Abstract
Objective:
The purpose of this randomized controlled study was to examine sex as a moderator of the efficacy of a brief, web-based personalized feedback intervention (eCHECKUP TO GO) on decreasing cognitive risk factors for alcohol use, increasing protective behavioral strategies, and reducing alcohol use among high school seniors.
Method:
Participants (n = 311) were high school seniors randomized by class period to the eCHECKUP TO GO intervention or assessment-only control group. Participants completed online surveys at baseline and 30-day follow-up (91.0%; n = 283).
Results:
Students in the intervention group reported a significant reduction in normative perceptions of peer drinking, positive alcohol expectancies, and alcohol use relative to those in the control group. Intervention effects for perceptions of frequency of peer drunkenness and frequency of alcohol use were moderated by sex, with results favoring females. In contrast, we did not find evidence for sex as a moderator of intervention effects for normative perceptions of peer drinking frequency, sex-specific perceptions of peer heavy episodic drinking, positive alcohol expectancies, or peak drinking quantity. Further, we did not find significant intervention or moderator effects for protective behavioral strategies.
Conclusions:
Results of this study extend the literature by demonstrating the efficacy of the eCHECKUP TO GO for both males and females on reducing cognitive risk factors and alcohol use, although results were significant for a broader range of variables for females. Results also indicate that program content regarding normative feedback and protective behavioral strategies may need modification to be more effective for this age group.
Adolescent alcohol use is a serious public health concern in the United States, posing significant health risks among youth (National Institute on Alcohol Abuse and Alcoholism, 2020). Among high school students, seniors have the highest rates of alcohol use, with 58.5% of seniors reporting alcohol use and 42.9% reporting being drunk at least once in their lifetime (Johnston et al., 2019). Further, binge drinking—defined as four or more drinks in a row for females and five or more for males—escalates through high school, with 20.9% of seniors reporting binge drinking in the past 30 days (Centers for Disease Control and Prevention, 2017). Among high school students, binge drinking is associated with multiple negative consequences, including impaired neurocognitive functioning (Nguyen-Louie et al., 2015), academic problems (Patte et al., 2017), unwanted sexual activity (Arata et al., 2003), being a dating violence victim, attempting suicide, using illicit drugs, and riding with a driver who had been drinking (Miller et al., 2007). In addition, hazardous drinking (e.g., drinking games), which is more prevalent among older high school students (Borsari et al., 2013), peaking at ages 17–19 (Zamboanga et al., 2016), is associated with a wide range of negative consequences, including having a hangover, passing out, engaging in regretted sexual activities, and drinking and driving (Borsari et al., 2013).
Despite the need for alcohol interventions for high school seniors, a seminal review of alcohol preventive interventions concluded that the majority of research targets youth ages 10–15, with limited research conducted with high school students ages 16 and older (Spoth et al., 2008). More recent reviews echo these findings, indicating that the majority of alcohol intervention research targets students in their first 2 years of high school (Champion et al., 2013) or junior high school students (Strøm et al., 2014). These findings, coupled with the high rates of alcohol use and associated consequences, highlight the importance of identifying effective alcohol interventions for high school seniors.
The eCHECKUP TO GO (San Diego State University Research Foundation, n.d.) is a web-based personalized feedback intervention categorized in the National Institute on Alcohol Abuse and Alcoholism (2019) CollegeAIM as a highly effective, low-cost intervention. Because the eCHECKUP TO GO is brief, can be disseminated to large groups of students within one class period, and is inexpensive, the program is well suited for use as a school-based intervention. Although originally developed for college students, preliminary research provides some support for the efficacy of the eCHECKUP TO GO in reducing alcohol use among high school students (Doumas et al., 2014, 2017a). However, in a high school study comparing the eCHECKUP TO GO to traditional alcohol education, results indicated that the eCHECKUP TO GO was effective in reducing rates of alcohol use for females only (Hausheer et al., 2018). Thus, the research suggests that although the eCHECKUP TO GO is a promising strategy for high school students, the intervention may need modification to increase program efficacy for males in this age group.
