Dynamic Self-Efficacy and Outcome Expectancies: Prediction of SmokingLapse and Relapse

Chad J. Gwaltney, Saul Shiffman, Mark H. Balabanis, and Jean A. PatyUniversity of Pittsburgh

According to social learning models of drug relapse, decreases in abstinence self-efficacy (ASE) andincreases in positive smoking outcome expectancies (POEs) should foreshadow lapses and relapse. In thisstudy, the authors examined this hypothesis by using ecological momentary assessment data from 305smokers who achieved initial abstinence from smoking and monitored their smoking and their ASE andPOEs by using palmtop computers. Daily ASE and POEs predicted the occurrence of a 1st lapse on thefollowing day. Following a lapse, variations in daily ASE predicted the onset of relapse, even aftercontrolling for concurrent smoking. ASE and POEs generally neither mediated nor moderated eachother’s effects. These data emphasize the role of dynamic factors in the relapse process.

Keywords:self-efficacy, outcome expectancies, smoking, relapse, ecological momentary assessment

Social learning models of behavior (e.g., Bandura, 1977, 1997)have profoundly influenced smoking-relapse theories (e.g., Baker,Morse, & Sherman, 1987; Brandon, Juliano, & Copeland, 1999;Marlatt & Gordon, 1985; Niaura et al., 1988). In particular, thehypothesis that beliefs about smoking and quitting smoking influ-ence relapse has shaped smoking cessation research and treatment.Two types of beliefs are central in social learning models ofrelapse: abstinence self-efficacy, confidence in one’s ability toabstain from smoking, and smoking outcome expectancies, beliefsabout the outcomes of smoking, such as affect modulation andhealth risks. Although abstinence self-efficacy (ASE) has beenmuch more widely studied in the area of smoking cessation, bothbeliefs predict the outcome of smoking cessation attempts, suchthat individuals with weaker ASE (e.g., Condiotte & Lichtenstein,1981; Gwaltney et al., 2001) and stronger expectations that smok-ing will lead to positive outcomes (Copeland, Brandon, & Quinn,1995) are more prone to relapse.

Previous research has focused almost exclusively on presump-tively stable individual differences in ASE and outcome expect-ancies, while ignoring the potential role of within-person changesin these constructs in the relapse process. In other words, researchhas focused on who will relapse, rather than on when (i.e., underwhat conditions) relapse will occur (Shiffman, 1989). Althoughpredicting who is vulnerable to relapse has clear theoretical andclinical appeal, this research does not adequately address sociallearning relapse models. These models suggest (a) that ASE andoutcome expectancies should change over time in response tochanging internal and external contexts and challenges and (b) thatthis variation should be related to the timing of smoking lapses(discrete episodes of smoking). Lapses should occur when ASE isparticularly low and when expected positive outcomes of smokingare particularly high. Accordingly, in this study, we examinedday-to-day measures of ASE and outcome expectancies in the 6weeks following an attempt to quit smoking and assessed theirrelationship with smoking. This type of within-person analysisprovides a unique assessment of prominent relapse models.

Social-Learning Relapse Models

Variables influencing relapse can be grouped into three catego-ries differing primarily in their temporal proximity to the onset ofrelapse: enduring individual differences, background variables,and proximal precipitants (Shiffman, 1989; see also Piasecki,Fiore, McCarthy, & Baker, 2002). Stable individual differences arecharacteristics that are relatively static over time, such as person-ality characteristics and nicotine dependence. Background vari-ables change slowly over time and may set the stage for relapse.For example, negative affect may build over a period of days,making it more likely that other, more “phasic” events (e.g.,exposure to smoking cues) may lead to relapse. Alternatively, theslow-building process may reach a threshold, after which lapsingmay occur—when negative affect reaches a certain level, it maydirectly cause a smoking lapse. Relapse precipitants are phasic,episodic factors, such as offers to smoke a cigarette and alcohol

Chad J. Gwaltney, Saul Shiffman, Mark H. Balabanis, and Jean A. Paty,Department of Psychology, University of Pittsburgh.

Chad J. Gwaltney is now at the Center for Alcohol and AddictionStudies, Department of Community Health, Brown University. Mark H.Balabanis is now at the San Francisco Bay Area Center for CognitiveTherapy.

This paper is based on Chad J. Gwaltney’s dissertation at the Universityof Pittsburgh, under the supervision of Saul Shiffman. Committee membersTom Kamarck, Chris Martin, Ken Perkins, and Michael Sayette providedvaluable help. This research was supported by Grant DA006084 from theNational Institute on Drug Abuse. GlaxoSmithKline provided the nicotineand placebo patches used in the study under an unrestricted grant.

Saul Shiffman has undertaken research and consultancy for manufac-turers of smoking-cessation products, including nicotine replacement prod-ucts and bupropion, and is currently consulting exclusively for Glaxo-SmithKline. Saul Shiffman and Jean A. Paty are also founders ofinvivodata, inc., which provides electronic diaries for clinical trials.

Correspondence concerning this article should be addressed to Chad J.Gwaltney, Box G-BH, Brown University, Providence, RI 02912. E-mail:chad_gwaltney@brown.edu

Journal of Abnormal Psychology Copyright 2005 by the American Psychological Association2005, Vol. 114, No. 4, 661–675 0021-843X/05/$12.00 DOI: 10.1037/0021-843X.114.4.661

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consumption, that occur in close proximity to smoking (Shiffmanet al., 1996; Shiffman, in press).

Although early models of drug dependence and relapse focusedon the influence of stable individual differences (e.g., personality),more recent models emphasize changing background factors andprecipitants. For example, the “relapse prevention model” (Marlatt& Gordon, 1985; also Witkiewitz & Marlatt, 2004), the “dynamicregulatory feedback model” (Niaura, 2000; Niaura et al., 1988),and the “two affect model” (Baker et al., 1987) all underscore theinfluence of proximal, phasic variables in precipitating relapse.Other models (Piasecki et al., 2002) highlight background factors.For example, Piasecki and colleagues (2002) suggest that cessationfatigue—a latent construct encompassing loss of motivation, lossof hope in cessation success, a reduction in coping attempts,exhaustion of self-control resources, and decreased self-efficacy—may build over time, increasing the probability that an individualwill relapse. Although the relapse models differ slightly in whatprocesses are emphasized, they all agree (a) that factors relevantfor relapse, such as self-efficacy and outcome expectancies,change over time and (b) that these changes should foreshadowrelapse. In this study, we examined this fundamental hypothesis ofsmoking-relapse models.

Relapse models (e.g., Brandon et al., 1999; Marlatt & Gordon,1985; Niaura et al., 1988) emphasize the importance of expectan-cies regarding the positive effects of smoking, such as beliefs thatsmoking will improve a bad mood, further enhance a good mood,and boost arousal (Copeland et al., 1995). According to the mod-els, these positive expectancies are more strongly activated and aremore prone to influence behavior during high-risk situations (seealso, Brandon et al., 1999) than are beliefs about the negativeeffects of smoking. “Negative” expectancies—such as beliefsabout the deleterious health effects of smoking—may influencedecisions to make an attempt to quit smoking more strongly thanthey influence smoking behavior during a quit attempt (Brandon etal., 1999; Wetter et al., 1994). Therefore, in this study, we willfocus on dynamic variation in the expected positive effects ofsmoking (positive outcome expectancies; POEs).

Although social-learning models of relapse present similar con-ceptualizations of the relapse process, they provide slightly differ-ent views on how ASE and POEs should interact in determiningrelapse. The dynamic regulatory feedback model (Niaura et al.,1988) suggests that ASE should act as a final common pathway torelapse, mediating the influence of other constructs, includingPOEs. In contrast, the relapse prevention model (and more generalmodels of behavior, such as the theory of planned behavior; Ajzen,1991) implies that ASE and POEs should independently influencerelapse. Furthermore, others have suggested that self-efficacyshould moderate the influence of outcome expectancies on behav-ior (e.g., Dijkstra & Borland, 2003; Shadel & Mermelstein, 1993).For example, Bandura (1977, p. 193) states that “individuals canbelieve that a particular course of action will produce certainoutcomes, but if they entertain serious doubts about whether theycan perform the necessary activities such information does notinfluence their behavior.” In other words, the influence of outcomeexpectancies on behavior is dependent upon self-efficacy level. Inthe case of smoking cessation, if one doesn’t believe that he or shecan quit smoking, one’s outcome expectancies about smoking andquitting are irrelevant—he or she will fail to maintain abstinence.

