J Dev Phys Disabil. Author manuscript; available in PMC 2007 January 10.
Published in final edited form as:
Preventing Alcohol Abuse among Early Adolescents through Family and Computer-Based Interventions: Four-Year Outcomes and Mediating Variables
School of Social Work, Columbia University, 1255 Amsterdam Avenue, New York, NY 10027
III-timed and excessive use of alcohol is associated with multiple and irreversible disabilities. The relationship between perinatal alcohol use and developmental disabilities, including fetal alcohol syndrome, is well documented. Empirical evidence also links alcohol use to a host of other developmental and physical problems among the offspring of drinkers and among drinkers themselves. Toward advancing the science of how to reduce alcohol abuse risks, this study developed and tested family and computer-based approaches for preventing alcohol use among a community sample of inner-city minority youth. Original findings from 4-year follow-up data obtained from over 90% of the study sample document continued positive program outcomes and shed light on cognitive problem solving, peer, and family mediators of alcohol use risk and protective factors among target youth.
Keywords: Alcohol use, adolescents, prevention, family and computer interventions
Alcohol use can antecede multiple and irreversible disabilities. The relationship between perinatal consumption of alcohol and developmental disabilities, including fetal alcohol syndrome, is well documented (Coles et al., 1997
; Jones and Smith, 1973
; Mattson and Riley, 1996
; Streissguth et al., 1995
). An equally large literature attests to the untoward social, economic, and mental health sequelae of early and heavy alcohol consumption (Bonnie and O’Connell, 2004
; National Institute on Alcohol Abuse and Alcoholism, 2000
). That drinking is widely practiced and socially sanctioned in America and in much of the world underscores the value of alcohol abuse prevention programs. And, of the many target groups for alcohol prevention programs, young people deserve greatest attention.
Because alcohol abuse is best responded to before it emerges, prevention is a preferred intervention strategy. A growing body of research supports the efficacy of prevention programming for youth. Investigators have demonstrated the impact of a range of prevention strategies – from media campaigns, to efforts to limit alcohol access, to school-based approaches (Hingson and Howland, 2002
; National Institute on Drug Abuse, National Institutes of Health, 1997
; U.S. Department of Education, 2000
; Wagenaar et al., 2000
). Little research investment, however, has gone into approaches that enlist family supports in helping youth avoid problems with alcohol.
The centrality of familial influences in youths’ development and socialization points toward the logic of family approaches to alcohol prevention. For poor minority youths, family variables are particularly salient to inform prevention programming. Youths who grow up in families that lack clear rules against alcohol and other substance use are at greater risk for alcohol abuse than youths from families with such rules (Hawkins et al., 1994
). Other risk factors for early and problem drinking include low parental attachment, low self-esteem and self-concept, and association with substance using peers and boyfriends (Blum et al., 2000
). Low parental monitoring and an unstructured home environment are also associated with youths’ alcohol use (Murray et al., 1993
Yet, positive parental influence can help protect poor youth from the myriad risk factors associated with alcohol use (Bahr et al., 1995
; Blum et al., 2000
; Hawkins et al., 1992
). In particular, family activities, including meals and religious services, and parental praise and monitoring, can reduce alcohol use risks (The National Center on Addiction and Substance Abuse at Columbia University [CASA], 1999
). Notwithstanding the risks associated with single-parent families, engaged single mothers can compensate for absent fathers and lower their children’s substance use risk (CASA, 1999
). For example, early adolescent Black youth are strongly protected by parental control and supervision (Brody et al., in press
; Vega and Gil, 1998
). In addition, high-achieving low-income youths tend to be raised in homes with predictable routines (Garmezy, 1985
Drawing from such risk and protective factors, successful programs have been developed to prevent alcohol use among youth. Illustrative is Project Northland that targets parent-child communication, peer influence, and community supports (Perry et al., 1996