The eCHECKUP TO GO was designed to reduce alcohol use by targeting cognitive risk factors (e.g., normative perceptions of peer alcohol use and alcohol expectancies) and protective behavioral strategies (e.g., behaviors used to minimize risk associated with alcohol use). However, results from an initial study conducted with high school seniors demonstrated that when comparing outcomes between students receiving eCHECKUP TO GO and those in an assessment-only control group, intervention effects for cognitive risk factors, including normative perceptions of peer alcohol use and positive alcohol expectancies, were significant for females only (Doumas et al., 2017b). Further, the authors found no significant effects for protective behavioral strategies, although it is possible that the lack of significant findings was due to examining protective behavioral strategies changes for the whole sample, including nondrinkers. Overall, findings suggest that the content of the intervention may not be optimal for addressing cognitive risk factors for males or protective behavioral strategies for high school seniors.
The purpose of the present study was to replicate and extend prior research examining sex differences in the efficacy of the eCHECKUP TO GO among high school seniors. We selected seniors because they have the highest rates of alcohol use and high-risk drinking among high school students, yet alcohol intervention research for this age group is limited (Champion et al., 2013; Spoth et al., 2008; Strøm et al., 2014). Our first objective was to assess sex differences in intervention effects on cognitive risk factors and protective behavioral strategies to identify areas of program content that may need to be revised for this age group. To achieve this aim, we built on initial findings reported by Doumas et al. (2017b). First, we included additional measures of normative beliefs to gain a better understanding of sex differences in this area. Peer drinking norms have been identified as the most consistent mediator of normative feedback interventions (Reid & Carey, 2015), and gender-specific norms are more strongly associated with problematic drinking than gender-nonspecific norms (Lewis & Neighbors, 2004). Second, we used a revised measure of protective behavioral strategies (Protective Behavioral Strategies Scale-20 [PBSS20]; Treloar et al., 2015) that includes additional items and has higher reliability than the original PBSS measure. We also examined changes on the PBSS-20 for a subsample of students who reported alcohol use. Our second objective was to extend the literature by assessing sex differences in intervention effects on alcohol use among high school seniors, as only one study (Hausheer et al., 2018) has examined sex as a moderator of intervention effects among high school students. Based on the findings reported by Doumas et al. (2017b) and Hausheer et al. (2018), we hypothesized that relative to students in the control group, students in the eCHECKUP TO GO group would report (a) a decrease in cognitive risk factors for alcohol use with intervention effects favoring for females, (b) no difference in the use of protective behavioral strategies, and (c) a decrease in alcohol use with intervention effects, with results favoring for females.
Method
Design
This study evaluated the efficacy of the eCHECKUP TO GO for high school seniors using a randomized controlled design. Classroom periods were randomized to the eCHECKUP TO GO or control condition. Participants completed baseline and 30-day follow-up assessments online. All study procedures were approved by the university institutional review board and the school district research board.
Participants
Participants were high school seniors recruited from two urban high schools in the Northwest. Because the school district research board required parental consent for students regardless of age, consent forms were sent to parents of all students (N = 867). A total of 39.4% (n = 342) provided consent and of these, 91% of students (n = 311; 44.4% male, 55.6% female) agreed to study participation. See Figure 1 for the participant flow diagram. Table 1 presents demographic information by study group.
Figure 1.
Participant flow diagram
Table 1.
Demographics by study group
| Demographics | Control group (n = 137) | Intervention group (n = 174) | Total sample (n = 311) |
| Age in years, M (SD) | 17.13 (0.45) | 17.14 (0.46) | 17.15 (0.47) |
| Sex | |||
| Male | 46.7% | 42.5% | 44.4% |
| Female | 53.3% | 57.5% | 55.6% |
| Race/ethnicity | |||
| White | 81.8% | 87.9% | 85.2% |
| Hispanic | 6.6% | 3.5% | 4.8% |
| Asian | 5.1% | 4.0% | 4.5% |
| African American | 2.9% | 0.6% | 1.6% |
| American Indian/Alaska Native | 1.5% | 1.2% | 1.3% |
| Hawaiian/other Pacific Islander | 0.0% | 1.2% | 0.7% |
| Other | 2.2% | 1.7% | 1.9% |
Procedures
Data were collected in fall 2018. At the beginning of the fall semester, parents of all seniors were contacted by the school principal via letter by mail that included a parental consent form and a project-addressed, stamped envelope. Parents were asked to return signed consent forms indicating permission (yes or no) for their adolescent’s study participation. Reminder letters were sent to the student’s home address and sent home with the student. Students who received active parental consent were recruited during a common core class period and were asked to assent before participating in the baseline survey. Because research indicates that on family-oriented holidays such as Thanksgiving, drinking patterns change such that the number of youth who drink increases but the number of drinks consumed decreases (Goldman et al., 2011), we selected early October for baseline assessment to allow time for recruitment and so that the 30-day followup would occur before Thanksgiving break.
