Deborah M. Scharf

Analyzing Milestones in Smoking Cessation: Illustration in a Nicotine Patch Trial in Adult Smokers

Tests of addiction treatments seldom reveal where treatment exercises its effect (i.e., promoting initial abstinence, preventing lapses, and/or impeding progression from lapse to relapse). The authors illustrate analyses distinguishing effects on these milestones in a randomized trial of high-dose nicotine patch (35 mg; n = 188) versus placebo (n = 136) in adult smokers, who used electronic diaries to monitor smoking in real time during 5 weeks of treatment. High-dose patch promoted initial abstinence (hazard ratio [HR] = 1.3) and decreased the risk of lapsing among those who achieved abstinence (HR = 1.6). The biggest effect of treatment was to prevent progression to relapse among those who had lapsed (HR = 7.1). Analysis of effects by milestones may enhance understanding of cessation treatments and their mechanisms of action.

Patterns of intermittent smoking: An analysis using Ecological Momentary Assessment

Non-daily smokers comprise a substantial proportion of US smokers, but there has been little study of their patterns of smoking, which are often assumed to reflect social smoking. We used Ecological Momentary Assessment methods to study smoking patterns in 27 non-daily smoking adults who recorded each cigarette smoked over three weeks by leaving a voice mail message indicating their circumstances at the time of smoking. All told, 689 cigarettes were recorded over 589 person-days of observation. On average, participants smoked on 67% of days, averaging 2.1 (SD=0.91) cigarettes per day on days they smoked; 22% of all cigarettes were smoked in bouts (within an hour of another cigarette). Altogether, 19% of cigarettes were smoked when drinking alcohol and 29% when participants were socializing. Smoking patterns varied widely across participants. A pair of hierarchical cluster analyses distinguished three groups: Those who smoked primarily (81% of cigarettes) in the daytime (Early smokers; n=15, 58% of total sample), those who smoked primarily (75% of cigarettes) at night (Late smokers; n=7, 27%), and a distinct, classic Social smoking group (n=4, 15% of total sample), who smoked mostly at night but also primarily when socializing or drinking (86% of their cigarettes), in the evening (71% of their cigarettes), on weekends (65% of their cigarettes), and in bouts (71% of their cigarettes). Overall, results suggest that non-daily smoking patterns are quite heterogeneous, and that many non-daily smokers may not be primarily social smokers.

Daily smoking patterns, their determinants, and implications for quitting.

In this article, the authors examine daily temporal patterns of smoking in relation to environmental restrictions on smoking and cessation outcomes. Time-series methods were used for analyzing cycles in 351 smokers who monitored their smoking in real time for 2 weeks. The waking day was divided into 8 bins of approximately 2 hr, cigarette counts were tallied for each bin, and temporal patterns of smoking and restriction were analyzed. Cluster analyses of smoking patterns by time of day resulted in 4 clusters: daily decline (n = 30; 9%), morning high (n = 43; 12%), flatline (n = 247; 70%), and daily dip-evening incline (n = 31; 9%). Clusters differed in baseline demographic, smoking, and psychosocial variables. Results suggest that smoking behavior can be characterized by regular patterns of smoking frequency during the waking day: Smoking in the flatline cluster was within +/-0.5 standard deviation at all times. For the other clusters, smoking was high in the morning (daily dip-evening incline: +1.7 standard deviations; morning high: +2.8 standard deviations; daily decline: +1.7 standard deviations); moderate (morning high: -0.8 standard deviations; daily decline: +0.3 standard deviations) or low (daily dip-evening incline: -1.0 standard deviations) midday; and high (daily dip-evening incline: +2.0 standard deviations), moderate (morning high: +0.5 standard deviations), or low (daily decline: -1.5 standard deviations) in the evening. Daily smoking patterns were related to environmental smoking restrictions, but the strength of this relationship differed among clusters and by time of day. Clusters differed in lapse risk.

Do smokers crave cigarettes in some smoking situations more than others? Situational correlates of craving when smoking.