). Northland is supported by 3-year data showing that relative to control youths, those who received intervention smoked and drank less, experienced less peer influence to use alcohol and other drugs, perceived fewer drinking peers, and communicated more with parents about drinking. Life Skills Training (LST), another illustrative program, teaches youths to resist pressure to use alcohol, assert their rights, and analyze peer, situational, and environmental cues that precede drinking. Studies indicate that LST leads to reductions in alcohol use, heavy drinking, and drinking to intoxication (Botvin et al., 1995
). Other programs have similarly reduced alcohol-related risks and use among adolescents (Eggert et al., 1994
; Johnson et al., 1996
Toward advancing the science of alcohol use prevention, investigators must learn how to reliably, cheaply, and effectively deliver prevention programs to poor and minority youth. Most programs are delivered by teachers or other youth services professionals. Expressly trained for their roles, these delivery agents employ didactic instruction, modeling, role play, and other behavioral techniques. Live program delivery lets professionals adapt programs to meet the requirements of their context and audiences. But live delivery can also constrain the dissemination of alcohol and other substance use prevention programs. Too often, program replications are limited in scope and reach because of resource and human capital limits. Moreover, live delivery by professionals of differing backgrounds, capacities, and motivations can introduce unwelcome variations in program fidelity. Without question, better methods for alcohol prevention program delivery must be found.
Computer technology is one such promising venue for delivering alcohol prevention programs. Computer-based interventions are not only cost-effective and portable, but also their novelty and game-like appearance appeals to youths. Computers are ideal for delivering and discussing sensitive information (Paperny, 1997
; Paperny et al., 1990
). Branched programming allows intervention content to be tailored to the cultural-, age-, and gender-specific needs of different groups. Youths’ interest is easily sustained via animation, audio tracks, multiple menu choices, and high-speed graphics (Domel et al., 1994
). Variations in program delivery are reduced through computer approaches: all respondents receive the same information, rendering delivery consistent over repeated administrations. Finally, computer programs can collect, store, aggregate, and analyze information, making data available for quick, objective interpretation.
Though nascent, computer-based programs are emerging. For example, the Body Awareness Resource Network (BARN) is a computer program on alcohol and other drug use, sexuality, smoking, AIDS, and stress management (Bosworth et al., 1998
). After 2 years, youths exposed to BARN were less likely to engage in risk-taking behaviors than control youths. Other data as well suggest the potential of interactive computer programs (Bosworth, 2003
; Schinke et al., 2004a
; Schinke et al., 2004b
; Schinke et al., 2005
; Schinke and Schwinn, in press
; Rotheram-Borus, 2000
;). The present study explored the promise of family and computer-mediated approaches to preventing alcohol use among poor minority youth.
Sample and Setting
At the time of recruitment, study participants were 513 youths, aged 10 to 12 years (M = 11.5; SD = 0.53); 51% female, 54% Black, 30% Latino, 11% White, and 5% Asian or other ethnic-racial minority group.
Youths were recruited from community-based agencies in greater New York City. Youths were informed of the study’s requirements, procedures, and risks, and given assent statements for themselves and consent forms for their parents to read and complete. Assenting and consenting youths were pretested, and collaborating agencies were stratified by geography and ethnic-racial background of youth population served. Randomly within strata, agencies were divided into three study arms: computer intervention, computer intervention plus family involvement, and control.
Following intervention delivery to the first two arms, all youths completed posttests. Four annual follow-up measurements were administered to the entire sample, beginning 1 year after posttest. Approximately 6 months after first and second follow-up measurements, youths in both intervention arms received annual booster sessions. Booster sessions for parents in the respective arm of the study coincided with youth boosters.
Youths were asked for contact information on themselves, their families, and three persons not in their households who always knew how to reach them.
Youths reported the extent to which their parents were involved in family discussions, monitoring, and rule setting regarding their children’s alcohol use (Williams et al., 1999
). Test-retest reliabilities across these items ranged from 0.82 to 0.93.
