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Temporalanalysisoftherelationshipofsmokingbehaviorandurgestomoodstatesinmenversuswomen

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Ralph J. Delfino, MD, PhD, Epidemiology Division, Department ofMedicine, School of Medicine, and Department of Psychology andSocial Behavior, School of Social Ecology, University of California,Irvine; Larry D. Jamner, PhD, and Carol K. Whalen, PhD, Departmentof Psychology and Social Behavior, School of Social Ecology,University of California, Irvine, California, USA.

Correspondence to: Larry D. Jamner, Department of Psychology andSocial Behavior, University of California, Irvine, 3340 Social EcologyII, Irvine, CA 92697, USA. E-mail: ljamner@uci.edu

Temporal analysis of the relationship of smokingbehavior and urges to mood states in men versuswomen

Ralph J. Delfino, Larry D. Jamner, Carol K. Whalen

Epidemiological investigations of mood and smoking have relied largely on retrospective self-reports, with littleresearch on real-time associations. We examined the relationship of mood states to contemporaneous smokingurges and to subsequent smoking and also assessed the effects of smoking on subsequent mood. For 2 days, 25female and 35 male smokers aged 18–42 made three prompted diary entries per hour plus pre- and post-smoking entries (6882 entries). Data were analyzed with generalized estimating equations. We found significantpositive associations between smoking urge and anger, anxiety, and alertness in women and men; fatigue in menonly; sadness more strongly in men than women; and happiness in women only. Decreased alertness andincreased anxiety predicted subsequent smoking in men only. Smoking was followed by decreased anger levelsin men and women and decreased sadness in men only. In men with lower overall anger episodes, increasedanger was associated with subsequent increased smoking. These findings suggest that smoking is related tonegative affect and energy level, more clearly in men, and has palliative effects on sadness in men and on angerin men and women. These data demonstrate that ambulatory research can reveal targets for early interventionand smoking cessation.

Introduction

A key question in discovering how to prevent tobaccosmoking is why some people (usually adolescents) whoexperiment with tobacco go on to become regularusers, whereas most do not. Pomerleau, Collins, Shiff-man, and Pomerleau (1993) have proposed that somepeople are more sensitive to nicotine than others; thereinforcing consequences of nicotine on mood andperformance are greater for this subgroup, and toler-ance is more likely to develop. Pomerleau et al. (1993)further propose that, among individuals less sensitive tonicotine who become smokers, environmental factors

such as social cues play a much larger role indetermining smoking persistence. Host factors, possiblygenetic, may underlie individual predispositions topersistent tobacco use and nicotine addiction (Pomer-leau, 1995). These individual differences probablyexplain findings that nicotine strongly influences emo-tional and cognitive functions in specific groups ofpeople, including those with clinical syndromes such asmajor depressive disorder (Hall, Munoz, & Reus, 1994)and Attention-Deficit/Hyperactivity Disorder (Connerset al., 1996). Nicotine’s effects on the function andmetabolism of neurotransmitters such as serotonin anddopamine (Gamberino & Gold, 1999) may partlyexplain such individual differences in the addictivenature of tobacco use, given that there are known andexpected host differences in neurotransmitter biochem-istry and neurophysiology. Neurophysiological differ-ences between individuals in responses to the addictiveeffects of tobacco may be partly gender-based, includ-ing the relationship between smoking and levels ofanger and hostility.

ISSN 1462-2203 print/ISSN 1469-994X online/01/030235-14 © 2001 Society for Research on Nicotine and Tobacco

DOI: 10.1080/14622200 110050466

Nicotine & Tobacco Research (2001) 3, 235– 248

Gender differences in personality–smoking linkageshave been proposed, but the empirical literature isinconsistent. The Bogalusa Heart Study of 2092 childrenfound that hostility was positively associated withcigarette use in white boys, but not in white girls or inAfrican-American boys or girls (Johnson, Hunter, Amos,Elder, & Berenson, 1989). In contrast, no genderdifferences emerged in a prospective study of 4700college students followed-up 20 years later; high levelsof hostility at baseline were associated with initiation andpersistence of smoking in both men and women (Lipkus,Barefoot, Williams, & Siegler, 1994).

It has been hypothesized that pharmacological effectsof nicotine may be less reinforcing for women than menand that non-nicotinic (psychosocial) aspects of tobaccosmoking may be stronger reinforcers in women than men(Perkins, 1996). Killen et al. (1997) provided evidencefor this hypothesis in a prospective study of 1026 never-smoker adolescents. Boys with higher depressive symp-toms and girls with higher sociability scores were morelikely to report tobacco use at the 3–4 year follow-up. Insummary, although the above referenced evidence to dateon smoking-mood links is not entirely consistent, severalsets of findings support the value of delineating theputative emotion-regulation and anger-palliative effectsof nicotine in males vs. females.

There is little research on the short-term effects ofsmoking on anger or other mood states in everydaysettings (Hatsukami, Morgan, & Pickens, 1990; Shiff-man, Hufford, Hickcox, Paty, Gnys, & Kassel, 1997;Shiffman, Paty, Gnys, Kassel, & Hickcox, 1996; Shiff-man & Prange, 1988). A recent study of 30 smokers and30 non-smokers compared 24-h ambulatory diary reportsof anger during the administration of placebo vs. nicotinepatch (Jamner, Shapiro, & Jarvik, 1999). High hostileindividuals, identified by the Cook–Medley HostilityScale, reported significantly less anger in the nicotinepatch (13% of diary entries) than in the placebocondition (24%). In contrast, among low-hostile individ-uals the opposite pattern was observed, though thesedifferences did not achieve significance. Results wereconsistent across smoking status and gender. Theseresults suggest that smoking may have an anger-palliative effect that reinforces smoking behavior amongpeople prone to anger and hostility.

Shiffman (2000) further highlights the value ofambulatory data in a comparison of smoking typologyquestionnaires administered at study entry with diaryresults in 275 smokers. The diary reports (approximately35 subject reports per week of smoking situations) werecollected with an electronic palm-top diary to verify thatthe time of the report was proximal to the smoking event.The diary reports were summarized by smoking behavioracross 14 mood domains, seven location options, and 10activities. The diary data were compared to the baselinequestionnaire, which was modeled on the diary assess-ments. The average correlations for mood, location, andactivity were small (0.09, 0.09 and 0.08, respectively),and all individual correlations were low to negative.

Diary records are potentially more accurate than retro-spective questionnaires because they are obtained inclose temporal proximity to actual events and are thusless subject to recall bias.

Recall of smoking lapses is also prone to notable bias,despite the expectation that such salient single eventswould be memorable (Shiffman, 2000). Shiffman et al.(1997) examined diary data on smoking relapse triggersfrom 127 people who had quit smoking and found thatretrospective accounts given an average of 72 days latercorrelated poorly with the real-time data (kappas of 0.18to 0.27 for the four content domains of mood, activity,episode triggers, and abstinence violation effects). Recallof the most important trigger matched the diary data onlyaround a third of the time (kappa=0.19). The diary datashowed that temptation to smoke was associated withgreater negative affect, restlessness, attention disturb-ance, and exposure to smoking cues, especially insituations of eating and drinking (Shiffman et al.,1996).

With one notable exception (Shiffman et al., 1996),most of these ambulatory studies have used self-collected data on smoking cues reported only at the timeof smoking. Little is known about the degree to whichthe same moods or activities occur at other times,particularly before or after smoking events.