Students with parental consent met at the school’s computer lab. A member of the research team described the study. Students were given a unique personal identification number (PIN) and URL to access the survey. Students logged onto the website, where they read a welcome screen explaining the research. Once students gave assent by clicking “Agree,” they were taken to a screen that asked them to enter their PIN and were directed to begin the survey. Students without parental consent or who did not provide assent remained in their classroom. After survey completion, students in the intervention group completed the eCHECKUP TO GO and students in the control group returned to their classroom. All participants who completed the baseline survey were invited to participate in a 30-day follow-up survey. Incentives included $100 deposited to the teacher’s school account for supplies and a bagel or pizza party for classrooms with a parental consent form (yes or no) return rate of at least 60%.
Measures
Demographics.
A brief demographic questionnaire designed for this study included basic participant characteristics (e.g., sex, race/ethnicity, age).
Normative beliefs about peer alcohol use.
A modified version of the Quantity/Frequency/Peak questionnaire (Dimeff et al., 1999; Marlatt et al., 1998) was used to assess perceptions of frequency of peer alcohol use. Participants were asked to indicate the frequency of drinking of a typical high school senior on an 8-point scale with options ranging from 1 (every day) to 8 (do not drink alcohol at all). Items were reverse scored so that high scores represent high frequency of use. Participants were also asked to indicate the frequency of peer drunkenness with the following question: “During the past 30 days (about 1 month), how many times do you believe a typical 12th grade student has gotten drunk or very high from alcohol?” This item was rated on a 6-point scale with options ranging from 1 (never) to 6 (9 or more). We also asked participants to report on sex-specific perceptions of the frequency of peer heavy episodic drinking in the past 2 weeks, defined as having five or more or three or more drinks in a row for males and females, respectively, on a single occasion (Donovan, 2009).
Positive alcohol expectancies.
The Brief Comprehensive Effects of Alcohol questionnaire (Addictive Behaviors Research Center, 1997) is a 15-item measure used to assess expectancies concerning alcohol. It includes 11 items that assess the positive expectancy factors sociability, tension reduction, liquid courage, and sexuality. Individuals responded to each of the items on a 4-point Likert-type scale ranging from 1 (disagree) to 4 (agree). A total score was created by summing the items (α = .85).
Protective behavioral strategies.
The PBSS-20 (Treloar et al., 2015) is a 20-item measure that assesses three types of protective behaviors: stopping/limiting drinking, manner of drinking, and serious harm reduction. Respondents rate the degree to which they engage in protective behaviors when using alcohol on a scale ranging from 1 (never) to 5 (always). A total score was created by summing the items (α = .98).
Alcohol use.
Past-30-day alcohol use was assessed with the following question: “During the past 30 days, how many days did you drink alcohol (beer, wine, liquor)?” (Bachman et al., 1996). Peak drinking was assessed by the question, “What is the most number of drinks that you have consumed on any given night in the past month?” (Quantity⁄Frequency⁄Peak questionnaire; Dimeff et al., 1999; Marlatt et al., 1998).
