ObjectiveSome researchers have raised concerns that pictorial health warning labels (HWLs) on cigarette packages may lead to message rejection and reduced effectiveness of HWL messages. This study aimed to determine how state reactance (i.e., negative affect due to perceived manipulation) in response to both pictorial and text-only HWLs is associated with other types of HWL responses and with subsequent cessation attempts.MethodsSurvey data were collected every 4 months between September 2013 and 2014 from online panels of adult smokers in Australia, Canada, Mexico, and the US were analyzed. Participants with at least one wave of follow-up were included in the analysis (n = 4,072 smokers; 7,459 observations). Surveys assessed psychological and behavioral responses to HWLs (i.e., attention to HWLs, cognitive elaboration of risks due to HWLs, avoiding HWLs, and forgoing cigarettes because of HWLs) and cessation attempts. Participants then viewed specific HWLs from their countries and were queried about affective state reactance. Logistic and linear Generalized Estimating Equation (GEE) models regressed each of the psychological and behavioral HWL responses on reactance, while controlling for socio-demographic and smoking-related variables. Logistic GEE models also regressed having attempted to quit by the subsequent survey on reactance, each of the psychological and behavioral HWL responses (analyzed separately), adjustment variables. Data from all countries were initially pooled, with interactions between country and reactance assessed; when interactions were statistically significant, country-stratified models were estimated.ResultsInteractions between country and reactance were found in all models that regressed psychological and behavioral HWL responses on study variables. In the US, stronger reactance was associated with more frequent reading of HWLs and thinking about health risks. Smokers from all four countries with stronger reactance reported greater likelihood of avoiding warnings and forgoing cigarettes due to warnings, although the association appeared stronger in the US. Both stronger HWLs responses and reactance were positively associated with subsequent cessation attempts, with no significant interaction between country and reactance.ConclusionsReactance towards HWLs does not appear to interfere with quitting, which is consistent with its being an indicator of concern, not a systematic effort to avoid HWL message engagement.
Background In June 2012, Canada implemented new pictorial warnings on cigarette packages, along with package inserts with messages to promote response efficacy (i.e., perceived quitting benefits) and self-efficacy (i.e., confidence to quit). This study assessed smokers’ attention towards warnings and inserts and its relationship with efficacy beliefs, risk perceptions and cessation at follow-up. Methods Data were analysed in 2015 from a prospective online consumer panel of adult Canadian smokers surveyed every four months between September 2012 and September 2014. Generalized Estimating Equation models assessed associations between reading inserts, reading warnings and efficacy beliefs (self-efficacy, response efficacy), risk perceptions, quit attempts of any length, and sustained quit attempts (i.e., 30 days or more) at follow-up. Models adjusted for socio-demographics, smoking-related variables, and time-in-sample effects. Results Over the study period, reading warnings significantly decreased (p<0.0001) while reading inserts increased (p=0.004). More frequent reading of warnings was associated independently with stronger response efficacy (Boften/very often vs never=0.28, 95% CI: 0.11–0.46) and risk perceptions at follow-up (Boften/very often vs never=0.31, 95% CI: 0.06–0.56). More frequent reading of inserts was associated independently with stronger self-efficacy to quit at follow-up (Btwice or more vs none=0.30, 95% CI: 0.14–0.47), quit attempts (ORtwice or more vs none= 1.68, 95% CI: 1.28–2.19), and quit attempts lasting 30 days or longer (ORtwice or more vs none=1.48, 95% CI: 1.01 – 2.17). Conclusions More frequent reading of inserts was associated with self-efficacy to quit, quit attempts, and sustained quitting at follow-up, suggesting that inserts complement pictorial HWLs.
BackgroundSmoking is the leading cause of preventable death in the world today. Ecological research on smoking in context currently relies on self-reported smoking behavior. Emerging smartwatch technology may more objectively measure smoking behavior by automatically detecting smoking sessions using robust machine learning models.ObjectiveThis study aimed to examine the feasibility of detecting smoking behavior using smartwatches. The second aim of this study was to compare the success of observing smoking behavior with smartwatches to that of conventional self-reporting.MethodsA convenience sample of smokers was recruited for this study. Participants (N=10) recorded 12 hours of accelerometer data using a mobile phone and smartwatch. During these 12 hours, they engaged in various daily activities, including smoking, for which they logged the beginning and end of each smoking session. Raw data were classified as either smoking or nonsmoking using a machine learning model for pattern recognition. The accuracy of the model was evaluated by comparing the output with a detailed description of a modeled smoking session.ResultsIn total, 120 hours of data were collected from participants and analyzed. The accuracy of self-reported smoking was approximately 78% (96/123). Our model was successful in detecting 100 of 123 (81%) smoking sessions recorded by participants. After eliminating sessions from the participants that did not adhere to study protocols, the true positive detection rate of the smartwatch based-detection increased to more than 90%. During the 120 hours of combined observation time, only 22 false positive smoking sessions were detected resulting in a 2.8% false positive rate.ConclusionsSmartwatch technology can provide an accurate, nonintrusive means of monitoring smoking behavior in natural contexts. The use of machine learning algorithms for passively detecting smoking sessions may enrich ecological momentary assessment protocols and cessation intervention studies that often rely on self-reported behaviors and may not allow for targeted data collection and communications around smoking events.
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