We hypothesize that anger and negative affect moregenerally trigger smoking in some people and thatsmoking in turn has mood-palliative effects that couldserve to reinforce smoking behavior. In the case of anger,people with higher levels of anger may show strongerlinks between anger and smoking and may gain greaterpalliative benefits from smoking. We also hypothesizethat women may show weaker links between smokingand moods or arousal levels because nicotine may be lessreinforcing for women than men, with men more likelyto use smoking to regulate moods and performance. Inthe present study, we employed real-time ambulatorymethodologies with young adults to examine the rela-tionships of specific moods to smoking urges andsubsequent smoking behaviors, and to examine theeffects of cigarette smoking on moods. We also com-pared the strength of associations in men vs. women andexamined potential differences in anger–smoking rela-tionships between people with high vs. low rates of angerepisodes. This study extends the experimental studydiscussed above by Jamner et al. (1999) by examiningthe anger palliative effects of smoking rather thannicotine patches. We also evaluated mood–smokinglinks after controlling for three factors that are oftenassociated with cigarette smoking: social environment,caffeine intake, and alcohol consumption.

Methods

Design

In this repeated-measures study, events and psycho-logical states were assessed at the time or soon after they

236 GENDER DIFFERENCES IN SMOKING AND MOOD

occurred. As noted above, a major advantage of thisapproach is a reduction in the likelihood of recall biasbecause of the close or immediate proximity of eventsand psychological states. Also, each participant serves ashis or her own control over time. The design enablesinvestigators to determine the temporality of associationsand to observe between- and within-individual patternsof change in exposure and response over time, therebygaining insight into host susceptibility. The signal-to-noise ratio is enhanced because multiple exposures orexposure conditions/ levels are studied in each participantand because data can be analyzed within the participant’scluster of exposure–response measurements, which con-trols the variability in exposure–response relationshipsdue to between-subject characteristics. This variability iscommonly overcome with large sample sizes in typicalepidemiological designs such as cohort or cross-sectionalstudies. Repeated measures, on the other hand, reducethe variability of the response variable(s) withoutreducing the magnitude of association, thereby enhanc-ing both power and precision while allowing smallersample sizes (Weiss & Ware, 1996).

Participants

Twenty-five women and 35 men aged 18–42 years wererecruited using advertisements in local Orange County,CA newspapers. Eligible participants were smokers whoreported smoking at least 10 cigarettes/day and wereotherwise in good health. The daily cigarette consump-tion from self-reports given in a background ques-tionnaire at intake was: 10 cigarettes/day for five womenand four men, 11–15 cigarettes/day for eight women andseven men, 16–20 cigarettes/day for eight women and 16men, 21–25 cigarettes/day for three women and six men,and >25 cigarettes/day for one woman and two men. Intheir background questionnaires, only three women andtwo men reported being completely restricted fromsmoking while at home, and only two women and threemen reported being completely restricted from smokingwhile at work. Nevertheless, during the 2 days ofambulatory monitoring the restricted subjects eachsmoked 21–56 cigarettes. One man reported bothrestrictions, and he smoked 21 cigarettes during ambula-tory monitoring. Most men smoked regular lengthcigarettes (81%), whereas nearly half of the womensmoked 100s (46%).

The sample included 33 white non-Hispanics (18 men,15 women), one Hispanic male, 15 Asians (nine men, sixwomen), five African-Americans (three men, twowomen), and six other race/ethnicity (four men, twowomen). The educational background of participants wasthat 76% of women and 71% of men had attended highschool only and the rest at least some college. Fortyparticipants were married (24 men, 16 women), 11 werenever married (eight men, three women), and nine weredivorced or separated (three men, six women). Sixty-eight per cent of women and 70% of men wereemployed.

The median age at smoking initiation was 17 years forboth men and women. Evidence that participants weredependent smokers was found in self-reported datacollected at baseline. The median number of attempts toquit was two for both men (range 0–10, excluding oneoutlier of 50) and women (range 0–10; Wilcoxon ranksums test, p=0.40). Participants answered additionalquestions at intake representing smoking dependencewith up to five ordinal levels. Answers to these questionswere dichotomized into categorical variables for highervs. lower levels of dependence, and then comparedbetween men and women with x 2 tests. Men did notdiffer significantly from women for several dependencevariables, including inhaling deeply (60% vs. 52%,respectively); smoking the first cigarette of the day morethan half the time within 30 min of waking (51% vs.48%, respectively); difficulty giving up the usual firstcigarette of the day (49% vs. 60%, respectively);smoking half or more of the time when sick with a cold,flu, or so ill that they are in bed most of the day (49% vs.52%, respectively); smoking all of each cigarette smoked(46% vs. 60%, respectively); or always inhaling (86% vs.80%, respectively). Men were somewhat more likely toreport holding cigarette smoke in their lungs a momentor two before exhaling (43% vs. 24%, respectively), butthe difference was not significant (p<0.14). More menthan women reported smoking more in the morning thanduring the rest of the day (51% vs. 28%, respectively,p<0.07), but men were significantly less likely thanwomen to find it difficult to refrain from smoking inplaces where it is forbidden (26% vs. 68%, respectively,p<0.002). These results suggest little difference insmoking dependence between men and women in thisstudy.

Ambulatory monitoring

The influence of moods on smoking urges and behaviorand the effects of smoking on subsequent mood wereevaluated using a compact diary. Participants reportedmood states using adjectival word scales that werechosen by each person to describe six mood levels from0 (none) to 5 (highest level). Moods included anger,stress, anxiety, sadness, happiness, wellbeing, fatigue,and alertness. Participants chose words they wouldcommonly use to describe their mood states, and theythen rated each word using a cross-modality matchingtechnique (Stevens & Mack, 1959). They were instructedto first think of a word to describe their maximum levelof a mood, then a word for their minimum level,followed by a word for an intermediate level, and finally,adjectives describing the mood between maximum andintermediate and between minimum and intermediatelevels. The aim of this approach is to enhance theaccuracy of self-reported mood levels by employingwords used by each participant in his or her own life.These methods and their advantages are more thoroughlydescribed in Jamner, Shapiro, and Alberts (1998).Smoking urge was also scored from 0 to 5, but we used

NICOTINE & TOBACCO RESEARCH 237

the same wording for all subjects (none, just noticeable,weak, moderate, strong, intense).

Procedure. Participants completed two 24-h ambulatorymonitoring sessions. The median length of time betweenthe first and second sessions was 9 days, with over 80%of participants completing the two sessions within 12days. Data were collected primarily on weekdays (83%)vs. weekends (17%). Sessions began between 7:00 and11:00 a.m. For each participant, the same ‘start’ time wasused across sessions. Smokers were instructed to abstainfrom smoking for at least 6 h prior to their sessions sothat we could capture the cardiovascular responses to thefirst cigarette of the day as compared to later responses.Upon arrival at the laboratory, research assistantschecked smokers’ compliance with the instructions toabstain by asking participants to exhale into an air bagthat was subjected to CO analysis (National Draeger,Model 2000, Portable CO Analyzer). Smokers with COreadings greater than 20 ppm were rescheduled fortesting on another day.

Participants were then outfitted with an ambulatoryblood pressure (ABP) monitor, which was programmedto operate approximately every 20 min on a variable timeschedule over 24 h. In addition to collecting the regularblood pressure data, the ABP unit served to cue diaryentries during waking hours. Participants were given adiary and instructed that, for each cuff inflation, theywere to indicate their location, activity, and mood states.In addition to these signaled entries, participants wereinstructed to initiate a blood pressure determination andmake a diary entry immediately preceding and sub-sequent to smoking a cigarette (cigarette-promptedentries), regardless of intervening cuff prompts. There-fore, the time between diary reports was as short as10 min. The total number of completed diary observa-tions for cigarette-prompted entries was 2472 [1236 pre-and post-smoking paired observations, i.e., cigarette-prompted entries include a before-smoking entry (smo-king=no) and an after-smoking entry (smoking=yes)].Cigarette-prompted entries comprise about a third of thetotal number of diary entries including data on thepresence or absence of smoking (6882 observations).

