Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2008 Aug 1.
Published in final edited form as: Prev Med. 2007 Jun 4;45(2-3):215–225. doi: 10.1016/j.ypmed.2007.05.018

Addressing Challenges in Adolescent Smoking Cessation: Design and Baseline Characteristics of the HS Group-Randomized Trial

Jingmin Liu a, Arthur V Peterson Jr a,b, Kathleen A Kealey a, Sue L Mann a, Jonathan B Bricker a,c, Patrick M Marek a
PMCID: PMC2040060  NIHMSID: NIHMS30972  PMID: 17628650

Abstract

Objective

Well-documented challenges have hampered both intervention development and research in teen smoking cessation. Addressing these challenges, the Hutchinson Study of High School Smoking (HS Study), the largest group-randomized trial in adolescent smoking cessation to date, incorporates several design innovations to investigate the effect of a counselor-initiated, individually tailored telephone counseling smoking cessation intervention for older adolescents. This paper presents and discusses these innovative design features, and baseline findings on the resulting study population.

Method

The trial used a population-based survey to proactively identify and recruit all high school juniors who had smoked in the past month—potentially expanding intervention reach to all smokers, even those who smoked less than daily and those not motivated to quit. For ethical and intervention reasons, some nonsmokers were enrolled in the intervention, also. Other important design features included the random allocation of schools into experimental conditions (intervention vs. no-intervention control) and a multi-wave design.

Results & Conclusion

The design innovations address problems and challenges identified in adolescent smoking cessation literature. The heterogeneous baseline characteristics of the study population, well-balanced between the two arms, have three significant implications: They (1) demonstrate the effectiveness of the trial’s design features, (2) highlight several intervention-related issues, and (3) provide assurance that the trial’s evaluation of intervention effectiveness will be unbiased.

Keywords: adolescent, smoking cessation, randomized trial, group-randomized, cessation intervention, proactive intervention, telephone counseling, youth

Introduction

Despite an 8-year gradual decline, U.S. adolescent smoking rates remain unacceptably high: One in four high school seniors smokes at least monthly, and 15% smoke daily (Johnston et al., 2005). These rates are alarming because adolescents who smoke regularly are at increased risk for a variety of health problems. Moreover, their risk of serious health consequences increases with the length of time and amount they smoke (U.S. Department of Health and Human Services, 1989; Hozawa et al., 2006), and for the majority of adolescent smokers, smoking will be a long-term addiction (Chassin et al., 1996; Chen and Kandel, 1995; Kandel and Logan, 1984; Bachman et al., 1997; Pierce and Gilpin, 1996).

Many young smokers, however, want to quit and seriously attempt to do so (Centers for Disease Control and Prevention, 2006; Allen et al., 1993; Burt and Peterson, 1998). Unfortunately, their self-initiated cessation rate is low (Zhu et al., 1999; Sussman et al., 1998; Stanton et al., 1996), and quitting grows more difficult with age as the amount smoked and level of dependence increase (COMMIT, 1995; U.S. Department of Health and Human Services, 1989). Young smokers’ desire to quit, the low rates of abstinence achieved without assistance, and the consequences of smoking into adulthood all support the need for strong, theory-based cessation interventions and rigorous randomized trials to evaluate their effectiveness.

Despite this pressing need, multiple, well-documented challenges have hampered development of interventions for teen smokers. First, teens smoke with greater variability and irregularity than adults: While some youth are regular, daily smokers exhibiting symptoms of addiction, many are light or infrequent smokers, and still others smoke only under limited circumstances (Mermelstein, 2003; Backinger, McDonald et al., 2003; Mermelstein et al., 2002; Baillie et al., 2005; Kishchuk et al., 2004). Also, some adolescents do not view their smoking as a serious problem, and even those who want to quit have been unenthusiastic about potentially helpful cessation strategies (Balch, 1998; Patten et al., 2004; Balch et al., 2004; Massey et al., 2003; Leatherdale and McDonald, 2005). Other noted intervention obstacles include adolescents’ concerns regarding privacy (McCormick et al., 1999; Massey et al., 2003; Gillespie et al., 1995; Balch, 1998; Moolchan and Mermelstein, 2002) and loss of autonomy (Balch, 1998; Balch et al., 2004), and limited relevance and accessibility to teens of cessation programs (Balch, 1998; Patten et al., 2003; Balch et al., 2004; Massey et al., 2003; Leatherdale and McDonald, 2005).

Conducting methodologically rigorous research with adolescent smokers has also been a challenge. First, identifying and reaching teen smokers is difficult: Prior teen smoking cessation studies have been conducted primarily in self-selected or convenience samples of small size (McDonald et al., 2003; Sussman 2002), limiting the trials’ generalizability. Second, past adolescent cessation studies have mostly focused on daily smokers, with little attention paid to infrequent smokers (Backinger, Fagan et al., 2003): In a recent meta-analysis of 48 adolescent cessation trials, the level of baseline smoking averaged 10.44 cigarettes per day (Sussman, 2006), reflecting the prevalent research interest in daily smokers. Because infrequent smokers constitute a sizable portion of adolescent smokers (Centers for Disease Control and Prevention, 2006; Johnston et al., 2006; Mowery et al., 2004), are at high risk of smoking escalation and adult smoking (Bricker et al., 2006; Centers for Disease Control and Prevention, 1998; Choi et al., 1997; Chassin et al., 1990), and have difficulties quitting smoking (CDC, 1994), expanding research to include infrequent smokers is an important next step. Third, much of the published literature on adolescent smoking cessation consists of uncontrolled, nonrandomized, or single-group studies (Sussman, 2002), providing weak tests of program effectiveness. Finally, in-depth reporting of study populations, methods and outcomes is often neglected, despite its importance for research communication and assessment. Underreporting of such important information has impeded comprehensive literature reviews (McDonald et al., 2003; Sussman 2002) and meta-analyses (Sussman 2006), and has limited knowledge accumulation in the field of adolescent smoking cessation.

Designed to overcome identified methodological challenges and rigorously test an innovative smoking cessation intervention for older adolescents, the Hutchinson Study of High School Smoking (HS Study) is the largest group-randomized trial in adolescent smoking cessation to date, with 2,151 adolescent smokers from 50 high schools. The trial used a population-based survey to proactively identify and recruit all high school juniors who had smoked in the past month, thus potentially expanding intervention reach to all smokers and capturing a heterogeneous sample of older teen smokers. With its rigorous group-randomized design, the study will evaluate the extent to which a counselor-initiated, individually tailored telephone counseling intervention can effect smoking cessation among a heterogeneous population of older adolescent smokers.

This paper presents the trial’s intervention and experimental design, with a focus on innovative features that were developed to overcome the identified challenges, and describes the resulting study population of older teen smokers and the degree of balance in important baseline characteristics of these teens between the two experimental conditions.

Intervention Design

The smoking cessation intervention incorporates multiple strategies to address known intervention obstacles (Kealey et al., 2007), including (1) proactively identify smokers in the population and directly invite their participation; (2) include both smokers and nonsmokers as participants to protect smokers’ privacy; (3) use the telephone to proactively deliver the intervention; (4) allow unlimited calls to reach adolescents; (5) tailor the intervention to the individual; and (6) communicate respect for adolescents and their choices.

