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Incerto, et. al., 2011 [32], “Fourth Year Fifth”event, determine effectiveness of social norms marketing campaign to prevent participating in the “Fourth Year Fifth” event 1.
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Observational cross-sectional. Web-based survey to random sample. Wilcoxon Signed-Rank tests. Descriptive statistics.
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n = 536/1,000, response rate = 53.6%.
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Participation in the “Fourth Year Fifth” event: 19.6% participated. Application of protective behaviours: 86.3% diluted alcohol; 80.6% had sufficient sleep; 78.6% ate large breakfast. Relation participation and exposure to campaign elements : χ2 = 34.81, d.f. = 6, p ≤ 0.001.
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No control group. No pretest. Different response prompts to questions: no comparison possible. Short-term effect only.
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Glassman, et. al., 2010 [33], “Less is more”, determine effectiveness of the social marketing campaign “Less is more”.
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Observational longitudinal. Standardized quantitative survey with random sample. Data collection was done six times, from fall 2004 until spring 2008. Descriptive statistics.
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n = 473/2,400 in fall 2004, 19.7%
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Impact on high-risk drinking: significant decrease from 56.5% to 37.8%. Impact on drinking and driving: significant decrease from 37.5% to 20.6%. Impact on the perception that alcohol use increases sexual opportunities: significant decrease from 64.0% to 50.7%.
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No control group. Low response rates. Other prevention efforts may have caused the effect. Short-term effect only.
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n = 1,006/4,000 in fall 2005, 25%
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n = 785/4,000 in fall 2006, 19.6%
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n = 835/4,000 in spring 2007, 20.9%
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n = 745/4,000 in fall 2007, 18.6%
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n = 546/4,000 in spring 2008, 13.7%.
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Slater, et. al., 2006 [34], “Be under your own influence”, determine effectiveness of an in-school media campaign “Be under your own influence” reinforced by community-based media efforts, on the reduction of increase of substance uptake. For this review study, only results of “Be under your own influence” are discussed.
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Experimental longitudinal. Randomised community crossed design: 8 communities received social marketing in-school media intervention and 8 communities did not. Four waves of data collection, during two years. Generalised linear mixed models (four-level random-intercept model).
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n = 4,216 Response rates: 68.6% provided data at 4 measurements, 16.8% at 3, 10.9% at 2, and 3.7% at 1.
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Alcohol use: odds ratio (OR) = 0.40, p ≤ 0.01. Effect on rate of change in alcohol use: OR = 0.82, p > 0.05. Recognition of campaign messages: Time 2, OR = 4.70, p ≤ 0.01; time 3, OR = 6.80, p ≤ 0.01; time 4, OR = 10.13, p ≤ 0.01.
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Short-term effect only. Other prevention efforts may have caused the effect.
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Rothshild, et. al., 2006 [35], “Road Crew”, determine effectiveness of social marketing intervention “Road Crew”.
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Experimental longitudinal. Treatment for 1 year, with pre- and post-test. Three treatment communities and five control communities. Generalized linear models.
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n = 710 and n = 693 at pre-test in treatment and control groups. n = 573 and n = 371 at post-test in treatment and control groups.
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Count of all rides taken in treatment communities: 10,097 rides taken by 21-34-year-olds. Self-report of drinking and driving behaviour: less likely to drive themselves or ride with someone else (OR = 0.40, p ≤ 0.05); no significant changes in alcohol-impaired driving (χ2 = 0.82, p > 0.05); decrease in reported number of alcohol-impaired driving (χ2 = 4.85, p ≤ 0.05).
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Observing changes in the number of actual crashes was not possible. Possible differences between communities of treatment and control groups. Self-reported data of bar patrons possibly underestimated.
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Gomberg, et. al., 2001 [37], “Just the facts”, analyse the results of the “Just the Facts” (JTF) campaign.
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Observational longitudinal. Survey with random sample. Three times of data collection (1 pretest and 2 posttests, just after the two campaign phases). Two-sample independent t tests. Chi-square analyses. Linear regression analyses. Logistic regression analyses.
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n = 785 for pretest, n = 698 for first posttest n = 583 for second posttest.
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Recognition of campaign logo: 6.2% at pretest, 55.4% at first posttest, 78.5% at second posttest. Alcohol use: decrease of mean number of drinks from 15.80 at pretest to 12.61 at second posttest; decrease in mean number of days from 2.96 at pretest to 2.65 at second posttest. High risk drinking: decrease for male students from 65.6% at pretest to 58.4% at posttest and for female students from 40.5% at pretest to 34.7% at second posttest. Perceived drinking norms: significant increases in correctly answered questions about the drinking norms.
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Shortcomings in research design. No control group. Decreasing response rates for three surveys. Not asked to recall campaign messages, only logo and advertisements. Measurement for high-risk drinking is not comparable.
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Caverson, et. al., 1990 [36], “Thanks for being a sober driver”, determine how the “Thanks for being a sober driver” intervention was received by the community.
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Observational cross-sectional. Field experiment of 1 year. Telephone interview, conducted several months after end of pilot. Other measures: number of cars stopped, number of offences and number of folders handed out at spot-checks. Further, interviews with key informants from police department and senior officers. Descriptive statistics.
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n = 445/667, response rate = 67%.
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Awareness of intervention: 76%. Knowledge of slogan: 13%. Stopped by the police: 79% not been drinking prior to driving. Reaction to this and other equivalent interventions: 93% good idea to reward sober drivers.
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No control group. Short-term effect only. Other drinking-driving countermeasure programs were run simultaneously: not clear whether results can be attributed to “Thanks for being a sober driver”.
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