We limit our presentation to the examination of theinterrelationships among mood, smoking urge, andcigarette smoking. Data on smoking and cardiovascularrelationships will be described in a separate report. Wealso briefly examine the influence of social environmentand alcohol and caffeine intake on smoking urges andbehavior and on the modeled smoking–mood relation-ships. The primary aim was to assess confounding ofmood–smoking relationships rather than to test possiblemood effects of specific social environments or ofalcohol or caffeine intake. Diary questions on alcoholand caffeine intake were presented in dichotomous yes/no format. Diary questions on social environment wereopen-ended and were classified as follows: (1) alone (thereferent condition); (2) family, friends, and coworkers(responses included, spouse, family, friend, boyfriend,

girlfriend, coworker, room-mate); (3) others (answersincluded client, manager, supervisor, professor, staff,stranger, other). We chose these crude classifications tocontrast social environments in which a participant mightfeel more at ease due to familiarity (family, friends,coworkers) vs. social environments where a participantmight feel less at ease (professional superiors, school, orunfamiliar social environments). The grouping was alsoaimed at increasing the sample size of these explanatoryvariables for regression analyses (e.g., there were only 20diary reports with a girlfriend/boyfriend out of 6882observations).

Statistical analysis

Independent and dependent variables in the analysiswere either ordinal or dichotomous. We created somedichotomous variables to represent the presence orabsence of current psychological states (moods orsmoking urges), whereas others were inherently dichot-omous, namely, the presence or absence of smoking,caffeine intake, or alcohol consumption. The psycho-logical states were measured on ordinal scales from 0 to5 as discussed above. The selection of cut-points for thebinary expressions of these variables is discussed in theResults section.

The analysis focuses on the repeated measures repor-ted in diaries by individual participants. Their responsesto questions formed the dependent and independentvariables. The temporal distance between two reportedtime periods varied. Participants’ reports in each diaryentry covered behaviors for the prior time periodbeginning after the last diary entry and ending at themoment of diary completion. Mood states were thoseperceived at the moment of diary completion. Theinterval between two consecutive diary entries rangedbetween 10 and 40 min. The previous entry is referred toas being time lagged by one period. The reason for thevariation in time periods is that there were regular cuffprompts on average every 20 min as well as diary entriesinitiated by participants before and after smoking. Thisquasi-random schedule was used to reduce the influenceof subject expectancies on diary and blood pressure data.Therefore, two schedules could overlap so that, say, ascheduled prompt occurred at 1:00 p.m., but the subjectdecided to smoke a cigarette just 10 min later at 1:10p.m., and at that time he/she self-initiated another pair ofdiary entries before and after smoking. The nextscheduled prompt at 1:20 p.m. could be used as the post-smoking prompt entry.

This time series was analyzed by taking independentand dependent variables from diary responses to ques-tions reported at the same observation time (t0 ), or bylagging the independent variable to the previouslyreported observation time (t– 1 ), as compared with thedependent variable at t0 . For lagged variables, theinterest is in the likelihood that the lagged explanatoryvariable predicts the subsequent occurrence of theresponse variable. Four basic models were examined: (1)

238 GENDER DIFFERENCES IN SMOKING AND MOOD

smoking urge as the dependent variable was modeled onmood states, with both variables being psychologicalstates reported contemporaneously at t0 ; (2) smoking asthe dependent variable at t0 was modeled on mood states10–40 min ago as reported at t– 1 ; (3) current mood stateas the dependent variable was modeled on smoking inthe immediately adjacent observation period up to20 min ago, with both variables reported at t0 ; and (4)current mood state as the dependent variable at t0 wasmodeled on smoking 10–40 min ago as reported at t– 1 .

For the regression analyses we utilized GeneralizedEstimating Equations (GEE). The GEE approach wasdeveloped by Liang and Zeger (1986) for the analysis ofnon-normal dependent variables that are correlated(within-individual clusters of the measured response).The present repeated ambulatory measurements ofresponses in a single individual constitute a cluster ofobservations. The model is in some ways a set ofseparate regression models on repeated measurements ofeach participant. As such, every participant acts as his orher own control, much like a clinical crossover design.The GEE models were tested using the logit link functionin the SAS generalized linear model procedure Genmod(SAS, 1996). The Genmod procedure uses a ridge-stabilized Newton–Raphson algorithm to maximize thelog likelihood function for the regression parameters.

The GEE approach of the Genmod procedure is wellsuited to the present type of ambulatory diary databecause: (1) it can be applied to repeated measures thatare unbalanced, have unequal numbers of observations indifferent individuals, or have missing observations intime; and (2) it accounts for temporally correlatedresponses (on t0 vs. t– 1 , t– 2 , . . . tn ) and the dependenceof repeated observations in single individuals (thecluster). Temporal correlation for binary outcomes wasaccounted for with autoregressive parameters (AR1)because it led to autocorrelation of residual errors andpossible bias in the standard error estimates. Statisticalsignificance was attributed to p-values <0.05 for two-sided t statistics.

Because dichotomization of ordinal variables leads toa loss of some information, we performed an alternateGEE analysis to model the ordinal data. We againemployed the SAS Genmod program for GEE analysis,which has recently been extended to modeling cumu-lative probabilities at each time with a logistic linkfunction, and fitting a proportional odds model to thecumulative logits (Lipsitz & Kim, 1994). This providedinformation about the overall slope of the mood–smoking relationships, although we expect non-linearityin the true underlying exposure–response relationshipsfor many of the ordinal mood variables. The current SASprogram only allows for the independent workingcorrelation, not the AR1 working correlation, which isavailable for the binary GEE model as discussed above.Therefore, there may be some bias in not adjusting forthe autocorrelation of ordinal data within subjects(Lipsitz & Kim, 1994). This bias would likely result insmaller standard errors for the independence structure.

Autocorrelation bias is not at issue for the binary GEEanalyses, though, and is the primary justification forpresenting both binary and ordinal data results side-by-side. Also, the dichotomization is more easily inter-pretable and controls for individual differences in the useof the mood scales.

For the GEE models incorporating ordinal mood orsmoking urge variables, when the dependent variablewas ordinal the data were analyzed using the cumulativelogit link function; when the dependent variable wasbinary smoking (no/yes), the data were analyzed with thelogit link function and adjusted for autocorrelation. Oddsratios (OR) for ordinal outcome models representincreased risk per unit increase in the independentexplanatory variable (which could be on an ordinal orbinary scale) and pertain to risk of being in highercategories of the ordinal outcome. Risk of smoking dueto increases in an ordinal mood is also per ordinal unitincrease in the explanatory mood variable. Therefore, themagnitude of the OR in ordinal data models is usuallysmaller than the magnitude for models based on binarydata.

Examination of the binary anger variable revealed thatnine men and six women reported no anger episodes overa score of 1. The binary cut-point for anger was between1 and 2, so a score of 0 or 1 was set to the binary valueof 0. Because the GEE approach models exposure–response relationships in clusters of data by individualparticipants, participants with all 0 values for the binaryresponse or explanatory variables contribute no informa-tion to the final parameter estimates. The binary modelsdo, however, capture episodes of more intense anger.This can be contrasted to the ordinal models, which useinformation from those subjects with anger scores thatnever exceed 1.