There has been little research addressing how ASE and POEsjointly influence relapse. Niaura (2000) presented some evidencesupporting the dynamic regulatory feedback model: ASE in re-sponse to a simulated high-risk situation in the laboratory mediatedthe influence of urge to smoke, affect, and coping skills ability oncessation outcome at 6 months follow-up. Although these data areinteresting, they did not strictly test the hypotheses of relapsemodels, because POEs were not assessed and because of the longlatency between measurement of the predictor variables and out-come. In two studies, researchers have found that ASE and POEsmay interact to predict cessation outcome (Dijkstra & Borland,2003; Shadel & Mermelstein, 1993). Several other researchershave measured both self-efficacy and outcome expectancies buthave focused on the expected health effects of smoking or notsmoking (e.g., Maddux & Rogers, 1983; Norman, Conner, & Bell,1999), ignoring the positive effects of smoking. This omission issignificant, because positive and negative smoking expectanciesmay differentially influence behavior (Brandon et al., 1999). Stud-ies are needed that address more proximal changes in ASE andPOEs and their relation to lapsing. In this study, we will use dailyratings in an exploratory look at how ASE and POEs jointlypredict lapse and relapse.

Evidence Supporting Dynamic Variabilityin ASE and POEs

Although relapse models clearly underscore the importance ofdynamic changes in ASE and POEs, few studies have addressedvariability in these constructs during a quit attempt. Elsewhere, wehave shown that ASE (Gwaltney, Shiffman, & Sayette, 2005) andPOEs (Gwaltney, Shiffman, Paty, & Balabanis, 2005) are reactive tosituational context, especially affective contexts: During a quit at-tempt, ASE decreased and POEs increased when cigarette craving andnegative affect were particularly high. Further, Shiffman and col-leagues (2000) used ecological momentary assessment (EMA) tocapture momentary measures of ASE over 4 weeks during an attemptto quit smoking. EMA methods (Stone & Shiffman, 1994) emphasizerepeated real-time assessments of subjects’ momentary states in theirnatural environments, to avoid the problems of retrospective recalland to ensure ecological validity. ASE was assessed multiple timeseach day, via palm top computers. As predicted, ASE was lower onthe day preceding the first lapse than on other days prior to the firstlapse. However, this proximal effect disappeared when individualdifferences in ASE were accounted for, suggesting that between-person differences in ASE were of primary importance in predictingfirst lapses. Similarly, ASE on the day prior to the initiation of relapse(return to regular smoking) was generally lower than on other daysfollowing the first lapse. However, in contrast to first lapses, thisproximal relationship between ASE and relapse remained after con-trolling for individual differences in ASE and concurrent smoking.Thus, these data provide some support for the hypotheses of social-learning relapse models.

No study has attempted to intensively assess POEs following aquit attempt. However, several studies have assessed acute effectsof craving, nicotine deprivation, or both on POEs in the laboratory,addressing the hypothesis that POEs are reactive to changes inaffect–motivational state. For example, it has been suggested thatdrug craving may produce systematic changes in cognitive pro-cessing, including activation of POEs (Sayette, 1999). In fact,

662 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

smokers view smoking in a more positive manner (Brandon,Wetter, & Baker, 1996; Kozlowski, Pillitteri, Sweeney, Whitfield,& Graham, 1996; Sayette & Hufford, 1997; Zinser, Baker, Sher-man, & Cannon, 1992) and judge positive consequences of smok-ing to be more likely than negative consequences when craving isheightened (Sayette, Martin, Wertz, Shiffman, & Perrott, 2001).Thus, the motivational properties of cigarette craving may causesmoking to be viewed through “rose-colored glasses”—during thecraving, POEs are more accessible and easily activated than neg-ative expectancies. Although less well-studied, negative moodmay increase the number of positive drug use expectancy itemsendorsed (Hufford, 2001; Zinser et al., 1992). These studies sup-port the hypothesis that outcome expectancies may be state-dependent, but they cannot address the fundamental assumption ofsocial-learning relapse models—that momentary changes in ex-pectancies influence relapse risk.

Study Goals

In this study, we assessed how changes in daily measures of ASEand POEs jointly predict smoking cessation outcomes (first lapse andrelapse). Understanding the relationship between self-efficacy, out-come expectancies, and relapse may inform social-learning models ofsmoking cessation. The study introduced methodological innovations.Past studies have generally not distinguished between-person andwithin-person influences on cessation outcome. If self-efficacy andoutcome expectancies play a proximal role in determining relapse,changes in these constructs (reflecting within-person variation) shouldoccur shortly before smoking occurs. We used EMA (Stone & Shiff-man, 1994) to assess self-efficacy, outcome expectancies, and smok-ing in real time in the participant’s natural environment during asmoking cessation attempt. Our analyses carefully controlled forbetween-persons differences at baseline, the quit day, and the day ofthe first lapse. Thus, consistent with social-learning theories, we wereable to assess dynamic changes in self-efficacy and outcome expect-ancies, in addition to individual differences.

We hypothesized that decreases in daily ASE and increases indaily POEs would foreshadow impending smoking (first lapse orinitiation of relapse) on the following day, even after controllingfor individual differences and, in the case of prediction of relapse,concurrent smoking. Furthermore, consistent with the dynamicregulatory feedback model, we hypothesized that ASE and POEswould be inversely correlated and that ASE will mediate the effectof POEs on lapse and relapse.

Method

Participants

Participants were 305 smokers who enrolled in a smoking cessationresearch clinic and achieved 24 hours of abstinence from smoking. (Of 401original participants, 70 dropped out before the target quit date, 19 failedto quit smoking for 24 hours, and 7 had ambiguous quit days.) Participantswere recruited via advertisements for smoking cessation treatment andwere paid $150. To qualify, participants had to (a) be between 21 and 65years old; (b) currently smoke at least 15 cigarettes per day; (c) havesmoked for at least 5 years; (d) be in general good health, as determined bya medical screening and examination; and (e) report high motivation andefficacy to quit (combined score of 150 on the sum of two 0–100 scales).

The sample was typical of a smoking cessation cohort. On average,participants were 39.5� 9.3 years old, smoked 24.3� 8.7 cigarettes perday, and had been smoking for 22.0� 9.5 years. Nicotine dependence(Fagerstrom Test for Nicotine Dependence; Heatherton, Kozlowski,Frecker, & Fagerstrom, 1991) scores averaged 6.0� 2.0. Of the partici-pants, 51% were women; 97% were high school graduates, 85% wereCaucasian, and 11% were African American. As required, participantsreported high screening levels of confidence in their ability to quit (87.2�12.4) and motivation to quit (92.2� 8.5).

Procedure

On enrolling in the program, participants were trained in the use of apalm-top computer, the Electronic Diary (ED; see Shiffman et al., 1996);they monitored ad lib smoking for 2 weeks before quitting and monitoredtheir quit experiences for up to 6 weeks after cessation. Subjects wereinstructed to quit on a designated Target Quit Day. At that time, partici-pants were randomly assigned to nicotine or placebo transdermal patches(nicotine replacement therapy; NRT) and began wearing their assignedpatches. The 1st full day of abstinence (i.e., no self-reported smoking for�

24 hr, as recorded on ED) was designated the Actual Quit Day, andsubsequent days were numbered as Days Since Quit (DSQ, where ActualQuit Day is DSQ 0). Once per week following the Target Quit Day,self-reported abstinence was verified by using expired CO levels (� 10ppm) at each visit. If CO level contradicted a participant’s claim ofabstinence, data from the previous week were excluded. (Abstinenceclaims of 11 participants were contradicted by CO, resulting in the loss of220 days of data—out of approximately 10,000 total days—and threefirst-lapse episodes.) The ED administered three kinds of assessments thatwere used in these analyses.

Random assessments.ED audibly prompted participants to completeassessments approximately 5 times per day (M � 5.2; SD � 1.9). Thetiming of the prompts was random with the constraint that no prompts wereissued for 30 min after a temptation (an episode during which a participantexperienced an increased urge to smoke or came to the brink of lapsing; seeShiffman et al., 1996) or a lapse episode (any smoking, even a puff).Participants completed 90% of the scheduled random assessments.