The intervention, based on Social Cognitive Theory (Bandura, 1977, 1986), incorporates both Motivational Interviewing (MI) (Miller and Rollnick, 1991, 2002), to increase smokers’ own motivation and commitment for quitting smoking, and cognitive behavioral skills training (Baer et al., 1999), to build skills for smoking cessation and relapse prevention. MI was chosen for its early promise in reducing adult smoking (Zhu et al., 1996; Britt et al., 1994; Curry et al., 1995), and its nonjudgmental, nonprescriptive style is appropriate for adolescent smokers (Lawendowski, 1998).

The intervention was delivered via counselor-initiated telephone calls, providing the privacy and personalization of face-to-face counseling but at a lower cost. Also, because many teens have continuous access to a telephone, proactive telephone interventions make participation easy for teens, and allow programs to involve all smokers, including precontemplative and infrequent smokers who typically are not reached by cessation programs that respond to teen initiative. Furthermore, personalized counseling tailored the intervention’s content and dose to each smoker’s needs and readiness to quit, thereby enhancing relevance, and increasing teen receptivity.

The intervention was implemented (in the experimental condition) during the participants’ senior year of high school. Prior to contacting adolescents, consent for intervention participation from parents of minor-age teens was obtained via mail, with telephone follow-up. Informed consent was conducted with adolescents via the telephone, following receipt of an informational mailer.

As noted, smokers in the trial were identified via a confidential baseline survey and then invited to participate in the intervention. Such a proactive approach enables the recruitment of a wide array of adolescent smokers; however, if self-reported survey data were used to contact only smokers, the confidentiality of those data could be breached, because contacting a smoker trial participant would reveal to an observer, e.g. anyone in the household, that the participant was smoker (Moolchan & Mermelstein, 2002). Including nonsmokers in the intervention, so it’s understood that both smokers and nonsmokers will be contacted, is an innovation designed to overcome this obstacle. (In accordance with this design feature, parents of trial participants were informed that both nonsmokers and smokers were enrolled in the intervention.) In addition, contacts with nonsmokers served two significant intervention functions: (1) to reinforce smoking abstinence among nonsmokers, and (2) to build motivation, skills and confidence for supporting peers’ efforts to quit smoking, thus capitalizing on positive social interactions among teens.

Experimental Design

The experimental design of this two-arm group-randomized trial, shown in Figure 1, is described below.

Figure 1.

Figure 1

Open in a new tab

Study population

To provide a population basis for evaluating the intervention effectiveness, 50 high schools were randomly selected from 168 Washington State public high schools that met the study’s eligibility criteria: located within 200 miles of the Fred Hutchinson Cancer Research Center and with an enrollment of 100–500 seniors. In the 50 participating high schools, 12,141 (93.1% of 13,042 eligible) high school juniors completed the baseline survey. (Ineligible for survey were 1,188 juniors who were foreign exchange students, enrolled only in off-campus classes, or unable to read/understand English). Through the baseline survey, all smokers who were currently smoking at least monthly or had smoked in the past 30 days were identified for inclusion, based on a positive response to any of the following three survey items: (a) How often do you currently smoke cigarettes? (responded, “once a month or more”); (b) Have you smoked one or more cigarettes in the last 30 days? (responded, “yes”); (c) When was the last time you smoked, or even tried, a cigarette? (responded, “within the past 30 days”).

The selected sample of nonsmokers consisted of former smokers (those who reported they had smoked regularly before but had been abstinent for at least three months) and never smokers (those who reported smoking no more than two cigarettes in their lifetime, and none in the past month). To capitalize on the existing social network and positive social interactions, nonsmokers were preferably selected who self-reported having more smoking friends, higher level of willingness to help friends quit smoking, and greater interest in knowing more about how to help friends quit smoking.

A total of 2,175 smokers and 745 nonsmokers were identified and selected. Excluding 24 smokers and 2 nonsmokers who declined further contact in the baseline survey, the trial cohort consisted of 2,151 smokers and 743 nonsmokers.

Experimental unit and conditions/random assignment

The experimental unit is the high school. This design choice not only prevented possible contamination due to social mixing of intervention-exposed students and unexposed students, but also enabled the trial to target all high school juniors in the school, and to capitalize on the interaction among smokers, and the interaction between nonsmokers and smokers as possible enhancement of intervention effectiveness. The 50 participating high schools were randomly assigned to either the experimental or control condition. To further ensure balance beyond what the randomization achieves between the two conditions, the randomization of schools was pair-matched by prevalence of smoking, number of smokers, cessation stage and percentage of students eligible for free/reduced-price school meals. The matched random assignment was performed via a computerized coin flip for each randomly ordered pair.

Baseline data collection

Baseline data were collected on both schools and students. Information on school demographics and smoking-related characteristics (e.g., rural/urban composition, staff smoking) was obtained via the Survey of High School Principals, which was mailed to each principal, with mail and telephone follow-up of non-responders.

The Survey of High School Juniors was used to collect baseline information on older teens’ smoking as well as to identify trial participants. The in-class survey was administered in regular school classrooms by trained study staff, with a scripted explanation of survey procedures and study information. “Bogus pipeline” saliva samples were collected to encourage accurate self-reporting of smoking behavior (Murray et al., 1987; Murray and Perry, 1987). Students absent from the initial survey were contacted 2 weeks later for an in-class clean-up survey; those still absent were asked to complete a mailed or telephone survey.

Prior to the baseline survey, parents/guardians were mailed a letter describing the study, the survey, the saliva sample procedure, the confidential treatment of the data collected, and the voluntary nature of participation. The letter invited them to call a toll-free telephone number with questions or to decline their teen’s participation.

Follow-up and outcomes

Multiple outcomes, including abstinence at 7 days, 1, 3 and 6 months, will be collected at approximately 6 months post intervention to answer the trial’s main scientific questions. Follow-up procedures were designed to attain the highest possible response rates and minimize attrition, essential for reducing bias in the trial’s findings.

Evaluation methods

Intervention effect, represented by differences between the two arms in endpoint outcomes (e.g. 6-month abstinence rate, 3-month abstinence rate), will be evaluated by group permutation-based methods (Lehman, 1975; Edgington, 1987; Gail et al., 1992). The permutation method of analysis acknowledges the intraclass correlation within individuals in the same school by permuting school, as opposed to individual, among a set of all possible intervention assignments, to calculate the test statistics and form a null distribution. The test statistic observed on the basis of the actual assignment is then referenced against this null distribution for hypothesis testing. Such analytic method avoids modeling and distribution assumptions, relying solely on the random assignment of intervention, and is flexible for covariate adjustment (Gail et al., 1996; Murray et al., 2006). Analysis of subgroups will also be conducted on the basis of strong empirical or theoretical evidence to detect differential intervention impact in specific subgroups.

Sample size calculation

The trial design acknowledged the group nature of the experimental unit and accounted for the within-school intraclass correlation in its sample size calculation, which used the following parameters: (1) 6-month abstinence rate of 0.06 at the study endpoint in the control group (Sussman et al., 1999; U.S. Department of Health and Human Services, 1989; Lando et al., 1992), (2) 90% participation rate in endpoint data collection, based on prior experience on the Hutchinson Smoking Prevention Project (HSPP), a 12-year trial on adolescent smoking (Peterson et al., 2000), (3) intraclass correlation coefficient for cessation among high school senior smokers, conservatively estimated as 0.025 from the prior HSPP data on cessation among high school seniors, (4) resulting variance inflation factor of 2.11, (5) two-sided test that accommodates the intraclass correlation, and (6) alpha = .05. Thus, the trial has included enough schools and trial participants to evaluate intervention effectiveness. Specifically, 50 high schools with 2,150 smoker trial participants (43 per school) provide a statistical power of 89% for a cessation fraction of .12 in the intervention arm.