For the analysis of anger, we performed an additionalstratified analysis to determine whether individual differ-ences in anger levels were associated with the putativeanger-palliative effects of tobacco smoking. Oneapproach to defining this anger propensity construct iswith standard psychological screening questionnairessuch as the Cook–Medley Hostility Scale (see Jamner etal., 1999). We assumed that anger propensity could beused as a surrogate for trait measures of hostility oraggressivity and may in fact be a more valid indexbecause current emotional states may be misrepresentedby background questionnaires, which can lead to recallbias. Therefore, we defined anger propensity using theambulatory data in the diaries. Although this approachmay result in misclassification of personality because theobserved time is short, it provides a useful snapshot ofemotional states during the study.

More specifically, we divided the male cohort into twogroups: 23 participants who reported anger episodes(defined as a score over 1) on £ 5% of the time, and 12participants reporting episodes >5% of the time. Thefrequency distribution of anger episodes in men withepisodes >5% of the time showed three men with afrequency of 6–9%, five men 10–20%, four men >20%

NICOTINE & TOBACCO RESEARCH 239

with one outlier at 92% (range 6–47%, excluding theoutlier). We also divided the female cohort into twogroups: 16 participants reporting anger episodes £ 10% ofthe time, and nine participants reporting episodes >10%of the time. The higher percentage cut-point in womenwas needed because some of the women frequently usedwords for personal anger codes for ordinal scores >1 thatwere less frequently used among men and that conveylittle or no hostility (e.g., sulky, moody). It is for thisreason that most women (15 of 25) who expressed‘anger’ episodes (>1 on 0–5 scale) reported suchepisodes on more than 5% of observation occasions. Thefrequency distribution of anger episodes in women withepisodes >10% of the time showed three women with afrequency of 11–20% and six women reporting angermore than 20% of the time (range 11–34%). Note thatnine men and six women in the low anger frequencygroups reported no anger episodes over an ordinal scoreof 1.

Results

Descriptive results

The frequency of smoking events reported in diaries wassimilar in men and women. Out of 2971 observationsfrom the 25 women, there were 692 smoking reports(23.3%), and out of 3911 observations from the 35 men,there were 937 smoking reports (24.0%). In GEE modelspredicting smoking in the following period from cur-rently reported smoking urge, women were around sixtimes more likely to smoke if they had any urge, and men

were five times as likely compared with periods withoutany urge (both genders models were p<0.00001).

Table 1 shows the frequency of ordinal mood statesincluding smoking urges among the 35 men and 25women. The frequency distribution shows that the dataare relatively sparse at levels over 3 for all ordinalvariables, and over 2 for many. This pattern diminishesthe statistical power for testing associations for mostbinary variables defined with cut-points greater than thatbetween 1 and 2. An examination of regression modelsfor both genders using cut-points between 0 and 1 incomparison with 1 and 2 revealed stronger associationsfor cut-points between 1 and 2 for all mood variablesexcept alertness and fatigue, which were more stronglyassociated with smoking behavior, and urges at cut-points between 0 and 1. For smoking urge, a cut-pointbetween 0 and 1 (any urge) showed the strongestassociations to mood variables. For simplicity, binaryregression results presented below are based on cut-points showing the stronger effect estimates. These arecompared to ordinal regression results.

Regression analysis of smoking and smoking urges vs.feelings of anger

Table 2 shows the relationship of smoking urges andbehavior to feelings of anger by gender. In both men andwomen, smoking urge was positively associated withanger states, with the binary OR for all men beingsomewhat higher than that for all women (1.89 vs. 1.50,respectively), although confidence intervals overlappedmarkedly. The ordinal anger outcome model showed that

240 GENDER DIFFERENCES IN SMOKING AND MOOD

Table 1. Distribution of ordinal mood variables reported in diaries for 35 male and 25 female smokers

Mood variable

Ordinal scale: number of observations (row %)

0 (none) 1 2 3 4 5

Smoking urgeMen 1074 (28.3) 494 (13.0) 714 (18.8) 1028 (27.1) 397 (10.4) 93 (2.4)Women 941 (33.0) 263 (9.2) 385 (13.5) 609 (21.3) 441 (15.5) 215 (7.5)

AngerMen 3002 (80.7) 367 (9.9) 216 (5.8) 116 (3.1) 9 (0.2) 12 (0.3)Women 2227 (79.4) 329 (11.7) 146 (5.2) 50 (1.8) 35 (1.2) 17 (0.6)

HappinessMen 519 (14.2) 1121 (30.6) 736 (20.1) 849 (23.2) 236 (6.5) 197 (5.4)Women 559 (20.8) 796 (29.6) 587 (21.8) 601 (22.3) 107 (4.0) 41 (1.5)

SadnessMen 3228 (85.5) 388 (10.3) 115 (3.0) 41 (1.1) 2 (0.0) 1 (0.0)Women 2448 (87.1) 213 (7.6) 61 (2.2) 47 (1.7) 22 (0.8) 19 (0.7)

AlertnessMen 747 (20.2) 846 (22.9) 914 (24.7) 883 (23.9) 226 (6.1) 77 (2.1)Women 668 (24.3) 607 (22.1) 578 (21.0) 622 (22.6) 203 (7.4) 74 (2.7)

FatigueMen 1854 (50.1) 742 (20.0) 550 (14.9) 374 (10.1) 149 (4.0) 33 (0.9)Women 1318 (46.4) 545 (19.2) 451 (15.9) 258 (9.1) 199 (7.0) 68 (2.4)

AnxietyMen 2903 (78.0) 405 (10.9) 219 (5.9) 124 (3.3) 31 (0.8) 40 (1.1)Women 2123 (74.8) 327 (11.5) 214 (7.5) 88 (3.1) 46 (1.6) 39 (1.4)

NICOTINE & TOBACCO RESEARCH 241

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*p<

0.05

; **

p<0.

01;

***p

<0.

001.

aT

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naly

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does

not

incl

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subj

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who

had

no

ange

r ep

isod

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of 1

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1, t

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or c

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s 1–

5, t

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var

iabl

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1 f

or c

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orie

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–5,

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ply

no =

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s =

1. T

he d

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imat

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usi

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R1)

.c

Sm

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/yes

; the

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urge

and

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re o

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om 0

to 5

in in

crea

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inte

nsity

. Whe

n th

e de

pend

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aria

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was

ord

inal

, the

dat

a w

ere

anal

yzed

with

gene

raliz

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atin

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the

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depe

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ure

(SA

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Gen

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as b

inar

ysm

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ta w

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yzed

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and

adju

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R1)

. Odd

s ra

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(OR

) re

pres

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ncre

ased

risk

per

uni

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the

inde

pend

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pred

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d pe

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n to

ris

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hig

her

cate

gorie

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the

ord

inal

out

com

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here

fore

, th

e m

agni

tude

of

the

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is u

sual

ly s

mal

ler

than

the

OR

for

the

bin

ary

data

mod

el.

anger was significantly diminished with reports ofsmoking in the prior 20 min for both men and women.However, there were no associations in the binarymodels between anger and smoking behavior usingeither reports of anger in the previous reporting period(t– 1 ) to predict subsequent smoking behavior, or reportsof smoking in either of the last two periods (t0 or t– 1 ) topredict subsequent anger states.

