Remote Carbon Monoxide Capture via REDCap: Evaluation of an Integrated Mobile Application.

INTRODUCTION: To improve the feasibility of remote biochemical verification of smoking status, our team developed "COast", a mobile app integrated with REDCap that allows a research participant to complete self-report research assessments and provide a breath sample via the iCOQuit® Smokerlyzer® for the purposes of carbon monoxide (CO) testing. The aims of the present study were to examine: 1) the validity of remote CO data capture using COast as compared to gold standard approaches (salivary cotinine, stand-alone CO monitor) and 2) the feasibility of remote CO data capture using COast as applied to both daily and weekly CO collection schedules. METHODS: Participants (N=143, 59% Female), including recently quit (n=36) and current (n=107) smokers, completed a baseline video session to capture validity data, and then were randomized to daily or weekly CO monitoring for a period of one month. RESULTS: Balancing both sensitivity and specificity, optimal cut-points for defining abstinence using the COast system were < 4 parts per million (ppm) with salivary cotinine as the referent (Sensitivity = 100%, Specificity = 92.8%) and < 8ppm with the stand-alone CO monitor as the referent (Sensitivity = 100%, Specificity = 88.9%). Compliance across groups with CO monitoring was high with average compliance of 74% for the daily group and 84% for the weekly group. Self-reported feasibility and acceptability of using the system were strong. CONCLUSIONS: Pairing the iCOQuit with REDCap via the COast app was both valid and feasible among a sample of adults who smoke cigarettes enrolled remotely. This integration may help to improve the rigor of decentralized smoking cessation trials. IMPLICATIONS: With increasing prevalence of decentralized trial designs, innovative methods are needed to remotely capture biomarkers. Methods that leverage existing widely available research data capture platforms may be particularly useful for promoting adoption. The COast app, which integrates a Bluetooth-enabled carbon monoxide monitor with REDCap, is a fitting, valid, feasible solution to remotely biochemically verify smoking status.

Behavioral Activation-Based Digital Smoking Cessation Intervention for Individuals With Depressive Symptoms: Randomized Clinical Trial.

BACKGROUND: Depression is common among adults who smoke cigarettes. Existing depression-specific cessation interventions have limited reach and are unlikely to improve smoking prevalence rates among this large subgroup of smokers. OBJECTIVE: This study aimed to determine whether a mobile app-based intervention tailored for depression paired with a mailed sample of nicotine replacement therapy (NRT) is efficacious for treating depression and promoting smoking cessation. METHODS: A 2-arm nationwide remote randomized clinical trial was conducted in the United States. Adults (N=150) with elevated depressive symptoms (Patient Health Questionnaire-8≥10) who smoked were enrolled. The mobile app ("Goal2Quit") provided behavioral strategies for treating depression and quitting smoking based on Behavioral Activation Treatment for Depression. Goal2Quit participants also received a 2-week sample of combination NRT. Treatment as usual participants received a self-help booklet for quitting smoking that was not tailored for depression. Primary end points included Goal2Quit usability, change in depression (Beck Depression Inventory-II) across 12 weeks, and smoking cessation including reduction in cigarettes per day, incidence of 24-hour quit attempts, floating abstinence, and 7-day point prevalence abstinence (PPA). RESULTS: In total, 150 participants were enrolled between June 25, 2020, and February 23, 2022, of which 80 were female (53.3%) and the mean age was 38.4 (SD 10.3) years. At baseline, participants on average reported moderate depressive symptoms and smoked a mean of 14.7 (SD 7.5) cigarettes per day. Goal2Quit usability was strong with a mean usability rating on the System Usability Scale of 78.5 (SD 16.9), with 70% scoring above the ≥68 cutoff for above-average usability. Retention data for app use were generally strong immediately following trial enrollment and declined in subsequent weeks. Those who received Goal2Quit and the NRT sample reported lower mean depressive symptoms over the trial duration as compared to treatment as usual (difference of mean 3.72, SE 1.37 points less; P=.01). Across time points, all cessation outcomes favored Goal2Quit. Regarding abstinence, Goal2Quit participants reported significantly higher rates of 7-day PPA at weeks 4 (11% vs 0%; P=.02), 8 (7-day PPA: 12% vs 0%; P=.02), and 12 (16% vs 2%; P=.02). CONCLUSIONS: A mobile app intervention tailored for depression paired with a sample of NRT was effective for depression treatment and smoking cessation. Findings support the utility of this intervention approach for addressing the currently unmet public health treatment need for tailored, scalable depression-specific cessation treatments. TRIAL REGISTRATION: ClinicalTrials.gov NCT03837379; https://clinicaltrials.gov/ct2/show/NCT03837379.