Phase-in of trial activities

The trial used a “wave” design in which recruitment of high schools and subsequent activities were phased-in over 3 years. A total of 14 high schools with 515 smoker trial participants were recruited in the first year (2002), 20 schools with 966 smokers in the second year and 16 schools with 670 smokers in the third year.

Human subject approval

The HS Study intervention, experimental design, procedures and instruments were reviewed and approved by the Hutchinson Center’s Institutional Review Board.

Baseline Results

Baseline characteristics

All the percentages reported below used the number of valid responses as denominator. Unless stated otherwise, percentages of valid data range from 86% to 100%.

The 50 participating high schools had an average enrollment of 1,224 students (SD: 527.7), ranging from 204 to 2,376 students. The percentage of students eligible for free/reduced price school meals is one indication of a school’s socioeconomic status; the average of this percentage for participating schools was 24.6% (SD: 13.7%). For 21 (46.7%) high schools, more than 50% of families lived in rural areas; for 18 (40%) high schools, fewer than 25% of families did so.

Almost half (49.4%) of the 12,141 baseline survey respondents were female. Race/ethnic composition was 73.5% White, 7.2% multiple races/ethnicity, 6.4% Asian, 6.1% Hispanic, 2.9% Black, 1.4% Native Hawaiian or other Pacific Islander, 1.3% American Indian, and 1.5% other. Almost all (94.9%) survey participants were age 16 or 17 at the time of survey; only 28 (.2%) were younger than 16, and 597 (4.9%) were older than 17. Among these high school juniors, 2,175 (17.9%) were identified as smokers; 794 (6.8%) reported at least daily smoking.

Baseline data on important variables (i.e., population descriptors, known predictors of adolescent smoking cessation) are presented in Table 1 for the 2,151 smoker trial participants. Characteristics of smoker trial participants in each condition, as well as overall, are presented side-by-side, so that the degree of balance between the two arms can be observed. Because the randomization ensures that the null hypothesis of balance except for chance variation is true (Donner & Klar, 2000; Murray, 1998), statistical tests for such a null hypothesis are not needed. Nonetheless, at the suggestion of a referee, p-values are provided to show where in the randomized distribution the differences between experimental and control condition lie. They were calculated on the basis of two-sample t-test using school as the unit of analysis, appropriate for the group nature of the random assignment.

Table 1.

Baseline characteristics of 2,151 smoker trial participantsa, HS Study, WA, 2000–2008

Variable All (%) (n = 2,151) Control (%) (n = 1,093) Experimental (%) (n = 1,058)
Demographic variables
Gender
 Female 1,017 (47.3%) 505 (46.2%) 512 (48.4%)
 Male 1,134 (52.7%) 588 (54.5%) 546 (51.6%)
 p-value for gender: 0.647
Ethnicity/Race
 White 1,583 (74.9%) 796 (72.1%) 787 (75.9%)
 Multiple Races/Ethnicity 153 (7.2%) 78 (7.3%) 75 (7.2%)
 Hispanic (unknown race) 114 (5.4%) 50 (4.7%) 64 (6.2%)
 Asian 103 (4.9%) 67 (6.2%) 36 (3.5%)
 American Indian or Alaska Native 54 (2.6%) 27 (2.5%) 27 (2.6%)
 Black or African American 53 (2.5%) 32 (2.9%) 21 (2.0%)
 Native Hawaiian or other islander 29 (1.4%) 12 (1.1%) 17 (1.6%)
 p-value for % of white: 0.612
Age at baseline data collectionb
 <16 years old 2 (0.1%) 2 (0.2%) 0 (0.0%)
 16 years old 656 (30.5%) 309 (28.3%) 347 (32.8%)
 17 years old 1,333 (62.0%) 695 (63.6%) 638 (60.3%)
 > 17 years old 160 (7.4%) 87 (8.0%) 73 (6.9%)
 p-value: 0.064
Current smoking and smoking history
Number of cigs smoked in lifetime
 ≤1 144 (6.9%) 71 (6.7%) 73 (7.0%)
 2–100 865 (41.0%) 463 (43.4%) 402 (38.7%)
 101–400 379 (18.0%) 199 (18.6%) 180 (17.3%)
 > 400 723 (34.2%) 336 (31.4%) 387 (37.1%)
 p-value for % of ≥ 100 cigs: 0.226
Smoking frequency
 < Monthly 593 (28.2%) 314 (29.4%) 279 (26.9%)
 ≥ Monthly, < weekly 367 (17.4%) 190 (17.8%) 177 (17.1%)
 ≥ Weekly, < daily 362 (17.2%) 195 (18.3%) 167 (16.1%)
 ≥ Daily 782 (37.2%) 368 (34.5%) 414 (39.9%)
 p-value for % of daily smokers: 0.019
Current use of other tobacco productsc
 Not at all 902 (43.6%) 463 (44.2%) 439 (42.9%)
 Less than monthly 520 (25.1%) 261 (24.9%) 259 (25.3%)
 At least monthly, but not daily 412 (11.3%) 214 (20.5%) 198 (19.3%)
 Daily 237 (11.4%) 110 (10.5%) 127 (12.4%)
 p-value for % of no use: 0.659
Nicotine dependence among daily smokersd
 No symptoms 96 (12.5%) 47 (13.0%) 49 (12.1%)
 1 or 2 symptoms 186 (24.3%) 90 (24.9%) 96 (23.7%)
 3 or 4 symptoms 485 (63.2%) 225 (62.2%) 260 (64.2%)
 p-value for % of having 3 or 4 symptoms: 0.795
Age at first use
 ≤ 8 153 (8.0%) 73 (7.6%) 80 (8.5%)
 9–12 572 (30.0%) 267 (27.8%) 305 (32.4%)
 13–16 1,008 (52.8%) 532 (55.0%) 476 (50.6%)
 > 16 175 (9.2%) 95 (9.8%) 80 (8.5%)
 p-value for % of first use before 13 yrs: 0.371
Quitting variables
Made at least 1 quit attempt in the last yeare 872 (47.2%) 420 (44.9%) 452 (49.6%)
 p-value:0.810
Longest quit attempt in the last 12 months
 > 3 months 155 (19.4%) 79 (20.5%) 76 (18.3%)
 > 1 month, ≤ 3 months 168 (21.0%) 79 (20.5%) 89 (21.4%)
 > 1 week, ≤ 1 month 246 (30.7%) 120 (31.2%) 126 (30.3%)
 > 1 day, ≤ 1 week 209 (26.1%) 100 (26.0%) 109 (26.2%)
 ≤ 1 day 23 (2.9%) 7 (1.8%) 16 (3.8%)
 p-value for % of quit attempt lasted > 1 month: 0.334
Plan to stop smoking entirely someday?
 In the next 30 days 365 (19.8%) 183 (19.5%) 182 (20.1%)
 1 – 6 months from now 340 (18.5%) 166 (17.7%) 174 (19.2%)
 Not in the next 6 months/No 698 (37.9%) 360 (38.4%) 338 (37.4%)
 Don’t know 438 (23.8%) 228 (24.3%) 210 (23.2%)
 p-value for % of having plan in next 6 months: 0.617
Intention to smoke in the future
 No 1,008 (48.5%) 510 (48.3%) 498 (48.7%)
 Yes 687 (33.1%) 357 (33.8%) 330 (32.3%)
 Don’t know 382 (18.4%) 188 (17.8%) 194 (19.0%)
 p-value for % of no intention: 0.683
Motivation to quitf
 Strong 563 (31.0%) 266 (29.0%) 297 (33.1%)
 Medium 706 (38.9%) 368 (40.1%) 338 (37.7%)
 Weak 546 (30.1%) 284 (30.9%) 262 (29.2%)
 p-value for % of strong motivation: 0.423
Quitting self-efficacyg
 Strong 1,279 (69.3%) 665 (71.2%) 614 (67.4%)
 Medium 446 (24.2%) 208 (22.3%) 238 (26.1%)
 Weak 120 (6.5%) 61 (6.5%) 59 (6.5%)
 p-value for % of strong efficacy: 0.192
Other smoking-related variables
Often & sometimes around smokers age over 25 1522 (73.2%) 764 (72.5%) 758 (74.0%)
 p-value: 0.816
Often & sometimes around high school smokers 1823 (87.8%) 913 (86.7%) 910 (88.9%)
 p-value: 0.082
Number of friends who smoke
 0 188 (9.0%) 101 (9.5%) 87 (8.5%)
 1 or 2 688 (33.0%) 360 (34.0%) 328 (31.8%)
 3 or more 1210 (58.0%) 598 (56.5%) 612 (59.6%)
 p-value for % of having ≥3 friends who smoke: 0.197
Desire for help with quitting
 Yes 208 (11.4%) 106 (11.5%) 102 (11.3%)
 No 1371 (75.1%) 699 (75.9%) 672 (74.3%)
 Don’t know 247 (13.5%) 116 (12.6%) 131 (14.5%)
 p-value for % of wanting help: 0.509
Open in a new tab
a

