Developing and pre-testing a digital decision-tree smartphone application for smoking prevention and cessation among HIV care providers

Abstract

Objectives: The diagnosis and continuous care of chronic conditions such as HIV infection present potential teachable moments for delivering smoking prevention and cessation interventions for patients. We designed and pre-tested a prototype of a smartphone application(app), Decision-T, specifically designed to assist healthcare providers when providing personalized smoking prevention and cessation services to their patients. Methods: We developed the Decision-T app based on transtheoretical algorithm for smoking prevention and cessation following the 5-A's model. We employed a mixed-methods approach among 18 HIV-care providers recruited from Houston Metropolitan Area for pre-testing the app. Each provider participated in three mock sessions, and the average time spent at each session was measured. We measured accuracy by comparing the smoking prevention and cessation treatment offered by the HIV-care provider using the app to that chosen by the tobacco specialist who designed the case. The system usability scale (SUS) was used to assess usability quantitatively, while individual interview transcripts were analyzed to determine usability qualitatively. STATA-17/SE and Nvivo-V12 were used for quantitative and qualitative analysis, respectively. Results: The average time for completing each mock session was 5 min 17 s. The participants achieved an overall average accuracy of 89.9%. The average SUS score achieved was 87.5(±10.26). After analyzing the transcripts, five themes (app's contents are beneficial and straightforward, design is easy to understand, user's experience is uncomplicated, tech is intuitive, and app needs improvements) emerged. Conclusions: The decision-T app can potentially increase HIV-care providers’ engagement in offering smoking prevention and cessation behavioral and pharmacotherapy recommendations to their patients briefly and accurately.