Youths responded to items regarding their peer network, including the number of their closest friends who drink, smoke, use drugs, and have been drunk; perceptions of the influence peers have on their drinking and other substance use; and their ability to resist negative peer influences and positively shape their friends’ behavior (Centers for Disease Control and Prevention [CDC], 1999
; Oetting et al., 1994
). Test-retest reliability for peer influences items averaged 0.84.
Alcohol and other substance use
Youths reported their alcohol and other substance use on scaled response items (CDC, 1999
; Oetting et al., 1994
). Test-retest data for these questions ranged from 0.78 to 0.94.
The prevention approach drew from social learning and problem behavior theories (Bandura, 1986
; Jessor and Jessor, 1977
). From social learning theory, the program included components on norm correcting, media literacy, refusal skills, goal setting, and decision-making. From problem behavior theory, the prevention approach included components to impart more positive functioning (e.g., coping, assertiveness, goal setting, problem solving, effective communication). These theory-based components were programmed into a CD-ROM intervention called “Thinking Not Drinking: A SODAS City Adventure.”
Youths in both intervention arms completed 10, 45-minute initial sessions plus two booster sessions of the CD-ROM program. Each session started with skill objectives youths needed to acquire to advance to the next session. Along the way, youths encountered realistic obstacles and distractions presented by animated characters who mirrored their ages and ethnic-racial backgrounds. Each 30-minute, CD-ROM booster session began with a review of prior components. Youths were then exposed to developmentally appropriate content indexed to theoretical and empirical risks for alcohol use. For example, boosters covered issues of increasing peer pressure – particularly that from dating partners – greater access to alcohol, media influences, and increased amounts of unsupervised time.
Concurrent with the delivery of youth intervention, parents of youths in the computer intervention plus family involvement arm received initial and booster sessions. Family intervention was rooted in family interaction theory positing that strong parent-child bonds can protect youth (Brook et al., 1990
). Initial family intervention consisted of a videotape and newsletters sent to youths’ homes. Available in English and Spanish, the 30-minute videotape mimicked CD-ROM material to acquaint parents with the goals and processes of youths’ intervention. The videotape demonstrated how parents could help their children avoid problems with alcohol, and explained and illustrated the value of family rituals, rules, and bonding. Newsletters sent to parents at the end of CD-ROM intervention reinforced prevention content and offered tips on how to help children make sound decisions regarding alcohol use and other problem behavior.
The first parent booster session was a 4-hour workshop held 1 year after initial intervention. In small groups, workshop leaders focused on the stresses parents face when raising adolescent children in an urban environment. Leaders and parents shared tips on ways parents can attend to their own needs while acting as positive role models. Parents also learned the value of setting clear family rules around alcohol. The second parent booster provided separate content for parents of girls and boys, responding to the gender specificity of alcohol use and risk taking that becomes pronounced in the middle-adolescent years. Delivered via CD-ROM, the second booster consisted of exercises for parents and their children aimed at increasing communication, bonding, and discussions about alcohol and other substance use.
Findings on the first 3 years of the research revealed that over time, youths in all three arms of the study reported increased use of alcohol, tobacco, and marijuana; albeit youths in either intervention arm reported smaller increases than control youths (Schinke et al., 2004b
). At 3-year follow-up, alcohol use was lower for CD-ROM plus family involvement youths than for CD-ROM only youths who, in turn, reported less use than controls. Cigarette use was lower for youths in either intervention arm than for control youths at posttest and at 1-, 2-, and 3-year follow-ups. Marijuana use was lower for youths in either intervention arm than for controls at 1-, 2-, and 3-year follow-ups. Youths in both intervention arms outperformed control arm youths at posttest and at 1- and 3-year follow-ups on levels of negative and peer influence toward substance use. Finally, at 3-year follow-up, youths in the CD-ROM plus family involvement arm reported more family involvement in their alcohol use prevention efforts than youths in the CD-ROM arm who, in turn, reported more positive levels of family involvement than youths in the control arm. Against this backdrop, the present analyses focus on outcome data and mediator variables from the latest follow-up measurement, completed 4 years after initial intervention delivery.
