Drug and Alcohol Dependence 76 (2004) 247–259

Shaping smoking cessation using percentile schedules

R.J. Lamba,∗, Andrew R. Morralb, Kimberly C. Kirbyc, M.Y. Iguchid, G. Galbickae

a Department of Psychiatry-MC 7792, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229-3900, USAb RAND Arlington, VA 22202, USA

c Treatment Research Institute and the University of Pennsylvania, Philadelphia, PA 19106, USAd RAND, Santa Monica, CA 90407, USA

e Aventis Pharmaceuticals, Bridgewater, NJ 08807, USA

Received 15 March 2004; received in revised form 26 May 2004; accepted 26 May 2004

Abstract

Behavioral interventions that provide incentives contingent upon abstinence are effective addiction treatments. Nevertheless, these treatmentsoften fail for individuals whose recent behaviors are very different from those reinforced. These hard-to-treat individuals may require shapingto achieve abstinence. We used percentile schedules to shape smokers’ delivery of breath samples indicative of recent smoking abstinence(breath carbon monoxide (BCO)<4 ppm). Percentile schedules deliver incentives to current behaviors proximal to the target. Participants (N= 102) were assigned to treatments delivering incentives for breath COs at or below the 10th, 30th, 50th, or 70th percentile of recent breathCOs. Each condition effectively ensured contact with available contingencies, and resulted in BCO<4 ppm in >90% of the 30th, 50th and70th percentile groups versus 63% in the 10th percentile. The 30th, 50th and 70th percentiles were especially effective in a sub-sample ofhard-to-treat participants who did not deliver a breath CO<4 ppm during an initial abstinence test or during a nine-visit baseline period,suggesting the value of shaping for this important sub-sample.© 2004 Elsevier Ireland Ltd. All rights reserved.

Keywords:Contingency management; Smoking cessation; Shaping; Percentile schedules

1. Introduction

Contingency management interventions provide a contin-gency between some behavior of the patient and some envi-ronmental event under the treatment provider’s control. Forexample, contingency management treatments for substanceabuse have provided incentive delivery for treatment atten-dance (Iguchi et al., 1996; Jones et al., 2001; Petry et al.,2001), completion of treatment plan items (Iguchi et al.,1997; Petry et al., 2001), compliance with medication use(Carroll et al., 2001; Preston et al., 1999), and abstinence.Contingency management interventions for substance abusetypically aim to increase the frequency of abstinence by pro-viding incentives contingent upon evidence of abstinence.Such treatments are effective in increasing the overall rateof abstinence from a variety of behaviors including drinkingby alcoholics (Miller, 1972; Miller et al., 1974) or problem

∗ Corresponding author. Tel.:+1-210-567-5483;fax: +1-210-567-5381.

E-mail address:lamb@uthscsa.edu (R.J. Lamb).

drinking adolescents (Bringham et al., 1981), drug use byheroin addicts (e.g.Stitzer et al., 1992; Milby et al., 1978;Hall et al., 1979; Havassy et al., 1979) or cocaine addicts(Higgins et al., 1993, 2000), and cigarette smoking (e.g.Elliott and Tighe, 1968; Shoptaw et al., 2002; Winett, 1973).

Although contingency management treatments for drugabuse are often effective, they are not inevitability so. Fail-ures are seen at a group level, when the contingency grouphas no better outcomes than their control counterparts (e.g.Kirby et al., 1998, study 1), or at an individual level, whensome individuals show no improvements even while receiv-ing a contingency management treatment effective at a grouplevel. One reason contingency management treatment failsmay be that the individuals do not effectively contact theprogrammed incentives. For instance, in a study byIguchiet al. (1996; Morral et al. 1997) of the 17 treatment failuresin an abstinence-contingencies group, 15 failed to submitenough urine samples indicating abstinence to earn even asingle incentive. Additional evidence that treatment failureresults from a failure to contact the programmed incentivescomes from a study by Kirby et al. (1998, study 2). An ini-

0376-8716/$ – see front matter © 2004 Elsevier Ireland Ltd. All rights reserved.doi:10.1016/j.drugalcdep.2004.05.008

248 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

tially ineffective contingency management program in theirstudy was modified to reduce the difficulty of attaining anincentive, and to increase the value of the initial incentives.These changes increased the frequency with which individ-uals contacted the programmed contingencies, and resultedin the modified program being more effective than the orig-inal one.

TheKirby et al. (1998)study suggests that increasing thelikelihood of receiving an incentive by, for instance, reduc-ing task difficulty, may facilitate later achievement of moredifficult target behaviors. Reinforcing successively closerapproximations to the desired behavior, or shaping, can leadto desired behaviors initially outside the individual’s currentbehavioral repertoire.

Preston et al. (2001)provide one example of shaping usedin a contingency management treatment of substance abuse.During the first 3 weeks of this study, methadone main-tenance patients who were abusing cocaine were assignedeither to a group receiving incentives contingent upon de-livering urine samples indicating abstinence from cocaine,or a group receiving incentives for the delivery of samplesindicating either abstinence or at least a 25% reduction incocaine metabolite levels from the previous sample. As ex-pected, the shaping group was more likely to earn vouchersduring the first 3 weeks (96% of participants in the shapinggroup as compared to 53% the traditional abstinence crite-rion). Moreover, the shaping group also produced more urinesamples indicating abstinence from cocaine in later stagesof the study when both groups received incentives only forsamples indicating cocaine abstinence. Thus, in this studythe shaping procedure appeared to enhance success.

