For the pilot, the health system will run a workbench report in Epic to identify English-speaking tobacco users aged 18 or older with an upcoming appointment with any of the 5 participating clinicians from primary care/internal medicine. Invitations for patients with an upcoming appointment in the next 42 days will be sent via an alert in MyHealth, the patient portal. These proactive communications will be sent on behalf of the clinician whom the patient is scheduled to see and will invite the patient to visit the study landing page. The landing page will provide an overview of the study, include frequently asked questions, and will include a link to a brief screener. Potentially interested patients will be asked whether they currently use any tobacco products; own an iPhone 6s or newer (available since 9/2015); and are able and willing to download a tobacco cessation app. Patients will be excluded if they do not use tobacco, do not own a compatible iPhone, or are unwilling/unable to download the app. Eligible patients will be provided with an informed consent document to review and a link to a specific section of the app store to download FHIR-ed Up. Recruitment will continue until each clinician has recruited 20 patients. Once the participant completes the informed consent and screener (i.e., baseline assessment of patient's smoking history, current rate of tobacco use, psychosocial expectancies, and readiness to quit smoking, tobacco products, and electronic nicotine delivery systems (ENDs), they will be asked to download the app. As part of the app onboarding process, users will be invited to enable the app to access their health records via their Health app. Users will be invited to interact with the app as often as they like over the following 30 days. Each user's experience will be tailored to their responses. All new data elements that become available from user assessments or the Health app will be used to further tailor the user's feedback, nudges, and prompts. Data on the patient's readiness to quit, current smoking status, number of interactions with the app, and the evidence-based behavior change message that would be most helpful for the patient will be transmitted to a research assistant at the health system who will manually enter it into the patient's chart in the pre-visit encounter note to simulate an automated transmission and ensure it is available for the upcoming visit. At that visit, the data elements will thus be in a chart note at the clinician's fingertips in the patient visit view of Epic. Within one week of the visit, participants will receive an email or text message inviting them to complete a follow-up survey, including readiness to change, psychosocial expectancies, acceptability measures, and open-ended questions regarding what they liked most and least about each intervention component and how the app could be improved. Patients who don't complete it within one week will be called and asked to complete by phone. In addition to the specified outcomes of feasibility, acceptability, and preliminary effects, the app will provide an array of utilization and engagement data at the participant level. Key utilization metrics will include app opens and activities accessed by user.

Upon the completion of the pilot test, the 5 participating clinicians will be asked to complete an online survey. They will be asked what they liked most and least about the EHR prompts, major facilitators and barriers to implementation, and how the prompts could be improved. Their feedback on what went well and ideas for addressing any challenges will be used to revise procedures to help ensure successful implementation in Phase II. They will also be asked to complete a modified version of Weiner's 3-dimensional measure (acceptability, appropriateness, feasibility) of implementation outcome. Data with regard to the number of patients for whom each clinician delivered the behavior change message will be captured.

This pilot involves a single-group, pre-post design. Simple descriptive statistics will be calculated to describe the study sample and to evaluate app utilization and acceptability. Data analysis for the final outcome, psychosocial expectancies, will be analyzed using general linear mixed models (GLMM). Residuals will be examined to make a final determination regarding type of distribution - discrete count, ordinal, or continuous. Count and ordinal outcomes will be analyzed with Statistical Analysis System (SAS) Proc GLIMMIX; continuous outcomes will be analyzed using General Linear Models (a subclass of GLMM) with SAS Proc Mixed. The primary model will assess whether psychosocial expectancies increased over time. Based on accumulated data from Pro-Change intervention trials and pilot tests, we estimate an effect size d=.30 for psychosocial expectancies. Assuming study retention rate of 75%, we will recruit 100 patients so that N=75 remain at follow-up to provide a power estimate that will exceed β= .80 in a one-tailed test at α= .05. Secondary analyses will be conducted using GLMM to examine the impact of the intervention on patient's readiness to quit tobacco; confidence for quitting, and tobacco use behavior. Intervention dose-response effects based on level of engagement/utilization of FHIR-ed Up and clinician interaction will also be conducted.