Acceptability and compliance with a remote monitoring system to track smoking and abstinence among young smokers

ABSTRACT

Background: Similar to adult smokers, quit attempts among younger smokers almost inevitably result in relapse. Unlike adults, less is known about the process of relapse in this younger age group. A technology-based remote monitoring system may allow for detailed and accurate characterization of smoking and abstinence and would help to improve cessation strategies. Objectives: This study describes a mobile system that captures smoking using breath carbon monoxide (CO) and real-time self-reports of smoking behavior. Compliance, feasibility, acceptability, and accuracy of the system were measured during a quit attempt and subsequent monitoring period. Methods: The mobile application (My Mobile Monitor, M³) combined breath CO with ecological momentary assessment, delivered via smartphone. Participants (N = 16; 75% female) were daily smokers between the ages of 19 and 25, who used the app for 11 days during which they agreed to make a quit attempt. Acceptability, compliance, and abstinence were measured. Results: Participants averaged 22.3 ± 2.0 years old and smoked an average of 13.0 ± 6.1 cigarettes per day. Overall session compliance was 69% and during the quit attempt, 56% of participants abstained from smoking for at least 24 hours. Agreement between self-reported smoking compared to breath CO was generally high, when available for comparison, though underreporting of cigarettes was likely. Conclusion: This study demonstrates feasibility of a remote monitoring app with younger smokers, though improvements to promote compliance are needed. Remote monitoring to detect smoking and abstinence represents a step forward in the improvement of cessation strategies, but user experience and personalization are vital.

KEYWORDS:

• Technology
• smoking
• tobacco
• mobile health
• remote monitoring
• adolescents
• youth

Acknowledgments

We would like to thank Chad Gwaltney for his assistance in protocol development and assessment use, and additional collaborators and mentors on this project, including, Jesse Dallery, Kathleen Brady, David Gustafson, Stephen Tiffany, and Viswanathan Ramakrishnan. Additionally, we would like to thank the team at the Technology Center for Healthful Lifestyles at MUSC (Sachin Patel, Kenneth Ruggiero, Christina Sithideth, Aaron Allsbrook, and Alex Umrysh).  Finally, we would like to thank the research and medical staff in the Addiction Sciences Division of the Department of Psychiatry and Behavioral Sciences at the Medical University of South Carolina who were integral in study development and execution, including, Danielle Schwartz, Kathryn Mase, Lori Ann Ueberroth, Kayla McAvoy, Breanna Tuck, Patrick Cato, Jaclyn Condo, Taylor York, Elhaam Borhanian, and Casy Johnson.

Declaration of interest

The authors have no financial disclosures to report.

Additional information

Funding

This work was supported by NIDA [grant number K01 DA036739] and K12 [grant number DA031794], and the South Carolina Clinical and Translational Research Institute––Biomedical Informatics Center (BMIC) at MUSC (NIH/NCATS UL1 TR001450). This study was also supported in part by pilot research funding from an American Cancer Society Institutional Research Grant awarded to the Hollings Cancer Center, Medical University of South Carolina [grant number IRG 97-2919-14]. Effort to support the preparation of this manuscript was provided by NIDA [grant number R01 DA042114], [grant number U01 DA031779] and NIAAA [grant number T32 AA007474].