Daily reports. Each evening, participants were prompted by ED to com-plete a daily report. Participants had a 2-hr time interval in which to completethe report (8 p.m. to 10 p.m.). If the report was not completed during thisinterval, the participants were required to complete it when they went to sleepat night. (ED was programmed to serve as an alarm clock. If the daily reportwas not completed previously, the ED asked the participant to complete itbefore setting the alarm.) On average, daily reports were completed at 8:40p.m. (SD� 61 min); 89% were completed before 10 p.m.

Lapses. The ED was also used to record any smoking. Participantswere instructed to make an entry following any smoking episode (even apuff) that occurred during the quit period. On reaching a smoking rate of5 cigarettes per day for 3 consecutive days, the ED informed participantsthat they should then record each cigarette, rather than recording specificlapses; they had functionally relapsed at this point (see Outcomes section).

Treatment

Participants were stratified on baseline smoking rate and baseline crav-ing and then randomly assigned to receive NRT (n � 183) or placebo (n �122) patches at the Target Quit Day. More participants were assigned toNRT to provide roughly equal samples of lapses from each group, on theassumption that NRT would prevent lapses. Each participant wore twopatches during the 6 weeks of ED monitoring. Participants assigned to theactive NRT group received a 35-mg daily nicotine dose (one 21-mg patchand one 14-mg patch; NicoDerm CQ, GlaxoSmithKline, Pittsburgh, PA)

663DYNAMIC SELF-EFFICACY AND EXPECTANCIES

during the first 3 weeks,1 21 mg for the next 2 weeks, and no nicotineduring the final week (both patches inactive). Those in the placebo groupwore inactive patches throughout the monitoring period. All participantsreceived from seven to nine sessions of group behavioral treatment thatfocused on providing a supportive environment and psychoeducationalapproaches. The present analyses did not evaluate treatment effects.2 NRTassignment was used as a covariate in all analyses.

Baseline Questionnaire Assessments

Abstinence Self-Efficacy Questionnaire (ASEQ).We used a 29-itemassessment to measure participants’ confidence in their ability to resist thetemptation to smoke in a variety of affective and environmental contexts (1�not at all confident; 5� extremely confident). The ASEQ is based on a 75-itemself-efficacy measure (Gwaltney et al., 2001) that consisted of 7 first-orderfactors and a second-order factor (measuring general ASE). For the ASEQ,items with the highest loadings on each factor were selected. Four additionalitems measuring restlessness (“restless,” “jittery,” “can’t sit still,” “fidgety”)were added to the revised version, because restlessness is associated with bothad lib smoking (Shiffman et al., 2002) and lapsing (Shiffman et al., 1996).Participants completed the ASEQ 2 days prior to the Target Quit Day. Becausewe were interested in general ASE strength in this study, the ASEQ was scoredas a single factor (Cronbach’s alpha� .95).

Smoking Consequences Questionnaire–Adult (SCQ; Copeland et al.,1995). The SCQ is a 55-item questionnaire that assesses possible conse-quences of smoking. Participants use a scale ranging from 0 (completelyunlikely to occur) to 9 (completely likely to occur) to rate the probabilitythat a specific consequence will occur. Previous analyses of the question-naire’s latent structure suggest a 10-factor solution, 6 factors on positiveeffects of smoking, 3 factors on negative effects, and 1 factor (Craving/Addiction) containing a mix of positive (e.g., “Smoking will satisfy mynicotine cravings”) and negative (“I become more addicted the more Ismoke”) effects. Participants completed the SCQ 2 weeks before the TargetQuit Day. (The SCQ was not administered to participants run early in thestudy; data were available for 270 participants.) We calculated a single,global positive smoking-outcome expectancy factor, reflecting our interestin global positive expectancies rather than in particular types of expect-ancy. Reliability analysis indicated a high degree of internal consistencyamong the positive expectancy items (� � .93).

Nicotine dependence.In addition to the ASEQ and SCQ, we alsoobtained measures of nicotine dependence (Nicotine Dependence Syn-drome Scale or NDSS; Shiffman, Waters, & Hickcox, 2004) and baselinesmoking rate. Although it is possible to derive multiple NDSS factorscores, a single, total score was calculated for the purposes of this study.

Daily EMA Assessments

Daily measures of ASE: Random assessments.At each random assess-ment, participants responded to a single efficacy item that asked “Confident inability to abstain?” Participants responded on a 0–10 visual-analog scale (0�NO!!; 10 � YES!!). In a previous study, daily changes in responses to thissingle item (scored on a 1–4 scale) predicted relapse on the following day,even when controlling for concurrent smoking rate (Shiffman et al., 2000). Itwas also highly correlated with the 75-item ASE questionnaire. Thus, thesingle item appears to be a valid and sensitive measure of general ASE. Wecomputed daily ASE by averaging the ASE responses from random assess-ments for each day.3 ASE data were missing on 2.5% of days, because ofcomputer malfunction or participant noncompliance. ASE was also assessed inthe daily report; results from analyses of this measure were similar to resultsusing the aggregate score and are not presented here.

Daily measures of POEs: Daily reports.At each daily report, partici-pants responded to 7 items assessing the expected positive effects of smoking,one each from the 7 positive expectancy SCQ factors, including the Craving/Addiction factor. Items assessed the degree to which participants believed that

smoking would (a) “help me relax if I were angry or nervous” (Negative AffectReduction), (b) “energize me” (Stimulation/State Enhancement), (c) “tastegood” (Taste/Sensorimotor Manipulation), (d) “help me feel comfortablearound others” (Social Facilitation), (e) “keep my weight down” (WeightControl), (f) “help pass idle time” (Boredom Reduction), and (g) “satisfy mycravings” (Craving/Addiction). Participants responded on a 0 (NO!!) to 10(YES!!) visual-analog scale. The internal consistency of the items was high(� � .85); a single positive expectancy score was calculated. POEs data weremissing on 10.3% of days. This is higher than the ASE missing data rate,reflecting the fact that POEs were assessed at a single time point, rather thanat multiple times during the day.

Outcomes: Lapse and Relapse

After quitting (abstinence for 24 hr), participants were asked to initiatean entry whenever they lapsed, defined as any occasion of smoking, evenif only a puff. Participants could also report unrecorded lapses during theirevening assessment. The day in which participants entered a first smokingepisode (either in real time or in the daily report) was defined as the firstlapse day. Relapse was defined as smoking at least 5 cigarettes per day for3 consecutive days; the 1st of these days was counted as the relapse day.4

ED System Hardware and Software

The ED system was implemented on the PalmPilot Professional Version2.0, manufactured by 3Com Inc. It is compact, 3.1 in.� 4.8 in.� 0.5 in.,and weighs only 5.7 oz. It has a touch-screen LCD display with a soft greenbacklight. Software was developed specifically for this application (invi-vodata, inc., Pittsburgh, PA). Required actions are always specified

1 The 35-mg dose is higher than the standard over-the-counter dose (21mg). This dose was chosen to increase the probability of 100% nicotinereplacement, as standard doses often achieve only 50% replacement.

2 NRT treatment significantly increased the probability of achievinginitial abstinence and reduced the probability of experiencing a first lapseand progressing from a first lapse to full-blown relapse in this sample.These effects are discussed elsewhere (Shiffman et al., in press).

3 We used only randomly sampled assessments for this summary, withoutincluding efficacy data entered just after temptations or lapses because: (a)analysis of efficacy following temptation episodes showed that efficacy wasnot acutely affected by temptations (Shiffman, Gnys, et al., 1996); (b) valuesfor posttemptation or postlapse efficacy would be missing for person/dayswithout temptations or lapses on a particular day; (c) in a previous study(Shiffman et al., 2000), the results did not change when efficacy reports fromtemptation and lapse assessments were added to models, and (d) conceptually,the relevant, nontransient influence of temptation and lapse experiences shouldbe reflected in the random assessment ratings.

