Toward an mHealth Intervention for Smoking Cessation

G.M. Tanimul Ahsan, Ivor D. Addo, S. Iqbal Ahamed, Daniel Petereit, Shalini Kanekar, Linda Burhansstipanov, Linda U. Krebs
2013 2013 IEEE 37th Annual Computer Software and Applications Conference Workshops  
The prevalence of tobacco dependence in the United States (US) remains alarming. Invariably, smoke-related health problems are the leading preventable causes of death in the US. Research has shown that a culturally tailored cessation counseling program can help reduce smoking and other tobacco usage. In this paper, we present a mobile health (mHealth) solution that leverages the Short Message Service (SMS) or text messaging feature of mobile devices to motivate behavior change among tobacco
more » ... s. Our approach implements the Theory of Planned Behavior (TPB) and a phase-based framework. We make contributions to improving previous mHealth intervention approaches by delivering personalized and evidence-based motivational SMS messages to participants. Our proposed solution implements machine learning algorithms that take the participant's demographic profile and previous smoking behavior into account. We discuss our preliminary evaluation of the system against a couple of pseudo-scenarios and our observation of the system's performance. Index Terms: Theory of Planned Behavior, Phase-based Framework, Smoking cessation, mHealth Smoking cigarettes, pipes and cigars leads to several types of health-related problems including lung cancer and heart diseases [1]. According to the US Center for Disease Control and Prevention (CDC) [1], smoke-related health issues accounts for more deaths than the aggregate of drug abuse, suicide, motor accidents, murder, and AIDS. About 500,000 people die each year from firsthand or secondhand smoking. Smoking cessation and the avoidance of secondhand smoking is a definite way to reduce the risk of smoke-related health problems. This epidemic can be curtailed by using effective methods to influence behavior change among tobacco users. Several intervention techniques can be used to treat smoking cessation. In some instances, medicinal approaches including nicotine patches and others can be used to control the issue. Personal counseling is another popular method for smoking cessation intervention. Our proposed intervention strategy incorporates a phase-based model that makes use of the theory of planned behavior to influence behavior change towards smoking cessation. Our work-in-progress solution logs the demographic information of the smoking cessation program participants in addition to their self-annotated smoking behavior. We propose the use of an unsupervised machine learning algorithm to identify patterns in the mined data. The selection of personalized SMS messages is consequently driven by the evidence gathered for SMS messages that are known to positively influence tobacco use patterns for the profile segment that the targeted user belongs to. We discuss the motivation for this study in section II and highlight a number of related works in section III. We then describe the characteristics of the system and implementation details in section IV and V respectively. Our evaluation, approach and findings are described in section VI and VII correspondingly. With tobacco dependence noted as the leading preventable cause of death in the US [11], we seek to use our proposed intervention strategy to drive down tobacco use in communities that have a high smoking prevalence rate. The production-ready solution will be used as part of a smoking dependence study tailored for Native American communities in South Dakota. The prevalence of tobacco dependence among the Northern Plains Native American community, in comparison with other communities in the United States (US), remains very alarming.
doi:10.1109/compsacw.2013.61 pmid:24172662 pmcid:PMC3809119 dblp:conf/compsac/AhsanAAPKBK13 fatcat:pl6nh3kg3ffvxekq66xz6ih5me