). The AUDIT is a survey containing 10 items ( ¼ 0.75 at baseline; ¼ 0.73 at follow-up). Only the quantity and frequency items of the AUDIT (questions 1–3) were considered in the analysis because they were mainly adapted to verify the change in the time (one month) between subjects ( ¼ 0.62 at baseline; ¼ 0.69 at follow-up). Measures used to generate profile-specific feedback Drinking motives were assessed with the Drinking Motives Questionnaire Revised Short Form (DMQ-R SF) (15), alcohol use and abuse was measured using eight items (frequency of drinking four different types of alcoholic beverages in the last 30 days, frequency of binge drinking in the last 30 days and frequency of drunkenness – intoxication from drinking alcoholic beverages – in the last 12 months and in the last 30 days). Procedure Students participating in an online study for course credit were asked to complete an alcohol assessment through which profile-specific feedback was identified during the first days of the undergraduate and graduate courses. This profile was based on alcohol use and abuse (high-low risk status) and drinking motives (enhancement, social, conformity and coping). Thus, the intervention customizes information based on the eight different profiles obtained from combining risk status and drinking motives. At this baseline session, students were assigned to one of two group conditions: intervention and control group. Both groups received profile-specific feedback and then the intervention group received profile-specific online training for four weeks [one online activity for four weeks; i.e. Coping/high risk status: (i) Advantages and disadvantages of drinking alcoholic beverages; (ii) Coping strategies; (iii) Calculator of alcoholic units, calories and prices for drink;
Outcomes of an e-SBI
DOI: 10.3109/00952990.2014.991022
(iv) Didactic information]. The training utilizes multimedia presentations, such as streaming video, interactive web pages and static text within three content areas (see Supplementary Material for further details). Data analysis plan Descriptive analyses were conducted to describe baseline characteristics for the total sample and for each condition. The primary analytic strategies were to conduct repeated measures analyses of variance for a design including two betweensubject factors, each with two levels (Condition, Control or Intervention; Hazardous drinkers at baseline, Yes or No), and one- within-subjects factor with two levels (Time, Baseline or one month later).
Results Alcohol involvement at baseline In Table 1 the demographic characteristics are presented. At baseline, students reported a mean of 0.96 (SD ¼ 0.52) frequency and quantity of alcohol use AUDIT score mean. Of the 124 students, 34% reported two or more heavy drinking episodes in the past month. There were no differences between intervention or control at baseline on AUDIT score mean, F(1,124) ¼ 0.20; p ¼ 0.65, and on two or more reported heavy drinking episodes in the past month, c2 (1) (N ¼ 124) ¼ 0.26; p ¼ 0.61. With regard to sociodemographic characteristics, although there was no difference as a function of condition group rates in students’ gender, c2 (1) (N ¼ 124) ¼ 0.14, p ¼ 0.71, there was a difference by intervention and control at baseline on age, F(1,124) ¼ 48.67; p50.001 (M ¼ 20.82, SD ¼ 2.19 vs. M ¼ 24.03, SD ¼ 2.20).
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and observations of mean values showed that AUDIT score means no significant decreases in the intervention condition (baseline: M ¼ 0.97; one month later: M ¼ 0.92) and they had not significantly increased in the control group (baseline: M ¼ 0.92; one month later: M ¼ 1.01). Moreover, a Generalized Linear Model for repeated measures was used to to examine whether specific training would be differentially effective for hazardous drinkers who had experienced more heavy drinking episodes at baseline. For the variable assessing the frequency and quantity of alcohol use, the analyses revealed a medium effect of the interaction time condition hazardous drinkers at baseline, F(1,120) ¼ 10.50, p ¼ 0.002, n2p ¼ 0.08. This finding indicates that the changes from baseline to one month later between participants of the two conditions were moderated by whether students were hazardous drinkers at baseline. Bonferroniadjusted pairwise comparisons indicated that for those who were hazardous drinkers at baseline, there was an improvement from baseline to follow-up, t(40) ¼ 2.03, p ¼ 0.04, d ¼ 0.25 for those in the intervention condition, while there was a deterioration from baseline to follow-up, t(40) ¼ 3.47, p ¼ 0.001, d ¼ 0.67 for those in the control condition. However, there were no differences between those who were non-hazardous drinkers at baseline, t(80) ¼ 0.80, p ¼ 0.80, for those in the control condition; t(80) ¼ 0.74, p ¼ 0.46, or for those in the intervention condition. These results and observation of mean values showed that AUDIT score means for the hazardous drinkers at baseline significantly decreased in the intervention condition (baseline: M ¼ 1.50; one month later: M ¼ 1.39) while they significantly increased in the control group (baseline: M ¼ 1.22; one month later: M ¼ 1.56) (Figure 1).