4 Other authors define relapse by using different criteria, such as smoking for 7consecutive days (Hughes, Keely, Niaura, Ossip-Klein, Richmond, & Swan,2003). However, this definition can be met with little escalation of smoking, andthus may not be apt for studying the progression from initial lapsing to relapsing.Indeed, in these data, more participants were identified as relapsers (45), when thesmoking-for-7-consecutive-days criterion was used. However, 16 of theseparticipants started the relapse progression (the first of the 7 days) on the dayof their first lapse, making dynamic changes in ASE and POE between lapseand relapse irrelevant. In any event, analyses using the 7 consecutive dayscriterion produced similar results. Therefore, we presented data on the ‘smok-ing 5 or more cigarettes on 3 consecutive days’ relapse criterion.

664 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

on-screen in simple English, and all entries into the machine are made bytapping directly on the screen with a stylus.

Data Reduction and Analysis

Natural history analyses. This study focused on daily summaries ofself-efficacy and outcome expectancies. For the purposes of this study, aday was defined as the period between two daily reports. (When a dailyreport was missing, the day was divided at the average time of thesurrounding days’ daily summaries.) All participants’ series began on thequit day, their first day of abstinence (DSQ� 0) and extended until theyreached relapse criteria or until the end of monitoring. Three critical dateswere the focus of this study: the quit day, the day of the first lapse afterhaving quit (lapse day; Days Since Lapse or DSL� 0), and the relapse day(Days Since Relapse or DSR� 0). The relapse day is defined here as the1st day of the 3-day sequence that met the relapse criterion (5 cigarettes/day for 3 consecutive days).

We first assessed the natural history (i.e., trends over time) of ASE andPOEs.5 We assessed these trends in two separate phases of observation orparticipant state: (a)abstinent, that is, quit day to first lapse or (forindividuals who did not lapse) end of data and (b)lapsed: following alapse, that is, from lapse to relapse or (for lapsed individuals who did notrelapse) end of data. For ease of presentation and because the amount ofdata dropped off dramatically following this point, natural history analysesinclude only 5 (of a possible 6) weeks of data in both the abstinent andlapsed intervals. Generalized Estimating Equations (GEE; Zeger, Liang, &Albert, 1988), which allow participants to contribute unequal numbers ofobservations to analysis (becausee of missing data), were used to examinetemporal trends.6 We also used GEE to assess trends in ASE and POEs inthe 4 days immediately preceding a lapse or relapse.

Prediction of lapse and relapse.The principal analyses of this studyfocused on predicting lapsing and relapsing from daily ASE and POEs. Weused event history analyses that can accommodate both between-personsand within-person sources of variation on a day-by-day basis. Theselogistic regression models (Breslow & Day, 1980; Collett, 1991) in effectcontrast the day preceding a lapse (considered a “case”) with all otherpreceding days treated as matched controls. To account for the overallhazard function for lapse and relapse over days, we entered terms for linearand quadratic functions of days (since the quit day or since the lapse day;see Shiffman et al., 1997). This analysis resembles proportional-hazardssurvival analysis when the time function of the baseline hazard is specified(Hosmer & Lemeshow, 1989). In analyses of dynamic covariates, datawere lagged so that lapse or relapse on a day was predicted from efficacyand outcome expectancies on the preceding day.

Following the hypotheses of the study, there are three major groups ofanalyses. (a) We first assessed the predictive power of ASE and POEsmeasures taken at key milestones: at baseline, the quit day, and the lapseday (controlling for NDSS scores and average prequit smoking rate), usingsurvival analysis.7 This addressed the “static” effects of between-persondifferences at key milestones coming into and during the quit effort. Quit-and lapse-day ASE and POEs were considered separately from the dailymeasures, as these critical days may be associated with “shifts” in between-person differences. (b) Next, we assessed the effect of dynamic day-to-daychanges in ASE and POEs, while controlling for baseline measures (nic-otine dependence, prequit smoking rate, ASE or POEs questionnaires) andtreatment group. Subsequently, we added quit- or lapse-day measures ascovariates in the model. By controlling for between-persons sources ofvariation at key milestones (essentially controlling for each individual’s“starting point”), we were better able to isolate the effects of the within-person, dynamic effects.8 (c) Last, we examined joint models includingboth ASE and POEs measures (and their interaction term, when appropri-ate) to assess mediation (and moderation) by using methods adapted fromBaron and Kenny (1986).9

Results

Lapse and Relapse: Descriptive Statistics

Of the 305 participants, 211 (69%) lapsed during the monitoringperiod, and 26 (9% of total sample, 12% of lapsers) relapsed. Onaverage, first lapses occurred 1 week after the quit day (6.9� 9.7days). Relapse (the 1st day of 3 day sequence) occurred, onaverage, 19.2� 9.2 days following the initial lapse to smoking. Norelapse sequence began on the day of the first lapse (latencybetween first lapse and relapse ranged from 5 to 35 days). Theperiod between lapse and relapse was characterized by intermittentsmoking. Relapsers recorded an average of 28.8� 15.3 discretelapse episodes (encompassing an average of 34.1� 16.1 ciga-rettes) before relapsing. Survival curves for both first lapse andrelapse are shown in Figure 1.

ASE: Abstinent Interval

Prediction of first lapse: Baseline and quit-day measures.Av-erage baseline ASEQ scores (see Table 1) suggested a moderate tohigh average level of confidence in ability to abstain from smok-ing. Although lapsers reported lower baseline ASE than did non-lapsers (see Table 1), baseline ASE predicted first lapses only at atrend level in survival analysis (OR� 0.81, 95% CI� 0.65–1.01,p � .06). Inclusion of covariates (NDSS, average baseline smok-ing rate, treatment group) did not change the magnitude of thisrelationship (OR� 0.82, 95% CI� 0.65–1.03,ns). The averagequit-day ASE scores for lapsers and nonlapsers are also listed inTable 1. Quit-day ASE (excluding participants who lapsed on thequit day) was lower for those who would subsequently lapse thanfor those who maintained abstinence (OR� 0.82, 95% CI�0.74–0.92,p � .001). Even after we controlled for covariates,

5 The impact of NRT group on temporal trends was assessed. Althoughthere were mean differences across groups, trends were similar and, there-fore, data for each group are not presented.

6 We also examined blocks of days in which all participants wereabstinent, because temporal trends may be influenced by the progressivedropout of participants, owing to lapsing or relapsing (e.g., an upward trendin ASE over time may reflect the dropout of participants with low ASE).For example, we assessed trends over the first 4 postquit days, amongindividuals who maintained abstinence for at least 4 days. Analyses ofthese subgroups and subintervals largely matched the results from analysesusing all participants and are not presented here.

7 Forty-three participants lapsed on their quit day. Thus, the quit-daymeasures of ASE and POEs are confounded, because they may reflect,rather than predict, smoking. In analyses using quit-day individual differ-ence measures to predict lapsing, individuals who lapsed on DSQ 0 wereexcluded. No participant began the relapse sequence on the lapse day.Thus, all participants were included in analysis of lapse day measures.

8 We assessed whether treatment group assignment moderated the rela-tionship between ASE and POEs and lapse and relapse. It did not, andresults from these analyses were not presented here.

9 We considered using more sophisticated analytic techniques to test formediation (e.g., MacKinnon, Lockwood, Hoffman, West, & Sheets, 2002).Because (a) newer methods are not easily used with repeated-measuresdata, as used here, and (b) these are exploratory analyses, we chose toreport only results from more traditional methods. However, analyses usingquantitative “indirect effects” tests (z’) produced results similar to those ofthe analyses presented here.

665DYNAMIC SELF-EFFICACY AND EXPECTANCIES

higher quit-day ASE scores were associated with lower lapse risk(OR � 0.85, 95% CI� 0.76–0.96,p � .01).

Natural history of daily measures.ASE was very high onabstinent days during the abstinent interval (see Table 1). Figure 2(A) displays the natural history of ASE during the abstinentinterval for all participants. We statistically tested the temporaltrends from the quit day forward. During the DSQ 0–34 interval,both linear (quadratic trend coefficient� .02, SE � .003, p �.001) and quadratic (linear trend coefficient� �.001,SE� .0002,p � .001) trends were significant, suggesting that an initial in-crease in ASE decelerates later in the monitoring period. Accord-ing to Figure 2, ASE plateaus approximately 2 weeks after the quitday. This was confirmed by a post hoc analysis: ASE increasedlinearly over DSQ 0–13 (linear trend coefficient� .04,SE� .01,p � .001), but the linear trend was not significant over DSQ 14–34(linear trend coefficient� .006,SE� .004,ns).