Five years after pretesting, 91.8% of the original sample completed 4-year follow-up measures. Youths’ tobacco, alcohol, and marijuana use rates for the past 30 days continue to show differences that favor both intervention arms. Graphed in , these differences are portrayed as odds ratios that compare each intervention arm with the control arm. In the graph, control youths’ rates of 30-day cigarette, alcohol, and marijuana use are set to the value 1.0. Rates for youths in CD-ROM and CD-ROM plus parent involvement arms are shown as ratios, with intervention arm rates divided by control arm rates. Rates of less than 1.0 in the intervention arms, indicate that those youths reported less cigarette, alcohol, and marijuana use relative to control youths at the 4-year follow-up measurement.
Odds Ratios (CD-ROM/Control and CD-ROM plus Family/Control) for 30-Day Tobacco, Alcohol, and Marijuana Use at 4-Year Follow-Up
As found at the 3-year follow-up, 4-year follow-up rates for tobacco, alcohol, and marijuana use in the CD-ROM plus family involvement arm were lower than in the CD-ROM only intervention arm, with rates in both intervention arms lower than in the control arm. Unsurprisingly, tobacco is the most used substance of the three measured. Marijuana use is higher than alcohol use, another expected finding in an urban early adolescent sample.
In longitudinal prevention studies, proximal variables – or mediators of behavioral outcomes – are of clear interest. By documenting mediator variables, prevention researchers can connect intervention components to ultimate outcomes, determine the accuracy of theoretically postulated models of behavior change, and measure empirically proven intermediate markers of behaviors and skills targeted by the prevention program. Variables serving these purposes for the present study include cognitive problem solving components, evidence of peer influences, and family supports for alcohol prevention objectives.
presents 4-year follow-up data on problem solving, peer behavior, and family influence variables across the three arms of the trial. The first component of problem solving, youths’ capacity to solve problems, showed across-arm differences, F(2, 408) = 2.96, p < 0.05. These differences slightly – albeit nonsignificantly – favored youths in the CD-ROM plus family involvement arm. The second problem-solving component, youths’ ability to weigh their options before acting, also differed among the three arms, F(2, 408) = 4.09, p < 0.02, with larger gains in the CD-ROM arm than in the CD-ROM plus family involvement arm or in the control arm.
Problem-Solving, Peer Behavior, and Parent Support Outcomes at 4-Year Follow-Up by Study Arm
Peer behavior was assessed when youths reported the alcohol use patterns of their five best friends. Relative to the number of youths’ five best friends who drink, 4-year outcomes favored youths in the CD-ROM plus family involvement arm, F(2, 406) = 5.31, p < 0.005. Youths’ reports of the number of their five best friends who have been drunk closely followed this pattern, with differences supporting the CD-ROM plus family involvement arm, F(2, 408) = 4.73, p < 0.01. In each arm of the study, most of youths’ five best friends drink or have been drunk – nearly all of control youths’ five best friends exhibited these behaviors.
Pertinent family mediators included parents’ conversations with their children about substance use. Relative to conversations about alcohol with their parents in the last month, youths differed across the study’s three arms, F(2, 408) = 3.77, p < 0.02. Post-hoc comparisons indicate that these differences are attributable to youths who received CD-ROM plus family involvement. These youth reported more conversations about alcohol with their parents than youths in either the CD-ROM or control arm. Such differences are not unexpected since the parents of youths in the CD-ROM plus family involvement arm received instruction on the value of talking with their daughters and sons about alcohol and of the importance of attending to their children when they initiated conversations about or made reference to alcohol. More than youths in the CD-ROM or control arm, those in the CD-ROM plus family involvement arm also reported more conversations with parents about cigarette use in the last month, F(2, 408) = 3.47, p < 0.03 – a serendipitously positive outcome since parents in this arm were not expressly instructed to discuss cigarette smoking with their children.