All percentages use the number of valid responses as the denominator. Valid percentages for demographics, smoking, and other smoking-related variables range from 96% to 100%. Valid data on smoking history and quitting were obtained on 84% to 89% of the smoker trial participants. The latter variables were designed specifically to be answered by those who self-reported having smoked one or more cigarettes in the last 30 days. Some smoker trial participants skipped these questions, either by mistake, intentionally, or simply perceiving themselves as not fitting the statement––even though they were identified by one or more of three questionnaire items as smokers.

b

The baseline data collection date varied over a three-month range within Wave.

c

Current use of other tobacco products: Measured the consumption frequency of chewing tobacco/snuff, cigars, bidis or clove cigarettes, pipe tobacco.

d

Nicotine dependence among daily smokers: Presence of the following nicotine-dependent symptoms: (1) Smokes >10 cigarettes per day; (2) Experiences urge to smoke within 2 hours after waking; (3) Smokes first cigarette of the day within 2 hours after waking; (4) Smokes first cigarette of the day within 2 hours after leaving home in the morning (Rojas, et al., 1998; Nonnemaker et al., 2004; Etter, et al., 2003).

e

Respondents were asked: “Think back over the last 12 months. Did you try to stop smoking completely anytime in the last 12 months?” Those who responded “Yes, I did try to stop smoking completely in the last 12 months” were directed to an open-ended question: “How many times did you try to stop smoking completely in the last 12 months?” Those who responded “one” or a larger number were in this category.

f

Motivation to quit: Mean score of 2 items, “How important is it for you to stop smoking completely?” and “How committed are you to becoming a nonsmoker someday?” Min = 0 (weak), max = 1 (strong); presented using 3 categories: strong (.67~1), medium (.34~.66), weak (0~.33).

g

Quitting self-efficacy: Mean score of 2 items, “Do you think that if you put your mind to it, you could stop smoking?” and “If you were to try to stop smoking, how confident are you that you could keep from smoking for at least 6 months?” Min = 0 (weak) and max = 1 (strong); presented using 3 categories: strong (.67~1), medium (.34~.66), weak (0~.33).

As shown in Table 1, the smoker trial participants have diverse smoking patterns. With regard to cumulative use of cigarettes, about as many (52.4%) reported having smoked more than 100 cigarettes as reported having smoked 100 cigarettes or fewer (47.6%). With regard to smoking frequency, 37.2% of these smokers were smoking at least daily, with 62.8% smoking less than daily, and a sizeable 28.2% smoking less than monthly. A majority of smokers (69.9%) reported having strong or medium motivation to quit, but a significant minority (30.1%) lacked motivation. Only 11.4% of these smokers reported wanting help with quitting.

Examination of the two rightmost columns in Table 1 indicates that the smoker trial participants in experimental and control conditions were very similar with regard to most characteristics. One exception is the percentage of daily smokers (39.9% in experimental vs. 34.5% in control).

In light of the diversity of smoking frequency among smokers, Table 2 presents quitting variables (e.g., past quit attempt, motivation to smoke in the future) by smoking frequency. Almost 60% of daily smokers reported past quit attempts, compared to 34.2% of less-than-monthly smokers and 36.5% of monthly-but-not-weekly smokers. However, a majority (73.3%) of the less-than-monthly smokers reported no intentions to smoke in the future, while almost half (48.8%) of daily smokers reported intentions to do so. Higher-frequency smokers consistently reported lower motivation to quit and lower self-efficacy for quitting.

Table 2.

Baseline quitting variables of smoker trial participants by smoking frequency a,b, HS Study, WA, 2000–2008

Variable < Monthly n = 593 (%) ≥ Monthly,< Weekly n = 367 (%) ≥ Weekly,< Daily n = 362 (%) ≥ Daily n = 782 (%)
Made ≥ 1 quit attempt in the last year 133 (34.2%) 112 (36.5%) 146 (42.4%) 454 (59.1%)
Plan to stop smoking entirely someday?
 In the next 30 days 166 (43.0%) 70 (22.6%) 56 (16.2%) 63 (8.3%)
 1 – 6 months from now 29 (7.5%) 48 (15.5%) 70 (20.3%) 183 (24.1%)
 Not in the next 6 months/No 79 (20.5%) 101 (32.6%) 136 (39.4%) 370 (48.7%)
 Don’t know 112 (29.0%) 91 (29.4%) 83 (24.1%) 144 (18.9%)
Intention to smoke in the future
 No 421 (73.3%) 182 (50.8%) 154 (44.1%) 233 (30.9%)
 Yes 64 (11.1%) 111 (31.0%) 127 (36.4%) 368 (48.8%)
 Don’t know 89 (15.5%) 65 (18.2%) 68 (19.5%) 153 (20.3%)
Motivation to quit
 Strong 174 (45.1%) 90 (29.7%) 89 (26.3%) 191 (25.5%)
 Medium 125 (32.4%) 121 (39.9%) 146 (43.1%) 302 (40.3%)
 Weak 87 (22.5%) 92 (30.4%) 104 (30.7%) 256 (34.2%)
Quitting self-efficacy
 Strong 355 (90.3%) 267 (86.1%) 264 (76.7%) 369 (48.6%)
 Medium 29 (7.4%) 38 (12.3%) 69 (20.1%) 297 (39.1%)
 Weak 9 (2.3%) 5 (1.6%) 11 (3.2%) 93 (12.3%)
Open in a new tab
a

All percentages use the number of valid responses as the denominator. Ranges of valid percentages for each of the following groups were 65% – 95.1% for less than monthly smokers, 82.6% – 97.5% for at least monthly but not weekly smokers, 93.4% – 96.4% for at least weekly but not daily smokers, and 95.8% – 98.2% for daily smokers.

b

Smoking frequency was determined by one survey item: “How often do you currently smoke cigarettes?” Although smoking less than monthly, 593 respondents also reported that they smoked one or more cigarettes in the past 30 days, and therefore, they were still considered as smokers by the trial’s definition.