Rationale, design, and protocol for a hybrid type 1 effectiveness-implementation trial of a proactive smoking cessation electronic visit for scalable delivery via primary care: the E-STOP trial.

BACKGROUND: Cigarette smoking remains the leading cause of preventable disease and death in the United States. Primary care offers an ideal setting to reach adults who smoke cigarettes and improve uptake of evidence-based cessation treatment. Although U.S. Preventive Services Task Force Guidelines recommend the 5As model (Ask, Advise, Assess, Assist, Arrange) in primary care, there are many barriers to its implementation. Automated, comprehensive, and proactive tools are needed to overcome barriers. Our team developed and preliminarily evaluated a proactive electronic visit (e-visit) delivered via the Electronic Health Record patient portal to facilitate evidence-based smoking cessation treatment uptake in primary care, with promising initial feasibility and efficacy. This paper describes the rationale, design, and protocol for an ongoing Hybrid Type I effectiveness-implementation trial that will simultaneously assess effectiveness of the e-visit intervention for smoking cessation as well as implementation potential across diverse primary care settings. METHODS: The primary aim of this remote five-year study is to examine the effectiveness of the e-visit intervention vs. treatment as usual (TAU) for smoking cessation via a clinic-randomized clinical trial. Adults who smoke cigarettes are recruited across 18 primary care clinics. Clinics are stratified based on their number of primary care providers and randomized 2:1 to either e-visit or TAU. An initial baseline e-visit gathers information about patient smoking history and motivation to quit, and a clinical decision support algorithm determines the best evidence-based cessation treatment to prescribe. E-visit recommendations are evaluated by a patient's own provider, and a one-month follow-up e-visit assesses cessation progress. Main outcomes include: (1) cessation treatment utilization (medication, psychosocial cessation counseling), (2) reduction in cigarettes per day, and (3) biochemically verified 7-day point prevalence abstinence (PPA) at six-months. We hypothesize that patients randomized to the e-visit condition will have better cessation outcomes (vs. TAU). A secondary aim evaluates e-visit implementation potential at patient, provider, and organizational levels using a mixed-methods approach. Implementation outcomes include acceptability, adoption, fidelity, implementation cost, penetration, and sustainability. DISCUSSION: This asynchronous, proactive e-visit intervention could provide substantial benefits for patients, providers, and primary care practices and has potential to widely improve reach of evidence-based cessation treatment. TRIAL REGISTRATION: NCT05493254.

Electronic cigarette dependence and demand among pod mod users as a function of smoking status.

Electronic cigarette (e-cigarette) use continues to proliferate with fast-paced product evolution. Pod mod e-cigarettes emerged in the market in 2015 and have changed the tobacco landscape again. However, little is known regarding their addiction potential among users. The current study describes e-cigarette dependence and demand among pod mod users as a function of smoking status (current smokers/dual users, former smokers, and never smokers). Participants were 593 young adult (Mage = 25.9 years) JUUL users recruited via Amazon's Mechanical Turk. Respondents were specifically recruited based on current use of pod mods (but may also be using other e-cigarette devices) and smoking status (never, former, and current/dual users). Participants completed online measures assessing e-cigarette dependence and demand. Dual users of pod mods and cigarettes displayed greater e-cigarette dependence compared to current pod mod users with no history of cigarette smoking (p = .033). Similarly, dual users showed the greatest levels of e-cigarette demand compared to both former smokers and those without a history of smoking (ps < .05). Dual users displayed the greatest e-cigarette dependence and demand. Future research should directly assess potential mechanisms for this effect and continue to monitor e-cigarette dependence as the tobacco landscape changes with the emergence of new e-cigarette products and innovations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).