Identifying Content-Based Engagement Patterns in a Smoking Cessation Website and Associations With User Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis

Abstract

Introduction Using WebQuit as a case study, a smoking cessation website grounded in Acceptance and Commitment Therapy, we aimed to identify sequence clusters of content usage and examine their associations with baseline characteristics, change to a key mechanism of action, and smoking cessation. Methods Participants were adult smokers allocated to the WebQuit arm in a randomized controlled trial (n = 1,313). WebQuit contains theory-informed content including goal setting, self-monitoring and feedback, and values- and acceptance-based exercises. Sequence analysis was used to temporally order 30-s website usage segments for each participant. Similarities between sequences were assessed with the optimal matching distance algorithm and used as input in an agglomerative hierarchical clustering analysis. Associations between sequence clusters and baseline characteristics, acceptance of cravings at 3 months and self-reported 30-day point prevalence abstinence at 12 months were examined with linear and logistic regression. Results Three qualitatively different sequence clusters were identified. “Disengagers” (576/1,313) almost exclusively used the goal-setting feature. “Tryers” (375/1,313) used goal setting and two of the values- and acceptance-based components (“Be Aware,” “Be Willing”). “Committers” (362/1,313) primarily used two of the values- and acceptance-based components (“Be Willing,” “Be Inspired”), goal setting, and self-monitoring and feedback. Compared with Disengagers, Committers demonstrated greater increases in acceptance of cravings (p = .01) and 64% greater odds of quit success (ORadj = 1.64, 95% CI = 1.18, 2.29, p = .003). Discussion WebQuit users were categorized into Disengagers, Tryers, and Committers based on their qualitatively different content usage patterns. Committers saw increases in a key mechanism of action and greater odds of quit success. Implications This case study demonstrates how employing sequence and cluster analysis of usage data can help researchers and practitioners gain a better understanding of how users engage with a given eHealth intervention over time and use findings to test theory and/or to improve future iterations to the intervention. Future WebQuit users may benefit from being directed to the values- and acceptance-based and the self-monitoring and feedback components via reminders over the course of the program.

Digital multiple health behaviour change intervention targeting online help seekers: protocol for the COACH randomised factorial trial

“…This factorial trial investigates the components of a novel multiple behaviour intervention. While our aim of the trial is to estimate the effects of the components on behaviour, we plan to conduct exploratory studies of engagement, 54 which in combination with effect estimates will be used to determine future directions of study. Decisions to retain or remove components will therefore not be based solely on the statistical analyses in this study, but rather combined with engagement data and the evidence from the literature more widely.…”

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

“…This study contributes to the nascent literature on longitudinal use trajectories of digital health interventions and their prediction of health outcomes [20][21][22][23][24][25]. The study found (1) 1-, 4-, and 26-week trajectories for iCanQuit versus 1-and 3-week trajectories for the QuitGuide smoking cessation apps; (2) that these trajectory groups differentially predicted smoking outcomes at 12 months for iCanQuit but not for QuitGuide; and (3) that certain user characteristics were associated with membership in certain trajectory groups.…”

“…Engagement with the assigned app was measured objectively using Google Analytics. The measure of engagement was the number of days each application was opened, which was consistent with other digital interventions' measures of engagement [7,24,25]. For each participant, time-and date-stamped log file records of each page opening were recorded.…”

“…There is a dearth of studies analyzing use trajectories for digital smoking cessation interventions. We are aware of 3 publications for SMS text messaging interventions [20][21][22], 4 publications for website interventions [5,[23][24][25], and none for smartphone interventions. Regarding SMS text messaging interventions, a study identified 5 distinct use trajectories of an SMS text messaging-based smoking cessation program over the 6 weeks after quit date, namely high engagement, increasing engagement, rapid decrease, delayed decrease, and low engagement [20].…”

Subtypes of smokers in a randomized controlled trial of a web-based smoking cessation program and their role in predicting intervention non-usage attrition: Implications for the development of tailored interventions

“…However, this line of research is still in its infancy in the field of web-based smoking cessation interventions (c.f., Beatty and Binnion, 2016 ). Only few studies have investigated pre-treatment characteristics as predictors of non-usage attrition in web-based smoking cessation interventions, and they have mainly focused on demographics and smoking-related variables ( Balmford et al, 2008 ; Bricker et al, 2018b ; Cantrell et al, 2016 ; Graham et al, 2006 ; Perski et al, 2021 ; Strecher et al, 2008 ; Wangberg et al, 2008 ). These studies showed that smokers who are younger, male, less educated, less addicted to tobacco, less ready to change, and do not receive other smoking cessation aids (e.g., e-cigarettes or pharmacotherapy) are more likely to stop using web-based smoking cessation interventions.…”