Four-year data on the efficacy of computer-mediated intervention with and without family intervention to help adolescents avoid alcohol abuse point toward continued positive outcomes. Youths in both intervention arms reported less use of tobacco, alcohol, and marijuana than youths in a control arm, with youths who received CD-ROM plus family involvement reporting the lowest rates of 30-day substance use. Mediator variables of youths’ cognitive problem solving, peer influences, and family supports also suggest the impact of intervention components. As with 30-day substance use rates, mediator variables favor youths who received CD-ROM intervention and whose parents received intervention to supplement their children’s intervention.
Together, outcome data and those yielded by mediator variable assessments shed empirical light on potential mechanisms for behavior change from prevention programming. The prevention approach employed in this study presumes that adolescents choose to use or to not use alcohol and other substances based on their cognitive-behavioral skill set, their ability to withstand or at least analyze peer and other pressures toward substance use, and the relevant support they receive from their parents. Those presumptions are borne out in part by mediator variables and by self-reported behavioral outcomes.
Measured mediator variables suggest that the prevention approach impacted youths’ problem-solving skills which, according to other data, equip youth to make sound decisions about substance use, other risks, and health promoting choices (Brezina, 2000
). Peer influences, a major contributor to alcohol and other substance use behavior, are evident in youths’ reports about drinking among their best friends. That fewer best friends of youths in the CD-ROM plus family involvement arm drink and get drunk than best friends in the other arms and that alcohol (as well as other substance use) reports were lowest in the CD-ROM plus family involvement arm implies that not only do peers influence youths’ own drinking, but also that intervention elements aimed to discourage associations with peers who drink may have operated in a salubrious manner. Family influences, another intervention component and a factor in adolescent drinking, were differentially affected by CD-ROM plus family involvement and may have contributed to lower rates of substance use for youths engaged in this intervention and whose parents were similarly engaged.
Confidence in study findings is strengthened by methodological design and by happenstance. By design, the group trial controlled for potential contagion effects: intact community sites, rather than individual youths, were randomized to the study arms. Four-year follow-up data lend credence to the durability of prevention program outcomes. And, the good fortune of over 90% sample retention across the longitudinal study reduces the likelihood that differences among arms resulted from heavy or uneven attrition. High fidelity rates for intervention delivery to youths and to parents further strengthen causal links between the prevention program and its outcomes.
Notwithstanding its strengths, the study has flaws. These include self-reported outcomes, a problem in many prevention trials with youth. Moreover, the full potential of the group trial cannot be exploited since too few community sites were available to analyze outcomes at that level. Rates of parent involvement, albeit relatively high by comparable standards, were nonetheless disappointing. Clearly, better ways of engaging parents in youth-oriented programs must be found. Finally, odds ratios for cigarette, alcohol, and marijuana use rates among the three arms were relatively small and are probably dampened by overall low levels of substance use among the study’s still youthful sample.
Overall, the study’s limits do not detract from its conclusions about the lasting effects of a modest prevention program to lower risks of alcohol and other substance abuse in a vulnerable population. Once developed, the computer intervention was inexpensive to deliver and offers easy replications. Family intervention too did not entail the human capital investment typical of most family involvement approaches. Assuming continued positive effects as youths mature and enter the highest risk years for problem drinking, the program may prove cost-beneficial. Young people who receive responsive prevention programs can avoid or at least lower the chances of experiencing the devastation that alcohol and other substance use can visit upon themselves, their children, their families, and larger society. Prevention programming for youth is a humane and preferable approach to avert many of the developmental and physical disabilities caused by alcohol abuse and misuse. Perhaps the lessons of this study will inform other efforts to help youths and their families prevent harmful lifestyle behaviors.
- Bahr SJ, Marcos AC, Maughan SI. Family, educational, and peer influences on the alcohol use of female and male adolescents. J Stud Alcohol. 1995;56:457–469. [PubMed]
- Bandura A. Prentice Hall; Englewood Cliffs, NJ: 1986. Social foundations of thought and action: A social cognitive theory.