The eCHECKUP TO GO
The eCHECKUP TO GO (www.echeckuptogo.com) is a brief, web-based personalized feedback intervention. The online assessment consists of demographic information and information on alcohol consumption and drinking behavior assessed through standard alcohol measures. Individualized feedback is provided in the following domains: quantity and frequency of drinking, estimated risk status for negative consequences associated with drinking, genetic risk, tolerance, approximate financial cost of drinking in the past year, and normative feedback comparing one’s own drinking to national survey data for sex-specific adult normative data, sexspecific college student normative data, and sex-nonspecific youth ages 12–17 normative data, as well as comparisons of perceptions of peer drinking to local high school data. The program assesses student willingness to use several protective behavioral strategies (e.g., avoid drinking games, space my drinks out over time, spend time with friends who don’t drink alcohol, identify alternative social activities instead of partying). The program then provides resources for services in the local community. To ensure standardized intervention delivery, a member of the research team read a script to the participants. Research team members were present to assist participants and serve as monitors, ensuring participants completed the program, kept their eyes on their own screens, were engaged in the program, and that there was no discussion among participants.
Statistical analyses
All outcome variables were examined for outliers at baseline and follow-up and were adjusted to 3.3 SD above the mean before conducting analyses (Tabachnick & Fidell, 2007). We examined differences on demographic and outcome variables between the two study conditions at baseline. Students in the intervention group reported a significantly higher frequency of drinking, t(281) = 3.46, p < .001, and peak drinking, t(281) = 3.23, p < .001, than students in the control group. Thus, we included these variables as control variables in the analyses of risk factors and protective behavioral strategies. Because students were recruited from two schools and class periods were randomized to treatment, we assessed the importance of incorporating random effects (i.e., participants nested within school and within period) using Akaike information criterion (AIC; Burnham & Anderson, 2002) under the restricted maximum likelihood algorithm. We examined the intraclass correlations (ICCs) to evaluate the degree of nonindependence among students within school and within period. Because the ICCs for both within school and within period ranged from .01 to .09 and .04 to .06, respectively, compared to a within-student ICCs ranging from .91 to .93, and the change in AIC was negligible, we did not consider these random effects further. We fit study outcomes with a repeated measures analysis of variance with group (intervention; control) and sex (male; female) as grouping factors and time (baseline; follow-up) as the within factor. The analysis for protective behavioral strategies was conducted with a subsample of students who reported alcohol use in the past 30 days (49.9%). Analyses were considered significant at p < .05 and were conducted in SPSS Statistics for Windows, Version 24.0 (IBM Corp, Armonk, NY).
Results
Overall, 91.0% (n = 283) of the 311 participants completed the 30-day follow-up assessment. Chi-square and one-way analyses of variance revealed no differences on demographic or outcome variables between 30-day survey completers and noncompleters. In addition, there was no difference in the rate of attrition across the two groups. Means and standard deviations for outcome variables by group and sex at baseline and follow-up are reported in Tables 2–4.
Table 2.
Means and standard deviations for cognitive risk factors at baseline and follow-up assessments by study condition and sex
| Sex |
|||
| Outcomes | Malea M (SD) | Femaleb M (SD) | Total samplec M (SD) |
| Perception of frequency of peer drinking | |||
| Control | |||
| Baseline | 5.47 (1.31) | 5.49 (1.02) | 5.48 (1.16) |
| Follow-up | 5.37 (1.47) | 5.57 (0.89) | 5.48 (1.19) |
| Intervention | |||
| Baseline | 5.37 (1.09) | 5.74 (0.88) | 5.57 (0.99) |
| Follow-up | 5.11 (1.15) | 5.35 (1.05) | 5.24 (1.10) |
| Perception of frequency of peer drunkenness | |||
| Control | |||
| Baseline | 2.93 (1.15) | 3.40 (1.40) | 3.19 (1.31) |
| Follow-up | 2.97 (1.16) | 3.57 (118) | 3.29 (1.21) |
| Intervention | |||
| Baseline | 3.11 (1.06) | 3.90 (1.18) | 3.55 (1.19) |
| Follow-up | 2.83 (1.08) | 3.09 (1.18) | 2.98 (1.14) |
| Sex-specific perception of heavy episodic drinking | |||
| Control | |||
| Baseline | 2.61 (2.93) | 2.69 (2.19) | 2.65 (2.55) |
| Follow-up | 2.95 (2.81) | 3.45 (3.23) | 3.22 (3.04) |
| Intervention | |||
| Baseline | 2.87 (2.46) | 3.03 (2.45) | 2.96 (2.44) |
| Follow-up | 2.20 (1.82) | 2.59 (2.47) | 2.42 (2.21) |
| Positive alcohol expectancies | |||
| Control | |||
| Baseline | 27.73 (7.42) | 27.73 (7.11) | 27.73 (7.23) |
| Follow-up | 27.46 (8.53) | 27.49 (6.70) | 27.48 (7.57) |
| Intervention | |||
| Baseline | 30.52 (6.34) | 30.40 (5.38) | 30.45 (5.81) |
| Follow-up | 28.76 (6.20) | 28.77 (7.57) | 28.77 (6.97) |
Control group, n = 57; intervention group, n = 71.