Shaping requires highly individualized behavioral plans,which may account for the fact that it is commonly usedin pre-clinical laboratory settings, but rarely in clinical tri-als. An innovation of thePreston et al. (2001)procedure isthe use of a standardized contingency management protocol,but one that allows individualized incentive criteria for eachclient. Nevertheless, by offering incentives only for behav-iors that exceed recent performance, this procedure fails toensure that shaping will, in fact, occur. That is, participantswho fail to deliver a urine sample with at least a 25% reduc-tion in cocaine metabolite will never receive a single incen-tive, so will not have longer periods of abstinence shaped.

Galbicka (1994)has described a standardized shaping pro-tocol known as percentile schedules that also provides indi-vidualized behavioral contingencies, but which maximizesthe likelihood that ongoing behaviors closest to the targetbehavior will earn incentives. Percentile schedules set thecriteria for earning an incentive not in terms of a fixed, ab-solute standard, but rather in terms of how the current re-sponse ranks relative to a sample of an individual’s recentbehaviors. Thus, for instance, instead of requiring a 25% re-duction in cocaine metabolites, a percentile schedule mightrequire a urine sample with metabolite levels better than twoof the last four urine samples delivered. This would ensurethat on average, even without behavior change, individuals

could expect to receive shaping incentives on 60% of theoccasions that they deliver a urine sample, and specificallyfor those urine samples with metabolite levels closest to thedesired target behavior of delivering a sample indicative ofcocaine abstinence. If these incentives successfully reinforcereduced cocaine use, then the “better than two of the lastfour” criterion moves progressively closer to the target be-havior. If behavior change does not occur immediately, thecriterion ensures that those current behaviors closest to thetarget behavior earn an incentive, and those farthest do not.

Recently, we reported the results of two small studies us-ing percentile schedules in smokers to shape lower breathCO levels (Lamb et al., 2004). The first of these involvedtwo smokers without plans to quit smoking in the near futurewho could earn a fixed amount for each breath CO samplethey delivered that was less than the median breath CO levelof the five most recent samples collected (50th percentilecontingency). This resulted in both participants receivingsome incentives and in reduced breath CO levels over the 5weeks of the breath CO contingency. In a subsequent study,five smokers seeking to quit could earn escalating incen-tives for breath CO samples that were either lower than thelowest breath CO level of the nine most recently collectedsamples or<4 ppm. Again, all five participants received in-centives, and their breath CO levels were reduced over thecourse of the 12-week breath CO contingency. In this sub-sequent study, breath CO levels were more dramatically re-duced than in the initial study, raising the possibility that themore difficult incentive criterion, requiring breath CO’s bet-ter than 9 of the last 10 (a “10th percentile contingency”),may be more effective than the less demanding 50th per-centile contingency used in the first study. However, manyother important differences between the two studies precludereaching such a conclusion.

In order to study whether shaping procedures usingpercentile schedules can enhance outcomes over fixed ab-stinence criteria, the optimum parameters to use for the per-centile schedule must first be understood. In this paper, wereport the results of a study targeting the lowest 10th, 30th,50th, or 70th percentile of daily breath CO levels deliveredby smokers seeking to quit smoking to examine (a) if per-centile schedules are a practical means of setting the criteriafor earning incentives; (b) if percentile schedules actually seta floor of incentive contact; (c) if percentile schedules shapelower breath CO levels; (d) if percentile schedules result inour target behavior of breath CO levels<4 ppm; and (e) ifsome percentile values are more effective than others partic-ularly in those hard-to-treat participants who do not deliverbreath CO levels<4 ppm during the initial baseline period.

We were particularly interested in the last question. Shap-ing procedures, such as percentile schedules, should onlybe necessary in those who are unable or unwilling to pro-duce the behavior for which contingencies are available.In past studies, contingency management treatment failurecould often be predicted by the failure to meet the abstinencecriterion that was to be used in the period before the pro-

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grammed contingency was put into place, or immediatelyupon implementation of the contingency. In this study, wehave a 10-visit period before randomization to the differenttreatment conditions. On the first of these visits, participantscould earn an incentive for the delivery of a breath CO level<4 ppm, our abstinence criterion and target behavior (theinitial abstinence test). On the subsequent nine visits, noprogrammed contingencies were in place and participantswere told that we were observing how they did on their own.Thus, based on the results of past studies, we would predictthat the participants most likely to be successful with ab-stinence incentives would be those who delivered a breathCO level<4 ppm during these initial 10 visits. Therefore,shaping would be expected to be needed only for those whodid not meet the abstinence criterion during these initial 10visits.

2. Methods

2.1. Participants

Participants were men and women age 18 years or olderwho reported smoking 15 or more cigarettes per day, wereseeking to quit smoking, and willing to deliver a breath sam-ple each weekday for 3 months. Delivery of a breath samplecontaining at least 15 ppm of CO at intake was also required.One individual with a breath CO level of 13 ppm was acci-dentally enrolled and assigned to the 50th percentile sched-ule. Participants were recruited by flyers posted around theUniversity of Texas Health Science Center at San Antonioand other nearby sites, by word of mouth and by occasionalbrief news stories. One hundred and nineteen individualsprovided informed consent. One hundred and two individ-uals completed the initial 2-week baseline period and wererandomized to one of the four treatment conditions. Partici-pants who left before randomization were demographicallysimilar to those who were randomized (75% Caucasian; 50%female; mean age 37 years; and 50% married). Of the 102randomized participants, 82 completed the approximately3-month intervention.