- Blum RW, Beuhring T, Shew ML, Bearing LH, Sieving RE, Resnick MD. The effects of race/ethnicity, income, and family structure on adolescent risk behaviors. Am J Public Health. 2000;90:1879–1884. [PubMed]
- Bonnie RJ, O’Connell ME, editors. The National Academies Press; Washington, D.C.: 2004. Reducing underage drinking: A collective responsibility.
- Bosworth K. Application of computer technology to drug abuse prevention. In: Bukowski W, Slaboda Z, editors. Handbook of drug abuse theory, science, and practice. New York: Plenum Publishing; Spring 2003.
- Bosworth K, Espelage D, DuBay T. A computer-based violence prevention intervention for young adolescents: Pilot study. Adolescence. 1998;33:785–795. [PubMed]
- Botvin GJ, Baker E, Dusenbury L, Botvin EM, Diaz T. Long-term follow-up results of a randomized drug abuse prevention trial in a White middle-class population. JAMA. 1995;273:1106–1112. [PubMed]
- Brezina T. Delinquent problem solving: An interpretive framework for criminological theory and research. J Res Crime and Delinq. 2000;37:3–30.
- Brick J, editor. Haworth Press; New York: 2004. Handbook of the medical consequences of alcohol and drug abuse.
- Brody GH, Flor DL, Gibson NM. Linking maternal efficacy beliefs, developmental goals, parenting practices, and child competence in rural single-parent African American families. Child Dev in press.
- Brook JS, Brook DW, Gordon AS, Whiteman M, Cohen P. The psychological etiology of adolescent drug use: A family interactional approach. Genet Soc Gen Psychol Monogr. 1990;116 Whole No. 2.
- Centers for Disease Control and Prevention. Youth Risk Behavior Surveillance System. 1999. [Retrieved July 16, 2004]. http://www.cdc.gov/nccdphp/dash/yrbs/index.htm.
- Coles CD, Platzman KA, Raskind-Hood CL, Brown RT, Falek A, Smith LE. A comparison of children affected by prenatal alcohol exposure and attention deficit, hyperactivity disorder. Alcohol Clin Exp Res. 1997;21:150–161. [PubMed]
- Domel SB, Thompson WO, Baranowski T, Smith AF. How children remember what they have eaten. J Am Diet Assoc. 1994;94:1267–1272. [PubMed]
- Eggert LL, Thompson EA, Herting JR, Nicholas LJ. Prevention research program: Reconnecting at-risk youth. Issues Ment Health Nurs. 1994;15:107–135. [PubMed]
- Garmezy N. Stress-resistant children: The search for protective factors. In: Stevenson JE, editor. Recent research in developmental psychopathology. Elsevier; New York: 1985. pp. 213–233.
- Hawkins JD, Arthur MW, Catalano RF. Preventing substance abuse. Crime and Justice. 1994;8:197–277.
- Hawkins JD, Catalano RF, Miller JY. Risk and protective factors for alcohol and other drug problems in adolescence and early adulthood: Implications for substance abuse prevention. Psychol Bull. 1992;112:64–105. [PubMed]
- Hingson RW, Howland J. Comprehensive community interventions to promote health: Implications for college-age drinking problems. J Stud Alcohol Suppl No. 2002;14:226–240.
- Jessor R, Jessor SL. Academic Press; New York: 1977. Problem behavior and psychosocial development: A longitudinal study of youth.
- Johnson K, Strader TN, Berbaum M, Bryant D, Bucholtz G, Collins D, Noe T. Reducing alcohol and other drug use by strengthening community, family, and youth resiliency: An evaluation of the Creating Lasting Connections Program. J Adolesc Res. 1996;11:36–67.
- Jones KL, Smith DW. Recognition of fetal alcohol syndrome in early infancy. Lancet. 1973;2:999–1001. [PubMed]
- Mattson SN, Riley EP. Brain anomalies in fetal alcohol syndrome. In: Abel EL, editor. Fetal Alcohol Syndrome: From mechanism to prevention. CRC Press; Boca Raton, FL: 1996. pp. 51–68.
