The accurate assessment of nicotine withdrawal is important theoretically and clinically. A 28-item scale, the Wisconsin Smoking Withdrawal Scale, was developed that contains 7 reliable subscales tapping the major symptom elements of the nicotine withdrawal syndrome. Coefficients alpha for the subscales range from .75 to .93. This scale is sensitive to smoking withdrawal, is predictive of smoking cessation outcomes, and yields data that conform to a 7-factor structure. The 7 scales predicted intratreatment smoking, chi2(7, N = 163) = 15.19, p = .034. Moreover, the questionnaire is sufficiently brief so that it can be used in both clinical and research contexts.

Recent models of addiction posit that drug outcome expectancies are influential determinants of drug use. The current research examines the dimensional structure, predictive validity, and discriminant validity of expectancies for cigarette smoking in a prospective study. There was a good fit between the factor structure of the Smoking Consequences Questionnaire and the observed data. In addition, the internal consistency of each scale was satisfactory. Moreover, there was considerable evidence for the predictive and discriminant validity of expectancies. Expectancies of positive outcomes (positive reinforcement, negative reinforcement, and appetite-weight control) predicted withdrawal severity. Negative reinforcement expectancies and expectancies of negative consequences predicted cessation success. Predictive relations remained significant after controlling for related constructs: negative affect, stress, and dependence measures.

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BackgroundDespite substantial public health progress in reducing the prevalence of smoking in the United States overall, smoking among socioeconomically disadvantaged adults remains high.ObjectiveTo determine the feasibility and preliminary effectiveness of a novel smartphone-based smoking cessation app designed for socioeconomically disadvantaged smokers.MethodsParticipants were recruited from a safety-net hospital smoking cessation clinic in Dallas, Texas, and were followed for 13 weeks. All participants received standard smoking cessation clinic care (ie, group counseling and cessation pharmacotherapy) and a smartphone with a novel smoking cessation app (ie, Smart-T). The Smart-T app prompted 5 daily ecological momentary assessments (EMAs) for 3 weeks (ie, 1 week before cessation and 2 weeks after cessation). During the precessation period, EMAs were followed by messages that focused on planning and preparing for the quit attempt. During the postcessation period, participant responses to EMAs drove an algorithm that tailored messages to the current level of smoking lapse risk and currently present lapse triggers (eg, urge to smoke, stress). Smart-T offered additional intervention features on demand (eg, one-click access to the tobacco cessation quitline; “Quit Tips” on coping with urges to smoke, mood, and stress).ResultsParticipants (N=59) were 52.0 (SD 7.0) years old, 54% (32/59) female, and 53% (31/59) African American, and 70% (40/57) had annual household income less than US $16,000. Participants smoked 20.3 (SD 11.6) cigarettes per day and had been smoking for 31.6 (SD 10.9) years. Twelve weeks after the scheduled quit date, 20% (12/59) of all participants were biochemically confirmed abstinent. Participants responded to 87% of all prompted EMAs and received approximately 102 treatment messages over the 3-week EMA period. Most participants (83%, 49/59) used the on-demand app features. Individuals with greater nicotine dependence and minority race used the Quit Tips feature more than their counterparts. Greater use of the Quit Tips feature was linked to nonabstinence at the 2 (P=.02), 4 (P<.01), and 12 (P=.03) week follow-up visits. Most participants reported that they actually used or implemented the tailored app-generated messages and suggestions (83%, 49/59); the app-generated messages were helpful (97%, 57/59); they would like to use the app in the future if they were to lapse (97%, 57/59); and they would like to refer friends who smoke to use the Smart-T app (85%, 50/59). A minority of participants (15%, 9/59) reported that the number of daily assessments (ie, 5) was “too high.”ConclusionsThis novel just-in-time adaptive intervention delivered an intensive intervention (ie, 102 messages over a 3-week period), was well-liked, and was perceived as helpful and useful by socioeconomically disadvantaged adults who were seeking smoking cessation treatment. Smartphone apps may be used to increase treatment exposure and may ultimately reduce tobacco-related health disparities among socioeconomically disadvant...

Examined the effects of offender sex, offender status, and consequence severity on accounts following an embarrassing predicament. Subjects were induced to believe they had committed a gaffe with either relatively mild or severe consequences for a confederate/victim of either higher or lower status than they, and their verbal and nonverbal behaviors captured on videotape served as the source of dependent variable measures. Verbal accounts were coded using Schonbach's (1980) account taxonomy. Nonverbal behaviors were also coded, as were measures of subjects' verbal and behavioral helping. Results showed a main effect for sex on account length (p < .001), number of concessionary elements (p < .001), and verbal helping scores (p = .001). Mitigating accounts were proffered more than aggravating accounts. Two-way interactions among sex, status, account type, and severity also were obtained.Social interactions do not always proceed smoothly. More often than we would like to think, we say or do something we and/or others wish we had not said or done, or fail to say or do something we and/or others wish we had, that is, when norms or role-based expectations are violated or when untoward acts are intentionally or unintentionally committed. Because such disruptions in otherwise fluid social encounters occur frequently, they have received a great deal of attention from microsociologists, psychologists, and sociolinguists alike (e.g.,