Subsequently, using a sample of 168 lapsers who lapse after thequit day, we assessed whether ASE changed as the lapse dayapproached (see Figure 3). The decrease in ASE in the 4 daysleading up to a lapse reached only a statistical trend (linear trendcoefficient� �.07,SE� .03,p � .06). Thus, any linear decreasewas subtle—ASE decreased by .07 units (on a 0–10 scale) on eachday over the 4 days leading up to the first lapse.

Prediction of first lapse: Daily measures.Using event historyanalyses, we assessed the relationship between daily ASE scoresand subsequent day’s smoking behavior (lapse vs. no lapse). Byitself, lower daily ASE significantly predicted lapsing on thesubsequent day (OR� 0.75, 95% CI� 0.68–0.84,p � .001).This effect persisted when baseline measures (NDSS, ASEQ score,average smoking rate) and treatment group were included in themodel (OR� 0.76, 95% CI� 0.68–0.86,p � .001). The effectof daily ASE on lapse risk also remained when we included

participants’ ASE rating at the quit day milestone (OR� 0.68,95% CI � 0.57–0.82,p � .001). To further distinguish dynamicchanges in ASE from individual differences—that is, focusing onwhen participants lapsed, rather than on who lapsed—a subsequentanalysis included only lapsers. With all covariates in the model,ASE continued to predict lapsing (OR� 0.78, 95% CI� 0.65–0.94, p � .05). Thus, dynamic changes in ASE predicted thetiming of lapses, not just who did and who did not lapse.

POEs: Abstinent Interval

Prediction of first lapse: Baseline and quit-day measures.Av-erage POEs scores from the questionnaire reflected moderate ex-pectations for positive effects of smoking (see Table 1). In survivalanalyses, baseline questionnaire POEs scores were unrelated tofirst lapses (OR� 1.08, 95% CI� 0.94–1.24,ns).10 AveragePOEs on the quit day for lapsers and nonlapsers is shown in Table1. Those who maintained abstinence had lower POEs scores on thequit day (OR � 1.17, 95% CI� 1.08–1.27,p � .01). Thisrelationship remained even after we controlled for NDSS scores,baseline smoking rate, baseline SCQ POEs scores, and treatmentgroup (OR� 1.15, 95% CI� 1.05–1.26,p � .01).

Natural history of daily measures.In contrast to ASE, POEswere relatively low on abstinent days during the abstinent interval

10 The aggregation over all positive items is unique; separate factors havebeen calculated in previous studies. Therefore, in order to assess the possibilitythat the aggregation over items masked significant relationships between POEsand first lapses, the relationship between the individual POEs subfactors andlapsing was assessed. Only the Taste/Sensorimotor Manipulation factor sig-nificantly predicted lapsing, after we controlled for the NDSS and baselinesmoking rate (OR� 1.13, 95% CI� 1.03–1.25,p � .05).

Figure 1. Lapse and relapse survival curves. Lapse survival curve contains all participants (N � 305); relapsecurve contains lapsed participants (n � 211).

666 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

(see Table 1). Figure 2 (B) plots the natural history of POEs duringthe abstinent interval for all participants. Over the DSQ 0–34interval, GEE identified both linear (linear trend coefficient��.07,SE� .007,p � .001) and quadratic trends (quadratic trendcoefficient � .002, SE � .0004, p � .001). Examination ofFigure 2 suggests that the decrease in POEs is more pronouncedearly in the monitoring period. Indeed, follow-up analyses indi-cated that the linear change during the DSQ 0–10 interval (linear

trend coefficient� �.11,SE� .02,p � .001) was several timessteeper than during the DSQ 11–34 interval (linear trend coeffi-cient � �.03, SE � .01, p � .001), although both trends werestatistically significant.

Figure 3 (B) shows POEs in the 4 days leading up to the firstlapse. Despite some indication of increased POEs approaching thelapse day, no significant linear (linear trend coefficient� .04,SE� .05,ns) or quadratic trends were identified (quadratic trendcoefficient� .05, SE� .04, ns). POEs do not change over the 4days leading up to the lapse day.

Prediction of first lapse: Daily measures.Higher daily POEsscores were associated with increased lapse risk (OR� 1.19, 95%CI�1.10–1.28,p � .001). This relationship remained statisticallysignificant when baseline measures (NDSS, SCQ positive expect-ancy factor, average smoking rate) and treatment group wereincluded as covariates in the model (OR� 1.17, 95% CI�1.08–1.27,p � .001). The relationship also remained unchangedwhen quit day POEs was added to this cumulative model (OR�1.19, 95% CI� 1.05–1.34,p � .01). Daily POEs remained asignificant predictor of lapsing when only lapsers were included inthe analysis (controlling for covariates), OR� 1.21, 95% CI�1.04–1.40,p � .05. As with ASE, dynamic changes in POEsappear to predict the timing of first lapses.

Joint Effects of ASE and POEs: Abstinent Interval

The preceding analyses examined the effects of ASE andPOEs individually; we now turn to their interrelationships andjoint effects. Baseline questionnaire measures of ASE (from theASEQ) and POEs (from the SCQ) were significantly but mod-estly correlated (r � �.23, p � .001). In addition, ASE on thequit day was significantly correlated with quit-day POEs (r ��.31, p � .001). Similarly, daily ASE and POEs during theabstinent interval were reliably associated within-day for bothall participants (r using disaggregated daily data� �.42; GEEparameter estimate� �.06, SE � .02, p � .001) and lapsersonly (r using disaggregated daily data� �.34; GEE parameterestimate� �.09, SE � .03, p � .01). In all cases, strongerexpectancies for the positive effects of smoking were associatedwith reduced confidence in ability to abstain from smoking.

Because neither baseline questionnaire measure was signifi-cantly associated with lapsing, mediation tests were moot.However, we did test for moderation with these variables, toexamine the possibility that the relationship between positiveoutcome expectancies and smoking was dependent upon ASEstrength. The interaction between ASE (from the ASEQ) andPOEs (SCQ) was nonsignificant (OR� 1.03, 95% CI� 0.86 –1.24,ns).

Quit-day ASE and POEs measures did predict univariatelywhether someone lapsed. We tested for mediation by includingboth variables in the same analytic model (along with baselinemeasures of ASE and POEs, NDSS, smoking rate, and treat-ment group). Results of these tests are presented in Table 2. Themagnitude (indexed by the OR) and significance of the rela-tionships between the quit day measures and lapsing was es-sentially unchanged when both were included in the sameanalysis, indicating that both exercise independent influenceson lapses. Quit day ASE and POEs did not interact to predictlapsing (see Table 2).

Table 1Descriptive Statistics for Abstinence Self-Efficacy (ASE) andPositive Outcome Expectancies (POEs) Measures

MeasureAll participants

(N � 305)No lapse(n � 94)

Lapsed(n � 211)

Baseline questionnaire measures

ASEQ (1–5 scale)M � SD 3.48� 0.64 3.58� 0.68 3.43� 0.62Response range 1.52–5.00

SCQ (0–9 scale)M � SD 4.94� 1.19 4.81� 1.22 4.99� 1.17Response range 1.16–7.47

Quit day

ASE (0–10 scale)M � SD 8.49� 1.37 8.83� 1.14* 8.30� 1.45Response range 3.67–10.00

POEs (0–10 scale)M � SD 4.45� 2.09 3.83� 2.20* 4.79� 1.95Response range 0.00–9.14

Average daily measures (abstinent interval)

ASE (0–10 scale)M � SD 9.05� 1.12 9.22� 0.97* 8.68� 1.31Response range 0–10

POEs (0–10 scale)M � SD 2.98� 2.38 2.63� 2.34* 3.72� 2.29Response range 0.00–9.86

All participants(N � 211)

No relapse(n � 185)

Relapse(n � 26)

Lapse day

ASE (0–10 scale)M � SD 7.75� 1.83 7.85� 1.81 6.98� 1.82Response range 1–10

POEs (0–10 scale)M � SD 4.60� 2.12 4.46� 2.12 5.55� 1.84Response range 0.00–9.57

Average daily measures (lapsed interval)

ASE (0–10 scale)M � SD 7.82� 1.96 7.93� 1.94* 6.78� 1.85Response range 0.00–10

POEs (0–10 scale)M � SD 4.04� 2.21 3.90� 2.20* 5.33� 1.91Response range 0–10

Note. ASEQ � Abstinence Self-Efficacy Questionnaire; SCQ� Smok-ing Consequences Questionnaire. Asterisk indicates that differences be-tween nonlapsers and lapsers or between nonrelapsers and relapsers arestatistically significant.