2.2. Study timeline and groups

For each participant, the planned study duration was 70study visits. Study visits consisted of every non-holidayweekday. Participants could be excused from a study visitby pre-arranging an absence at least 24 h in advance. In rareemergencies, shorter advanced notice was allowed. How-ever, absences were never excused after the fact. Followingan intake session in which consent was obtained and in-take measures gathered, participants were scheduled to re-turn each weekday at a mutually determined time between11:00 am and 5:30 pm. These visits took about 5 min, dur-ing which participants (1) delivered a breath sample for COmeasurement; (2) filled out a form reporting the number of

cigarettes smoked in the last day and use of any medicationto stop smoking; and (3) signed a receipt for the money theywere given. In addition to any money they might earn bymeeting their CO criterion, participants earned US$ 1.00 foreach study visit they delivered a breath sample and filled outthe required forms. On the first study visit, participants tookpart in an initial-abstinence-test in which they could earn anextra US$ 2.50 for delivery of a breath CO sample readingless than 4 ppm. They were told that this CO level could beobtained by not smoking for 1 day. The next nine study vis-its (study visits 2–10) consisted of a baseline condition inwhich no contingencies were in effect. Participants were toldthat this was so we could see how they were doing on theirown. Following this baseline period (study visit 11) partici-pants were randomized to one of four conditions. These werein effect for 60 study visits. Randomization was stratifiedon three factors: (1) the results of the initial-abstinence-test;(2) reported intention to use a medication to aid in quittingsmoking; and (3) order of study entry.

Each of the four conditions was identical except for thepercentile schedules used. Specifically, participants receivedincentives for providing breath samples with CO levels be-low 4 ppm, or which were at or better than the best 10th,30th, 50th or 70th percentile of his or her own 10 most re-cently delivered breath samples (the 10th, 30th, 50th and70th percentile conditions). Thus, in the 10th percentile con-dition breath CO levels had to be lower than the lowest ofthe last nine delivered samples or 4 ppm to earn an incen-tive. In the 30th percentile condition, a sample would needto be lower than the third lowest of the last nine deliveredsamples or 4 ppm to earn an incentive. In the 50th percentilecondition, a sample would need to be lower than the 5thlowest sample of the last nine delivered samples or 4 ppm toearn an incentive. Finally, in the 70th percentile condition asample would need to be lower than the 7th lowest sampleof the last nine delivered samples or 4 ppm in order to earnan incentive.

Participants were told on each visit what the breath COcriterion was that they had to meet on their next visit inorder to earn an incentive. Participants were given no specificinformation about how these breath CO criterion were set.Rather, they were told that the criteria adjusted based onhow they were doing.

Payments for each breath sample meeting an individual’sreinforcement criterion were established using an escalatingpayment schedule (Higgins et al., 1994). Payments beganat US$ 2.50 and increased by US$ 0.50 with each sequen-tial criterion sample delivery. The delivery of five sequentialcriterion samples resulted in the delivery of a US$ 10.00bonus. When the delivered sample did not meet criterion, thepayment amount for the next criterion sample was reset toUS$ 2.50. The delivery of five sequential criterion samplesresulted in the payment amount delivered being set at thehighest amount earned to date. Participants were told howthis payment schedule worked upon intake into the study,and then later before the contingencies began given a writ-

250 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

ten explanation of how it worked. When participants expe-rienced a reset contingency, they were reminded that theycould get back to the highest level that they had achieved bydelivery of five sequential samples meeting criterion. Par-ticipants could earn up to US$ 1155.00 in incentives duringthe 60 visit contingency phase of the study.

2.3. Intake procedures

Participant’s completed several forms after giving in-formed consent. These forms included brief self-developeddemographic form and a smoking history and attitudes ques-tionnaire, as well as the Fagerstrøm Test of Nicotine De-pendence (Fagerstrøm, 1978) and the University of RhodeIsland Change Assessment (Prochaska and DiClemente,1983).

2.4. Measures

Participant’s breath CO level was measured using a Vi-talograph CO monitor (Vitalograph Inc., Lenexa, KS). Theparticipant was asked to take a deep breath, hold it for 20 sand then expire over 20 s into a disposable mouthpiece onthe monitor. The peak reading (the monitor is watched un-til the CO reading declines) is recorded as the participant’sbreath level. Examination of over 12,000 breath CO sam-ples and self-reports of smoking in this community indicatethat a CO criterion for abstinence lower than the typical8 ppm is more accurate and that a criterion of 4 ppm has aspecificity of 0.945, i.e. 94.5% of the individuals with val-ues of 4 ppm or greater had reported smoking in the last day(Javors, Hatch and Lamb, in press).

2.5. Data analysis

Differences in demographic variables between the per-centile schedule groups were analyzed using analysis ofvariance (ANOVA) or chi-square tests. Differences in atten-dance between groups were analyzed using ANOVA withpercentile group as a categorical variable. Differences inbreath CO level between days 10 and 11 were tested usingpairedt-tests. Changes in CO level across visits were ana-lyzed using a repeated measures ANOVA with group assign-ment as a categorical variable. For these analyses, missingbreath CO data was handled as follows: (1) for one or twomissing sequential samples the mean of the samples imme-diately before and following the missing value was used, orif the missing value was the last one scheduled to be col-lected, the last value collected was used; and (2) for morethan two sequential missing values the mean of the ninenon-contingent baseline values was used (missing data ac-counted for about 12% of total data). Between group differ-ences in obtaining at least a visit with a breath CO<4 ppmwere analyzed using a chi-square test. Total number of studyvisits<4 ppm was analyzed using ANOVA. Differences be-tween those with and without early success were analyzed

using a chi-square test or ANOVA. All analyses were con-ducted using Systat on an Apple Macintosh.

3. Results

3.1. Participant characteristics

Participant characteristics are shown inTable 1.The 102 participants averaged just less than 40 years old.

The range of ages of participants was wide (19–67). Mostparticipants were Caucasian and slightly more than half fe-male.

Similar numbers of participants were single, married, orseparated/divorced. Most were employed full time, with thenext largest group being students (13.7%). Income levelswere distributed evenly across conditions, as were educationlevels.

About one-third of the participants smoked a pack orless of cigarettes per day, while approximately two-thirdssmoked more than a pack of cigarettes per day. Participantsbegan smoking at age 15 on average and became regularsmokers by around age 18. Just less than half the partici-pants lived with another smoker. Most participants reportedprior quit attempts with an average number of about fourprevious attempts.

