Abstracts / 46 (2010) S17–S81S58

from pregnancy (baseline, p¼ .039; 3 months, p¼ .001), and higherembarrassment discussing contraception with partner (3-month,p¼ .003). Compared to SP reporting Low continuation, Highcontinuation correlated with the following: general family influ-ence (baseline, p¼ .008) and desire to protect family from preg-nancy (baseline, p¼ .033); lower perception that friends think SPis ‘‘looking for sex’’ if on birth control (6-month, p¼ .002); anddesire to protect partner from pregnancy (baseline, p¼ .048; 3months, p¼ .047).Conclusions: The attitudes of young women on the use of DMPAlargely integrate family, friend, and sex partner influences. Thisdata supports that the contraceptive decision-making process foradolescents-like their sexual development in general-is embeddedin a larger social context, which needs to be included in both contra-ceptive counseling and research.Sources of Support: HRSA/T71 MC00008, NIH/NCRR RR025761.

91.

EARLY WEIGHT GAIN AND ITS RELATIONSHIP TO

LATER WEIGHT GAIN IN ADOLESCENTS ON DMPA

Andrea Bonny, MD1, Michelle Secic, MS2, Barbara Cromer1.1MetroHealth Medical Center; 2Secic Statistical Consulting, Inc

Purpose: To examine if early weight gain in adolescents on DMPApredicts continued excessive weight gain and to identify riskfactors of early weight gain.Methods: Adolescents (age: 12-19 years) initiating DMPA wereeligible to participate. Height and weight were assessed at baseline,6, 12, and 18 months. Early weight gain was defined as more than5% weight gain over baseline after 6 months of DMPA use. Meanweight gain at 12 and 18 months was estimated based on earlyweight gain status (� 5% gain vs. > 5% gain). ANOVA techniqueswere used to identify risk factors for weight change at 12 and 18months. Logistic regression techniques were used to identify riskfactors of early weight gain. Risk factors analyzed included: race,ethnicity, baseline weight, baseline BMI, chronological age, gyneco-logic age, physical activity, and parity.Results: Baseline characteristics of subjects (n¼ 97) included: meanage 15.8 (SD 1.4) years, mean weight 61.9 (SD 14.4) kilograms, meanBMI 23.6 (SD 4.6) kg/m2, 64% African American, 34% White.Twenty subjects (21%) had early weight gain. Mean weight gainfor the� 5% group and> 5% group at 6, 12, and 18 months isshown in the Table below.

Time Point Mean Wt. Gain £5% Mean Wt. Gain >5% p-value

Group Group

6 Months

-.1 kg 5.8 kg <.001 12 Months 1.6 kg 9.1 kg <.001 18 Months 4.1 kg 12.4 kg <.001

In multivariate modeling, early weight gain was the only factorsignificantly associated with weight change at 12 (F-value 34.8, p <

.001) and 18 (F-value 4.2, p¼ .04) months. No risk factors for earlyweight gain were found.Conclusions: In this study, adolescents who experienced >5%weight gain after 6 months of DMPA use were at risk for continuedexcessive weight gain. Although prior studies have found AfricanAmerican race and baseline obesity to be risk factors for DMPA-associated weight gain, our current study found early weightgain status more predictive. Weight gain at 6 months on DMPA,regardless of baseline weight, can be used to identify adolescentsat risk for continued weight gain and appropriate counseling canbe done at this time point.

Sources of Support: Supported by U.S.P.H.S Grant Number M1 RR0008012 General Clinical Research Centers and N.I.H. GrantRO1HD39009.

SESSION II: TOBACCO, ETOH AND DRUGS

92.

