The Effects of Personalized Boosters for a Computerized Intervention Targeting College Student Drinking

Heavy episodic alcohol use within the college student population is both widespread and problematic (Benton et al., 2004; Core Institute, 2006; Hingson, Zha, & Weitzman, 2009; O\u27Malley & Johnston, 2002; Perkins, 2002; Singleton, 2007). More than 40% of college students report at least one symptom of alcohol abuse or dependence (Knight et al., 2002). Computerized interventions are widely used because of their advantages over in-person interventions. They are more cost-effective and can quickly deliver tailored individual feedback to more students. Computerized interventions can be administered to large groups of students (e.g., incoming students, athletes, fraternities/sororities). However, a (2007) meta-analysis by Carey and colleagues found that in-person interventions are generally more efficacious than interventions delivered via other mediums. The current study is a prospective examination of intervention efficacy, the ability of personalized feedback to boost efficacy, and protective behavioral strategies (PBS) as a possible mediator for these relationships. The intervention for the current study, Alcohol 101 Plus™ (Century Council, 2003), incorporates a number of intervention components, including alcohol education, college student drinking norms, skills training, and personalized feedback. The current study sought to improve the efficacy of the online intervention with personalized feedback via email boosters. Content was created based on a comparison of 2-week data to baseline. Boosters provided personalized feedback based on reported alcohol consumption, alcohol-related problems, and PBS use. They included normative data and emphasized PBS. Data were collected from N = 233 college students. Eligibility criteria included drinking 4+ alcoholic drinks within two weeks of the assessment and being between the ages of 18 and 24. Participants were randomized into one of three conditions: 1) control, 2) intervention only, or 3) intervention plus booster. Participants were assessed at baseline (pre-intervention), 2 weeks post, and 4 weeks post. The intervention was administered during the baseline procedure, immediately following assessment. After the 2-week assessment, participants in the intervention-plus-booster condition were sent a booster email. Piecewise latent growth models revealed no intervention effect among alcohol use indicators or alcohol-related problems. However, knowledge about alcohol and related consequences was significantly increased after the intervention. Interestingly, a significantly indirect effect was found, such that intervention receipt significantly increased growth trajectories for PBS, which in turn was associated with reduced trajectories for alcohol use and related problems. Additionally, the booster emails with personalized feedback had a significant effect. All alcohol use indicators and alcohol-related problems were significantly reduced for those in the experimental booster group. There was limited support for PBS as a mediator of both intervention and booster effects. The implications of these findings are far-reaching, given the prevalence of online interventions targeting college student drinking and the ability of easily-disseminated, cost-effective emails to boost efficacy

Strategies Young Adults Use to Curb Distracted Driving

Distracted driving is a well-established risk for young drivers, as they have disproportionately higher vehicle fatalities relative to miles driven. Although many studies have examined the danger of distracted driving, less is known about countermeasures young drivers use to protect themselves from getting distracted. Study 1 included focus groups of young adult drivers to learn different strategies used. From these responses, 25 items were generated. In Study 2, we administered these items to a larger sample of young adult drivers (N = 157). Using exploratory factor analysis (including scree plots, Velicer’s MAP, Cronbach’s alpha, item loadings), we determined a unidimensional structure. Countermeasure use was curvilinearly related to distracted driving. Future research can use this scale to examine how the Health Belief Model applies to distracted driving.https://digitalcommons.odu.edu/reu2021_psychology/1010/thumbnail.jp

Personalized Boosters For a Computerized Intervention Targeting College Drinking: The Influence of Protective Behavioral Strategies

Objective: Computerized interventions are cost-effective and can quickly deliver individual feedback to many students. However, in-person interventions are more efficacious. The current study sought to improve the efficacy of a popular online intervention via e-mailed boosters with personalized feedback. Participants: Participants were 213 student drinkers at a southeastern public university, ages 18-24. Methods: Students were randomized into (1) intervention only, or (2) intervention plus booster. Alcohol consumption and related problems were assessed at baseline, 2weeks post, and 4weeks post. Results: Boosters yielded reductions in drinking, but not alcohol-related problems. Boosters were associated with significant reductions for drinking frequency, heavy drinking days, peak drinks, and associated blood alcohol concentration (BAC). Protective behavioral strategies (PBS) moderated this effect, with significant reductions for students low in PBS, but not students already highly engaged in PBS use. Conclusions: Easy dissemination and low cost make e-mailed boosters a very efficient way to promote student health