Discussion Effectiveness of the intervention A Generalized Linear Model for repeated measures was used to investigate the effectiveness of the intervention in reducing frequency and quantity of alcohol use. For the variable assessing the frequency and quantity of alcohol use, the analyses showed a not significant main effect of Time, F(1,122) ¼ 0.32, p ¼ 0.57, and of Condition, F(1,122) ¼ 0.04, p ¼ 0.84. There was only an effect of the interaction Time Condition, F(1,122) ¼ 5.21, p ¼ 0.02, n2p ¼ 0.04. This finding indicates that there were changes from baseline to one month later between participants of the two conditions. Bonferroni-adjusted pairwise comparisons indicated that those in the intervention condition had no significant improvement from baseline to follow-up, t(122) ¼ 1.74, p ¼ 0.08. In addition, there was no significant deterioration between those in the control condition, t(122) ¼ 1.63, p ¼ 0.10. These results
In this quasi-experimental pilot study, a four-week web-based personalized feedback intervention tailored to individual drinking motives demonstrated significant reductions in frequency and quantity of alcohol use post treatment among only college students who were hazardous drinkers at baseline. Baseline hazardous drinkers (college students) receiving only the personalized feedback, without additional training related to drinking motivation, showed significant increases in frequency and quantity of alcohol use post treatment. Although promising results were obtained using a small sample, as the differences between means are smaller, and only a small number of participants were classified as hazardous drinkers in the control group, the figure must be interpreted with caution. Nonetheless, the results are consistent with Palfai and colleagues’ finding, in which feedbackbased computerized intervention is more effective for
Table 1. Demographic and baseline characteristics.
n Age, mean (SD) Female, no. (%) AUDIT score, mean (SD) Hazardous drinkers, no. (%)
Intervention
Control
Statistic (df)
p Value
94 20.8 (2.1) 24 (80.0) 0.97 (0.54) 9 (30.0)
30 24.0 (2.2) 78 (83.0) 0.92 (0.45) 33 (35.1)
F(1,124) ¼ 48.67 2(1) ¼ 0.14 F(1,124) ¼ 0.20 2(1) ¼ 0.26
50.001 0.71 0.65 0.61
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Am J Drug Alcohol Abuse, 2015; 41(2): 183–187
Acknowledgements Funding for this study was provided in part by ESU C91J11001520002. ESU had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The authors wish to thank C. Marino, F. Disperati and M. Bergamin who assisted in the developing of the intervention.
Declaration of interest Figure 1. Association between quantity and frequency items of the AUDIT score means (questions 1–3) and time (baseline and one month later) according to being hazardous drinkers at baseline (yes/no).
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper.
References hazardous drinking students who are experiencing higher levels of alcohol-related consequences (6) since students who are heavier drinkers may experience feedback as more relevant and salient (16). Thus, web-based interventions tailored to specific drinking motives may be a novel variant to such interventions. Specifically providing students with a summary/ activity of their motivation to drink could create intrapersonal or intrinsic discrepancies around people’s goals and desired behavior (17), which may result in behavioral change, according to a recent study on the effectiveness of a web-based decisional balance feedback, providing students with a summary of their motivation to change (18). Limitations and strengths There are some limitations to this study. Firstly, although the AUDIT questionnaire may be useful as a measure for overall mean level of alcohol risk in college students as well as in broader adult populations (e.g. 19,20), other instruments are more frequently used and can better capture the outcome that demonstrates alcohol use specifically. Secondly, because of the quasi-experimental design and the selection of courses, the samples were biased with fewer students in the control arm, a significant difference between control and experimental arms in age, and a sample comprised primarily of women (and likely other unmeasured characteristics). These biases may be associated with the results found in this study. Future evaluation of the program on a larger random sample, with more male students and participants in the control condition and on groups of students quite homogeneous in terms of demographic characteristics is needed. Finally, the study uses follow-up in the short term. Replications with follow-up in the long term (i.e. 3–6–12 months) are also needed. In conclusion, the results suggest that hazardous drinkers (college students) who completed the specific training and received personalized feedback seemed to do better on frequency and quantity of alcohol use than hazardous drinkers (college students) who received only personalized feedback. This result provides support for a larger trial of the intervention. This study provides some evidence for targeting motivation to use alcohol among college-age students and integrating didactic information on motivation within broader substance abuse prevention interventions.
