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Population-Level Dissemination of a Smoking Cessation Smartphone App: Quasi-Experimental Comparison of Values-Based Messages in Social Media Advertisements

Population-Level Dissemination of a Smoking Cessation Smartphone App: Quasi-Experimental Comparison of Values-Based Messages in Social Media Advertisements

Our group developed i Can Quit, an Acceptance and Commitment Therapy (ACT)-based smartphone smoking cessation app [13]. Unlike standard interventions for smoking cessation [14], ACT-based smoking cessation interventions teach people to observe, acknowledge, and accept their cravings to smoke rather than avoid them, and focus on life values, instead of expectations, as motivation to quit [13,15]. i Can Quit targets 2 core theoretical processes of ACT, acceptance and values.

Jonathan B Bricker, Margarita Santiago-Torres, Kristin E Mull, Brianna M Sullivan, Ravi Mehrotra

JMIR Mhealth Uhealth 2025;13:e71619

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Evaluating the Impact of Pharmacotherapy in Augmenting Quit Rates Among Hispanic Adults in an App-Delivered Smoking Cessation Intervention: Secondary Analysis of a Randomized Controlled Trial

Evaluating the Impact of Pharmacotherapy in Augmenting Quit Rates Among Hispanic Adults in an App-Delivered Smoking Cessation Intervention: Secondary Analysis of a Randomized Controlled Trial

In the i Can Quit parent RCT, pharmacotherapy use was an exclusion criterion, but participants could opt to use it on their own after randomization [25]. In fact, both the i Can Quit and Quit Guide app–based interventions educate users on Food and Drug Administration (FDA)–approved pharmacotherapy to aid cessation [25].

Margarita Santiago-Torres, Kristin E Mull, Brianna M Sullivan, Ana Paula Cupertino, Ramzi G Salloum, Matthew Triplette, Michael J Zvolensky, Jonathan B Bricker

JMIR Form Res 2025;9:e69311

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Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

For the main aim of training a model to predict early dropouts, data were drawn from the i Can Quit app arm of a 2-arm randomized controlled trial (RCT) comparing i Can Quit with the NCI Quit Guide app for smoking cessation, with full protocol details previously described [26].

Jonathan Bricker, Zhen Miao, Kristin Mull, Margarita Santiago-Torres, David M Vock

J Med Internet Res 2023;25:e43629

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Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

Smoking Cessation Smartphone App Use Over Time: Predicting 12-Month Cessation Outcomes in a 2-Arm Randomized Trial

The data retention rate was 88% (1886/2133) and differed slightly between arms (90% in Quit Guide vs 87% in i Can Quit; P=.01). Participants randomized to the i Can Quit arm received access to download the i Can Quit smartphone app (version 1.2.1). i Can Quit intervenes on the ACT-focused processes of acceptance of internal cues to smoke and enacting one’s values that guide smoking cessation [12].

Jonathan B Bricker, Kristin E Mull, Margarita Santiago-Torres, Zhen Miao, Olga Perski, Chongzhi Di

J Med Internet Res 2022;24(8):e39208

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