Faculty Bibliography

PURPOSE/UNASSIGNED:This study describes the frequency of JUUL e-cigarette (referred to as JUUL) quit attempts and identifies characteristics associated with confidence in quitting and perceived difficulty quitting JUUL. DESIGN/UNASSIGNED:Cross-sectional study from a self-administered online survey. SETTING/UNASSIGNED:Two public southern California universities. PARTICIPANTS/UNASSIGNED:A total of 1,001 undergraduate students completed the survey from February to May 2019. MEASURES/UNASSIGNED:Self-report measures about JUUL included use, history of quit attempts, time to first use, perceived difficulty with cessation/reduction, and confidence in quitting. ANALYSIS/UNASSIGNED:Binary logistic regressions were used to identify demographic and tobacco-related behavioral correlates of JUUL cessation-related perceptions and behaviors. RESULTS/UNASSIGNED:Nearly half of ever-JUUL users (47.8%) reported a JUUL quit attempt. Adjusting for demographic factors and other tobacco product use, shorter time to first JUUL use after waking was associated with lower confidence in quitting JUUL (aOR = 0.02, 0.00-0.13) and greater perceived difficulty in quitting JUUL (aOR = 8.08, 2.15-30.35). Previous JUUL quit attempt history was also associated with greater odds of perceived difficulty quitting JUUL (aOR = 5.97, 1.74-20.53). CONCLUSIONS/UNASSIGNED:History of JUUL quit attempts among college students was common. Those who had previously tried quitting were more likely to perceive difficulty with cessation. Time to first JUUL use, a marker of dependence, was linked with greater perceived cessation difficulty and lower confidence in quitting. These findings suggest that there is a need for cessation and relapse prevention support for college student JUUL users.

INTRODUCTION/BACKGROUND:People with serious mental illness (SMI) have a high smoking prevalence and low quit rates. Few cessation treatments are tested in smokers with SMI. Mental health (MH) providers are reluctant to address smoking. Proactive tobacco cessation treatment strategies reach out directly to smokers to offer counseling and medication and improve treatment utilization and quit rates. The current study is a secondary analysis of a randomized controlled trial of proactive outreach for tobacco cessation treatment in VA MH patients. METHODS:Participants (N=1938, 83% male, mean age 55.7) across 4 recruitment sites, who were current smokers and had a MH visit in the past 12 months, were identified using the electronic medical record. Participants were randomized to Intervention (telephone outreach call plus invitation to engage in MH tailored telephone counseling and assistance obtaining nicotine replacement therapy [NRT]) or Control (usual care). The current study assessed outcomes in participants with SMI (N=982). RESULTS:Compared to the Control group, participants assigned to the Intervention group were more likely to engage in telephone counseling (22% vs. 3%) and use NRT (51% vs. 41%). Participants in the Intervention group were more likely to be abstinent (7-day point prevalence; 18%) at 12 months than participants in the Control group; 11%) but equally likely to make quit attempts. CONCLUSIONS:Proactive tobacco cessation treatment is an effective strategy for tobacco users with SMI. Proactive outreach had a particularly strong effect on counseling utilization. Future randomized clinical trials examining proactive tobacco treatment approaches in SMI treatment settings are needed. IMPLICATIONS/CONCLUSIONS:Few effective treatment models exist for smokers with serious mental illness. Proactive tobacco cessation outreach with connections to mental health tailored telephone counseling and medication promotes tobacco abstinence among smokers with serious mental illness and is an effective treatment strategy for this underserved population.

Mobile technology has created the opportunity for health systems to provide low cost tobacco cessation assistance to patients. The goal of the present study was to examine the feasibility and effectiveness of an intervention (Text2Connect) that uses text messages to offer proactive connection to the New York State Smokers' Quitline. The electronic health record at two urban health systems was queried for patients who were current smokers and who had an outpatient visit between March 2015 and February 2016. Smokers (N = 4000) were sent an informational letter. Those who did not opt out (N = 3719) were randomized to one of 6 message sequences in order to examine the effect of theoretically informed message frames on response rates. Participants were sent a series of text messages at baseline and at 1 month and were asked to reply in order to be contacted by the state quitline (QL). After removing 1403 nonworking numbers, texts were sent to 2316 patients, and 10.0% (205/2060) responded with a QL request. Almost one quarter (23.6%, 486/2060) replied STOP and 66.4% (1369/2060) never responded. QL request rates were significantly higher when response efficacy messages were not used (p < .001). There were no differences by message framing on STOP requests (p > .05). The Text2Connect intervention was well accepted with a minority opting out. A 10% QL response rate is noteworthy given that only 5-7 brief outreach text messages were used. Results indicate that simple self-efficacy-focused messaging is most effective at supporting response rates.

We sought to qualitatively explore how those at highest risk for lung cancer, current smokers, experienced, understood, and made decisions about participation in lung cancer screening (LCS) after being offered in the target setting for implementation, routine primary care visits. Thirty-seven current smokers were identified within 4 weeks of being offered LCS at seven sites participating in the Veterans Health Administration Clinical Demonstration Project and interviewed via telephone using semi-structured qualitative interviews. Transcripts were coded by two raters and analyzed thematically using iterative inductive content analysis. Five challenges to smokers' decision-making lead to overestimated benefits and minimized risks of LCS: fear of lung cancer fixated focus on inflated screening benefits; shame, regret, and low self-esteem stemming from continued smoking situated screening as less averse and more beneficial; screening was mistakenly believed to provide general evaluation of lungs and reassurance was sought about potential damage caused by smoking; decision-making was deferred to providers; and indifference about numerical educational information that was poorly understood. Biased understanding of risks and benefits was complicated by emotion-driven, uninformed decision-making. Emotional and cognitive biases may interfere with educating and supporting smokers' decision-making and may require interventions tailored for their unique needs.