Control group, n = 67; intervention group, n = 88.
Control group, n = 124; intervention group, n = 159.
Table 4.
Means and standard deviations for alcohol use at baseline and follow-up assessments by study condition and sex
| Sex |
|||
| Outcomes | Malea M (SD) | Femaleb M (SD) | Total samplec M (SD) |
| Frequency of alcohol use | |||
| Control | |||
| Baseline | 1.81 (1.26) | 1.60 (0.97) | 1.69 (1.11) |
| Follow-up | 1.67 (0.97) | 1.54 (0.94) | 1.60 (0.95) |
| Intervention | |||
| Baseline | 1.99 (1.08) | 2.26 (1.25) | 2.14 (1.78) |
| Follow-up | 1.99 (1.15) | 1.96 (1.09) | 1.97 (1.12) |
| Peak drinking quantity | |||
| Control | |||
| Baseline | 3.83 (6.74) | 1.81 (2.99) | 2.73 (5.15) |
| Follow-up | 3.49 (6.03) | 1.62 (2.98) | 2.48 (4.71) |
| Intervention | |||
| Baseline | 4.82 (5.52) | 5.25 (6.26) | 5.06 (5.93) |
| Follow-up | 3.86 (4.56) | 3.56 (4.40) | 3.69 (4.46) |
Control group, n = 57; intervention group, n = 71.
Control group, n = 67; intervention group, n = 88.
Control group, n = 124; intervention group, n = 159.
Table 3.
Means and standard deviations for protective behavioral strategies at baseline and follow-up assessments by study condition and sex
| Sex |
|||
| Variable | Malea M (SD) | Femaleb M (SD) | Total samplec M (SD) |
| Control | |||
| Baseline | 65.81 (10.58) | 70.06 (13.32) | 68.08 (12.17) |
| Follow-up | 63.43 (14.15) | 70.63 (12.00) | 67.27 (13.40) |
| Intervention | |||
| Baseline | 64.19 (11.91) | 68.31 (11.70) | 68.60 (11.90) |
| Follow-up | 65.92 (12.25) | 69.36 (15.06) | 67.94 (14.00) |
Control group, n = 21; intervention group, n = 39.
Control group, n = 39; intervention group, n = 55.
Control group, n = 60; intervention group, n = 79.
Cognitive risk factors
Perceptions of frequency of peer drinking.
Results revealed a significant interaction effect for Time × Group, Wilks’ λ = .99, F(1, 277) = 3.01, MSE = 0.62, p = .05, η2p = .01. The interaction effect for the Time × Group × Sex, Wilks’ λ = 1.00, F(1, 277) = 0.99, MSE = 0.62, p = .32, η2p = .00, was not significant. Students in the intervention group reported a reduction in perceptions of frequency of peer drinking compared with no change in the control group.
Perceptions of frequency of peer drunkenness.
Results revealed a significant interaction effect for Time × Group, Wilks’ λ = .95, F(1, 277) = 14.61, MSE = 0.78, p = .001, η2p = .05, and for Time × Group × Sex, Wilks’ λ = .99, F(1, 277) = 3.82, MSE = 0.78, p = .05, η2p = .01. Post hoc analyses indicated a significant Time × Group interaction for females, Wilks’ λ = .91, F(1, 151) = 15.08, MSE = 0.88, p = .001, η2p = .09, but not for males, Wilks’ λ =.98, F(1, 124) = 2.01, MSE = 0.68, p = .16, η2p = .02. Females in the intervention group reported a decrease in perceptions of frequency of peer drunkenness compared with an increase in the control group.