1. Fromme K, Corbin WR, Kruse MI. Behavioral risks during the transition from high school to college. Dev Psychol 2008;44: 1497–1504. 2. Hingson RW, Zha W, Weitzman ER. Magnitude of and trends in alcohol-related mortality and morbidity among US college students ages 18–24, 1998–2005. J Stud Alcohol Drugs 2009;16:12–20. 3. Cunningham JA. Comparison of two internet-based interventions for problem drinkers: randomized controlled trial. J Med Internet Res 2012;14:e107. 4. Barak A, Hen L, Boniel-Nissim M, Shapira NA. A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions. J Technol Hum Serv 2008;26: 109–160. 5. Hester RK, Delaney HD, Campbell W. The College Drinker’s Check-Up: outcomes of two randomized clinical trials of a computer-delivered intervention. Psychol Addict Behav 2012;26: 1–12. 6. Palfai TP, Zisserson R, Saitz R. Using personalized feedback to reduce alcohol use among hazardous drinking college students: the moderating effect of alcohol-related negative consequences. Addict Behav 2011;36:539–542. 7. Miller MB, Leffingwell T, Claborn K, Meier E, Walters S, Neighbors C. Personalized feedback interventions for college alcohol misuse: an update of Walters & Neighbors (2005). Psychol Addict Behav 2013;27:909–920. 8. Adams ZW, Kaiser AJ, Lynam DR, Charnigo RJ, Milich R. Drinking motives as mediators of the impulsivity-substance use relation: pathways for negative urgency, lack of premeditation, and sensation seeking. Addict Behav 2012;37:848–855. 9. Kuntsche E, Gabhainn SN, Roberts C, Windlin B, Vieno A, Bendtsen P, Wicki M. Drinking motives and links to alcohol use in 13 European countries. J Stud Alcohol Drugs 2014;75:428–437. 10. Cooper ML. Motivations for alcohol use among adolescents: development and validation of a four-factor model. Psychol Assessment 1994;6:117–128. 11. Conrod PJ, Castellanos-Ryan N, Mackie C. Long-term effects of a personality-targeted intervention to reduce alcohol use in adolescents. J Consult Clin Psych 2011;79:296–306. 12. Patrick ME, Lee CM, Larimer ME. Drinking motives, protective behavioral strategies, and experienced consequences: identifying students at risk. Addict Behav 2011;36:270–273. 13. Disperati F, Canale N, Vieno A, Marino C, Chieco F, Andriolo S, Santinello M. ‘‘Scopri che bevitore sei’’: intervento di prevenzione online per ridurre il consumo di alcol e i problemi alcol relati [‘‘Which type of drinker are you?’’: an online prevention programme to reduce alcohol consumption and alcohol related problems]. Ital J Psychol (in press). 14. Saunders JB, Aasland OG, Babor TF, Grant M. Development of the alcohol use disorders identification test (AUDIT): WHO collaborative project on early detection of persons with harmful alcohol consumption – II. Addiction 1993;88:791–804. 15. Mazzardis S, Vieno A, Kuntsche E, Santinello M. Italian validation of the drinking motives questionnaire revised short form (DMQ-R SF). Addict Behav 2010;35:905–908.
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16. Elliott JC, Carey KB, Bolles JR. Computer-based interventions for college drinking: a qualitative review. Addict Behav 2008;33: 994–1005. 17. Miller WR. Enhancing motivation for change in substance abuse treatment. Vol. 35. Rockville (MD): U.S. Department of Health and Human Services; 1999. 18. Collins SE, Kirouac M, Lewis MA, Witkiewitz K, Carey KB. Randomized controlled trial of web-based decisional balance
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feedback and personalized normative feedback for college drinkers. J Stud Alcohol Drugs 2014;75:982–992. 19. Chung T, Colby SM, Barnett NP, Rohsenow DJ, Spirito A, Monti PM. Screening adolescents for problem drinking: performance of brief screens against DSM-IV alcohol diagnoses. J Stud Alcohol Drugs 2000;61:579–587. 20. Kelly TM, Donovan JE, Kinnane JM, Taylor DM. A comparison of alcohol screening instruments among under-aged drinkers treated in emergency departments. Alcohol Alcsm 2002;37:444–450.