INTRODUCTIONnSmokers tend to smoke when experiencing craving, but even within smoking occasions, craving may vary. We examine variations in craving when people were smoking in various real-world situations.nnnMETHODSnUsing Ecological Momentary Assessment, 394 smokers recorded smoking, craving, and smoking context in real time on electronic diaries over 2 weeks of ad libitum smoking. Assessments occurred immediately prior to smoking. Mixed modeling was used to analyze associations between craving and situational variables.nnnRESULTSnCraving varied across smoking situations, but the differences were small (<1 on a 0-10 scale). Specifically, craving was higher in smoking situations where smoking was restricted, likely because high craving leads smokers to violate restrictions. Controlling for restrictions, craving was higher when cigarettes were smoked while eating or drinking, were with other people (vs. alone), were in a group of people (vs. other people simply in view), during work (vs. leisure), and during activity (vs. inactivity). In addition, craving was higher for cigarettes smoked early in the day. No differences in craving were observed in relation to drinking alcohol or caffeine (vs. doing anything else), being at work (vs. home), being at a bar or restaurant (vs. all other locations), interacting with others (vs. not interacting), or other people smoking (vs. no others smoking).nnnDISCUSSIONnEven though most craving reports prior to smoking were high, and situations were thus expected to have little influence on craving, results suggest that some cigarettes are craved more than others across different smoking situations, but differences are small.

Within-day temporal patterns of smoking, withdrawal symptoms, and craving ☆

We examined the temporal relationships between smoking frequency and craving and withdrawal. 351 heavy smokers (≥15 cigarettes per day) used ecological momentary assessment and electronic diaries to track smoking, craving, negative affect, arousal, restlessness, and attention disturbance in real time over 16 days. The waking day was divided into 8 2-h bins during which cigarette counts and mean levels of craving and withdrawal were computed. Cross-sectional analyses showed no association between restlessness and smoking, and arousal and smoking, but craving (b=0.65, p<0.01) was positively associated, and negative affect (b=-0.20, p<0.01), and attention disturbance (b=-0.24, p<0.01) were inversely associated with smoking. In prospective lagged analyses, higher craving predicted more subsequent smoking and higher smoking predicted lower craving (ps<0.01). Higher restlessness also predicted more subsequent smoking and higher smoking predicted lower restlessness (ps<0.01). Higher negative affect did not predict later smoking, but more smoking preceded lower negative affect (p<0.01). Neither attention disturbance nor arousal predicted, or were predicted by variations in smoking. In short, smoking exhibits time-lagged, reciprocal relationships with craving and restlessness, and a one-way predictive relationship with negative affect. Temporal patterns of craving and restlessness may aid in the design of smoking cessation interventions.

Education and Treatment for Boys Who Set Fires: Specificity, Moderators, and Predictors of Recidivism

Given the relative absence of treatment outcome studies, information about the specificity and utility of interventions for children who set fires has not been reported. In a treatment outcome study with young boys referred for firesetting that compared brief home visitation from a firefighter, fire safety education (FSE), and cognitive—behavioral treatment (CBT), we examined the specificity, potential moderators, and predictors of recidivism. FSE exerted specific effects on some fire knowledge and safety measures, as expected; CBT tended to show specific effects only on positive problem solutions. Potential moderators of FSE and CBT were suggested in an exploratory analysis (e.g.,exposure to fire models/materials,childs general fire knowledge, and family functioning). Fire history,fire attraction, and externalizing behaviors were among the predictors of firesetting recidivism. Implications of these findings for assessment and intervention are discussed in the context of future research directions.

Focusing on the five A's: A comparison of homeless and housed patients' access to and use of pharmacist-provided smoking cessation treatment

INTRODUCTIONnThe prevalence of smoking remains high among the medically underserved and could be related to disparities in access to and use of smoking cessation treatments.nnnMETHODSnThis study implemented and tracked providers use of the 5 As intervention for tobacco use (Ask, Assess, Advise, Assist, Arrange) with homeless (n = 260) and housed (n = 226) adults attending a free medical clinic, including referrals to and use of an on-site pharmacist-led smoking cessation service.nnnRESULTSnAmong patients whose tobacco use was Asked about and Assessed (97%), homeless (vs. housed) patients were more likely to smoke (59% vs. 39%; P = 0.008). Among current smokers, there were no homeless-housed disparities in receipt of Advice to quit smoking (84% vs. 78%; P = 0.22) or Arrangement of treatment (36% vs. 31%; P = 0.46). Overall, among patients for whom treatment was Arranged, homeless patients were less likely than housed patients to attend the smoking cessation program (25% vs. 48%; P = 0.04). However, among those that attended any treatment (i.e., were Assisted to quit), homeless and housed patients attended similar numbers of sessions and used pharmacotherapy at similar rates.nnnCONCLUSIONSnProviders may reduce homeless-housed disparities in smoking by offering special Assist(ance) to homeless smokers that reduces barriers to initially accessing treatment services.

Can Big Tobacco's Power Wall Be Breached?

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