Exploring the Intersection of Sexual Identity and Route of Administration in Relation to Cannabis Use Among Young Adult Females

Background and Objective: Rates of cannabis use continue to increase with sexual minority women (SMW) reporting greater use than heterosexual women. Along with these increasing trends, the routes of administration (ROA) for cannabis are evolving. The current study examined associations between cannabis ROA and frequency of use, as well as differences across sexual identity (heterosexual vs. SMW). Methods: Participants were 949 young adult (18–25 years old) women (29.8% SMW) who reported past month cannabis use and were recruited through Amazon Mechanical Turk. Number of cannabis use days and each ROA used (joint, pipe, blunt, bong, vape, edible, and ointment) in the past 30 days were measured. Analysis of covariance models examined if sexual identity moderated the association between each ROA and cannabis use frequency. Results: Among the full sample, joints were the most common ROA (78.6%); cannabis vaping was the most common noncombustible ROA (25.9%). SMW were more likely than heterosexual women to use each ROA except for joints. SMW who used pipes or edibles reported greater cannabis use frequency, compared to those who did not; there were no differences in frequency of use across ROA for heterosexual women. Discussion and Conclusions: SMW may use a greater variety of ROA, potentially increasing the harms associated with cannabis. Marketing strategies targeting the sexual minority community may increase the likelihood of using various cannabis ROA and subsequent use. Scientific Significance: Findings further our knowledge about how young adult women are using cannabis, and highlight how ROA may contribute to the disparities observed among SMW

Data Quality and Study Compliance Among College Students Across 2 Recruitment Sources: Two Study Investigation

Background: Models of satisficing suggest that study participants may not fully process survey items and provide accurate responses when survey burden is higher and when participant motivation is lower. Participants who do not fully process survey instructions can reduce a study’s power and hinder generalizability. Common concerns among researchers using self-report measures are data quality and participant compliance. Similarly, attrition can hurt the power and generalizability of a study. Objective: Given that college students comprise most samples in psychological studies, especially examinations of student issues and psychological health, it is critical to understand how college student recruitment sources impact data quality (operationalized as attention check items with directive instructions and correct answers) and retention (operationalized as the completion of follow-up surveys over time). This examination aimed to examine the following: whether data quality varies across recruitment sources, whether study retention varies across recruitment sources, the impact of data quality on study variable associations, the impact of data quality on measures of internal consistency, and whether the demographic qualities of participants significantly vary across those who failed attention checks versus those who did not. Methods: This examination was a follow-up analysis of 2 previously published studies to explore data quality and study compliance. Study 1 was a cross-sectional, web-based survey examining college stressors and psychological health (282/407, 69.3% female; 230/407, 56.5% White, 113/407, 27.8% Black; mean age 22.65, SD 6.73 years). Study 2 was a longitudinal college drinking intervention trial with an in-person baseline session and 2 web-based follow-up surveys (378/528, 71.6% female; 213/528, 40.3% White, 277/528, 52.5% Black; mean age 19.85, SD 1.65 years). Attention checks were included in both studies to assess data quality. Participants for both studies were recruited from a psychology participation pool (a pull-in method; for course credit) and the general student body (a push-out method; for monetary payment or raffle entry). Results: A greater proportion of participants recruited through the psychology pool failed attention checks in both studies, suggesting poorer data quality. The psychology pool was also associated with lower retention rates over time. After screening out those who failed attention checks, some correlations among the study variables were stronger, some were weaker, and some were fairly similar, potentially suggesting bias introduced by including these participants. Differences among the indicators of internal consistency for the study measures were negligible. Finally, attention check failure was not significantly associated with most demographic characteristics but varied across some racial identities. This suggests that filtering out data from participants who failed attention checks may not limit sample diversity. Conclusions: Investigators conducting college student research should carefully consider recruitment and include attention checks or other means of detecting poor quality data. Recommendations for researchers are discussed. JMIR Form Res 2022;6(12):e3948