Dividing the participants into high and low fre-quencies of anger episodes and re-testing binary andordinal models led to some different results for men, butpersistent results for women. Among men, the relation-ship of smoking urge to binary anger was no longersignificant in the subgroups, but smoking urge remainedsignificantly associated with ordinal anger in womenwith higher anger frequency. Both binary and ordinalanger in the previous reporting period (t– 1 ) werepositively associated with subsequent smoking behavior(p<0.05) only among men with anger frequencies £ 5%.Smoking up to 20 min prior to diary entries (t0 ) wasinversely associated with binary anger in men with ahigher overall frequency of anger episodes (p<0.05), butpositively associated with binary anger in men withlower frequency of anger (p<0.01). However, this wasnot consistent with model results for the ordinal angeroutcome, which showed non-significant negative param-eter estimates for men with both higher and lower angerfrequencies. Smoking 10–40 min prior to diary entries(t< 1 ) was not significantly associated with anger in eithergroup of men.

Dropping the man with a high outlying frequency(92%) for binary anger episodes did not change resultsfor any model. Also, testing models in women using the5% cut-point to define anger groups as in men did notchange the results (this included 15 women withepisodes >5% and four women £ 5%). The OR forsmoking urge and binary anger in the higher anger-frequency group was nearly the same as that for allwomen (OR 1.55, 95% CI 1.01–2.39).

Differences in associations between anger-frequencygroups were not due to the frequency of smoking. Bothgroups of men smoked on 23% of diary reports. Forwomen, the group with high anger frequency smoked on22% of diary reports compared with 24% in the groupwith low anger frequency. We also indirectly testedwhether differences in associations between male anger-frequency groups may have been due to differences intobacco dependence as reported in the backgroundquestionnaire. To account for the small cell sizes weconsidered comparisons with a Fisher’s Exact testp-value of <0.20 as being ‘potentially different’. Itshould be noted that this study was not designed forcross-sectional comparisons, and thus, lacks the statis-tical power required for such comparisons. Low vs. highanger frequency men did not differ across severaldependence variables, including: smoking the firstcigarette of the day more than half the time within 30 minof waking (36% vs. 58%, respectively); difficulty givingup the usual first cigarette of the day (43% vs. 50%,

respectively); smoking all of each cigarette smoked(43% vs. 58%, respectively); always inhaling (86% vs.83%, respectively); or holding cigarette smoke in theirlungs a moment or two before exhaling (29% vs. 58%,respectively). Low vs. high anger-frequency men didpotentially differ on several other dependence variables,including: inhaling deeply (50% vs. 83%, respectively,p<0.11); smoking more in the morning than during therest of the day (57% vs. 25%, respectively, p<0.13);difficulty refraining from smoking in places where it isforbidden (14% vs. 42%, respectively, p<0.19); smokinghalf or more of the time when sick with a cold, flu, or soill that they are in bed most of the day (21% vs. 67%,respectively, p<0.05). Except for smoking more in themorning, these last results suggest that the higher angerfrequency men may be somewhat more dependent onsmoking than low anger frequency men. The othercomparisons, although having Fisher’s Exact testp-values of >0.20, did show in most cases higherpercentages representing dependence in the higher angerfrequency men.

Regression analysis of smoking and smoking urges vs.other mood states

Table 3 shows results of GEE models for smoking urge,smoking behavior, and moods. There was a positiveassociation between smoking urge and being happy forwomen (p<0.01), but not men. Ordinal happiness wasweakly associated with increased smoking in the nextperiod in women. There was a significant OR for binaryhappiness and smoking during the current reportingperiod for men, but the magnitude was also weak(1.14).

There was a moderately strong positive associationbetween smoking urge and sadness in men using thebinary model (OR 2.60, p<0.003); the ordinal modelswere significant for both women and men. Most ORs forsmoking and sadness were close to 1.0 except thatsmoking in the prior period was associated withdecreased binary sadness in men.

Both men and women were approximately twice aslikely to report an urge to smoke when feeling anxiousthan when feeling non-anxious. Men who reportedanxiety in the prior interval were somewhat more likelyto then smoke (binary OR 1.28, p<0.06), which isconsistent with findings for smoking urges. Women whoreported smoking in either of the last two periods hadlower levels of anxiety for the ordinal but not binarymodels.

Smoking urges were also related to contemporaneousalertness in both genders. Alertness in the prior reportingperiod was inversely associated with subsequent smok-ing in men (OR 0.73, p<0.05), but not in women.Smoking did not appear to affect subsequent alertnessstates in either gender. A significant association alsoemerged between smoking urge and fatigue in men only(OR 1.68, p<0.01). However, smoking itself was notassociated with fatigue in either men or women.

242 GENDER DIFFERENCES IN SMOKING AND MOOD

Multivariate models of repeated measures: gender,social environment, caffeine, and alcohol

We tested the above models for interaction with genderby including a product term of the ordinal mood orsmoking variable with gender. There were no genderinteractions for anger, but we found some consistencywith the subgroup models in Table 3. Only one of theinteraction terms for ordinal mood or smoking urgemodels reached statistical significance. Men had astronger positive slope than women for anxiety as apredictor of smoking (p<0.02). Several interaction termswere borderline significant and showed women had astronger positive slope for anxiety as a predictor ofsmoking urge (p<0.06); women had a stronger negativeslope for smoking as a predictor of anxiety (p<0.06 forsmoking last 20 min, p<0.08 for smoking last10–40 min); and men had a stronger negative slope foralertness as a predictor of smoking (p<0.09). Theinteraction models for ordinal happiness and fatigue vs.

smoking urge were in the expected direction based on thegroup models, but significance levels were onlyp<0.20.

An interaction term for anger frequency group wasalso tested. The only significant interaction was that lowanger men showed a stronger positive slope than highanger men for anger as a predictor of smoking(p<0.05).

To assess whether social environment confounded theassociations between mood states and smoking behavioror urges shown above, we used two indicator variables,one for family, friends and coworkers vs. being alone(Soc_1), and the other for all other people vs. being alone(Soc_2; Table 4). Men (p<0.05) and women (p<0.11)were half as likely to smoke when around people otherthan family, friends, and coworkers as compared withbeing alone (Table 4). At the same time, men (p<0.03)and women (p<0.15) were more likely to have the urge tosmoke when around people other than family, friends,and coworkers as compared with being alone. The lack

NICOTINE & TOBACCO RESEARCH 243

Table 3. The relationship of smoking urges and behavior to mood states: differences by gender

Dependent variable Independent variable

Men (n=35)

Odds ratios(95% CI) onbinary dataa

Odds ratios(95% CI) onordinal datab

Women (n=25)

Odds ratios(95% CI) onbinary data

Odds ratios(95% CI)

on ordinal data

HappinessSmoking urge Contemporaneous happiness 1.02 (0.73–1.42) 1.10 (0.94–1.30) 1.71 (1.19–2.44)** 1.22 (1.06–1.40)**Smoking Happiness prior 10–40 min 1.03 (0.84–1.25) 1.02 (0.93–1.11) 1.17 (0.91–1.50) 1.09 (1.01–1.18)*Happiness Smoking prior £ 20 min 1.14 (1.02–1.27)* 1.12 (0.91–1.38) 1.00 (0.89–1.13) 1.14 (0.95–1.38)Happiness Smoking prior 10–40 min 0.93 (0.84–1.04) 0.98 (0.82–1.18) 0.95 (0.82–1.11) 1.10 (0.90–1.35)