- Murray M, Swan AV, Bewley BR, Johnson MRD. The development of smoking during adolescence: The MRC/Derbyshire smoking study. Int J Epidemiol. 1993;12:185–192. [PubMed]
- National Institute on Alcohol Abuse and Alcoholism. U.S. Department of Health and Human Services; Bethesda, MD: 2000. Tenth special report to the U.S. Congress on alcohol and health: Highlights from current research. (NIH publication No. 00–1583)
- National Institute on Drug Abuse [NIDA], National Institutes of Health. Author; Rockville, MD: 1997. Preventing drug use among children and adolescents: A research-based guide.
- Oetting ER, Beauvais F, Edwards R. RMBSI; Fort Collins, CO: 1994. The American Drug and Alcohol Use Survey™
- Paperny DMN. Computerized health assessment and education for adolescent HIV and STD prevention in health care settings and schools. Health Educ Behav. 1997;24:54–70. [PubMed]
- Paperny DM, Aono JY, Lehman RM, Hammar SL, Risser J. Computer-assisted detection and intervention in adolescent high-risk health behaviors. J Pediatr. 1990;116:456–462. [PubMed]
- Perry CL, Williams CL, Veblen-Mortenson S, Toomey T, Komro KA, Anstine PS, McGovern PG, Finnegan JR, Forster JL, Wagenaar AC, Wolfson M. Project Northland: Outcomes of a community-wide alcohol use prevention program during early adolescence. Am J Public Health. 1996;86:956–965. [PubMed]
- Rotheram-Borus MJ. Expanding the range of interventions to reduce HIV among adolescents. AIDS. 2000;14:S33–S40. [PubMed]
- Schinke SP, Di Noia J, Glassman JR. Computer-mediated intervention to prevent drug abuse and violence among high-risk youth. Addict Behav. 2004a;29:225–229. [PMC free article] [PubMed]
- Schinke SP, Schwinn TM. Gender-specific intervention for preventing drug abuse among girls: A computer-based approach. Am J Drug Alcohol Abuse in press
- Schinke SP, Schwinn TM, DiNoia J, Cole KC. Reducing the risks of alcohol use among urban youth: 3-year effects of a computer-based intervention with and without parent involvement. J Stud Alcohol. 2004b;65:443–450. [PMC free article] [PubMed]
- Schinke SP, Schwinn TM, Ozanian AJ. Alcohol abuse prevention among high-risk youth: Computer-based intervention. J Prev Interv Community. 2005;29:117–130. [PMC free article] [PubMed]
- Streissguth AP, Bookstein FL, Sampson PD, Barr HM. Attention: Prenatal alcohol and continuities of vigilance and attentional problems from 4 through 14 years. Dev Psychopathol. 1995;7:419–446.
- The National Center on Addiction and Substance Abuse at Columbia University. Author; New York: 1999. Back to School 1999: National Survey of American Attitudes on Substance Abuse V: Teens and Their Parents.
- U.S. Department of Education. U.S. Dept. of Education, Office of the Under Secretary, Planning and Education Service; Rockville, MD: 2000. Progress in prevention: Report on the national study of local education agency activities under the Safe and Drug Free Schools Act. (Doc. 2002-003)
- Vega WA, Gil AG. Plenum Press; New York: 1998. Drug use and ethnicity in early adolescence.
- Wagenaar AC, Murray DM, Gehan JP, Wolfson M, Forster JL, Toomey TL, Perry CL, Jones-Webb R. Communities mobilizing for change on alcohol: Outcomes from a randomized community trial. J Stud Alcohol. 2000;61:1–11.
- Williams CL, Perry CL, Farbakhsh K, Veblen-Mortenson S. Project Northland: Comprehensive alcohol use prevention for young adolescents, their parents, schools, peers, and communities. J Stud Alcohol. 1999;13:112–124.