SUCCESSFUL RECRUITMENT AND DISTANCE

TRAINING OF CLINICIANS IN AN ADOLESCENT

SMOKING CESSATION PILOT STUDY IN AAP

PROS PRACTICES

Jonathan Klein, MD, MPH7, Tracy Sesselberg, MA, MPH6,

Lori Pbert, PhD5, Jennifer Steffes, MSW2, Donna Harris, MA3,

Erika Sutter, MPH6, Edward Gotlieb, MD3, James Davis, MD2,

Eric Slora, PhD1, Richard Wasserman, MD, MPH4. 1AAP Division of

Primary Care Research; 2AAP PROS Network; 3American Academy of

Pediatrics; 4PROS, American Academy of Pediatrics; 5University of

Massachusetts Medical School; 6University of Rochester; 7University

of Rochester and the AAP Julius B. Richmond Center

Purpose: Many adolescents who smoke cigarettes report wantingto quit. However, evidence for cessation counseling for adolescentsremains limited. The purpose of our study was: (1) to recruit andtrain providers in smoking cessation intervention using the PublicHealth Service 5As (Ask, Advise, Assess, Assist, Arrange) usinga distance learning curriculum, and assess adherence to interven-tion; and (2) to establish whether primary care pediatric practicescan recruit adolescents to a tobacco intervention trial withoutenrollment bias.Methods: We recruited 35 practitioners from the AAP’s practice-based research network – Pediatric Research in Office Settings(PROS) - for a double-blind smoking cessation intervention pilot.Practices were randomized to a brief cessation counseling interven-tion or comparison condition. Both involved a 45-minute self-studyclinician training curriculum. Fidelity to protocols was assessedduring teach-back role-playing follow-up calls with clinicians.Practices recruited eligible adolescents at well visits. Full studyparticipants completed a baseline survey at the visit. All smokersand a sample of nonsmokers completed follow-up phone surveys;youth who initially refused were asked to provide anonymousbaseline data only to examine whether differential non-recruitmentof smokers affected study sample yield.Results: 23 (66%) of providers completed training and met stan-dards for intervention delivery. Of 976 teens recruited for the fullstudy and 150 teens for the anonymous survey, no statistical differ-ences in smoking prevalence were observed between these groups,with smoking rates of 5.7% and 6.6%, respectively (p¼ .65). Follow-up surveys were conducted with 94 teens. Teens in the interventiongroup were significantly more likely to report that their physiciandiscussed smoking (91% vs 72%; c2¼ 5.405, p¼ .02). Althoughnot reaching significance, more clinicians in the intervention groupasked teens if they smoke (90% vs 81%; p¼ .24), and asked if friendssmoked (60% vs 49%; p¼ .32). Smokers in the intervention groupwere more often asked if they wanted to quit (55% vs 36%;p¼ .39) and given materials to help them quit (33% vs 14%; p¼ .42).Conclusions: This field pilot demonstrates successful cliniciantraining in delivery of smoking cessation counseling throughdistance learning and recruitment of teens without differentialnon-enrollment of smokers. Intervention clinicians were signifi-cantly more likely to discuss smoking with teens, and whilea lack of statistical power precluded other significant findings,clinicians in the intervention group delivered more cessationservices than their control counterparts. A full scale RCT is

Abstracts / 46 (2010) S17–S81 S59

methodologically feasible and appears warranted to examine theimpact of brief counseling interventions on adolescent cessation.Sources of Support: NIH, FAMRI, MCHB, AAP.

93.

DESIRE FOR ALCOHOL AND OTHER DRUGS AMONG

ADOLESCENTS AND YOUNG ADULTS WHO

FREQUENTLY USE MARIJUANA

Ashley Kendall, BA, Courtney Walls, MPH, Emily Blood, PhD,

Lydia Shrier, MD, MPH. Children’s Hospital Boston

Purpose: Although retrospective surveys and longitudinal cohortstudies have provided evidence in support of marijuana useleading to other substance use, little is known about the associa-tions between wanting to use marijuana and wanting to use othersubstances as they are experienced in real time. This study usedmomentary sampling techniques to examine associations of desirefor marijuana with desire to use alcohol and other drugs amongyouth who frequently use marijuana.Methods: For two weeks, medical clinic patients aged 15 to 24 yearswho reported using marijuana� 2 times/week carried handheldcomputers that signaled randomly 4-6 times/day. In response tosignals, participants answered separate questions on current desireto use marijuana, alcohol, and other drugs (0, no desire, to 8, strongdesire), perceived availability of each substance (5 response options,very easy to probably impossible, dichotomized as very easy vs. notvery easy), companionship, and location. Reports were date- andtime-stamped. Separate mixed effect models that adjusted for clus-tering of observations within an individual were used to examineassociations of desire to use marijuana with desire to use alcoholand with desire to use other drugs. Models adjusted for perceivedavailability of marijuana and alcohol or other drugs, companion-ship, location, weekend vs. weekday, time of day, gender, and age.Interactions were explored between desire to use marijuana andperceived availability of marijuana, gender, and age.Results: 39 youth (22 female, mean age 18.7 years) completed 2778reports, 2763 (99.5%) of which included responses to the desireitems and were considered for analyses. Participants reportedany desire to use marijuana, alcohol, and other drugs on 1525(55.2%), 529 (19.2%) and 225 (8.1%) of reports, respectively. Meanstrength of desire was, in order, 2.6, .8, and .4. Substances wereperceived as very easy to get on 1568 (56.8%), 1085 (39.3%) and445 (16.1%) of reports, for marijuana, alcohol, and other drugs,respectively. In the adjusted model, desire for marijuana was posi-tively associated with desire for alcohol (p < .0001). Youth also ex-pressed greater desire to drink when they regarded marijuana asnot very easy to get (p < .0001) and alcohol as very easy toget (p < .0001). Adjusting for covariates, greater desire for mari-juana (p < .0001) and other drugs perceived as very easy to get(p¼ .0194) were independently associated with greater desire forother drugs. In the interaction models, the association betweendesire for marijuana and desire for alcohol was stronger whenmarijuana was not very easy vs. very easy to get (p < .0001) andfor males vs. females (p < .0001). The association between desireto use marijuana and desire for other drugs was also stronger formales (p < .0001) and for older youth (19-24 vs. 15-18, p < .0001).Conclusions: Momentary desire for marijuana was positively asso-ciated with increased desire to use both alcohol and other drugs,and perceived difficulty obtaining marijuana predicted greaterdesire for alcohol use. To inform the development of interventionsfor substance use progression, future research should seek to betterunderstand the relations of marijuana use and availability withdesire for other substances, and how these relations vary by genderand age.Sources of Support: NIH R21DA021713-01A1, MCH T71MC00009.