SadnessSmoking urge Contemporaneous sadness 2.60 (1.41–4.76)** 1.36 (1.10–1.68)** 1.25 (0.89–1.77) 1.25 (1.09–1.42)**Smoking Sadness prior 10–40 min 1.13 (0.86–1.50) 1.03 (0.90–1.18) 0.97 (0.59–1.59) 1.03 (0.89–1.19)Sadness Smoking prior £ 20 min 1.05 (0.78–1.41) 1.05 (0.80–1.38) 1.07 (0.87–1.31) 0.94 (0.73–1.20)Sadness Smoking prior 10–40 min 0.76 (0.60–0.95)* 0.97 (0.80–1.17) 1.01 (0.76–1.34) 0.93 (0.67–1.29)

AnxietySmoking urge Contemporaneous anxiety 2.13 (1.37–3.31)** 1.28 (1.10–1.48)*** 1.90 (1.28–2.81)** 1.50 (1.23–1.83)****Smoking Anxiety prior 10–40 min 1.28 (0.99–1.66) 1.12 (1.00–1.26)* 0.88 (0.65–1.19) 0.94 (0.84–1.04)Anxiety Smoking prior £ 20 min 0.88 (0.73–1.07) 1.05 (0.82–1.35) 0.95 (0.82–1.10) 0.78 (0.64–0.94)*Anxiety Smoking prior 10–40 min 0.99 (0.83–1.18) 1.07 (0.82–1.38) 0.92 (0.75–1.13) 0.80 (0.65–0.97)*

AlertnessSmoking urge Contemporaneous alertness 1.89 (1.22–2.93)** 1.15 (1.00–1.32)* 1.42 (1.05–1.92)* 1.23 (1.07–1.40)**Smoking Alertness prior 10–40 min 0.73 (0.54–0.99)* 0.92 (0.83–1.01) 1.18 (0.85–1.62) 1.03 (0.94–1.12)Alertness Smoking prior £ 20 min 1.11 (0.93–1.33) 0.98 (0.77–1.25) 1.15 (0.99–1.35) 1.08 (0.88–1.34)Alertness Smoking prior 10–40 min 0.98 (0.86–1.12) 0.95 (0.74–1.20) 0.93 (0.82–1.07) 1.07 (0.88–1.30)

FatigueSmoking urge Contemporaneous fatigue 1.68 (1.18–2.40)** 1.13 (1.01–1.27)* 0.92 (0.69–1.24) 1.00 (0.91–1.10)Smoking Fatigue prior 10–40 min 0.99 (0.81–1.22) 0.98 (0.90–1.06) 1.00 (0.80–1.25) 0.97 (0.89–1.06)Fatigue Smoking prior £ 20 min 0.99 (0.89–1.10) 0.99 (0.80–1.21) 1.10 (0.99–1.23) 1.06 (0.84–1.34)Fatigue Smoking prior 10–40 min 0.95 (0.85–1.05) 0.96 (0.79–1.16) 0.94 (0.84–1.06) 0.95 (0.74–1.22)

*p<0.05; ** p<0.01; ***p<0.001; ****p<0.0001.a All variables are dichotomous 0/1; the smoking urge variable is 1 for categories 1–5; the anger variable is 1 for categories 2–5;

smoking is simply no = 0 or yes = 1. The data were analyzed with generalized estimating equations using the logit link function andadjusting for autocorrelation (AR1).

b Smoking is dichotomous no/yes; the remaining smoking urge and mood variables are ordinal from 0 to 5 in increasing intensity. Whenthe dependent variable was ordinal, the data were analyzed with generalized estimating equations using the cumulative logit linkfunction and assuming an independent working correlation structure (SAS Proc Genmod). When the dependent variable was binarysmoking, the data were analyzed with generalized estimating equations using the logit link function and adjusting for autocorrelation(AR1). Odds ratios (OR) represent increased risk per unit increase in the independent predictor variable and pertain to risk of beingin higher categories of the ordinal outcome. Therefore, the magnitude of the OR is usually smaller than the OR for the binary datamodel.

of statistical significance for women may be due to thesmaller sample size of 25 women (with around 2800observations) vs. 35 men (3800 observations). Usingsocial environment to predict mood states (not shown)served to confirm the validity of the present panel databecause, as expected, it was found that both men andwomen were happier (ORs 1.4 and 1.3, respectively) andwere less sad (ORs 0.61 and 0.86, respectively) whenwith family, friends, and coworkers. All were significantexcept the sadness variable in women (p<0.15). Therewere no associations between social environment and theother mood variables.

We then tested multivariate models by adding thesocial environment variables to those discussed abovefor mood states and smoking behavior and urges. Socialenvironment did not confound relationships of mood tosmoking urge or smoking for either men or women.There was also no confounding of anger and smokingrelationships for men or women in any of the angerfrequency groups.

The final set of analyses examined the possibleconfounding effects of caffeine and alcohol consumption(yes/no) on the associations between mood states andsmoking behavior or urges shown above. Smoking urgesand behavior were positively associated with bothcaffeine and alcohol consumption in men and women(Table 4). The strongest effects on smoking were fromcaffeine or alcohol consumption in the prior 10–40-minperiod (Table 4), but consumption in the immediatelyprior 20-min period was also positive for all models andat least p<0.10 (not shown). Fatigue was inverselyassociated, and alertness was positively associated, withcaffeine and with alcohol consumption in both men andwomen, with most models statistically significant ornearly so (Table 4). The one exception was that alcoholwas not associated with fatigue in women (p<0.35).

We then tested multivariate models by adding thecaffeine and alcohol variables to those discussed abovefor mood states and smoking behavior and urges.Caffeine and alcohol consumption did not confound anyof the associations between mood states and smokingurge or behavior.

Discussion

Anger and smoking

We found positive associations between smoking urgeand anger in the same reporting period for both men andwomen. Anger was associated with subsequent smokingonly in men with a lower anger frequency. The ordinalanger models showed that smoking in the prior 20-minperiod was associated with decreased anger in both menand women, but this was not confirmed in the binarymodels. Smoking was associated with decreased binaryanger within the same reporting period among men whohad a higher frequency of anger episodes, but wasassociated with increased binary anger in men with alower frequency of anger episodes. These latter twomodels were not significant using ordinal anger. Therewas no association of smoking at t–1 with anger in thesubsequent diary-reporting period for either group.

The descriptive results comparing estimates of smok-ing dependence from baseline self-reports suggest thatthe higher anger-frequency men may be somewhat moredependent on smoking than low anger-frequency men,despite smoking a similar number of cigarettes. Thissuggests etiological differences in smoking motivations.An alternative explanation is that men who are higher intrait anger experience different social and smoking-restriction environments than do those low in trait anger.However, of four male subjects reporting complete

244 GENDER DIFFERENCES IN SMOKING AND MOOD

Table 4. The relationship of social environments, caffeine, and alcohol consumption to smoking urges and behavior, and to moodstates: selected modelsa

Dependent variable Independent variables Men (n=35); OR (95% CI) Women (n=25); OR (95% CI)

Smoking urge Soc_1 Contemporaneous 1.16 (0.94–1.43) 1.12 (0.91–1.38)Soc_2 Contemporaneous 1.60 (1.06–2.42)* 1.47 (0.87–2.48)

Smoking Soc_1 Contemporaneous 1.02 (0.84–1.24) 0.86 (0.64–1.14)Soc_2 Contemporaneous 0.48 (0.23–0.99)* 0.46 (0.18–1.19)

Smoking urge Alcohol prior £ 20 min 1.42 (1.01–2.00)* 3.11 (1.03–9.43)*Caffeine prior £ 20 min 1.88 (1.11–3.17)* 1.24 (0.93–1.66)*

Smoking Alcohol prior 10–40 min 1.80 (1.19–2.73)** 1.97 (1.04–3.74)*Caffeine prior 10–40 min 1.78 (1.39–2.27)** 1.34 (1.02–1.77)*

Fatigue Alcohol prior £ 20 min 0.69 (0.48–0.99)* 0.80 (0.50–1.27)Caffeine prior £ 20 min 0.82 (0.66–1.01) 0.84 (0.74–0.96)**

Alertness Alcohol prior £ 20 min 1.76 (0.95–3.26) 1.49 (0.93–2.39)Caffeine prior £ 20 min 1.40 (1.07–1.84)* 1.32 (0.96–1.82)

*p<0.05; ** p<0.01.a All variables are dichotomous 0/1. The smoking urge variable is 1 for ordinal categories > 0 (see text); smoking, caffeine and alcohol

consumption are simply no/yes; ‘Soc_1’ is for being with family, friends and coworkers versus being alone (the referent category); and‘Soc_2’ is for being with all other people versus being alone; fatigue and alertness variables are 1 for ordinal categories > 0 (see text).The data were analyzed with generalized estimating equations using the logit link function and adjusting for autocorrelation (AR1).

smoking restrictions at home and/or work, two had lowerand two had higher anger frequencies suggesting thatsmoking restrictions probably do not account for theresults observed in this study.