Nonsmoker trial participants consisted of 419 (56.4%) never smokers and 324 (43.6%) former smokers. Over half (58.3%) of them were female. The ethnic distribution was diverse: 71.7% White, 8.5% multiple ethnicity, 6.9% Hispanic, 5.1% Asian, 2.8% Black, 2% American Indian, 2% Native Hawaiian or other Pacific Islander, and 1% other. The majority (91.4%) of nonsmoker trial participants were 16 or 17 years old.

Discussion

Experimental design

The HS Study, involving 2,151 older adolescent smokers from 50 high schools, is the largest group-randomized trial in adolescent smoking cessation to date. Among the many design features included in the trial to overcome problems identified in adolescent smoking cessation literature, two major innovations are (1) proactive identification of smokers and (2) use of a liberal definition for smokers. The combination of these two innovations expanded intervention reach to even low-frequency teen smokers and smokers who were unmotivated to quit. To the authors’ knowledge, the HS Study is the first adolescent smoking cessation trial to use a population-based survey to proactively identify and recruit participants to the intervention, and is also one of a very few studies that targeted daily and less-than-daily smokers (Sussman, 2002).

The proactive identification of teen smokers enables the recruitment of a population-based sample of teen smokers, but it also carries responsibilities for protecting the rights of study participants. If provisions were not made to avoid it, the use of self-reported data to contact individual smokers identified as such by the data would constitute a breach of confidentiality (Moolchan & Mermelstein, 2002). One such provision is to have both smokers and nonsmokers in the trial. In addition, including nonsmokers provides the intervention an opportunity to harness nonsmokers’ support in helping their smoking friends to quit and, more important, an opportunity to evaluate the effectiveness of their doing so. Finally, as it turned out, including nonsmokers may have boosted recruitment of smokers to the intervention (Kealey et al, 2007).

The “Phase-in” or “Wave” design in the trial has provided significant advantages for management and design robustness. From the management/quality aspect, the use of Wave design is invaluable for it has (1) enabled project staff to participate fully in recruiting and establishing vital collaborative relationships with schools, (2) allowed the project to operate efficiently with high quality work, (3) reduced the annual cost of the study, and (4) permitted interim evaluation of the intervention process, and incorporation of refinements into subsequent waves as needed. From the aspect of experimental design, recruitment by wave enabled secular trends in population attributes used for sample size calculations to be accommodated. Specifically, in this trial, when a lower-than-expected smoking prevalence was identified in Wave I, the estimate of teen smoking prevalence used at the design stage was updated using the actual prevalence observed in Wave I, and then the sample size calculations were updated accordingly. This corrective action led to the addition of four more schools in Wave II, and an entire new Wave (III) of 20 schools––sufficient for attaining the desired statistical power. In sum, this favorable experience from the trial supports the use of a Wave design in large group-randomized trials. It not only improves trial management and quality, but also provides a study design with the robustness to handle eventualities––either ones like those encountered in this trial, or others not anticipated at the start.

It is important to note that the experimental condition in this trial is an intervention with multiple components, while the control condition is a no-intervention control. Such design provides a rigorous framework to evaluate the impact of an intervention as a whole, but it is not intended for separating the effects of the various intervention components. In particular, as pointed out by a referee, this study can not separately investigate the effectiveness of the telephone contact and the effect of the intervention content during telephone contact. In contrast, this two-arm design is used to address first the fundamental question of whether the intervention, with all the components combined, works. Once evidence of overall effectiveness becomes established, subsequent studies could be conducted to distinguish the effect of the intervention components.

Baseline characteristics

One significant result of the trial’s important design innovations is a heterogeneous study population of older adolescent smokers with varying rate and frequency of smoking, different level of motivation to quit, and various degree of receptivity to cessation assistance. The baseline findings describing the characteristics of this large and diverse population of older teen smokers not only provide valuable information for understanding young smokers, but also highlight intervention-related issues and challenges, many of which may have relevance to other studies in adolescent smoking cessation.

For example, over one-third of less-than-daily smokers reported having made at least one quit attempt in the past 12 months. That they were still smoking at the time of survey confirms the need to intervene with infrequent smokers. Additionally, the baseline survey found that infrequent smokers differed from frequent smokers with regard to their quitting behaviors and beliefs: More daily smokers at baseline had experience in trying to quit. Their past experience and knowledge in quitting could increase the probabilities of their achieving abstinence in the future (Redmond, 2002; Zhu et al., 1999); although infrequent smokers had less quitting experience than daily smokers, they reported stronger intentions not to smoke in the future, higher motivation to quit, and higher quitting self-efficacy, all strong predictors of successful quitting (Solomon et al., 2005; Redmond, 2002; Engels et al., 1998, Sussman et al., 1998; Zhu et al., 1999). Interventions with either one or both of these groups of teen smokers should be tailored to capitalize on their different quitting behaviors and beliefs.

The baseline results confirm data from previous studies that a majority of teen smokers do not want help with quitting (Balch et al., 2004; Leatherdale and MacDonald, 2005; Balch, 1998; Gillespie et al., 1995; Lynch and Bonnie, 1994). Interestingly, despite their self-reported resistance to cessation assistance, smokers in the experimental arm were receptive to proactive intervention recruitment (Kealey et al., 2007). Specifically, among eligible smokers who were successfully contacted, 81% participated in the intervention.

It is also noteworthy that the baseline survey observed a 30-day smoking prevalence of 17.9% among the high school juniors who participated in the survey (17.2%, 19.1%, 16.9% for Wave I in 2002, II in 2003 and III in 2004, respectively). These percentages were not only lower than expected, as discussed earlier, but also substantially lower than the national estimates for 12th graders (26.7% in 2002, 24.4% in 2003 and 25% in 2004 from Monitoring the Future Study) (Johnston et al., 2005). This observed difference may be due to Washington’s Tobacco Prevention and Control program: Cigarette smoking among Washington State youth has dropped by 48% since the program’s launch in 2000 (Washington State Department of Health, 2005). Indeed, similar lower magnitudes in adolescent smoking prevalence have been observed in other states as a result of statewide tobacco control programs (Pierce et al., 2005; Siegel, 2002). While tobacco control efforts in Washington State may provide a more favorable social environment for this smoking cessation trial, another implication is that the remaining adolescent smokers may be more resistant to cessation efforts.