Mobile technology has been designed to serve a number of functions relating to health, but we know little about individuals who use these tools to track sleep. This study utilized data from a cross-sectional, geographically diverse survey of adults in the USA (N = 934). Among the sample, 28.2% (n = 263) report current use of a mobile phone for sleep tracking. Income and gender were significant correlates of sleep tracking (p < 0.05). Compared to a poor diet, a reported "excellent" diet was associated with sleep tracking (p < 0.05). Interestingly, compared to individuals who never smoke, report of smoking "everyday" was associated with sleep tracking (p < 0.05). Finally, individuals who reported current use of their mobile device for other health functions (e.g., chat with their doctor or log symptoms) were more likely to report sleep tracking on their mobile device (p < 0.05). Results appear to suggest sleep tracking is common among individuals with good general health.

BACKGROUND:Although smoking cessation apps have become popular, few have been tested in randomized clinical trials or undergone formative evaluation with target users. OBJECTIVE:We developed a cessation app targeting tobacco-dependent cancer patients. Game design and behavioral rehearsal principles were incorporated to help smokers identify, model, and practice coping strategies to avoid relapse to smoking. In this randomized pilot trial, we examined feasibility (recruitment and retention rates), acceptability (patient satisfaction), quitting self-confidence, and other cessation-related indices to guide the development of a larger trial. METHODS:We randomized 42 English-speaking cancer patients scheduled for surgical treatment to either the Standard Care (SC; telecounseling and cessation pharmacotherapies) or the experimental QuitIT study arm (SC and QuitIT game). Gameplay parameters were captured in-game; satisfaction with the game was assessed at 1-month follow-up. We report study screening, exclusion, and refusal reasons; compare refusal and attrition by key demographic and clinical variables; and report tobacco-related outcomes. RESULTS:Follow-up data were collected from 65% (13/20) patients in the QuitIT and 61% (11/18) in SC arms. Study enrollees were 71% (27/38) females, 92% (35/38) white people, and 95% (36/38) non-Hispanic people. Most had either lung (12/38, 32%) or gastrointestinal (9/38, 24%) cancer. Those dropping out were less likely than completers to have used a tablet (P<.01) and have played the game at all (P=.02) and more likely to be older (P=.05). Of 20 patients in the QuitIT arm, 40% (8/20) played the game (system data). There were no differences between those who played and did not play by demographic, clinical, technology use, and tobacco-related variables. Users completed an average of 2.5 (SD 4.0) episodes out of 10. A nonsignificant trend was found for increased confidence to quit in the QuitIT arm (d=0.25, 95% CI -0.56 to 1.06), and more participants were abstinent in the QuitIT group than in the SC arm (4/13, 30%, vs 2/11, 18%). Satisfaction with gameplay was largely positive, with most respondents enjoying use, relating to the characters, and endorsing that gameplay helped them cope with actual smoking urges. CONCLUSIONS:Recruitment and retention difficulties suggest that the perihospitalization period may be a less than ideal time for delivering a smoking cessation app intervention. Framing of the app as a "game" may have decreased receptivity as participants may have been preoccupied with hospitalization demands and illness concerns. Less tablet experience and older age were associated with participant dropout. Although satisfaction with the gameplay was high, 60% (12/20) of QuitIT participants did not play the game. Paying more attention to patient engagement, changing the intervention delivery period, providing additional reward and support for use, and improving cessation app training may bolster feasibility for a larger trial. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov NCT01915836; https://clinicaltrials.gov/ct2/show/NCT01915836 (Archived by WebCite at http://www.webcitation.org/73vGsjG0Y).

Background/UNASSIGNED:Many tobacco dependent cancer survivors continue to smoke after diagnosis and treatment. This study investigated the extent to which hospital-based cancer registries could be used to identify smokers in order to offer them assistance in quitting. The concordance of tobacco use coded in the registry was compared with tobacco use as coded in the accompanying Electronic Health Records (EHRs). Methods/UNASSIGNED:We gathered data from three hospital-based cancer registries in New York City during June 2014 to December 2016. For each patient identified as a current combustible tobacco user in the cancer registries, we abstracted tobacco use data from their EHR to independently code and corroborate smoking status. We calculated the proportion of current smokers, former smokers, and never smokers as indicated in the EHR for the hospitals, cancer site, cancer stage, and sex. We used a logistic regression model to estimate the log odds of the registry-based smoking status correctly predicting the EHR-based smoking status. Results/UNASSIGNED:Agreement in current smoking status between the registry-based smoking status and the EHR-based smoking status was 65%, 71%, and 90% at the three participating hospitals. Logistic regression results indicated that agreement in smoking status between the registry and the EHRs varied by hospital, cancer type, and stage, but not by age and sex. Conclusions/UNASSIGNED:The utility of using tobacco use data in cancer registries for population-based tobacco treatment interventions is dependent on multiple factors including accurate entry into EHR systems, updated data, and consistent smoking status definitions and registry coding protocols. Our study found that accuracy varied across the three hospitals and may not be able to inform interventions at these hospitals at this time. Several changes may be needed to improve the coding of tobacco use status in EHRs and registries.