Supplementary material available online Table S1: Content Component Definitions and Facets Supplementary material can be viewed and downloaded at http://informahealthcare.com/ada
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ORIGINAL ARTICLE
The efficacy of computerized alcohol intervention tailored to drinking motives among college students: a quasi-experimental pilot study Natale Canale, MSC, Alessio Vieno, PhD, Massimo Santinello, BA, Francesca Chieco, MD, and Stefano Andriolo, HSD Department of Developmental and Social Psychology, University of Padova, Padova, Italy
Abstract
Keywords
Background: Although motivational processes may influence the intervention effects and help prevention programmes identify students at great risk for alcohol-related problems, no computerized alcohol intervention has yet to be tailored to drinking motives. Objective: To describe the development and initial pilot testing of a computer-delivered intervention tailored to drinking motives, to prevent alcohol abuse and its adverse consequences among university students in general and among baseline hazardous drinkers specifically. Methods: 124 college students attending a public university in northeastern Italy participated in this study in October of 2012 (89.2% female- mean age ¼ 21.64–34% baseline hazardous drinkers). Two classes (one undergraduate, one graduate) were assigned to one of two conditions: intervention and control group. Both groups received profile-specific feedback and then the intervention group received profile-specific online training for 4 weeks. This profile was based on their risk type (high-low) and drinking motives (enhancement-social-conformity-coping). Results: Controlling for corresponding baseline alcohol measures, analyses showed a significant interaction between intervention condition and hazardous drinkers at baseline. For hazardous drinkers at baseline, the alcohol intervention results showed a significant decrease in frequency and quantity of alcohol use at follow-up, while no difference was observed between intervention conditions for non-hazardous drinkers at baseline. Conclusions: The results suggest that hazardous drinkers (college students) who completed the specific training and received personalized feedback seemed to do better on frequency and quantity of alcohol use than hazardous drinkers (college students) who received only personalized feedback. These results seem to provide support for a larger trial of the intervention and for more appropriate evaluations.
Alcohol abuse, college students, computer-delivered intervention, drinking motives, prevention
Introduction Research shows that alcohol consumption peaks during the transition from high school to college (1). Findings from national surveys suggest that almost half of college students (44.7%) report heavy episodic (i.e. ‘‘binge’’) drinking in the last month (2). College students were more likely to sustain accidental injury and engage in crime, sexual violence, and suicide than their same aged peers who were not enrolled in college (2). One recent response to the problem of heavy drinking in college students is the development of computer-delivered interventions (CDIs) such as electronic-screening and brief intervention (e-SBI). At the very least, e-SBI involves screening individuals for excessive drinking and delivering a brief intervention, which provides personalized feedback about the risks and consequences of excessive drinking. The Address correspondence to Alessio Vieno, Department of Developmental and Social Psychology, University of Padova, Via Venezia 8, 35131 Padova, Italy, Tel: +39 (0) 49 82 76493. Fax. +39 (0) 49 82 76547. E-mail: [email protected]
History Received 22 August 2014 Revised 10 November 2014 Accepted 18 November 2014 Published online 20 February 2015
CDIs that span across multiple sessions are more effective than when compressed in a single-session feedback intervention (3); moreover, a higher degree of interactivity is associated with higher effect sizes (4). Computer-based personalized feedback has included different types of information such as drinking norms, costs, and consequences (5,6) but has not considered drinking motives (7,8). Considering that alcohol drinking motives are an important proximal factor for drinking behavior (9), online interventions tailored to specific drinking motives may be a novel variant of such interventions by providing students with a summary of their motivations to drink. The Motivational Model of Alcohol Use assumes that people display a certain behavior to achieve expected or desired effects (10). Identifying the specific needs for alcohol may help people develop more adaptive ways to meet these needs. For example, ‘‘personality-related’’ interventions were associated with both reductions in alcoholrelated problems and changes in drinking motivation; thus, it is possible that the mechanism by which these interventions work is to alter drinking motivations, which in turn result in behavioral change (11). Finally, motives may also be able to
