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BackgroundModifiable risk factors such as tobacco use, physical inactivity, and poor diet account for a significant proportion of the preventable deaths in Canada. These factors are also known to cluster together, thereby compounding the risks of morbidity and mortality. Given this association, smoking cessation programs appear to be well-suited for integration of health promotion activities for other modifiable risk factors. The Smoking Treatment for Ontario Patients (STOP) program is a province-wide smoking cessation program that currently encourages practitioners to deliver Screening, Brief Intervention, and Referral to treatment for patients who are experiencing depressive symptoms or consume excessive amounts of alcohol via a web-enabled clinical decision support system. However, there is no available clinical decision support system for physical inactivity and poor diet, which are among the leading modifiable risk factors for chronic diseases. ObjectiveThe aim of this study is to assess whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcomes. MethodsThis study is designed as a hybrid type 1 effectiveness/implementation randomized controlled trial to evaluate a web-enabled clinical decision support system for supporting practitioners in addressing patients’ physical activity and diet as part of smoking cessation treatment in a primary care setting. This design was chosen as it allows for simultaneous testing of the intervention, its delivery in target settings, and the potential for implementation in real-world situations. Intervention effectiveness will be measured using a two-arm randomized controlled trial. Health care practitioners will be unblinded to their patients’ treatment allocation; however, patients will be blinded to whether their practitioner receives the clinical decision support system for physical activity and/or fruit/vegetable consumption. The evaluation of implementation will be guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework. ResultsRecruitment for the primary outcome of this study is ongoing and will be completed in November 2020. Results will be reported in March 2021. ConclusionsThe findings of the study will provide much needed insight into whether adding a computerized/web-enabled clinical decision support system for physical activity and diet to a smoking cessation program affects smoking cessation outcome. Furthermore, the implementation evaluation would provide insight into the feasibility of online-based interventions for physical activity and diet in a smoking cessation program. Addressing these risk factors simultaneously could have significant positive effects on chronic disease and cancer prevention. Trial RegistrationClinicalTrials.gov NCT04223336; https://clinicaltrials.gov/ct2/show/NCT04223336 International Registered Report Identifier (IRRID)DERR1-10.2196/19157

Introduction Trial evidence suggests that e-cigarettes may aid in quitting smoking, while observational studies have found conflicting results. However, many observational studies have not adjusted for important differences between e-cigarette users and non-users. Aims and Methods We aimed to determine the association between e-cigarette use frequency and motivation to use e-cigarettes to quit smoking, and smoking cessation using data from Canada's largest smoking cessation program. Participants who completed a baseline assessment and 6-month follow-up questionnaire were divided post hoc into four groups based on their self-reported e-cigarette use during the 30 days before baseline: (1) non-users; (2) users of e-cigarettes not containing nicotine; (3) occasional users; and (4) frequent users. Occasional and frequent users were further divided into two groups based on whether they reported using e-cigarettes to quit smoking. Abstinence at 6-month follow-up (7-day point prevalence abstinence) was compared among groups. Results Adjusted quit probabilities were significantly higher (both p  < .001) for frequent baseline e-cigarette users (31.6%; 95% CI = 29.3%, 33.8%) than for non-users (25.8%; 25.3% and 26.3%) or occasional users (24.2%; 22.5% and 26.0%). Unadjusted proportions favored non-users over occasional users (p  < .001), but this was not significant after adjustment (p  = .06). People using e-cigarettes to quit smoking were not likelier than other users to be successful, but were likelier to report frequent e-cigarette use during follow-up. Conclusions Frequent baseline e-cigarette use predicted successful smoking cessation, compared to occasional and non-users. Use of e-cigarettes to quit did not predict smoking cessation but was associated with continued use during follow-up, perhaps due in part to planned transitions to e-cigarettes. Implications Prior observational studies investigating e-cigarette use for smoking cessation have found that occasional users have poorer outcomes than either frequent or non-users. Consistent with these studies, occasional users in our data also had poorer outcomes. However, after adjustment for variables associated with cessation success, we found that cessation probabilities did not differ between occasional and non-users. These findings are consistent with trial data showing the benefit of e-cigarette use among people trying to quit smoking. Results of this study suggest that differences between trials and previous observational studies may be because of unaddressed confounding in the latter. [ABSTRACT FROM AUTHOR]

Background Adhering to varenicline has been shown to signifcantly improve the chances of successfully quitting smoking, with studies indicating a twofold increase in 6-month quit rates. However, despite its potential benefts, many individuals struggle with maintaining good adherence to varenicline; thus there is a need to develop scalable strategies to help people adhere. As a frst step to inform the development of an intervention to improve adherence to varenicline, we conducted a rapid literature review to identify: 1) modifable barriers and facilitators to varenicline adherence, and 2) behaviour change techniques associated with increased adherence to varenicline. Methods We searched MEDLINE, Embase, APA PsycINFO, CINAHL, and the Cochrane Central Register of Controlled Trials for relevant studies published between 2006 and 2022. Search terms included “varenicline,” “smoking cessation,” and "adherence," and their respective subject headings and synonyms. We screened and included studies reporting modifable determinants of adherence to varenicline and then assessed quality, extracted modifable determinants and mapped them to the Theoretical Domains Framework version 2 and the Behaviour Change Technique Taxonomy version 1. Results A total of 1,221 titles were identifed through the database searches; 61 met the eligibility criteria. Most of the studies were randomized controlled trials and predominantly focused on barriers to varenicline. Only nine studies explicitly mentioned behaviour change techniques used to help varenicline adherence. Eight domains were identifed as barriers to varenicline adherence (behavioural regulation, memory, goals, intentions, beliefs about capabilities, beliefs about consequences, optimism/pessimism, and environmental context) and fve as facilitators (knowledge, behavioural regulation, beliefs about capabilities, social infuences, and environmental context). Conclusions This study identifes barriers and facilitators that should be addressed when developing a complex adherence intervention tailored to patients’ needs based on modifable determinants of medication adherence, some of which are under- used by existing adherence interventions. The fndings from this review will inform the design of a theory-based healthbot planned to improve varenicline adherence in people undergoing smoking cessation treatment. [ABSTRACT FROM AUTHOR]