667DYNAMIC SELF-EFFICACY AND EXPECTANCIES

To test mediation and moderation models in dynamic ASEand POEs, we included both in the same model (along withbaseline and quit-day ASE and POEs measures, NDSS, smok-ing rate, and treatment group). Results are summarized in Table2. Analyses including all participants and those with lapsersonly produced similar results. When ASE and POEs wereincluded in the same model, ASE remained a significant pre-dictor of lapsing, whereas POEs did not. Although no longerstatistically significant, the magnitude of the relationship be-tween POEs and lapsing was only marginally weaker than whenASE was not included in the same model (all participants: 1.12vs. 1.19; lapsers only: 1.14 vs. 1.21). Accordingly, we con-cluded that there was little mediation of POEs effects by ASE.There was no evidence that daily ASE and POEs interacted topredict first lapses (see Table 2).

ASE: Lapsed Interval

Prediction of relapse: Baseline and lapse-day measures.Sur-vival analysis was also used to assess the relationship betweenaverage ASEQ self-efficacy scores and relapse among participantswho experienced a lapse (starting at the lapse day). Results sug-gested that the ASEQ was not associated with progression torelapse (OR� 0.58, 95% CI� 0.30–1.13,ns).

Average ASE on the lapse day for relapsers and nonrelapsers(lapsed, but not relapsed) is displayed in Table 1. ASE was loweron the lapse day for those who subsequently relapsed (OR� 0.81,95% CI� 0.68–0.97,p � .05). When we controlled for baselinecovariates and treatment group, the OR for lapse-day ASE re-mained similar, but the 95% confidence interval now included 1.0(OR � 0.80, 95% CI� 0.64–1.01,p � .06).

Figure 2. Natural history of ASE and POEs: Abstinent interval (Days Since Quit).

668 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

Natural history of daily measures.ASE was reduced instrength and slightly more variable during the lapsed interval thanduring the abstinent interval (see Table 1).11 Figure 4displays thenatural history of ASE during the lapsed interval (DSL 0–34) forall lapsed participants. Analysis of temporal trends during thelapsed interval suggested that ASE did not systematically changeover time. When ASE was assessed from the lapse day forward,neither the linear (linear trend coefficient� �.01, SE� .01, ns)nor quadratic (quadratic trend coefficient� .0004,SE� .0004,ns)trends were significant. However, when we assessed ASE on the 4days prior to relapse (see Figure 3), ASE reliably decreased, lineartrend coefficient� �.18,SE� .06,p � .01. Despite the “elbow”in the curve at Relapse Day�3, the quadratic trend in ASE wasnonsignificant, quadratic trend coefficient� �.11, SE� .09, ns.

Thus, daily ASE decreases linearly over the 4 days precedingrelapse.

Prediction of relapse: Daily measures.Higher daily ASE wassignificantly associated with lower risk of relapse (OR� 0.67,95% CI � 0.57–0.77,p � .001). This relationship held whenbaseline measures (NDSS, ASEQ score, average smoking rate)

11 In comparisons of the abstinent and lapsed intervals, not only weredifferent time intervals being compared but also different samples—indi-viduals who did not lapse were not included in the lapsed interval. How-ever, ASE decreased in magnitude following a lapse among a stable cohort(Table 1; see abstinent interval for lapsed participants and lapsed intervalfor all participants).

Figure 3. Natural history of ASE and POEs: Days prior to lapse and relapse.

669DYNAMIC SELF-EFFICACY AND EXPECTANCIES

and treatment group were included as covariates in the model(OR � 0.68, 95% CI� 0.58–0.81,p � .001) and even whenlapse-day ASE was also included in the model (OR� 0.64, 95%CI � 0.52–0.80,p � .001). We then included only relapsers in theanalysis. ASE continued to predict relapsing when we controlledfor all covariates (OR� 0.58, 95% CI� 0.44–0.77,p � .001).Thus, as with first lapses, dynamic changes in ASE predict thetiming of relapse.

Daily ASE and concomitant smoking.Participants were smok-ing intermittently during the lapsed interval. It is possible that dailyASE may vary because of daily smoking: ASE may drop on dayswhen a participant smokes more. To control for this confound,number of cigarettes smoked per day during the lapsed intervalwas included in the analytic model. When all lapsers were includedin analysis, ASE continued to predict relapse, even after control-ling for daily smoking rate, as well as all other covariates (OR�0.71, 95% CI� 0.58–0.89,p � .01). This also held in analysislimited to relapsers (OR� 0.60, 95% CI� 0.45–0.79,p � .001).In other words, ASE predicted progression to relapse, even whensmoking rate was taken into account (see also Shiffman et al.,2000).

Self-efficacy theory suggests that ASE should completely me-diate the influence of previous smoking on relapse. Daily ASE andsmoking rate were moderately correlated during the lapsed interval(r using disaggregated daily data� �.31; GEE parameter esti-mate � �.09, SE � .02, p � .001),12 and daily smoking ratesignificantly predicted next-day relapse (OR� 1.37, 95% CI�1.22–1.54,p � .001). However, the predictive power of smokingrate was not substantially reduced when ASE was included in themodel (OR� 1.32, 95% CI� 1.16–1.51,p � .001). Thus, theeffect of daily smoking rate on relapse was not mediated by dailyASE: The two variables exercised independent influences on re-lapse risk.

POEs: Lapsed Interval

Prediction of relapse: Baseline and lapse-day measures.TheSCQ aggregate score was not related to relapse among individualswho lapsed (OR� 1.29, 95% CI� 0.90 –1.85,ns). None of theindividual positive expectancy subfactors predicted relapse.Average POEs on the lapse day is shown in Table 1. Therelationship between lapse-day POEs and progression to relapsewas only a statistical trend (OR� 1.22, 95% CI� 0.99 –1.49,p � .06). Controlling for baseline measures and treatment groupfurther diminished this relationship (OR� 1.16, 95% CI�0.93–1.44,ns).

Natural history of dynamic measures.The POEs factor scorewas higher during the lapsed interval than during the abstinentinterval (Table 1; see also Footnote 11). Figure 4 displays thenatural history of POEs during the lapsed interval (DSL 0–34).POEs decreased linearly over days during the lapsed interval(linear trend coefficient� �.03, SE � .01, p � .001). Thequadratic trend was not significant (quadratic trend coefficient�.0007,SE� .0004,p � .07). However, the linear trend was notseen when only relapsers were included in analysis (linear trendcoefficient � �.01, SE� .02, ns), suggesting that the temporaltrend was evident primarily among nonrelapsers (linear trendcoefficient� �.03,SE� .01,p � .001). We then assessed trendsin POEs backward from the day before relapse (see Figure 3).POEs remained relatively flat during the days immediately pre-ceding relapse; linear (trend coefficient� �.002,SE� .05, ns)and quadratic (trend coefficient� .006,SE� .04,ns) trends werenonsignificant. Thus, POEs did not show trends leading up torelapse.

Prediction of relapse: Daily measures.Higher daily POEsratings were associated with relapse risk when covariates were notin the model (OR� 1.34, 95% CI � 1.11–1.61,p � .01).However, this association disappeared when baseline measures(NDSS, SCQ positive expectancy factor, average smoking rate)and treatment group were included in the model (OR� 1.21, 95%CI � 0.95–1.53,ns). Adding lapse-day POEs as a covariate in themodel did not change this result (OR� 1.22, 95% CI� 0.90–1.64, ns). Analysis showed that this was primarily due to theassociation between daily POEs and baseline SCQ positive ex-pectancy factor scores, which was stronger (r � .43)13 than theobserved relationships between POEs and NRT group (r � .24),nicotine dependence (r � .22), or smoking rate (r � .10). DailyPOEs also did not predict the timing of relapse when only relaps-ers, with no covariates, were included in the analysis (OR� 1.02,95% CI � 0.83–1.26,ns).