184
N. Canale et al.
aid prevention programmes in identifying students at greater risk for alcohol-related problems (e.g. indicated prevention) (12). Therefore, the purpose of the current study was to deliver a theoretically-based and empirically supported prevention program form via email to Italian college students enrolled in two lecture courses and evaluate the preliminary efficacy in reducing risky alcohol use at a one-month follow-up. The ‘‘Which type of drinker are you?’’ online prevention program was developed together with a panel of prevention experts that implemented evidence-based substance abuse prevention strategies (mainly cognitive and behavioural). The current e-SBI includes the aforementioned different types of information (e.g. drinking profile, didactic information, practical cost, consequences) and in addition, alters its message/information on the drinking motives of the students identified depending on their responses in an online questionnaire (more detail available on Disperati et al. (13)). Since the effectiveness of the intervention is moderated by individual differences such as alcohol-related negative consequences (6), a secondary objective was to examine whether specific training would be differentially effective for hazardous drinkers who had experienced more heavy drinking episodes at baseline (before intervention).
Methods Participants Participants were students in two courses (one undergraduate, one graduate), randomly selected from psychology courses in which the professor administered the consent for the study. The research assistants attended classes to explain the research opportunity and how to access the website. All students in the undergraduate course were assigned to the experimental arm; all students in the graduate course were assigned to the control arm. This first assignment was determined because of that fact that, in order to test all online activities, we needed a great number of participants in the intervention condition (undergraduate courses are generally more frequently attended by students than graduate courses). In addition, we considered that undergraduate psychology students, who generally have a lower familiarity with the range of methods used to investigate psychological questions than graduate students, would have a lower likelihood of anticipating the various elements in conducting action research (e.g. aim, methodologies). A total of 131 college students took part on a voluntary basis. Due to seven participants being absent from the post-test, the data reported here concerned 124 college students (22 male and 102 female) with a mean age of 21.64 years (SD ¼ 2.58). Hazardous drinking students were identified as those who reported two or more heavy drinking episodes in the past month (6). The design was quasi-experimental (without randomization) and the two classes were assigned to two conditions: intervention (n ¼ 94, in undergraduate course) and control (n ¼ 30, in graduate course). Randomization was not considered possible under the circumstances of this study as it would interfere with ongoing relationships with students, and also be highly likely to suffer contamination. Although the intervention was completed individually on a computer outside of class, students probably knew one another well enough to
Am J Drug Alcohol Abuse, 2015; 41(2): 183–187
share information (e.g. students in the control condition could know the online activities which characterized students in the intervention condition). The intention was to recruit students naturally in their usual place of work (classes), and to deliver interventions within routine conditions. All were involved in the pretest and delayed post-test research design. Every student at the university had an official personal university e-mail address, with all official mail delivered to this address. The e-SBI was performed by the individual students on personal computers at a location of their preference. Measures All measures were completed during the computerized assessment at baseline/follow-up (one month later). Single items assessing age and gender were used to describe the sample and compare groups at baseline. Measures used to generate drinking outcome variables Heavy drinking episodes in the past month were measured through one item. Participants were asked to consider the frequency (number of occasions) of drinking five or more drinks on one occasion. The frequency and quantity of alcohol use was assessed with the Alcohol Use Disorders Identification Test (AUDIT) (14). The AUDIT is a survey containing 10 items ( ¼ 0.75 at baseline; ¼ 0.73 at follow-up). Only the quantity and frequency items of the AUDIT (questions 1–3) were considered in the analysis because they were mainly adapted to verify the change in the time (one month) between subjects ( ¼ 0.62 at baseline; ¼ 0.69 at follow-up). Measures used