One-way analysis of variance (ANOVA) and chi-squaretests revealed no differences among conditions on any ofthese participant characteristics.

3.2. Attendance

Attendance was excellent and similar across all four con-ditions. The median number of breath samples delivered was69, 69, 68 and 70 out of 70 in the 10th, 30th, 50th and70th percentile schedule conditions, respectively. The lowerquartiles were 61, 65, 59 and 69 samples delivered. Analy-sis of variance on the number of samples delivered acrossconditions was not significant (F = 2.09, d.f.= 3, 98,P >0.10).

3.3. Treatment integrity

One part of the rationale for using percentile scheduleswas to insure a minimum rate of contact with the pro-grammed contingencies (i.e. the percentile value). Overall,88 of 102 (86%) participants met the incentive criteria set bythe percentile schedule at least at the value of the percentileschedule.

Fig. 1 shows the results for the four different percentileschedules. As can be seen by looking for dots outside thestippled gray area, for the 30th, 50th and 70th percentileschedules only two or three participants in each conditiondid not meet the incentive criteria at the rate expected fortheir percentile condition, and these participants receivedincentives at rates close to the expected rate.

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Table 1Participant characteristics

Condition Percentile schedule

Total 10th 30th 50th 70th

DemographicsNumber 102 25 23 28 26Age [years; mean (S.D.)] 39 (11) 35 (11) 40 (11) 40 (12) 39 (11)Caucasian (%) 76 79 70 79 77Female (%) 60 48 61 61 69

Marital statusSingle (%) 31 24 26 36 36Married (%) 49 68 48 39 40Separated/divorced (%) 21 8 26 25 24

EmploymentFull time (%) 72 71 78 69 73

Income<US$ 15000 (%) 25 29 17 21 31US$ 15–25000 (%) 32 29 44 29 27>US$ 25–35000 (%) 22 17 17 32 19=US$ 35000 (%) 22 25 22 18 23

Education (highest level obtained)HS/GED (%) 37 56 30 22 42VoTech/AA (%) 37 24 44 39 43=BA (%) 25 20 26 39 15

Amount smoked=20 cigarettes per day (%) 36 29 44 48 23>20 cigarettes per day (%) 64 71 57 52 77

Smoking historyAge first smoked [years; mean (S.D.)] 15 (4) 16 (3) 15 (4) 15 (5) 15 (2)Age first smoked regularly [years; mean (S.D.)] 18 (4) 18 (3) 18 (3) 17 (5) 17 (5)Live with a smoker (%) 42 40 44 39 46

Quitting historyPrevious quit attempt (%) 85 84 87 82 89Number of previous quit attempts [mean (S.D.)] 4 (4) 4 (3) 4 (5) 4 (3) 4 (6)

Under the 10th percentile schedule, 19 of 25 (76%) par-ticipants met the incentive criteria on at least 10% of studyvisits. Four participants in this condition failed to meetthe incentive criteria even once. These four participantsremained in the study only very short periods of time afterexposure to the contingencies, i.e. one, one, four, and sevenbreath sample deliveries. Even the participant who deliv-ered seven breath samples had only about a 50% chance ofhaving his/her behavior reinforced if no change in smok-ing occurred as a result of being told of the reinforcementcriteria (0.9 is the probability that any single occasion isnot reinforced, and 0.97 is the probability of this happeningseven times in a row, assuming independence of events).

The percentile value systematically changed the lowerend of the distribution of the rate of incentive contact whilenot greatly affecting the central tendency of the distribu-tion. In other words, while the mean percent visits earn-ing an incentive was different across schedules (ANOVAF= 7, 21, d.f.= 3, 110,P < 0.05), this was a result of themean value for the 70th percentile schedule being larger

than the mean value of the 10th percentile schedule (TukeyHSD, P < 0.05 with all other comparisons withP > 0.05,means of 61.5± 43.1, 75.5± 28.4, 77.4± 20.4, 93.1±11.6 for the 10th, 30th, 50th, and 70th percentile sched-ules, respectively; mean± S.D.). However, median valueswere not systematically different (78, 92.3, 79.7, and 98.3).What this really reflects is the tendency of the percentileschedules to keep the rate of earning an incentive at orabove the percentile value. For instance, the 70th percentileschedule had fewer individuals not earning an incentive on≥70% of their visits as compared to the other schedules(7.9% versus 40.8%; chi-square= 13.1, d.f.= 1, P < 0.05).Similarly, the 50th percentile schedule had fewer individ-uals not earning an incentive on=50% of their visits ascompared to the 10th and 30th percentile schedules (7.4%versus 30.6%; chi-square= 5.4; P < 0.05). The 30th per-centile schedule and the 10th percentile schedule had similarnumbers of individuals not earning an incentive on≥ 30%of their visits (12.5% versus 36%; chi-square= 3.6; P =0.056).

252 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

Fig. 1. The figure is a dot-plot of the percent visits an incentive wasearned for each percentile schedule. TheY-axis represents the percentvisits on which an incentive was earned. Each dot represents the percentvisits on which an incentive was earn for a particular participant. TheX-axis is the percentile schedule to which a participant was assigned.Points within the stipled area represent participants who earned incentiveat or above the rate set by the percentile schedule.

3.4. Reductions in breath CO levels across Study Visits

Our goal for these contingency management programswas to reduce breath CO levels. As can be seen inFig. 2,there was an abrupt decrease in breath CO levels immedi-ately upon the availability of contingent incentives. BreathCO levels continued to decline over time and reached verylow values by the end of the study.