Joint Effects of ASE and POEs: Lapsed Interval

Neither the ASE nor the POEs questionnaire measure signifi-cantly predicted relapse; it was, therefore, impossible to test formediation. However, potential moderation was assessed. Question-naire ASE and POEs measures did not interact to predict progres-

12 Results from analyses using only relapsers were similar and are notpresented here.

13 This association was higher than that between baseline SCQ scoresand daily POEs in the abstinent interval, prior to a lapse, even though thatperiod was closer to the baseline (r � .26).

Table 2Joint Effects of Abstinence Self-Efficacy (ASE) and PositiveOutcome Expectancies (POEs) on First Lapses: Quit-Day andDynamic Measures

Predictorvariable

Between-personseffects: Quit-day

scoresWithin-person effects:

Daily scores

Parameterestimate (SE)

Oddsratio

Parameterestimate (SE)

Oddsratio

All participants

ASEa �.14 (.07)* 0.87 �.40 (.11)*** 0.67POEsa .12 (.05)* 1.12 .12 (.07)† 1.12Interaction termb .004 (.03) 1.00 .02 (.02) 1.02

Lapsers only

ASEa �.28 (.12)* 0.75POEsa .13 (.08)† 1.14Interaction termb .01 (.03) 1.01

a Results from models including both ASE and POEs simultaneously.b Results from models in which the interaction between ASE and POEswas tested.† p � .10. * p � .05. *** p � .001.

670 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

sion from lapse to relapse (OR� 1.32, 95% CI� 0.67–2.60,ns).Similarly, lapse day ASE did not moderate the effect of lapse dayPOEs on relapse (OR� 0.98, 95% CI� 0.88–1.09,ns). Last,daily ASE did not moderate the effect of daily POEs on relapserisk (OR � 1.00, 95% CI� 0.97–1.03,ns).

Discussion

Although the influence of dynamic changes in ASE and POEs ishighlighted in relapse models (e.g., Marlatt & Gordon, 1985;Niaura et al., 1988), few studies have examined within-personvariation in these constructs during a quit attempt (Shiffman et al.,2000). Using ecological momentary assessment (Stone & Shiff-man, 1994), we examined both the natural history of ASE andPOEs following initial abstinence from smoking and the relation-

ship between dynamic changes in each construct and the subse-quent risk of lapse and relapse.

Natural History of ASE and POEs

To our knowledge, this is the first study to examine the temporaldynamics of POEs following a quit attempt and only the second toaddress dynamics of ASE (Shiffman et al., 2000). As abstinencewas maintained following the quit day, ASE increased, and POEsdecreased. This effect was not due to the progressive loss ofparticipants to lapsing. There are several potential explanations forthese temporal trends: As nicotine withdrawal fades over time(among both placebo and NRT-maintained participants), confi-dence in ability to abstain from smoking may grow stronger, whereasexpectations for the positive effects of smoking may weaken. Fur-

Figure 4. Natural history of ASE and POEs: Lapsed interval (Days Since Lapse).

671DYNAMIC SELF-EFFICACY AND EXPECTANCIES

thermore, as conditioned associations are broken during abstinence,environmental cues may lose the ability to decrease self-efficacy andenhance positive smoking expectancies. The increase in self-efficacyover time is consistent with theories suggesting that self-efficacyshould be enhanced by mastery experiences (e.g., Bandura, 1977,1997). Self-efficacy may increase as quitters confront and success-fully negotiate challenges to abstinence.

Interpreting temporal trends following a first lapse is madedifficult by the confounding effect of concurrent smoking. Perhapsbecause of the effect of intermittent smoking, trends following alapse were relatively flat. The lack of change in self-efficacy is notsurprising: The mix of self-regulatory successes and failures fol-lowing a lapse may result in the lack of systematic trends inASE.14 A small, but significant, downward trend in the magnitudeof positive smoking expectancies was identified following a firstlapse. However, this trend was seen only among those who did notrelapse; no temporal trends were identified among relapsers. Bydefinition, relapsers smoke more following a lapse than nonrelaps-ers. This intermittent smoking may maintain positive expectancies,via activation of an appetitive motivational system (e.g., Baker etal., 1987; Stewart, de Wit, & Eikelboom, 1984), with indirecteffects on cognitions about smoking.

Prediction of First Lapse and Relapse

First lapses. A fundamental hypothesis of social learning re-lapse theories is that changes in ASE and POEs should foreshadowlapses to smoking. In this study, the day before the first lapse wasdistinguished by both low ASE and high POEs: ASE was partic-ularly low, and POEs particularly high prior to a first lapse. Thiseffect was substantial: For example, a one-unit increase in POEswas associated with a 20% increase in risk of lapsing on thefollowing day. Thus, an increase of 1 standard deviation (2.38units) would result in approximately a 45% increase in risk eachday. As these daily risks are cumulative over time, this wouldresult in a very large impact on long-term abstinence. Although ourcorrelational data do not allow for strong causal inferences, ourfindings are consistent with social-learning models that assignASE and POEs important roles as causes of lapses. It seemscompelling that, when smokers’ expectations that smoking willprovide significant benefits are growing, they are more likely to bedrawn to smoking. At the same time, when self-efficacy is drop-ping, coping efforts to resist temptation may also diminish, in-creasing the risk of smoking.

Shiffman (1989) has suggested that variables influencing re-lapse may be grouped into three categories: individual differences,background variables, and phasic relapse precipitants (see alsoShiffman, in press). Individual differences and relapse precipitantshave received the bulk of attention in studies of relapse to date(e.g., Ockene et al., 2000; Shiffman et al., 1996). However, ourfindings indicate that lapse risk was not wholly accounted for bybetween-persons differences observed at the launch of the quiteffort, nor was it completely due to rapid, emergent influencesoccurring in the minutes or hours immediately prior to smoking.Rather, self-efficacy and positive expectancies may show changesover the days between quitting and lapsing. In fact, in the daysleading up to the onset of relapse, ASE appears to steadily (thoughsubtly) decrease, providing evidence that the relapse process un-folds over time, as a smoker experiences circumscribed lapses

mixed with periods of abstinence. Although providing evidence forslowly emerging processes, our data also hint at the effects of morerapid risk factors: 20% of first lapses occurred on the quit day,suggesting that within-day changes, not evaluated here, may alsobe very important in promoting lapses (see Shiffman & Waters,2004). Our understanding of relapse may be enhanced by futureresearch that addresses the interplay between individual differ-ences, background processes, and phasic events in producinglapses and relapse (Shiffman, 1989). EMA methods are well suitedto measuring each of these domains.

It is unclear why ASE decreased and POEs increased prior to afirst lapse. It is possible, and even likely, that they are influencedby changes in affective state or craving. Negative affect andcraving rise prior to a first lapse (Shiffman et al., 1997; Shiffman& Waters, 2004), and craving and negative affect are, in turn,associated with reduced ASE (Gwaltney, Shiffman, & Sayette,2005) and increased POEs (Gwaltney, Shiffman, Paty, & Bala-banis, 2005). This could explain the shifts in ASE and POEsleading up to a lapse: Coping with craving and negative affect maylead to a depletion of self-regulatory resources (Muraven &Baumeister, 2000) that is indexed by reduced ASE. Such “cessa-tion fatigue” (Piasecki et al., 2002) may increase one’s vulnera-bility to lapsing. Craving may also prime positive expectanciesabout smoking, as seen in other studies (Brandon et al., 1996;Kozlowski et al., 1996; Sayette & Hufford, 1997; Sayette et al.,2001; Zinser et al., 1992). Some of the variance in postquit cravingis due to exposure to situational cues associated with smoking(e.g., Gwaltney, Shiffman, & Sayette, 2005; Niaura et al., 1988);the increased craving this induced may have cascading effects onASE and POEs that amplify the risk of lapsing. Although beyondthe scope of this study, it may be informative in the future toexamine a more comprehensive model of lapsing that includesnegative affect, craving, and situational variables, in addition toASE and POEs.