Sex-specific perceptions of peer heavy episodic drinking.
Results indicated a significant interaction effect for Time × Group, Wilks’ λ = .98, F(1, 277) = 5.91, MSE = 4.13, p = .02, η2p = .02. The interaction effect for the Time × Group × Sex, Wilks’ λ = 1.00, F(1, 277) = 0.03, MSE = 4.13, p = .86, η2p = .00, was not significant. Students in the intervention group reported a reduction in perceptions of frequency of sex-specific peer heavy episodic drinking compared with an increase in the control group.
Positive alcohol expectancies.
Results revealed a significant interaction effect for Time × Group, Wilks’ λ = .98, F(1, 277) = 4.42, MSE = 16.01, p = .04, η2p = .02. The interaction effect for Time × Group × Sex, Wilks’ λ = 1.00, F(1, 277) = 0.01, MSE = 16.01, p = .94, η2p = .00, however, was not significant. Students in the intervention group reported a greater reduction in positive alcohol expectancies compared with students in the control group.
Protective behavioral strategies
Neither the interaction effect for Time × Group, Wilks’ λ = .99, F(1, 133) = 1.36, MSE = 56.66, p = .25, η2p = .01, nor for Time × Group × Sex, Wilks’ λ = 1.00, F(1, 133) = 0.70, MSE = 56.66, p = .41, η2p = .01, were significant, indicating no differences between study conditions.
Alcohol use
Frequency of alcohol use.
Results revealed a significant interaction effect for Time × Group × Sex, Wilks’ λ = .99, F(1, 279) = 4.12, MSE = 0.31, p = .04, η2p = .02. Post hoc analyses indicated a significant Time × Group interaction for females, Wilks’ λ = .97, F(1, 153) = 4.21, MSE = 0.28, p = .04, η2p = .03, but not for males, Wilks’ λ = .99, F(1, 126) = 0.86, MSE = 0.36, p = .36, η2p = .01. Females in the intervention group reported a greater reduction in frequency of alcohol use than females in the control group.
Peak drinking.
Results indicated a significant interaction effect for Time × Group, Wilks’ λ = .98, F(1, 279) = 6.29, MSE = 6.29, p = .01, η2p = .02. The interaction effect for the Time × Group × Sex, Wilks’ λ = 1.00, F(1, 279) = 1.09, MSE = 6.29, p = .30, η2p = .00, was not significant. Students in the intervention group reported a greater reduction in peak drinking compared with students in the control group.
Discussion
The purpose of this study was to extend the literature by examining sex differences in the efficacy of the eCHECKUP TO GO among high school seniors. Our findings suggest that the eCHECKUP TO GO was effective in reducing perceptions of peer alcohol use, perceptions of sex-specific peer heavy episodic drinking, positive expectancies, and peak drinking for both males and females. However, consistent with prior research (Doumas et al., 2017b; Hausheer et al., 2018), intervention effects for perception of peer drunkenness and frequency of alcohol use were significant for females only and we found no significant effects for protective behavioral strategies.
Understanding sex differences in normative perceptions of peer drinking is important, as changing normative perceptions is the most consistently identified mediator of intervention effects for personalized feedback interventions (Reid & Carey, 2015). Because females typically report less alcohol consumption than males, when using sex-neutral normative data, females are presented with normative feedback that provides higher average alcohol consumption norms relative to their own drinking (Lewis & Neighbors, 2006). Thus, sex-neutral (e.g., typical student) normative feedback may be less effective for females. However, our results suggest the opposite pattern. One explanation is that at baseline, female students reported higher normative perceptions of frequency of peer drunkenness (M = 3.68, SD = 1.30) than male students (M = 3.03, SD = 1.10), t(281) = 4.51, p < .001. The discrepancy between perceptions and normative data may have been greater for females, potentially resulting in greater shifts in perceptions of peer drunkenness and frequency of alcohol use among females. In contrast, results for sexspecific heavy episodic drinking did not favor females. It is possible that our use of the 3/5 definition for heavy episodic drinking, rather than the 4/5 definition, may have resulted in lower normative estimates of female drinking. Also of note, the sex-specific normative data presented in the eCHECKUP TO GO are from adult and college student national surveys. Replacing these data with sex-specific local normative data from high school seniors may provide a more relevant normative comparison for both males and females.