This can be seen clearly inFig. 2by comparing the pointsbefore the vertical line indicating the onset of contingenciesto the points after this line. The mean delivered breath COlevel on visit 10 was 13.2 (1.0) [mean (S.D.)] and on visit 11it was 8.5 (0.8) (pairedt-test,t = 7.7, d.f.= 101,P < 0.05).Similar changes from study visit 10–11 were seen acrossall conditions with breath CO levels going from 13.0, 12.0,14.4, and 13.2 to levels of 8.7, 8.0, 7.6 and 10.0 ppm in the10th, 30th, 50th, and 70th percentile schedules, respectively(pairedt-tests:t = 2.36, d.f.= 24, P < 0.05; t = 2.77, d.f.= 23, P < 0.05; t = 4.3, d.f.= 26, P < 0.05; t = 2.65, d.f.= 25, P < 0.05). The delivered breath CO levels continuedto drop across days falling to an average level of 6.0 ppm(0.7) on day 70.

There were some apparent between condition differencesin the decline in breath CO levels across study days 11–70.Repeated measures analysis revealed no main effect forgroup membership (F = 0.94, d.f.= 3, 98,P > 0.10), butsignificant time (F = 5.0, d.f.= 59, 5782,P < 0.05) and asignificant group by time interaction (F = 1.64, d.f.= 177,

Fig. 2. Breath CO levels across the 70 study visits for each condition. TheY-axis represents Breath CO level in parts per million (ppm). TheX-axisrepresents consecutive study visits. Study visits occurred each weekdayin the afternoon. On visit one, participants could earn US$ 2.50 forbreath CO levels<4 ppm. On visits 2–10, no contingencies were placedon breath CO levels. On days 11–70, participants could earn bonuses setaccording to an escalating payment schedule for meeting a breath COcriterion set according to a percentile schedule. Each symbol representsmean results from participants assigned to that percentile schedule (10%;30%; 50%; and 70%).

5782, P < 0.05). Contrasts conducted to determine thesource of this interaction effect comparing each percentileschedule’s mean breath CO level on a given visit to themean breath CO level of the other three schedules on thatvisit indicate that this interaction effect is a result of theparticipants in the 70th percentile condition having lowerbreath CO levels than the other participants as the studyprogressed. Breath CO levels for participants in the 70thpercentile condition were lower than the mean of the otherthree conditions on 10 visits (30, 35, 39, 47, 48, 59, 62, 63,65, and 70;Fs > 4.3, d.f.= 1, 98, Ps < 0.05). Only oneother comparison had a probability<0.05 (50th percentileon visit 45 was larger than the mean of the other threeconditions;F = 4.1, d.f.= 1, 98,P < 0.05).

In summary, all four percentile schedule contingencymanagement programs were associated with reduced breathCO levels immediately following the institution of the con-tingent incentives, and these breath CO levels continued tofall over the course of the study until very low levels werereached. The lowest breath CO levels were delivered bythose assigned to the 70th percentile schedule.

3.5. Number of Individuals delivering a breath samplewith <4 ppm CO and number of study visits until a breathsample<4 ppm CO

The ultimate target of these percentile schedules was de-livery of breath CO samples with<4 ppm CO, a breath COlevel consistent with smoking abstinence. The 30th, 50th

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and 70th percentile schedules resulted in a greater propor-tion of individuals achieving this goal than the 10th per-centile schedule. Similar proportions of individuals undereach schedule delivered their first breath CO level<4 ppmbefore study day 15 (i.e. 5 days of contingencies). However,a smaller proportion of individuals assigned to the 10th per-

Fig. 3. The effects of the four percentile schedules on the obtainment of the target behavior of breath CO levels<4 ppm. The figure shows event recordsfor each subject. Each panel shows the results obtained for participants assigned to a different percentile schedule (10th–70th from top to bottom).Eachline in the panel represents an individual participant’s behavior over time. Results from the beginning of the study are on the left, while results fromthe end of the study are on the right. Light gray spaces represent visits on which a participant delivered a breath CO sample that was≥4 ppm. Darkgray spaces represent visits on which breath samples with a CO level<4 ppm were delivered. Open space represent missed visits. The vertical blackline marks when contingencies went into effect on breath CO levels.

centile schedule did so after study day 15 compared to thosein the other conditions.

A disproportionate number of participants never delivereda breath CO sample<4 ppm in the 10th percentile schedule.This can be seen by comparing the number of lines with-out any dark gray spaces in the event records inFig. 3 or

254 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

Fig. 4. The figure shows cumulative percent of subjects delivering a breath sample with a CO level<4 ppm across visits for each percentile schedule.The Y-axis is the cumulative percent of subjects delivering a breath sample with a CO level<4 ppm. TheX-axis is sequential study visits. Each symbolrepresents the results from a different percentile schedule condition (10%; 30%; 50%; and 70%).

by comparing the asymptote of the 10th percentile line inthe cumulative record of participants delivering breath COsamples<4 ppm inFig. 4. Of the 25 participants assignedto the 10th percentile schedules nine participants (37%)never delivered a breath CO sample<4 ppm compared to2/24 (8%), 2/27 (7%), and 2/26 (8%) of the participants inthe groups assigned to the 30th, 50th, and 70th percentileschedules. These differences were significant (likelihood ra-tio chi-square= 8.12, d.f.= 3, P < 0.05).

On the first study visit, similar proportions of individu-als delivered a breath sample with<4 ppm CO and earnedUS$ 2.50, as would be expected based on the stratificationscheme (6/25, 5/24, 6/27, and 6/26 participants delivered asample with<4 ppm CO out of all participants assigned tothat condition for the 10th, 30th, 50th and 70th percentileschedules conditions, respectively). During visits 2–10 whenno contingencies were in effect, again, similar proportions ofparticipants delivered their first breath sample with<4 ppmCO, as would be expected by simple randomization (6/19,3/19, 6/21, and 5/20 participants delivering their first breathsample with<4 ppm CO of those participants assigned tothat condition who had not already done so). In the first fivevisits contingencies were in effect (study days 11–15), pro-portions of participants delivering their first breath samplewith <4 ppm CO were similar across conditions (3/13, 6/16,4/15, and 4/15). By study visit 15, very similar proportions

of participants had delivered at least one breath sample with<4 ppm CO across all four conditions (15/25, 14/24, 16/27,and 15/26).

