Engagement and abstinence among users of a smoking cessation text message program for veterans

Abstract

BACKGROUND SmokefreeVET is a text messaging smoking cessation program available to veterans enrolled in the Veterans Health Administration. SmokefreeVET was developed in collaboration with the National Cancer Institute as part of the SmokefreeTXT initiative. PURPOSE To evaluate the real world use of and effectiveness of the SmokefreeVET program for SmokefreeVET users who enrolled between 2013 and 2014. METHODS Demographics and smoking behavior of 1,470 SmokefreeVET users who enrolled between 2013 and 2014 were analyzed. Latent growth mixture modeling was used to identify discrete classes of SmokefreeVET users based on engagement patterns. Multi-level modeling determined class differences in abstinence. RESULTS The average age of the SmokefreeVET user was 48, 75% of users were male, and 84% were daily smokers. After five weeks, 13% of all users reported abstinence from smoking. Five statistically distinct engagement classes of SmokefreeVET users were identified. Highly engaged classes were significantly less likely to opt-out and more likely to report abstinence. Over 60% of users who were classified as high engagers throughout the program reported abstinence 5 weeks after their quit date. Users were more likely to report abstinence after two weeks if they used smoking cessation medication than those that did not use medication (OR=9.01, p<.001). CONCLUSIONS SmokefreeVET may be effective at supporting abstinence among a real world group of highly engaged users. Smoking cessation medication use was also associated with abstinence in SmokefreeVET users. Engagement appears to be a critical component when assessing the efficacy of a text messaging smoking cessation intervention.

Using Mobile Health (mHealth) Technology in the Management of Diabetes Mellitus, Physical Inactivity, and Smoking

“…The purpose of SMS has been to assist participants in working towards a quit date and providing support through the process of smoking cessation. The recently published SMOKEFREEVET study, for example, was performed on 1470 veterans and required participants to decide on quit [35]. SMS were sent prior to and after the quit date with content based on the date selected.…”

Engagement Within a Mobile Phone–Based Smoking Cessation Intervention for Adolescents and its Association With Participant Characteristics and Outcomes

“…Engagement, for instance, has been conceptualized in previous studies both as the usage or the subjective experience with the program [ 14 ]. For texting-based programs, there is some evidence that the predominant engagers are female [ 15 ] and older and that they exhibit lower rates of daily cigarette consumption [ 16 ], but none of these studies were conducted on adolescents.…”

Trajectories of 12-Month Usage Patterns for Two Smoking Cessation Websites: Exploring How Users Engage Over Time (Preprint)

“…However, the study was limited by a small sample size (N = 84) as well as short duration (eight weeks) and there was no reporting of whether the trajectories were predictive of health outcomes. Other research identified five distinct usage trajectories of a text-based smoking cessation program over fiveweeks, namely "high engagement", "increasing engagement", "rapid decrease", "delayed decrease" and "low engagement" [13]. The study found that the high engagement and increasing engagement groups were more likely, compared to the other groups, to be abstinent over the course of five-weeks.…”

“…This was one of few studies to analyze usage trajectories of e-health interventions and examine the association between trajectory group membership and health outcomes [12,13]. The study found: (1) three distinct groups of login trajectories for two web-delivered interventions for smoking cessation, (2) that these trajectory groups differentially predicted smoking outcomes at 12 months, and (3) that certain user characteristics are associated with membership to certain trajectory groups.…”

“…Each study participant's login trajectory was obtained by transforming longitudinal sequences of login time stamps into a binary time series indicating login occurrence each day. Note that we chose not to use latent class growth curve approaches that have been used in other e-health intervention engagement studies [12,13] because these methods do not handle very densely recorded longitudinal data without substantial data reduction (e.g., reducing data into weekly or monthly login counts per participant) and often rely on restrictive assumptions on the shapes of trajectories.…”