Baseline comparability

Examination of the smoker baseline characteristics between experimental and control conditions indicates that the randomized assignment of high schools generally resulted in good balance between the two conditions. Some mild imbalances between the two arms were identified, mainly in the percentage of daily smokers. However, given that multiple baseline variables are reported here, it is not surprising that a few imbalances are observed (Altman, 1985); the random assignment ensures that these small imbalances have arisen purely by chance (Assmann et al., 2000; Pocock et al., 2002). Nevertheless, such baseline imbalance will be accounted for in outcome data evaluation with covariate adjustments.

Summary

The Hutchinson Study of High School Smoking is the largest group-randomized trial to test a smoking cessation intervention among older adolescents. The study population was proactively identified and a liberal definition was used to pursue older teens who smoked at various levels. For ethical and intervention reasons, nonsmokers were also included as intervention target. Other design features with significant benefits include the group (school) randomization and the multi-wave design. Finally, the heterogeneous baseline characteristics of the study population, well-balanced between the two arms, have three important implications: First, they demonstrate the usefulness and effectiveness of the trial’s experimental design features; Second, they highlight several intervention-related issues that would be encountered in other adolescent smoking cessation studies; Third, they provide assurance that the trial’s evaluation of intervention effectiveness will be unbiased.

Acknowledgments

This study is supported by NCI grant R01-CA082569 and was initiated and analyzed by the investigator.

We gratefully acknowledge the students who participated in the Survey of High School Juniors, their parents, and the staff and administrators of the 50 collaborating Washington State high schools. We thank Anya Luke-Killam and Maura Davis for formatting the manuscript.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