However, after study visit 15, the 30th, 50th, and 70thpercentile schedules continued to accrue participants deliv-ering a breath sample<4 ppm at a steady rate, while onlya small proportion of the participants did so who were as-signed to the 10th percentile schedule. Thus, after study visit15, only 20% (2/10) of participants assigned to the 10th per-centile schedule delivered their initial breath sample with<4 ppm CO. This compares to 72% (23/32) of those as-signed to the three other conditions (7/10, 7/11, 9/11 forthe 30th, 50th, and 70th percentile schedules, respectively).This difference was statistically significant (likelihood ratiochi-square= 9.61, d.f.= 3, P < 0.05).

3.6. Total number of study visits<4 ppm and last fivestudy visits<4 ppm

Ultimately, we desired this contingency managementprogram to not only produce a single breath sample with aCO level <4 ppm, but to produce many such breath sam-ples and to have this behavior occurring consistently by theend of the study. This hope was for the most part fulfilledwith participants typically delivering samples with breathCO levels<4 ppm on many study visits and about half the

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participants delivering samples meeting this criterion onthe last five study visits (48 of 102). All four percentileschedules appeared equally effective.

The number of study visits on which breath samples weredelivered with CO levels<4 ppm was similar across theparticipants assigned to each of the four percentile sched-ules. The median number (interquartile range) of study visitson which a breath sample was<4 ppm was 51 (0–59), 45(6–57), 34 (6–51), and 44.5 (15–58) for the 10th, 30th, 50th,and 70th percentile schedules respectively, and 42 (5–57)for the entire sample. These values were not significantlydifferent (ANOVA, F = 0.44, d.f.= 3, 98,P > 0.10).

Similarly, as can be seen inFig. 3, the number of sequen-tial breath CO samples with<4 ppm CO delivered by par-ticipants did not differ with the exception discussed earlierthat fewer subjects in the 10th percentile condition deliveredone or more breath CO levels<4 ppm. Thus, the mediannumber of sequential breath samples<4 ppm CO was 31,29, 19, and 38 and the means were 30.5 (25.7), 26.2 (21.3),24.9 (22.9) and 31.8 (21.6) for the 10th, 30th, 50th, and70th percentile schedule conditions, respectively. These dif-ferences were not statistically different (ANOVAF = 0.45,d.f. = 3, 98,P > 0.10).

3.7. Hard-to-treat

In most drug abuse treatments, early success, especiallysuccess that occurs before formal treatment begins, pre-dicts later successes even when those later successes canbe shown to result from the effects of a specific treatment(e.g. Kidorf et al., 1994; Morral et al., 1997). One reasonfor using percentile schedules was to make those withoutearly (pre-treatment) success more likely to succeed in treat-ment than they would be with more conventional treatments.Therefore, we looked at how some markers of early successinfluenced the likelihood of later success and whether thisinfluence depended upon the percentile schedule used.

In general, early success predicted the likelihood of latersuccess. This can be seen inTable 2. While the likelihood ofdelivering at least one breath sample with a value of<4 ppmduring study visits 11–70 was high regardless of perfor-

Table 2Success rates of subjects who produced a breath CO level<4 ppm onstudy visit 1 or on any of study visits 1–10 compared to the rates ofthose who did not

Visit 11–70,<4 ppm CO(%)

Visits 66–70,<4 ppm CO(%)

Number of visits<4 ppm CO(visits 11–70)a

<4 ppm CO on visit 1Yes (N = 23) 100 78 50 (15)No (N = 79) 80 38 28 (24)

<4 ppm CO on any of visits 1–10Yes (N = 44) 95 61 45 (19)No (N = 58) 76 36 24 (22)

a Mean (S.D.).

mance on study visits 1–10, it was higher in participants whohad breath samples with CO levels<4 ppm on study day1 (23/23 versus 63/79 participants; chi-square= 5.25, d.f.= 1, P < 0.05) or had values<4 ppm on any of study visits1–10 (42/44 versus 44/58 participants; chi-square= 7.26,d.f. = 1, P < 0.05). Similarly, the likelihood of deliveringbreath samples with CO values<4 ppm on each of studyvisits 66–70 was higher in participants who had breath sam-ples with CO levels<4 ppm on study visit 1 (18/23 versus30/49 participants; chi-square= 11.6, d.f.= 1, P < 0.05) orhad values<4 ppm on any of study visits 1–10 (27/44 versus21/58 participants; chi-square= 6.36, d.f.=1, P < 0.05).Further, the number of visits on which delivered breath sam-ples had values<4 ppm was greater in those who had breathsamples with CO levels<4 ppm on study visit 1 (49.6 (14.6)versus 28.4 (23.7) visits;t = 4.07, d.f.= 100,P < 0.05) orwho had values<4 ppm on any of study visits 1–10 (45.3(19.4) versus 24.0 (22.5);t = 5.03, d.f.= 100, P < 0.05).Similar results were obtained if delivery of breath sampleson visits 2–10 were used.

