Parental smoking and smoking cognitions among youth

$\textit {Aims:}$ We summarized and discussed the empirical evidence for an association between parental smoking and smoking-related cognitions among youth and for the mediating role of smoking-related cognitions in the relation between parental and youth smoking behaviour. $\textit {Methods:}$ We conducted a systematic review of articles published between 1980 and February 2015 using the databases PsychInfo and PubMed. $\textit {Results:}$ The systematic search resulted in 41 eligible studies. Only 4 studies investigated smoking-related cognitions as putative mediators in the association between parental and youth smoking. The synthesis of evidence showed a mix of significant and non-significant associations between parental smoking and smoking-related cognitions among youth. A majority of results reported positive associations even when non-significant findings were found. However, studies that report an effect suggest that the effect may be quite modest. $\textit {Conclusion:}$ Empirical evidence does not confirm the commonly applied assertions of social learning theories that parental smoking increases the risk of youth smoking through the development of favourable smoking-related cognitions. Methodological and theoretical aspects that might explain the lack of consistent findings are discussed

Association of Reduced Nicotine Content Cigarettes With Smoking Behaviors and Biomarkers of Exposure Among Slow and Fast Nicotine Metabolizers: A Nonrandomized Clinical Trial.

ImportanceThe US Food and Drug Administration (FDA) has announced its intention to reduce the nicotine content in combustible cigarettes but must base regulation on public health benefits. Fast nicotine metabolizers may be at risk for increased smoking following a national nicotine reduction policy. We hypothesized that using reduced nicotine content (RNC) cigarettes would be associated with increases in smoking behaviors and exposure among smokers with a fast-but not slow-nicotine-metabolite ratio (NMR).ObjectivesTo examine the association of RNC cigarettes with smoking behaviors and biomarkers of exposure and to compare these associations in fast and slow metabolizers of nicotine based on the NMR.Design setting and participantsA 35-day, 3-period, within-participant nonrandomized clinical trial was conducted at an academic medical center in Philadelphia, Pennsylvania. A 5-day baseline period using the smokers' preferred brand of cigarettes was followed by 2 consecutive 15-day periods using free investigational RNC cigarettes. A total of 100 daily, non-treatment-seeking, nonmenthol cigarette smokers (59 fast, 41 slow metabolizers) were recruited from December 24, 2013, to December 2, 2015. Data analysis was performed from December 12, 2016, to January 3, 2018.InterventionsTwo 15-day periods using cigarettes containing 5.2 mg (RNC1) and 1.3 mg (RNC2) of nicotine per gram of tobacco.Main outcomes and measuresSmoking behaviors (number of cigarettes per day [CPD], total puff volume) and biomarkers of exposure (carbon monoxide [CO], urine total nicotine equivalents [TNE], and 4-[methylnitrosamino]-1-[3-pyridyl]-1-butanol [NNAL]).ResultsSmokers (73 [73.0%] men; 74 [74.0%] white; mean [SD] age, 43.02 [12.13] years; mean [SD] CPD, 17.31 [5.72]) consumed 2.62 (95% CI, 1.54-3.70) more CPD during the RNC1 period vs their preferred brand during baseline (P < .001) and approximated baseline CPD during the RNC2 period (mean difference, 0.96 [95% CI, -0.36 to 2.28]; P = .24). Additional outcome measures were lower during both RNC periods vs baseline (total puff volume, mean [95% CI]: RNC1, 537 mL [95% CI, 479-595 mL]; RNC2, 598 mL [95% CI, 547-649 mL] vs baseline, 744 mL [95% CI, 681-806 mL]; TNE, mean [95% CI]: RNC1, 30.9 nmoL/mg creatinine [95% CI, 26.0-36.6 nmoL/mg]; RNC2, 22.8 nmoL/mg creatinine [95% CI, 17.8-29.0 nmoL/mg] vs baseline, 54.6 nmoL/mg creatinine [95% CI, 48.1-62.1 nmoL/mg]; and NNAL, mean [95% CI]: RNC1, 229 pg/mg creatinine [95% CI, 189-277 pg/mg]; RNC2, 190 pg/mg creatinine [95% CI, 157-231 pg/mg] vs baseline, 280 pg/mg creatinine [95% CI, 231-339 pg/mg]; all P < .001). Carbon monoxide measures were similar across study periods (CO boost [SD], RNC1, 4.6 ppm [4.1-5.1 ppm]; RNC2, 4.2 ppm [3.7-4.6 ppm]; and baseline, 4.4 ppm [3.8-4.9 ppm]). The RNC cigarette associations did not differ by NMR.Conclusions and relevanceBoth RNC cigarettes were associated with decreased puffing and urinary biomarker exposure but not with decreased daily cigarette consumption or CO levels. The NMR did not moderate associations at the nicotine levels tested, suggesting that fast metabolizers may not be at greater risk of increasing use or exposure from these products should the FDA mandate an RNC standard for cigarettes

The power of social influence over food intake: examining the effects of attentional bias and impulsivity

Contains fulltext : 116733.pdf (publisher's version ) (Closed access)Numerous studies have shown that people adjust their intake directly to that of their eating companions. A potential explanation for this modelling effect is that the eating behaviour of others operates as an external eating cue that stimulates food intake. The present study explored whether this cue-reactive mechanism can account for modelling effects on intake. It was investigated whether attentional bias towards dynamic eating cues and impulsivity would influence the degree of modelling. Participants completed one individual session and one session in which an experimental confederate accompanied them. In the first session, eye movements were recorded as an index of attentional bias to dynamic eating cues. In addition, self-reported impulsivity and response inhibition were assessed. The second session employed a between-participants design with three experimental conditions in which participants were exposed to a same-sex confederate instructed to eat nothing, a low or a large amount of M&Ms. A total of eighty-five young women participated. The participants' self-reported impulsivity determined the occurrence of modelling; only low-impulsive women adjusted their intake to that of their eating companion. Attention towards eating cues and response inhibition, however, did not moderate modelling of food intake. The present study suggests that cue-reactive mechanisms may not underlie modelling of food intake. Instead, the results emphasise the importance of social norms in explaining modelling effects, whereas it is suggested that the degree of impulsivity may play a role in whether or not women adhere to the intake norms set by their eating companion