References

  1. Allen KF, Moss AJ, Giovino GA, Shopland DR, Pierce JP. Teenage tobacco use: data estimates from the Teenage Attitudes and Practices Survey, United States, 1989. Advance Data. 1993;224:1–20. doi: 10.1037/e608902007-001. [DOI] [PubMed] [Google Scholar]
  2. Altman DG. Comparability of randomized groups. Statistician. 1985;34:125–136. [Google Scholar]
  3. Assmann SF, Pocock SJ, Enos LE, Kasten LE. Subgroup analysis and other (mis)uses of baseline data in clinical trials. Lancet. 2000;335:1064–1069. doi: 10.1016/S0140-6736(00)02039-0. [DOI] [PubMed] [Google Scholar]
  4. Bachman JG, Wadsworth KN, O’Malley PM, Johnston LD, Schulenberg JE. Smoking, Drinking, and Drug Use in Young Adulthood: The Impacts of New Freedoms and New Responsibilities. Mahwah, NJ: Lawrence Erlbaum Associates; 1997. [Google Scholar]
  5. Backinger CL, Fagan P, Matthews E, Grana R. Adolescent and young adult tobacco prevention and cessation: current status and future directions. Tob Control. 2003;12(Suppl IV):iv46–iv53. doi: 10.1136/tc.12.suppl_4.iv46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Backinger CL, McDonald P, Ossip-Klein DJ, Colby SM, Maule CO, Fagan P, Husten C, Colwell B. Improving the future of youth smoking cessation. Am J Health Behav. 2003;27(Suppl 2):S170–S184. [PubMed] [Google Scholar]
  7. Baer JS, Kivlahan DR, Donovan DM. Integrating skills training and motivational therapies. Implications for treatment of substance dependence. J Subst Abuse Treat. 1999;17:15–23. doi: 10.1016/s0740-5472(98)00072-5. [DOI] [PubMed] [Google Scholar]
  8. Baillie L, Lovato CY, Johnson JL, Kalaw C. Smoking decisions from a teen perspective: a narrative study. Am J Health Behav. 2005;29:99–106. doi: 10.5993/ajhb.29.2.1. [DOI] [PubMed] [Google Scholar]
  9. Balch GI. Exploring perceptions of smoking cessation among high school smokers: input and feedback from focus groups. Prev Med. 1998;27:A55–A63. doi: 10.1006/pmed.1998.0382. [DOI] [PubMed] [Google Scholar]
  10. Balch GI, Tworek C, Barker DC, Sasso B, Mermelstein RJ, Giovino GA. Opportunities for youth smoking cessation: findings from a national focus group study. Nic Tob Res. 2004;6:9–17. doi: 10.1080/1462200310001650812. [DOI] [PubMed] [Google Scholar]
  11. Bandura A. Social Learning Theory. Englewood Cliffs, NJ: Prentice-Hall; 1977. [Google Scholar]
  12. Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice-Hall; 1986. [Google Scholar]
  13. Bricker JB, Peterson AV, Andersen MR, Rajan KB, Leroux BG, Sarason IG. Childhood friends who smoke: do they influence adolescents to make smoking transitions? Addict Behav. 2006;31:889–900. doi: 10.1016/j.addbeh.2005.07.011. [DOI] [PubMed] [Google Scholar]
  14. Britt J, Curry SJ, McBride D, Grothaus L, Louie D. Implementation and acceptance of outreach telephone counseling for smoking cessation with nonvolunteer smokers. Health Educ Q. 1994;21:55–68. doi: 10.1177/109019819402100107. [DOI] [PubMed] [Google Scholar]
  15. Burt RD, Peterson AV. Smoking cessation among high school seniors. Prev Med. 1998;27:319–327. doi: 10.1006/pmed.1998.0269. [DOI] [PubMed] [Google Scholar]
  16. Centers for Disease Control and Prevention. Youth risk behavior surveillance – United States, 2005. MMWR CDC Surveill. 2006 Summ. 55, (No. SS-5) [Google Scholar]
  17. Centers for Disease ControlPrevention. Selected cigarette smoking initiation and quitting behaviors among high school students – United States, 1997. MMWR Morb Mortal Wkly Rep. 1998;47:386–389. [PubMed] [Google Scholar]
  18. Centers for Disease Control Prevention. Reasons for tobacco use and symptoms of nicotine withdrawal among adolescent and young adult tobacco users – United States, 1993. MMWR Morb Mortal Wkly Rep. 1994;43:745–750. [PubMed] [Google Scholar]
  19. Chassin L, Presson CC, Sherman SJ, Edwards DA. The natural history of cigarette smoking: predicting young-adult smoking outcomes from adolescent smoking patterns. Health Psychol. 1990;9(6):701–716. doi: 10.1037//0278-6133.9.6.701. [DOI] [PubMed] [Google Scholar]
  20. Chassin L, Presson CC, Rose JS, Sherman SJ. The natural history of cigarette smoking from adolescence to adulthood: demographic predictors of continuity and change. Health Psychol. 1996;15:478–484. doi: 10.1037//0278-6133.15.6.478. [DOI] [PubMed] [Google Scholar]
  21. Chen K, Kandel DB. The natural history of drug use from adolescence to the mid-thirties in a general population sample. Am J Public Health. 1995;85:41–47. doi: 10.2105/ajph.85.1.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Choi WS, Pierce JP, Gilpin EA, Farkas AJ, Berry CC. Which adolescent experimenters progress to established smoking in the United States. Am J Prev Med. 1997;13:385–391. [PubMed] [Google Scholar]
  23. The COMMIT Research Group. Community Intervention Trial for Smoking Cessation (COMMIT): I. Cohort results from a four-year community intervention. Am J Public Health. 1995;85:183–192. doi: 10.2105/ajph.85.2.183. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Curry SJ, McBride C, Grothaus LC, Louie D, Wagner EH. A randomized trial of self-help materials, personalized feedback, and telephone counseling with nonvolunteer smokers. J Consult Clin Psychol. 1995;63:1005–1014. doi: 10.1037//0022-006x.63.6.1005. [DOI] [PubMed] [Google Scholar]
  25. Donner A, Klar N. Design and Analysis of Cluster Randomization Trials in Health Research. New York: Oxford University Press; 2000. [Google Scholar]
  26. Donner A, Birkett N, Buck C. Randomization by cluster: sample size requirements and analysis. Am J Epidemiol. 1981;114:906–914. doi: 10.1093/oxfordjournals.aje.a113261. [DOI] [PubMed] [Google Scholar]
  27. Edgington ES. Randomization Tests. 2. New York: Marcel Dekker; 1987. [Google Scholar]
  28. Engels RCME, Knibbe RA, Vries HD, Drop MJ. Antecedents of smoking cessation among adolescents: who is motivated to change? Prev Med. 1998;27:348–357. doi: 10.1006/pmed.1998.0304. [DOI] [PubMed] [Google Scholar]
  29. Etter JF, Le Houezec J, Perneger TV. A self-administered questionnaire to measure addiction to cigarettes: the cigarette dependence scale. Neuropsychopharmacology. 2003;28:359–370. doi: 10.1038/sj.npp.1300030. [DOI] [PubMed] [Google Scholar]
  30. Gail MH, Mark SD, Carroll RJ, Green SB, Pee D. On design considerations and randomization-based inference for community intervention trials. Stat Med. 1996;15:1069–1092. doi: 10.1002/(SICI)1097-0258(19960615)15:11<1069::AID-SIM220>3.0.CO;2-Q. [DOI] [PubMed] [Google Scholar]
  31. Gail MH, Byar DP, Pechacek TF, Corle DK (for the COMMIT Study Group) Aspects of statistical design for the Community Intervention Trial for Smoking Cessation (COMMIT) Control Clin Trials. 1992;14:253–254. doi: 10.1016/0197-2456(92)90026-v. [DOI] [PubMed] [Google Scholar]
  32. Gillespie A, Stanton W, Lowe JB, Hunter B. Feasibility of school-based smoking cessation programs. J School Health. 1995;65:432–437. doi: 10.1111/j.1746-1561.1995.tb08208.x. [DOI] [PubMed] [Google Scholar]
  33. Hozawa A, Houston T, Steffes MW, Widome R, Williams OD, Iribarren C, Pletcher MJ, Daviglus ML, Carr JJ, Jacobs DR. The association of cigarette smoking with self-reported disease before middle age: The Coronary Artery Risk Development in Young Adults (CARDIA) study. Prev Med. 2006;42:193–199. doi: 10.1016/j.ypmed.2005.12.008. [DOI] [PubMed] [Google Scholar]
  34. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. (NIH Publication No. 06-5882) Bethesda, MD: National Institute on Drug Abuse; 2006. Monitoring the Future National Results on Adolescent Drug Use: Overview of Key Findings, 2005. [Google Scholar]
  35. Johnston LD, O’Malley PM, Bachman JG, Schulenberg JE. (NIH Publication No. 05-5507) Bethesda, MD: National Institute on Drug Abuse; 2005. Monitoring the Future National Survey Results on Drug Use, 1975–2004. Volume I: Secondary School Students. [Google Scholar]
  36. Kandel DB, Logan JA. Patterns of drug use from adolescence to young adulthood: I. Periods of risk for initiation, continued use, and discontinuation. Am J Public Health. 1984;74:660–666. doi: 10.2105/ajph.74.7.660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Kealey KA, Ludman EJ, Mann SL, Marek P, Phares MM, Riggs KR, Peterson AV. Overcoming barriers to recruitment and retention in adolescent smoking cessation. Nic Tob Research. 2007;9(2):257–270. doi: 10.1080/14622200601080315. [DOI] [PubMed] [Google Scholar]
  38. Kishchuk N, Tremblay M, Lapierre J, Heneman B, O’Loughlin J. Qualitative investigation of young smokers’ and ex-smokers’ views on smoking cessation methods. Nic Tob Research. 2004;6:491–500. doi: 10.1080/14622200410001696565. [DOI] [PubMed] [Google Scholar]