Fig. 5shows the mean breath CO levels under each sched-ule condition when participants are divided into those whodid (early successes) and those who did not (hard-to-treat)deliver a breath sample with<4 ppm CO during study vis-its 1–10. As can be seen by comparing the open circlesacross the four panels, the early successes fared very wellacross all four conditions, and there were no statistically sig-nificant differences between conditions (repeated measuresANOVA: condition main effectF = 1.16, d.f.= 3, 40, P> 0.10; within subjects factor blocks of 10 study visitsF= 1.53, d.f.= 5, 200,P > 0.10; condition by blocks of visitsinteractionF = 1.66, d.f.= 15, 200,P > 0.05).

For the hard-to-treat, there were differences between thefour schedules. The 10th percentile schedule appeared to bethe least effective. The 30th and 50th percentile schedulesappeared to have intermediate effectiveness; and the 70thpercentile schedule appeared to be the most effective forthe hard-to-treat. This difference in effectiveness becameapparent only over time with the treatment effects of the fourschedules being very similar in the first block of 10 studyvisits before treatment began, as would be expected; andsimilar in the next block of 10 study visits immediately aftertreatment began. This assertion is supported by a significantinteraction term between percentile schedule and study daysin a repeated measures ANOVA (condition main effectF= 1.39, d.f.= 3, 54, P > 0.10; study daysF = 8.41, d.f.= 5, 270,P < 0.05; condition by time interactionF = 2.04,d.f. = 15, 270,P < 0.05).

In the first two blocks of 10 study visits, the hard-to-treathad higher breath CO levels than the early successes underall four schedules (comparisons byt-test,P < 0.05). In thethird block, this was true for only the 10th and the 50th per-centile schedule; thus, by the third block hard-to-treat par-ticipants under the 30th and 70th percentile schedules weredoing as well as the early successes in these conditions. Bythe fourth block, only the hard-to-treat under the 10th per-

256 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

Fig. 5. The figure shows participants’ mean breath CO level for sequential 10-study visit blocks for the hard-to-treat (those who did not deliver a breathCO sample<4 ppm during study visits 1–10) and the early successes (those who did deliver a breath CO sample<4 ppm during study visits 1–10) foreach of the four percentile schedules. TheY-axis is mean breath CO level and theX-axis is sequential blocks of 10 study visits. The filled circles arethe hard-to-treat and the open circles are the early successes. Bars around the points represent standard errors of the means. The panels are for the 10th,30th, 50th and 70th percentile schedules starting from the upper left and preceding clockwise. Asterisks above points indicate thatt-tests comparing theearly successes to the hard to treat for that block were significant (P < 0.05).

centile schedule were not doing as well as the early suc-cesses. This continued for the remainder of the study.

When comparisons were made between conditions by10-day blocks in the hard-to-treat, sub-sample significantdifferences were observed between the 10th and the 70thpercentile schedules during the 3rd, 4th and 5th blocks of 10study days (t = 2.535, d.f.= 26, P < 0.05; t = 2.383, d.f.= 26,P < 0.05;t = 2.378, d.f.= 26,P < 0.05, for the 3rd,4th, and 5th blocks comparing the 10th and 70th percentileschedules; all other comparisonsP > 0.05).

4. Discussion

This study examined the utility of using percentile sched-ules to set the criteria for reinforcement in contingency man-agement treatments. In this comparison of four percentileschedules, we observed that percentile schedules could beeffectively implemented and that adherence was excellent.

As expected, percentile schedules allowed for the system-atic and effective reinforcement of a class of behaviors clos-est to the target behavior. Further, percentile schedules wereeffective in setting a “floor” rate of contingent incentives,and these contingencies resulted in the delivery of lowerbreath CO levels. Finally, we found that the target behav-ior of breath CO levels<4 ppm was obtained in many indi-viduals, and that more generous percentile schedules weredifferentially more effective for hard-to-treat smokers.

Despite requiring daily laboratory visits for 3 months,study completion and adherence rates were remarkably highby comparison, for instance, to those observed in studies oftransdermal nicotine treatments for smokers. The approxi-mately 80% study completion rate, and attendance (our onlytreatment adherance requirement) both compare favorablyto rates in transdermal nicotine studies (Abelin et al., 1989;Tønnesen et al., 1991; Transdermal Nicotine Study Group,1991). In our study, the median number of breath CO sam-ples delivered ranged from 68 to 70 out of 70 possible de-

R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259 257

liveries. In comparison,Tønnesen et al. (1991)reported thatdaily patch use was about 80% during week 1 in both theactive and inactive patch groups and fell to 48% by week6 of treatment in the active patch group and to 16% in theinactive patch group.

The high rates of completion and adherence in this studywere likely the result of several factors. First, a 2-weekbaseline period preceded randomization, and this likelyeliminated participants for whom the procedures were par-ticularly onerous. In fact, 118 participants were initiallyrecruited to obtain the 102 participants who were random-ized to treatment. Of these 17 pre-randomization dropouts,four never delivered a single breath CO sample. Addition-ally, the minimal study demands, the average visit lengthwas less than 5 min, and the direct reinforcement of atten-dance with a US$ 1.00 payment, all may have contributedto the high attendance rate. Finally, the use of the percentileschedule may also have served to enhance attendance byindirectly reinforcing attendance. The use of the percentileschedule insured that some proportion of participants’ vis-its would result in delivery of a bonus payment, and thus,breath CO delivery in and of itself was reinforced under anintermittent schedule.

Percentile schedules increase the likelihood that partici-pants’ current behaviors closest to the desired target behav-ior earn an incentive. All participants, with the exception offour participants in the 10th percentile condition, receivedsuch contingent incentives. Among the four participants whonever received a contingent incentive, all dropped out beforethey had even a 50% chance of receiving a single incentive.With the exception of these early dropouts, percentile sched-ules were quite effective at establishing minimum rates ofbehaviors that would receive contingent incentives.