Our finding that anger is followed by smoking in somemen is consistent with reports that hostility is associatedwith smoking in men but not in women (Musante,Treiber, Davis, Strong, & Levy, 1992; Muller, 1992).Still, other studies have demonstrated this association forboth men and women (Lipkus et al., 1994; Schifano,Forza, & Gallimberti, 1994; Siegler, Peterson, Barefoot,& Williams, 1992). Our findings of anger–smoking urgelinks in women suggest that a causal link between angerand subsequent smoking could exist in women, but itwas not detected in our data. The lack of significance forthe gender-by-smoking or gender by anger interactionterms suggests that our reported gender differences maybe between additive effects and multiplicative inter-action. It is possible that men may be more apt to smokewhen they get an urge to smoke and/or are more ablethan women to pace themselves in smoking-friendlyenvironments.

The ambulatory monitoring results in men suggest thatthe anger-palliative effects of smoking are immediateand short-term. There is a possibility that the effects ofsmoking on feelings of anger may go in oppositedirections for low vs. high anger groups, but the analysisof ordinal anger was inconclusive. The study by Jamneret al. (1999) discussed above suggests that there may bedistinct groups of men who respond differently withrespect to effects of nicotine on anger. They found hightrait hostile individuals had significant reductions inanger reports when on the nicotine patch, but there wasa non-significant increase in anger among low traithostile participants. Both smokers and non-smokersshowed the same results. Individual variation in nicotinemetabolism and function may explain some of thesedifferences. For instance, there is evidence that theeffects of nicotine on the mesolimbic dopamine systemof the brain, which plays a role in the rewardingproperties of nicotine, operate through a balance ofstimulation and desensitization that is likely to be highlyheterogeneous across individuals, given the structuraland functional variation in nicotine receptors (Balfour,1994). This may be related to the action of nicotine indesensitizing nicotinic-acetylcholinergic receptors thatare involved in behavioral reward and arousal pathways.The duration of such desensitization may explainindividual variability in neurobehavioral effects ofnicotine, including the level of tolerance, and mayultimately explain some individual differences in themagnitude of smoking addiction and in the determinantsof daily smoking behavior (Rosecrans & Karan, 1993).

Other emotions and smoking

Sadness was positively associated with smoking urgemore clearly in men than women, whereas feelings ofhappiness were associated with smoking urge in women,

but not men. Although sadness was not associated withsubsequent smoking in either sex, smoking was asso-ciated with a subsequent decrease in binary sadness inmen, not women, and a subsequent increase in happinessin men, not women. We were, therefore, unable tocapture all temporal associations that would support areinforcing causal pathway of sadness and smoking, i.e.,smoking urge occurs with feelings of sadness, smokingthen begins as a result and is followed by palliation ofsadness. Nevertheless, our findings for men are con-sistent with the literature, which shows a consistent linkbetween depressive symptoms and persistent smoking inUS populations (Anda, Williamson, Escobedo, Mast,Giovino, & Remington, 1990). The assumption of such acausal pathway may not apply to all individuals or alloccasions, nor is there necessarily an isomorphismbetween the mood that may trigger smoking and themood that may be changed by smoking. For instance,anxiety, rather than sadness may induce smoking, whichmay in turn palliate the sadness that can accompanyanxiety.

Both men and women were around twice as likely tohave an urge to smoke if they also reported beinganxious. However, increased anxiety was associated withincreased smoking only in men, suggesting that men maybe more likely than women to act when smoking urgeand anxiety are linked. A gender interaction term showedthat women had a stronger positive slope for anxiety asa predictor of smoking urge, whereas men had a strongerpositive slope than women for ordinal anxiety as apredictor of smoking (p<0.02). For women but not men,smoking predicted subsequent decreased anxiety (ordinalmodel), and a gender interaction term reached aborderline level of significance lending further support toa gender difference in the smoking–anxietyassociations.

In a review of the literature on gender differences insmoking, Gilbert (1995) has pointed out that women tendto report negative affect as their primary motivation forsmoking, in contrast to men who report that stimulationand other factors serve as primary motivations. Theseconclusions are based on retrospective survey data. Ourfindings on reports in real time for anger, anxiety, andsadness support an alternate view: men may use smokingfor negative affect regulation as much or even more thando women. These discrepancies between retrospectivesurveys and contemporaneous reports underscore thevalue of ambulatory diary approaches and the potentialfor biased assessments from global self-reports (Shiff-man, 2000).

Evidence that people use smoking as a stimulant issuggested by our findings that alertness is positivelyassociated with smoking urge in men and women. Onlyamong men was decreased alertness associated withsubsequent smoking. Also, fatigue was associated withsmoking urge only in men. These findings suggest thatmen more actively use tobacco as a stimulant than dowomen, a difference also suggested by survey data(reviewed by Gilbert, 1995).

NICOTINE & TOBACCO RESEARCH 245

The relationships of smoking urge or smoking tomood were not confounded by social environment foreither men or women. There were effects of social settingon smoking urge and behavior, however. Smoking urgesignificantly increased in men when among people otherthan family, friends, and coworkers vs. being alone,while at the same time actual smoking behavior sig-nificantly decreased. Effects were of similar magnitudein women but not statistically significant. Neithersmoking urge nor behavior was different when amongfamily, friends, and coworkers vs. being alone. Thispattern suggests that social stigma and/or non-smokingregulations beyond a person’s close social environmentswere exerting measurable influences on behavior, andwere accompanied by increased urges to smoke. Sub-sequent studies can test this hypothesis by includingdiary information on smoking restrictions.

As expected, smoking urge and behavior were sig-nificantly and positively associated with alcohol andcaffeine consumption in men and women. Neitheralcohol nor caffeine intake confounded associations ofsmoking with mood.

Comparisons of smoking dependence failed to revealany consistent differences between men and women.During follow-up, men and women also had similarsmoking frequencies. Therefore, the gender differences inthe associations between mood and smoking are not likelyattributable to gender differences in smoking habits.