to generate profile-specific feedback Drinking motives were assessed with the Drinking Motives Questionnaire Revised Short Form (DMQ-R SF) (15), alcohol use and abuse was measured using eight items (frequency of drinking four different types of alcoholic beverages in the last 30 days, frequency of binge drinking in the last 30 days and frequency of drunkenness – intoxication from drinking alcoholic beverages – in the last 12 months and in the last 30 days). Procedure Students participating in an online study for course credit were asked to complete an alcohol assessment through which profile-specific feedback was identified during the first days of the undergraduate and graduate courses. This profile was based on alcohol use and abuse (high-low risk status) and drinking motives (enhancement, social, conformity and coping). Thus, the intervention customizes information based on the eight different profiles obtained from combining risk status and drinking motives. At this baseline session, students were assigned to one of two group conditions: intervention and control group. Both groups received profile-specific feedback and then the intervention group received profile-specific online training for four weeks [one online activity for four weeks; i.e. Coping/high risk status: (i) Advantages and disadvantages of drinking alcoholic beverages; (ii) Coping strategies; (iii) Calculator of alcoholic units, calories and prices for drink;
Outcomes of an e-SBI
DOI: 10.3109/00952990.2014.991022
(iv) Didactic information]. The training utilizes multimedia presentations, such as streaming video, interactive web pages and static text within three content areas (see Supplementary Material for further details). Data analysis plan Descriptive analyses were conducted to describe baseline characteristics for the total sample and for each condition. The primary analytic strategies were to conduct repeated measures analyses of variance for a design including two betweensubject factors, each with two levels (Condition, Control or Intervention; Hazardous drinkers at baseline, Yes or No), and one- within-subjects factor with two levels (Time, Baseline or one month later).
Results Alcohol involvement at baseline In Table 1 the demographic characteristics are presented. At baseline, students reported a mean of 0.96 (SD ¼ 0.52) frequency and quantity of alcohol use AUDIT score mean. Of the 124 students, 34% reported two or more heavy drinking episodes in the past month. There were no differences between intervention or control at baseline on AUDIT score mean, F(1,124) ¼ 0.20; p ¼ 0.65, and on two or more reported heavy drinking episodes in the past month, c2 (1) (N ¼ 124) ¼ 0.26; p ¼ 0.61. With regard to sociodemographic characteristics, although there was no difference as a function of condition group rates in students’ gender, c2 (1) (N ¼ 124) ¼ 0.14, p ¼ 0.71, there was a difference by intervention and control at baseline on age, F(1,124) ¼ 48.67; p50.001 (M ¼ 20.82, SD ¼ 2.19 vs. M ¼ 24.03, SD ¼ 2.20).
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and observations of mean values showed that AUDIT score means no significant decreases in the intervention condition (baseline: M ¼ 0.97; one month later: M ¼ 0.92) and they had not significantly increased in the control group (baseline: M ¼ 0.92; one month later: M ¼ 1.01). Moreover, a Generalized Linear Model for repeated measures was used to to examine whether specific training would be differentially effective for hazardous drinkers who had experienced more heavy drinking episodes at baseline. For the variable assessing the frequency and quantity of alcohol use, the analyses revealed a medium effect of the interaction time condition hazardous drinkers at baseline, F(1,120) ¼ 10.50, p ¼ 0.002, n2p ¼ 0.08. This finding indicates that the changes from baseline to one month later between participants of the two conditions were moderated by whether students were hazardous drinkers at baseline. Bonferroniadjusted pairwise comparisons indicated that for those who were hazardous drinkers at baseline, there was an improvement from baseline to follow-up, t(40) ¼ 2.03, p ¼ 0.04, d ¼ 0.25 for those in the intervention condition, while there was a deterioration from baseline to follow-up, t(40) ¼ 3.47, p ¼ 0.001, d ¼ 0.67 for those in the control condition. However, there were no differences between those who were non-hazardous drinkers at baseline, t(80) ¼ 0.80, p ¼ 0.80, for those in the control condition; t(80) ¼ 0.74, p ¼ 0.46, or for those in the intervention condition. These results and observation of mean values showed that AUDIT score means for the hazardous drinkers at baseline significantly decreased in the intervention condition (baseline: M ¼ 1.50; one month later: M ¼ 1.39) while they significantly increased in the control group (baseline: M ¼ 1.22; one month later: M ¼ 1.56) (Figure 1).