Contact with the programmed contingencies was accom-panied by reductions in breath CO levels. Dramatic breathCO level reductions immediately followed the introductionof contingencies between days 10 and 11 and continued un-til the end of the study. Most participants achieved the targetbehavior of breath CO levels<4 ppm on at least one occa-sion. In fact, over 90% of the participants in the 30th, 50th, or70th percentile conditions delivered at least one breath COsample reading<4 ppm. A median of 42 samples less than<4 ppm were delivered, and slightly less than half (48/102)of the participants delivered a breath CO sample reading<4 ppm on each of the last five study days. These low breathCO levels seem likely to be a result of the imposed contin-gency. However, this was not directly tested in this study.

The results of this study support our hypothesis thatpercentile schedules might provide an effective means ofshaping behavior in contingency management programs.Shaping involves reinforcing successive approximations ofthe desired target behavior, in this case breath CO levelsconsistent with not smoking for a day. Percentile schedulesset criteria such that behavior nearer to the target behavioris reinforced, while behavior that is less close to the targetbehavior is not reinforced. Importantly, as the individual’s

behavior changes the criteria set by the percentile schedulechanges. This results in the percentile schedule setting cri-teria for reinforcement delivery that reinforces successiveapproximations of the desired target behavior. Thus, per-centile schedules can shape the desired target behavior, andthis is what appeared to happen in most cases in this study.

Shaping is especially needed by those for whom the targetbehavior is very rare, or those who do not succeed in produc-ing the target behavior when incentives are made contingenton it. There is, of course, no need to shape the target behaviorin those for whom incentives suffice to encourage productionof the target behavior. Percentile schedules with values ofthe 30th, 50th, or 70th percentile appear to be more effectivethan a percentile schedule with a value of the 10th percentileat shaping behavior in those unlikely to produce the behavior.For instance, in those who had one or more day with a breathsample<4 ppm during the first 10 visits of the study 89to 100% of the participants under all four conditions deliv-ered another breath CO sample<4 ppm during study visits11–70, but in those individuals who did not deliver a breathCO sample<4 ppm during the first 10 study visits (i.e., thehard-to-treat participants) only 38% assigned to the 10th per-centile schedule delivered a breath CO sample<4 ppm dur-ing study visits 11–70 compared to 87% of the individualsassigned to the 30th, 50th, or 70th percentile schedules.

Shaping improvement in those without initial successcould lead to significant improvements in the outcomesseen with contingency management treatments of substanceabuse. Early success is a strong predictor of later successin contingency management treatment or conversely, earlyfailure is a strong predictor of subsequent failure (Downeyet al., 2000; Preston et al., 1998; Silverman et al., 1996,1998; Stitzer et al., 1992). In this study, early success wasalso a predictor of later success, but this was only par-ticularly true for the 10th percentile schedule, indicatingthat these other schedules might increase the likelihoodof successful treatment outcomes in those least likely tosucceed with conventional contingency management pro-grams that provide incentives only for the target behaviorof abstinence. Thus, shaping procedures, such as percentileschedules, may provide an important way of improvingtreatment outcomes in contingency management.

One surprising result in this study was the similar ef-fectiveness of the 30th, 50th and 70th percentile schedules,with a possible trend indicating the superior effectiveness ofthe 70th percentile schedule. We had expected that the 30thand 50th percentile schedules would offer the best blend ofdifferential reinforcement. These schedules ensure that re-inforcement occurs frequently, but differentially. This unex-pected finding likely results from two factors. First, in ourstudy, participants are told what criterion they will need tomeet in order to earn an incentive. While extinction likelyexerts less selective pressure with the pervasive reinforce-ment under the 70th percentile schedule, the discriminativerole of the instructions likely mitigates this loss. Second,combined with the effects of instructions about the next cri-

258 R.J. Lamb et al. / Drug and Alcohol Dependence 76 (2004) 247–259

terion are the effects of the escalating payment schedule un-der which individuals earn greater amounts of money withgreater levels of sequential criterion responses. This bringsindividuals in the 70th percentile schedule in more frequentcontact with greater bonus values. Higher bonus paymentshave been frequently associated with greater treatment effec-tiveness in contingency management treatment (Silvermannet al., 1999; Stitzer and Bigelow, 1983). Thus, the greaterbonus amounts that come with an increased sequential cri-terion responses under the 70th percentile schedule and themitigating effects of instructions make the 70th percentileschedule at least equally effective with the 30th or 50th per-centile schedules.

This study was not designed to test whether percentileschedules more effectively reduce smoking than abstinenceincentives. Nevertheless, our finding of the inferiority of the10th percentile condition suggests that the other percentileconditions may be superior to abstinence contingencies. Likewith abstinence contingencies, but unlike the 30th, 50th and70th percentile conditions, the 10th percentile condition re-quires participants to produce a rarely emitted behavior,currently outside their behavioral repertoire. Additional re-search will be required to answer this question.

In summary, percentile schedules appear to be practi-cal and effective procedures for systematically shaping be-havior in contingency management programs. Shaping withpercentile schedules or “hill-climbing” procedures such asthose used byPreston et al. (2001)may be important meansfor improving contingency management treatments. As mostfailures in contingency management treatments can be ex-plained as the result of failure to contact the programmedcontingency even once, and shaping procedures, as this studydemonstrates, can overcome this problem by insuring con-tact with the programmed contingency. Because this contactwith the programmed contingencies follows behavior near-est the desired terminal behavior, behavior nearer and nearerthe desired behavior results, such that ultimately the desiredbehavior is shaped. Thus, percentile schedules and othersimilar shaping procedures may make contingency manage-ment procedures more helpful for those who are typicallyhardest to treat.

Acknowledgements

We would like to acknowledge the support of NIHGrant DA 13304, and we would like to thank FloydJones, Christina Talamentez, Jessica Barrientos, WendiStewart-Rodriguez, Iris Guerra, and Chris Fau for their helpin conducting this study.

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