94.

SUBSTANCE USE AMONG ADOLESCENTS WITH TYPE 1

DIABETES

Jessica Pan,4 Sherry Van Blyderveen, PhD2, Zubin Punthakee, MD,

MSc1, Christina Grant, MD, FRCP3. 1Department of Medicine and

Pediatrics, McMaster University; 2Department of Pediatrics,

McMaster University; 3Division of Adolescent Medicine, Department

of Pediatrics, McMaster University; 4McMaster University

Purpose: Adolescents with diabetes are as likely to engage in riskybehaviors as healthy adolescents. Substance use in particular canadversely affect their ability to manage their diabetes and overallhealth. In the adult diabetic population, smoking is found to behighly correlated with diabetes mismanagement, leading to highermorbidity and mortality in later life. However, prospective studiesof substance use among youth with Type 1 Diabetes are lacking.The limited available evidence is inconclusive about whether a rela-tionship exists between substance use and glycemic control amongyouth with Type 1 Diabetes. The purpose of the present study wasto examine the prevalence and frequency of substance use amongyouths with Type 1 Diabetes, who are receiving treatment at a Dia-betes Transition Clinic.Methods: Study participants included 66 patients (39 M and 27 F),consecutively enrolled from December, 2007 to August, 2009,who had their first visit with the Diabetes Transition Clinic atour institution. During clinical interviews conducted by anAdolescent Medicine Specialist and an Adult Endocrinologist,data was prospectively collected regarding presence, frequencyand quantity of drug use, and indicators of diabetic control,specifically glycated hemoglobin (A1C) and hospitalization fordiabetic ketoacidosis or severe hypoglycemia. Spearman’s RankCorrelation Coefficient was used to test associations betweencontinuous variables, and Chi-Square Tests were used for categor-ical variables.Results: The mean age of patients was 17.7 years (range 15.6 to19.2). The mean A1C was 9.13% (SD 2.34). Twenty-three percentof patients were admitted to the hospital due to complications inthe past year, 16.7% for diabetic ketoacidosis and 6.3% for severehypoglycemia. Although 12.3% of youth reported ever smokingcigarettes, only 4.5% reported ongoing use. Although 63.6% ofyouths reported ever drinking alcohol, only 16.5% drank alcoholon a weekly basis. Binge drinking (>5 drinks at one time for malesand>4 drinks at one time for females) was reported by 33.8% ofmales and 14.8% of females. Thirty-two percent of patients reportedever using marijuana, while 13.5% reported regular use (one ormore times a month). Eight percent of males and 3.7% of femalesreported using marijuana weekly. In addition, two percent ofpatients smoked cigarettes only, 30.3% drank alcohol only, 33.3%percent of patients used more than one substance and 34.8% hadnot used any substances. Youth who used more than one substanceprimarily used both alcohol and marijuana. No significant correla-tions were found between the number of substances used, or thequantity of alcohol used monthly, with either A1C values or hospi-talization in the past year.Conclusions: Similar to a recent study in Chile, our study demon-strated lower rates of most substance use in adolescents with Type 1Diabetes, compared to reports of substance use in otherwisehealthy teens in North America. However, rates of marijuana usewere similar to reports from healthy teens. Further, patients who re-ported substance use did not exhibit significantly poorer diabeticcontrol than those who do not use substances. As many factorscan influence diabetes management, substance use alone mayonly be one factor in the larger picture of diabetic control.Sources of Support: Hamilton Health Sciences New InvestigatorFund.