Discussion Effectiveness of the intervention A Generalized Linear Model for repeated measures was used to investigate the effectiveness of the intervention in reducing frequency and quantity of alcohol use. For the variable assessing the frequency and quantity of alcohol use, the analyses showed a not significant main effect of Time, F(1,122) ¼ 0.32, p ¼ 0.57, and of Condition, F(1,122) ¼ 0.04, p ¼ 0.84. There was only an effect of the interaction Time Condition, F(1,122) ¼ 5.21, p ¼ 0.02, n2p ¼ 0.04. This finding indicates that there were changes from baseline to one month later between participants of the two conditions. Bonferroni-adjusted pairwise comparisons indicated that those in the intervention condition had no significant improvement from baseline to follow-up, t(122) ¼ 1.74, p ¼ 0.08. In addition, there was no significant deterioration between those in the control condition, t(122) ¼ 1.63, p ¼ 0.10. These results
In this quasi-experimental pilot study, a four-week web-based personalized feedback intervention tailored to individual drinking motives demonstrated significant reductions in frequency and quantity of alcohol use post treatment among only college students who were hazardous drinkers at baseline. Baseline hazardous drinkers (college students) receiving only the personalized feedback, without additional training related to drinking motivation, showed significant increases in frequency and quantity of alcohol use post treatment. Although promising results were obtained using a small sample, as the differences between means are smaller, and only a small number of participants were classified as hazardous drinkers in the control group, the figure must be interpreted with caution. Nonetheless, the results are consistent with Palfai and colleagues’ finding, in which feedbackbased computerized intervention is more effective for
Table 1. Demographic and baseline characteristics.
n Age, mean (SD) Female, no. (%) AUDIT score, mean (SD) Hazardous drinkers, no. (%)
Intervention
Control
Statistic (df)
p Value
94 20.8 (2.1) 24 (80.0) 0.97 (0.54) 9 (30.0)
30 24.0 (2.2) 78 (83.0) 0.92 (0.45) 33 (35.1)
F(1,124) ¼ 48.67 2(1) ¼ 0.14 F(1,124) ¼ 0.20 2(1) ¼ 0.26
50.001 0.71 0.65 0.61
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Am J Drug Alcohol Abuse, 2015; 41(2): 183–187
Acknowledgements Funding for this study was provided in part by ESU C91J11001520002. ESU had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The authors wish to thank C. Marino, F. Disperati and M. Bergamin who assisted in the developing of the intervention.
Declaration of interest Figure 1. Association between quantity and frequency items of the AUDIT score means (questions 1–3) and time (baseline and one month later) according to being hazardous drinkers at baseline (yes/no).
The authors report no conflicts of interest. The authors alone are responsible for the content and writing of this paper.
References hazardous drinking students who are experiencing higher levels of alcohol-related consequences (6) since students who are heavier drinkers may experience feedback as more relevant and salient (16). Thus, web-based interventions tailored to specific drinking motives may be a novel variant to such interventions. Specifically providing students with a summary/ activity of their motivation to drink could create intrapersonal or intrinsic discrepancies around people’s goals and desired behavior (17), which may result in behavioral change, according to a recent study on the effectiveness of a web-based decisional balance feedback, providing students with a summary of their motivation to change (18). Limitations and strengths There are some limitations to this study. Firstly, although the AUDIT questionnaire may be useful as a measure for overall mean level of alcohol risk in college students as well as in broader adult populations (e.g. 19,20), other instruments are more frequently used and can better capture the outcome that demonstrates alcohol use specifically. Secondly, because of the quasi-experimental design and the selection of courses, the samples were biased with fewer students in the control arm, a significant difference between control and experimental arms in age, and a sample comprised primarily of women (and likely other unmeasured characteristics). These biases may be associated with the results found in this study. Future evaluation of the program on a larger random sample, with more male students and participants in the control condition and on groups of students quite homogeneous in terms of demographic characteristics is needed. Finally, the study uses follow-up in the short term. Replications with follow-up in the long term (i.e. 3–6–12 months) are also needed. In conclusion, the results suggest that hazardous drinkers (college students) who completed the specific training and received personalized feedback seemed to do better on frequency and quantity of alcohol use than hazardous drinkers (college students) who received only personalized feedback. This result provides support for a larger trial of the intervention. This study provides some evidence for targeting motivation to use alcohol among college-age students and integrating didactic information on motivation within broader substance abuse prevention interventions.
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Supplementary material available online Table S1: Content Component Definitions and Facets Supplementary material can be viewed and downloaded at http://informahealthcare.com/ada
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