Strengths and limitations

These findings demonstrate that the ambulatory monitor-ing approach is informative even with a relatively smallnumber of study participants. The key design featuresthat explain this advantage over larger studies usingsingle questionnaires (e.g., cohort studies) are: (1)frequent collection of real-time data related to moods,activities, locations, and smoking in natural environ-ments, which increases the accuracy of the estimatedexposures and responses; and (2) analytic strengths ofrepeated measures, including the ability to make tempo-rally robust causal inferences, and the ability to conductsusceptibility testing by individual characteristics such asmood frequency or baseline psychological dispositions.For instance, the ambulatory monitoring approach offersa solution to the dilemma of whether the associationbetween smoking and depression is due to self-medica-tion, i.e., whether depression induces the smoking habitbecause smoking elevates mood (Breslau, Kilbey, &Andreski, 1991), or due to an induction of depression bynicotine through an hypothesized effect of nicotine onneuroregulators (Pomerleau & Pomerleau, 1984). Ourfinding of associations between smoking urge or behav-ior and sadness or happiness supports the formerhypothesis, and argues against the existence of an acuteinduction of depression. If nicotine induces depressionthrough some chronic effect on neuroregulators, thentesting the latter hypothesis would require a longer-termfollow-up design.

We expect that most of our data represent proximalreports of exposures and responses, and thereby offer anadvantage over data from designs relying on distant orglobal recall. However, we had no way of verifying thatanswers to diary questions were given at the timesprompted by the cuff inflation or at times before andafter smoking. If answers were recorded later in time, thedata are subject to recall bias and temporal inaccuracies.An alternative approach providing a solution to thisproblem is to use electronic diaries that have time–datestamps for verification of timed entries (Shiffman et al.,1996; 1997; Shiffman, 2000; Whalen, Jamner, Henker, &Delfino, 2000).

The major limitation of the present design is that theintensive monitoring can have the effect of reducing thenumber of participants who can be followed. The resultsmay lack statistical significance for some models due tolimited statistical power, particularly for those modelssubdivided by both gender and anger frequency. Genderdifferences may be attributable, in part, to the fact thatthere were fewer women (25) than men (35). Underlyingdemographic differences or consistent differences inestimates of smoking dependence were not foundbetween genders. However, the number of participants(as opposed to the large number of repeated measures inthe ambulatory data) limited the statistical power todetect potential between-subject confounding factors.Also, the small sample sizes of ambulatory studies suchas the present one decrease the ability to generalizebroadly. Nevertheless, the real-life nature of ambulatorydata makes the information more externally valid insome respects than data obtained either in experimentalsettings or from recalled experiences (Shiffman, 2000).

The presence of more associations between smokingurges and moods than between smoking and moods,particularly for women, may have been partly due to thehigher frequency of urges (2/3) as compared withsmoking (1/4), and fewer environmental (e.g., social)constraints on urges. It may also be that the urge–affectlinks are partially driven by expectancies and learninghistories that may be independent of neurochemicalreward processes.

The significant findings in the present analysis are notlikely to be due to multiple testing bias. If the findingswere due to chance, we would expect three significantassociations to have emerged at the p<0.05 level for the64 binary regression models for mood-smoking linkages(Tables 2 and 3). There were actually 14 significantassociations, and seven were at the p<0.01 level.Moreover, some previous research results are consistentwith our findings, lending further confidence in theirvalidity.

It is possible that dichotomization weakened themagnitude of the underlying causal associations, assuggested by the ordinal models. We also expect the trueslopes for the ordinal variables to be non-linear. It isimportant to note that the data are not independent, i.e.,repeated measures from individual subjects are corre-lated, are unbalanced or have unequal numbers of

246 GENDER DIFFERENCES IN SMOKING AND MOOD

observations in different individuals, and have missingobservations. Therefore, the data cannot be analyzedwith standard regression models such as analysis ofvariance (ANOVA). Approaches to the analysis ofordinal mood ratings can be advanced through themodeling of clustered ordinal responses using emergingstatistical methods (Hedeker & Gibbons, 1994), butavailable software has been limited. SAS programs forordinal GEE models used in the present analysis and fornon-linear mixed models are conceptually suitable toemploy these methods but currently they only model theindependent working correlation and do not take intoaccount autocorrelation. For this reason, some of theordinal models reported above may have underestimatedstandard errors, and statistical significance may beinflated. S-Plus software may offer more advancedmodeling options to solve this problem.

Despite the limitations noted above, we argue that thegeneral approach we used may provide potentially richinsights into causal mechanisms in smoking addiction byassessing relationships between determinants and out-comes in real time. The present design and analyticapproach is underutilized by researchers. As statisticalsoftware programs improve, the sophisticated modelingrequired by these approaches should become moreaccessible and feasible.

Some of the gender differences could be attributed togender-based participation bias, i.e., those who volunteermay have different psychological characteristics fromnon-volunteers and these differences may be greater inone gender vs. the other. This type of bias could decreasethe relevance of the findings to the general population. Itis also possible that gender differences were confoundedby differences in underlying traits between men andwomen enrolled in this study, or by gender-baseddifferences in work and home environments and accessto smoking settings.

In women, menstrual phase may influence the associa-tions between mood states and nicotine effects (Gilbert,1995), and studies such as the present one that do notcontrol for menstrual phase may obscure some linkages.Future ambulatory studies should consider monitoringmenstrual phase and comparing mood–smoking relation-ships across menstrual phases.

Conclusions

Our findings suggest that anger and negative affect maytrigger smoking in some people, a process that mayexplain the higher relapse rates following smokingcessation that have been reported for high-hostile thanlow-hostile (Lipkus et al., 1994) and for depressed thannon-depressed individuals (Anda et al., 1990). Smokingcessation and preventive interventions may require newmethods that teach anger and stress management as wellas broader aspects of effective emotion regulation.

Our findings also add insight to the current evidenceof gender differences in the success of smoking cessationprograms, including those involving nicotine replace-

ment therapy (Brandon, 1994). Women may be lesssuccessful than men in such programs, because nicotinemay be less reinforcing for women in terms of a putativeneuroregulation of moods and performance. It has beenproposed that psychosocial aspects of tobacco may bemore important to sustained tobacco use in women thanin men (reviewed by Perkins, 1996). If smoking cues andreinforcing effects differ in men and women, smokingcessation interventions may require some level ofgender-specificity if they are to succeed.

The progress of smoking intervention trials willprobably remain limited until causal pathways ofnicotine addiction and smoking relapse are betterunderstood. Research using ambulatory data collectionmethods is needed to characterize within- and between-individual susceptibility to the day-to-day and evenminute-to-minute influences of smoking cues and moodstates. Such research can be extended to model theprogression of real-time associations of smoking,moods, behaviors, and contexts across stages of growthand development, particularly during adolescence whenthe smoking addiction process typically begins. Theprogression of real-time associations can also be mod-eled through the process of smoking cessation oncedependence or addiction has taken place. To accom-plish these goals, large repeated-measures studies areneeded in diverse populations that vary by potentiallyrelevant host susceptibility factors (sex, age, socio-economic status, race, and psychological profile). Theserecommendations are supported by evidence from thepresent study that the real-time factors driving smokingurges and behavior differ by gender and by short-termpsychological states, as well as by findings from arecent study of adolescents establishing links betweenthe frequency of smoking events collected in real-timeand aggressive and depressive personality dispositions(Whalen et al., 2000).

NICOTINE & TOBACCO RESEARCH 247

Acknowledgments

Some results were presented at the American Thoracic SocietyInternational Conference, San Diego, CA, 23–28 April 1999. Thisstudy was supported by grants from the California Tobacco-RelatedDisease Research Program (6RT–0154 and 6PT–3003), the NationalCancer Institute (CA 1R01 080301), and the National Institute of DrugAbuse (TTURC; CA 1P50 84723). The authors wish to thank Dr.Georjeanna Wilson-Doenges and Janel Alberts who assisted with thedata collection phases of the study; we are also indebted to Dr. DavidShapiro for his contributions to the logistic execution of the study.

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