Identifying Content-based Engagement Patterns in a Smoking Cessation Website and Associations with U
Saturday, June 19, 2021
Posted by: Natalia Gromov
Olga
Perski, Noreen L Watson, Kristin E Mull, Jonathan B Bricker.
Identifying Content-based
Engagement Patterns in a Smoking Cessation Website and Associations with User
Characteristics and Cessation Outcomes: A Sequence and Cluster Analysis.
Nicotine & Tobacco Research, Volume 23, Issue 7, July 2021, Pages
1103–1112, https://doi.org/10.1093/ntr/ntab008.
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.
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