Relapse. ASE systematically decreased in the 4 days leadingup to relapse and was also lower on the day before the beginningof relapse. This is consistent with earlier findings from our re-search group, using a different sample (Shiffman et al., 2000).However, the failure to find a relationship between POEs andrelapse was surprising. This null result does not appear to be dueto either a lack of variability in the measure or a ceiling effect(average POEs score was approximately a 4 on a 0–10scale).Indeed, daily POEs initially did predict relapse, but seemed to doso only because of its association to baseline, between-persondifferences in POEs. Perhaps, when smoking is reinitiated, POEsare “reactivated” and reemergent individual differences drive theprogression to subsequent relapse. Alternatively, our single dailymeasure of POEs may not have captured all relevant variance inthe construct (e.g., POEs might vary across situations within aday), which may have reduced the ability of the measure to predictrelapse.

Day-to-day changes in ASE significantly predicted the timing ofrelapse, even after we controlled for more static measures taken at

14 This null effect could also occur if trends among relapsers andnonrelapsers cancel each other out: Self-efficacy may decrease for the firstgroup and increase for the second. However, examination of these sub-groups did not find such an effect.

672 GWALTNEY, SHIFFMAN, BALABANIS, AND PATY

baseline or when the lapse first occurred, and even after controllingfor concurrent smoking. Thus, diminished ASE is an importantcorrelate (and possibly cause) of relapse. Diminishing ASE mayundermine attempts at self-control, which would allow smoking toaccelerate toward the smoker’s baseline levels, leading to relapse.

One of the strongest hypotheses in self-efficacy theory is thatself-efficacy should completely mediate the effect of previousbehavior on future behavior. If true, concurrent smoking shouldfail to predict subsequent smoking when ASE is included in thesame analytic model. However, in these data, both ASE andconcurrent smoking continued to predict the onset of relapse whenincluded in the same model. This replicates earlier analyses fromour laboratory of both dynamic (Shiffman et al., 2000) and indi-vidual difference (Gwaltney et al., 2001) measures. Therefore,there is little evidence to support the hypothesis that ASE whollytransmits the effect of today’s smoking on tomorrow’s smoking orthat ASE serves a final common pathway to relapse. Smokingtoday predicts smoking tomorrow above and beyond ASE effects,perhaps because today’s smoking behavior exerts both pharmaco-logical and behavioral effects that promote continued smoking.However, it is also possible that our single item assessment wasnot an optimal measure of momentary ASE. An enhanced measure(e.g., a multidimensional assessment; Gwaltney et al., 2001) maymore accurately reflect ASE changes following smoking and,therefore, more effectively mediate the effects of concurrent smok-ing on future smoking.

In sum, ASE and POEs showed dynamic changes over time,consistent with social-learning models of relapse. Most important,these dynamic changes were clinically relevant—they foreshad-owed lapses to smoking and the onset of relapse. Because changesin ASE and POEs warn of impending smoking, they may serve aclinical purpose as warning flags. However, at this point, thechanges appear to be too subtle to be of much value in providingsmokers with warnings to help direct their efforts to maintainabstinence. Increased clinical utility may result from improve-ments in measurement strategies (see below).

Joint Effects of ASE and POEs

Although ASE and POEs are important components of social-learning models of relapse, surprisingly little is known about therelationship between the two constructs during a smoking-cessation attempt. In this study, ASE and POEs largely exhibitedinverse relationships throughout the monitoring period: As POEsincreased, ASE decreased. Although the associations betweenASE and POEs were statistically significant, they were modest,indicating that the constructs overlap but are hardly redundant.

The analysis of the quit-day and dynamic measures suggestedthat ASE and POEs exerted independent effects on lapsing. (Eventhough daily POEs were no longer statistically significant after wecontrolled for ASE, the magnitude of the relationship betweenPOEs and lapsing was largely unchanged.) There was no evidencesuggesting that ASE moderated the effect of POEs on smoking(e.g., Bandura, 1977; Dijkstra & Borland, 2003; Shadel & Mer-melstein, 1993). Thus, these data primarily support a model inwhich ASE and POEs are independent predictors of smoking.However, it is difficult to address the exact predictions of therelapse models, because they typically address processes that occurin the moments immediately before lapsing. Therefore, these data

cannot provide unequivocal tests of the models. However, they doprovide an important improvement over most previous research, inwhich ASE and POEs have been operationalized as static andunchanging constructs, rather than as dynamic variables. We con-clude for now that evidence does not support mediation or mod-eration hypotheses.

Strengths and Limitations

This study has several strengths. We distinguished within-person, day-to-day dynamic changes in ASE and POEs frombetween-person differences. This is important, as all social-learning theories suggest (a) that each construct should change inresponse to ongoing behavior and interactions with the environ-ment and (b) that these changes should have an important impacton behavior. This is the first study to assess day-to-day changes insmoking outcome expectancies and only the second to assessmomentary ASE. In addition, concurrent behavior (smoking rate,in this case) was accounted for in all analyses, in an attempt toisolate the effect of our cognitive measures. Finally, an importantstrength was the use of computerized EMA methods (Shiffman etal., 2000; Stone & Shiffman, 1994), which ensured that data weregathered in real time, avoided retrospection, and prevented back-filling of diaries (Stone, Shiffman, Schwartz, Broderick, & Huf-ford, 2003).

However, the study was also limited in some respects. Mostnotably POEs and ASE were not experimentally manipulated,which prohibits strong causal inferences about their relationshipwith smoking cessation outcome. Also, in order to reduce subjectburden, only expectancies regarding the positive effects of smok-ing were assessed once daily. Thus, we ignored expected negativeeffects of smoking and/or other types of outcome expectancy (e.g.,expected effects of quitting, anticipated craving). Because relapsemodels (Brandon et al., 1999; Marlatt & Gordon, 1985; Niaura etal., 1988) and empirical studies (Copeland et al., 1995) suggest therelative importance of expected positive effects of smoking, thisassessment strategy seems acceptable. In our analysis of temporaltrends, we aggregated data to create a curve that best demonstrates“typical” ASE and POEs patterns. However, this strategy ignoresidiosyncratic variation in score profiles that may be relevant forpredicting cessation (e.g., Piasecki, Jorenby, Smith, Fiore, &Baker, 2003). Future studies may benefit by addressing suchvariability in individual trends. Finally, only individuals with highglobal ASE were allowed to enter the study. This may decreasegeneralizability of our results. However, individuals with low ASEare less likely to achieve abstinence, which would make theprediction of lapse and relapse more difficult and less relevant.

Our method of measuring ASE and POEs required the partici-pants to reflect on their experience and to self-report their currentstate. Thus, our assessments tap explicit processes, or processesthat are “cognitively accessible.” However, some models of druguse suggest that automatic, implicit cognitive and emotional pro-cesses (not available to conscious reflection) may also be impor-tant motivators of smoking (e.g., Baker et al., 1987, 2004; Gold-man, Darkes, & Del Boca, 1999; Tiffany, 1990). These modelsposit that drug use information, affect systems, craving (althoughsee Tiffany, 1990), and behavioral response templates are closelyintertwined (in neural networks) and are quickly activated inparallel, in the presence of drug cues or other relevant stimuli.

673DYNAMIC SELF-EFFICACY AND EXPECTANCIES

Importantly, the models suggest that changes in constructs such asoutcome expectancies may occur outside of conscious awareness,influencing behavior without the requirement that the smokerweigh their expectancies before “deciding” to smoke. It may beinformative to measure implicit/automatic processes using EMA infuture research.

Conclusion

Data from this study highlight the importance of dynamic,volatile processes during a smoking-cessation attempt: Changes inself-efficacy and positive smoking outcome expectancies predictedthe occurrence of a first lapse, whereas dynamic changes in self-efficacy predicted progression from lapse to relapse. These dataprovide a window into the complexity of the relapse process. Notonly are multiple mechanisms likely involved in the relapse pro-cess, but the relevant mechanisms may change from one phase ofthe quit effort (e.g., avoiding lapses while abstinent) to another(e.g., avoiding relapse after a lapse). Thus, theories of relapse needto provide different accounts for behavior before and after initiallapses to smoking. Theories of relapse already emphasize dynamicprocesses; empirical research needs to rise to the challenge ofcollecting relevant data on dynamic changes in behavior, affect,and cognition.

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Received October 13, 2004Revision received April 5, 2005

Accepted May 7, 2005�

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