  39. Lando HA, Hellerstedt WL, Pirie PL, McGovern PG. Brief supportive telephone outreach as a recruitment and intervention strategy for smoking cessation. Am J Public Health. 1992;82(1):41–46. doi: 10.2105/ajph.82.1.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Lawendowski LA. A motivational intervention for adolescent smokers. Prev Med. 1998;27:A39–A46. doi: 10.1006/pmed.1998.0424. [DOI] [PubMed] [Google Scholar]
  41. Leatherdale ST, McDonald PW. What smoking cessation approaches will young smokers use? Addict Behav. 2005;30:1614–1618. doi: 10.1016/j.addbeh.2005.02.004. [DOI] [PubMed] [Google Scholar]
  42. Lehmann EL. Nonparametrics: Statistical Methods Based on Ranks. San Francisco, CA: Holden-Day, Inc; 1975. [Google Scholar]
  43. Lynch BS, Bonnie RJ, editors. Committee on Preventing Nicotine Addiction in Children and Youths, Division of Biobehavioral Sciences and Mental Disorders, Institute of Medicine. Washington, DC: National Academy Press; 1994. Growing Up Tobacco-Free: Preventing Nicotine Addiction in Children and Youths. [PubMed] [Google Scholar]
  44. Massey CJ, Dino GA, Horn KA, Lacey-McCracken A, Goldcamp J, Kalsekar I. Recruitment barriers and successes of the American Lung Association’s Not-On-Tobacco Program. J School Health. 2003;73:58–63. doi: 10.1111/j.1746-1561.2003.tb03573.x. [DOI] [PubMed] [Google Scholar]
  45. McCormick LK, Crawford M, Anderson RH, Gittelsohn J, Kingsley B, Upson D. Recruiting adolescents into qualitative tobacco research studies: experiences and lessons learned. J School Health. 1999;69:95–99. doi: 10.1111/j.1746-1561.1999.tb07215.x. [DOI] [PubMed] [Google Scholar]
  46. McDonald P, Colwell B, Backinger CL, Husten C, Maule CO. Better practices for youth tobacco cessation: evidence of Review Panel. Am J Health Behav. 2003;27(Suppl 2):S144–S158. doi: 10.5993/ajhb.27.1.s2.5. [DOI] [PubMed] [Google Scholar]
  47. Mermelstein R. Teen smoking cessation. Tob Control. 2003;12(Suppl 1):i25–i34. doi: 10.1136/tc.12.suppl_1.i25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Mermelstein R, Colby SM, Patten C, Prokhorov A, Brown R, Myers M, Adelman W, Hudmon K, McDonald P. Methodological issues in measuring treatment outcome in adolescent smoking cessation studies. Nic Tob Research. 2002;4:395–403. doi: 10.1080/1462220021000018470. [DOI] [PubMed] [Google Scholar]
  49. Miller WR, Rollnick S. Motivational Interviewing: Preparing People To Change Addictive Behavior. New York: The Guilford Press; 1991. [Google Scholar]
  50. Miller WR, Rollnick S. Motivational Interviewing: Preparing People for Change. 2. New York: The Guilford Press; 2002. [Google Scholar]
  51. Moolchan ET, Mermelstein R. Research on tobacco use among teenagers: ethical challenges. J Adolesc Health. 2002;30:409–417. doi: 10.1016/s1054-139x(02)00365-8. [DOI] [PubMed] [Google Scholar]
  52. Mowery PD, Farrelly MC, Haviland ML, Gable JM, Wells HE. Progression to established smoking among U.S. youths. Am J Public Health. 2004;94:331–337. doi: 10.2105/ajph.94.2.331. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Murray DM, Hannan PJ, Pals SP, McCowen RG, Baker WL, Blitstein JL. A comparison of permutation and mixed-model regression methods for the analysis of simulated data in the context of a group-randomized trial. Stat Med. 2006;25:375–388. doi: 10.1002/sim.2233. [DOI] [PubMed] [Google Scholar]
  54. Murray DM. Design and Analysis of Group-Randomized Trials. New York: Oxford University Press; 1998. [Google Scholar]
  55. Murray DM, O’Connell CM, Schmid LA, Perry CL. The validity of smoking self-reports by adolescents: a reexamination of the bogus pipeline procedure. Addict Behav. 1987;12:7–15. doi: 10.1016/0306-4603(87)90003-7. [DOI] [PubMed] [Google Scholar]
  56. Murray DM, Perry CL. The measurement of substance use among adolescents: when is the ‘bogus pipeline’ method needed? Addict Behav. 1987;12:225–233. doi: 10.1016/0306-4603(87)90032-3. [DOI] [PubMed] [Google Scholar]
  57. Nonnemaker JM, Mowery PD, Hersey JC, Nimsch CT, Farrelly MC, Messeri P, Haviland ML. Measurement properties of a nicotine dependence scale for adolescents. Nic Tob Research. 2004;6:295–301. doi: 10.1080/14622200410001676413. [DOI] [PubMed] [Google Scholar]
  58. Patten CA, Lopez K, Thomas JL, Offord KP, Decker PA, Pingree S, Dornelas EA, Ahluwalia JS. Reported willingness among adolescent nonsmokers to help parents, peers, and others to stop smoking. Prev Med. 2004;39:1099–1106. doi: 10.1016/j.ypmed.2004.04.020. [DOI] [PubMed] [Google Scholar]
  59. Patten CA, Offord KP, Ames SC, Decker PA, Croghan IT, Dornelas EA, Pingree S, Boberg EW, Gustafson DH, Ahluwalis JS, Wolter TD, Hurt RD. Differences in adolescent smoker and nonsmoker perceptions of strategies that would help an adolescent quit smoking. Ann Behav Med. 2003;26:124–133. doi: 10.1207/S15324796ABM2602_04. [DOI] [PubMed] [Google Scholar]
  60. Peterson AV, Kealey KA, Mann SL, Marek PM, Sarason IG. Hutchinson Smoking Prevention Project: long-term randomized trial in school-based tobacco use prevention – results on smoking. J Natl Cancer Inst. 2000;92:1979–91. doi: 10.1093/jnci/92.24.1979. [DOI] [PubMed] [Google Scholar]
  61. Pierce JP, White MM, Gilpin EA. Adolescent smoking decline during California’s tobacco control programme. Tob Control. 2005;14:207–212. doi: 10.1136/tc.2004.010116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Pierce JP, Gilpin E. How long will today’s new adolescent smoker be addicted to cigarettes? Am J Public Health. 1996;86:253–256. doi: 10.2105/ajph.86.2.253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med. 2002;21:2917–2930. doi: 10.1002/sim.1296. [DOI] [PubMed] [Google Scholar]
  64. Redmond WH. Smoking reduction among high school seniors: a test of selected indicators. J Adoles Health. 2002;31:417–424. doi: 10.1016/s1054-139x(02)00406-8. [DOI] [PubMed] [Google Scholar]
  65. Rojas NL, Killen JD, Haydel FK, Robinson TN. Nicotine dependence among adolescent smokers. Arch Pediatr Adolesc Med. 1998;152:151–156. doi: 10.1001/archpedi.152.2.151. [DOI] [PubMed] [Google Scholar]
  66. Siegel M. The effectiveness of state-level tobacco control interventions: a review of program implementation and behavioral outcomes. Annu Rev Public Health. 2002;23:45–71. doi: 10.1146/annurev.publhealth.23.092601.095916. [DOI] [PubMed] [Google Scholar]
  67. Solomon LJ, Bunn JY, Pirie PL, Worden JK, Flynn BS. Self-efficacy and outcome expectations for quitting among adolescent smokers. Addict Behav. 2006;31:1122–1132. doi: 10.1016/j.addbeh.2005.08.001. [DOI] [PubMed] [Google Scholar]
  68. Stanton WR, Lowe JB, Gillespie AM. Adolescents’ experiences of smoking cessation. Drug Alcohol Depend. 1996;43:63–70. doi: 10.1016/s0376-8716(97)84351-7. [DOI] [PubMed] [Google Scholar]
  69. Sussman S, Sun P, Dent CW. A meta-analysis of teen cigarette smoking cessation. Health Psychol. 2006;25:549–557. doi: 10.1037/0278-6133.25.5.549. [DOI] [PubMed] [Google Scholar]
  70. Sussman S. Effects of sixty-six adolescent tobacco use cessation trials and seventeen prospective studies of self-initiated quitting. Tob Induced Dis. 2002;1:35–81. doi: 10.1186/1617-9625-1-1-35. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Sussman S, Lichtman K, Ritt A, Pallonen UE. Effects of thirty-four adolescent tobacco use cessation and prevention trials on regular users of tobacco products. Subst Use Misuse. 1999;34:1469–1503. doi: 10.3109/10826089909039411. [DOI] [PubMed] [Google Scholar]
  72. Sussman S, Dent CW, Severson H, Burton D, Flay BR. Self-initiated quitting among adolescent smokers. Prev Med. 1998;27:A19–A28. doi: 10.1006/pmed.1998.0379. [DOI] [PubMed] [Google Scholar]
  73. U.S. Department of Health and Human Services. The Surgeon General’s 1989 Report on Reducing the Health Consequences of Smoking: 25 Years of Progress. Washington, D.C.: United States Government Printing Office; 1989. [Google Scholar]
  74. Washington State Department of Health. Fact Sheet: Youth Cigarette Smoking Rates. 2005 March 30; Retrieved August 25, 2005 from http://www.doh.wa.gov/Tobacco/fact_sheets/hys_fact_sheet_2004-final.pdf.
  75. Zhu S-H, Sun J, Billings SC, Choi WS, Malarcher A. Predictors of smoking cessation in U.S. adolescents. Am J Prev Med. 1999;16:202–207. doi: 10.1016/s0749-3797(98)00157-3. [DOI] [PubMed] [Google Scholar]
  76. Zhu S-H, Stretch V, Balabanis M, Rosbrook B, Sadler G, Pierce JP. Telephone counseling for smoking cessation: effects of single-session and multiple-session interventions. J Consult Clin Psychol. 1996;64:202–211. doi: 10.1037//0022-006x.64.1.202. [DOI] [PubMed] [Google Scholar]

RESOURCES