Expectations and Preferences for Digital Cessation Treatment: Multimethods Study Among Older Adults Who Smoke Cigarettes.

BACKGROUND: To address enduring age-related tobacco disparities, it is critical to promote cessation treatment among older adults (aged 65+ years). Digital health platforms offer opportunities for wide dissemination of evidence-based behavioral cessation support. However, existing digital cessation treatments are not tailored to unique aging-related needs and preferences, resulting in low uptake. Detailed information is needed about how to best adapt these treatments for this age group. OBJECTIVE: We aimed to collect detailed, hypothesis-generating information about expectations and preferences for cessation digital treatment among older adults who smoke cigarettes. METHODS: Semistructured interviews were conducted with adults aged 65+ years currently smoking or who had quit within the past month. Interviews included open-ended questions regarding prior experiences with digital health platforms and expectations and preferences for cessation treatment via various modalities (app-delivered, texting-based, or videoconferencing counseling). Interviews also elicited questions regarding digital modalities that integrated social components (app-delivered social forums and group videoconferencing counseling). Using an iterative, team-based approach, the thematic analysis identified meaningful themes. Interviews were supplemented with quantitative measures assessing sociodemographics, digital literacy, and physical health symptoms. RESULTS: Participants (12/20, 60% men; 15/20, 75% White; 4/20, 20% Black or African American; 1/20, 5% Asian) were currently smoking (17/20, 85%) or had recently quit (3/20, 15%). Thematic analysis identified 3 meaningful themes across all digital modalities: convenience, accessibility, and personalization. Expected benefits of digital platforms included convenient treatment access, without reliance on transportation. Participants preferred treatments to be personalized and deliver content or strategies beyond standard education. Most (17/20, 85%) were unfamiliar with cessation apps but found them appealing given the potential for offering a novel quitting strategy. App ease of use (eg, easy navigation) was preferred. Half (10/20, 50%) would try a texting-based intervention, with many preferring texting with a counselor rather than automated messaging. Most (17/20, 85%) would use videoconferencing and expected this modality to deliver better quality counseling than via telephone. Expected videoconferencing challenges included looking presentable onscreen, technological difficulties, and privacy or security. Videoconferencing was regarded as the most personalized digital treatment, yet benefits unique to app-delivered and texting-based treatments included anonymity and access to treatment 24/7. Participants expected integrating social components into digital treatment to be useful for quit success and social connection, yet were concerned about possible interpersonal challenges. CONCLUSIONS: Because a long history of quit attempts and familiarity with standard quitting advice is common among older adults who smoke cigarettes, digital platforms might offer appealing and novel strategies for cessation that are accessible and convenient. Overall, this population was open to trying digital cessation treatments and would prefer that these platforms prioritize ease of use and personalized content. These findings challenge the bias that older adults are uninterested or unwilling to engage with digital treatments for behavioral health.

Designing a phase-III time-to-event clinical trial using a modified sample size formula and Poisson-Gamma model for subject accrual that accounts for the lag in site initiation using the PERT distribution.

A priori estimation of sample size and subject accrual in multi-site, time-to-event clinical trials is often challenging. Such trials are powered based on the number of events needed to detect a clinically significant difference. Sample size based on number of events relates to the expected duration of observation time for each subject. Temporal patterns in site initiation and subject enrollment ultimately affect when subjects can be accrued into the study. Lag times are common as the site start-up process optimizes, resulting in delays that may curtail observational follow-up and therefore undermine power. The proposed method introduces a Program Evaluation and Review Technique (PERT) model into the sample size estimation which accounts for the lag in site start-up. Additionally, a PERT model is introduced into a Poisson-Gamma subject accrual model to predict the quantity of study sites needed. The introduction of the PERT model provides greater flexibility in both a priori power assessment and planning the number of sites, as it specifically allows for the inclusion of anticipated delays in site start-up time. This model results in minimal power loss even when PERT distribution inputs are misspecified compared to the traditional assumption of simultaneous start-up for all sites. Together these updated formulations for sample size and subject accrual models offer an improved method for designing a multi-site time-to-event clinical trial that accounts for a flexible site start-up process.

The Impact of Older Age on Smoking Cessation Outcomes After Standard Advice to Quit.

Older adults are a high priority population for smoking cessation. This study observed the influence of older age on the relationship between quitting predictors and cessation. Secondary analysis was conducted of a primary care trial of adults who smoke cigarettes randomized to standard advice to quit or advice plus 2 week supply of nicotine replacement therapy. Logistic regressions assessed interactions of age (<60 vs. ≥ 60 years) by predictors in relation to quit attempts and 6-month cessation controlling for treatment. Predicting quit attempts, there was an interaction of age by motivation (p = .025) and trend of age by nicotine dependence (p = .057). Predicting 6-month cessation, an interaction of age by motivation was observed (p = .020) and a trend of age by dependence (p = .067). For those with high nicotine dependence, older adults had less successful outcomes compared to younger counterparts. Yet among participants unmotivated to quit, older adults had more successful outcomes than those younger.

"And then I found $5": Optimizing recruitment efficiency in remote clinical trials.

Introduction: As clinical trials adopt remote methodologies, there is need to optimize efficiency of remote enrollment. Within a remote clinical trial, we aim to (1) assess if sociodemographic factors differ among those consenting via mail vs. technology-based procedures (e-consent), (2) determine if, among those consenting via mail, a small unconditional monetary reward ($5) increases likelihood of subsequent enrollment, (3) economically evaluate additional cost per additional participant enrolled with $5 reward. Methods: In the parent nationwide randomized clinical trial of adult smokers (N = 638), participants could enroll via mail or e-consent. Logistic regression models assessed relationships between sociodemographics and enrollment via mail (vs e-consent). Mailed consent packets were randomized (1:4) to include $5 unconditional reward or not, and logistic regression modeling examined impact of reward on subsequent enrollment, allowing for a randomized study within a study. Incremental cost-effectiveness ratio analysis estimated additional cost per additional participant enrolled with $5 incentive. Results: Older age, less education, lower income, and female sex predicted enrolling via mail vs e-consent (p < .05's). In adjusted model, older age (AOR = 1.02, p = .016) and less education (AOR = 2.23, p < .001) remained predictive of mail enrollment. The $5 incentive (vs none) increased enrollment rate by 9% (AOR = 1.64, p = .007), with estimated cost of additional $59 per additional participant enrolled. Conclusions: As e-consent methods become more common, they have potential to reach many individuals but with perhaps diminished inclusion across all sociodemographic groups. Provision of an unconditional monetary incentive is possibly a cost-effective mechanism to increase recruitment efficiency for studies employing mail-based consenting procedures.

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.