In contrast to prior research, we did not find moderation effects for positive alcohol expectancies. It is not clear why this study did not replicate the sex differences reported by Doumas et al. (2017b), as both samples comprised high school seniors and had similar demographic characteristics, including distribution of males and females. However, the finding that positive expectancies were reduced for both males and females is particularly important, as positive alcohol expectancies have been identified as primary risk factors for adolescent alcohol use (Jester et al., 2015; Newton et al., 2014; Patrick et al., 2010) and are associated with high-risk drinking among high school students (Zamboanga et al., 2016). Similarly, we did not find sex differences in intervention effects for peak drinking. This parallel finding may be explained, in part, by the relationship between positive expectancies and high-risk drinking among high school students.
Although the eCHECKUP TO GO program is designed to increase protective behavioral strategies, we found no significant intervention effects. This finding has now been demonstrated across two samples, with both the original PBSS measure and the PBSS-20 measure, as well as with a sample including all seniors and a subsample of seniors reporting alcohol use. As suggested by Doumas et al. (2017b), how information regarding protective behaviors is presented in the eCHECKUP TO GO program may need to be modified for this age group. Alternatively, the lack of significant findings may be due to measurement issues. With the exception of a few items (i.e., alternate alcoholic and non-alcoholic drinks, avoid drinking games, drink slowly), the eCHECKUP TO GO items do not map exactly to the PBSS items, although the items do reflect strategies consistent with the PBSS domains. However, the eCHECKUP TO GO intervention includes strategies consistent with alcohol avoidance and identification of alternative activities that are not assessed by the PBSS (e.g., have a plan to say no when I am offered alcohol, spend time with friends who don’t drink alcohol, avoid situations where alcohol is present or I am likely to drink, and identify alternative social activities instead of partying). Thus, nonsignificant findings for protective behavioral strategies in both studies may be related to measurement issues and may not reflect actual changes in participant behavior.
Limitations of the study include a primarily White sample recruited from two high schools in the Northwest. Further, although the response rate in this study was low (35.9%), it is consistent with response rates of 30%–60% reported in school-based alcohol intervention research using active parental consent procedures (Smith et al., 2009). Further, although response bias should be considered, both the demographic makeup of our sample and alcohol use were similar to the data reported by the target school district. In addition, the duration of the follow-up was short. Future research should examine if intervention effects are sustained across the academic year and after graduation. Finally, this study did not examine how to increase program effectiveness for protective behavioral strategies or for males. Qualitative data collection may be an effective way to gather information needed to inform program modifications.
Findings from this study have important implications for developing intervention programs for high school seniors. First, this study suggests that the eCHECKUP TO GO is effective in reducing cognitive risk factors and alcohol use for both female and male students. Although we did find that females reported positive effects across a wider range of measures, data from this study demonstrate that male students also reported reductions in the majority of cognitive risk factors examined in this study, as well as reductions in peak drinking. Thus, even in the absence of intervention effects for protective behavioral strategies, this study adds to the literature supporting the use of the eCHECKUP TO GO with high school seniors.
Acknowledgments
The authors thank April Watts, Sherise Porchia, Andrea Pikes, Beverly Glouser, and Dayna Showalter for assistance with participant recruitment and data collection.
Footnotes
Research reported in this article was supported by National Institute on Alcohol Abuse and Alcoholism Grant Number R21AA023880. This project was also supported in part by Institutional Development Awards (IDeAs) from the National Institute of General Medical Sciences of the National Institutes of Health under Grants P20GM103408 and P20GM109095. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. We also acknowledge support from the Biomolecular Research Center at Boise State with funding from the National Science Foundation (Grant Numbers 0619793 and 0923535), the MJ Murdock Charitable Trust, and the Idaho State Board of Education.
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