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

Abstract

Background Little is known about how individuals engage over time with smartphone app interventions and whether this engagement predicts health outcomes. Objective In the context of a randomized trial comparing 2 smartphone apps for smoking cessation, this study aimed to determine distinct groups of smartphone app log-in trajectories over a 6-month period, their association with smoking cessation outcomes at 12 months, and baseline user characteristics that predict data-driven trajectory group membership. Methods Functional clustering of 182 consecutive days of smoothed log-in data from both arms of a large (N=2415) randomized trial of 2 smartphone apps for smoking cessation (iCanQuit and QuitGuide) was used to identify distinct trajectory groups. Logistic regression was used to determine the association of group membership with the primary outcome of 30-day point prevalence of smoking abstinence at 12 months. Finally, the baseline characteristics associated with group membership were examined using logistic and multinomial logistic regression. The analyses were conducted separately for each app. Results For iCanQuit, participants were clustered into 3 groups: “1-week users” (610/1069, 57.06%), “4-week users” (303/1069, 28.34%), and “26-week users” (156/1069, 14.59%). For smoking cessation rates at the 12-month follow-up, compared with 1-week users, 4-week users had 50% higher odds of cessation (30% vs 23%; odds ratio [OR] 1.50, 95% CI 1.05-2.14; P=.03), whereas 26-week users had 397% higher odds (56% vs 23%; OR 4.97, 95% CI 3.31-7.52; P<.001). For QuitGuide, participants were clustered into 2 groups: “1-week users” (695/1064, 65.32%) and “3-week users” (369/1064, 34.68%). The difference in the odds of being abstinent at 12 months for 3-week users versus 1-week users was minimal (23% vs 21%; OR 1.16, 95% CI 0.84-1.62; P=.37). Different baseline characteristics predicted the trajectory group membership for each app. Conclusions Patterns of 1-, 3-, and 4-week smartphone app use for smoking cessation may be common in how people engage in digital health interventions. There were significantly higher odds of quitting smoking among 4-week users and especially among 26-week users of the iCanQuit app. To improve study outcomes, strategies for detecting users who disengage early from these interventions (1-week users) and proactively offering them a more intensive intervention could be fruitful.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

“…Heavy drinking was defined as women who reported consuming ≥4 alcoholic drinks on a typical drinking day and men who reported consuming ≥5 alcoholic drinks on a typical drinking day [ 37 ]. These baseline variables were chosen as potential predictors of early dropout as they predicted engagement trajectory group membership in our previous studies and are commonly collected in digital intervention research [ 24 , 25 ].…”

“…As reported in our previous research [ 24 , 25 ], early dropouts were defined as users who were categorized as “one-week users” in each intervention using functional clustering analysis of log-in trajectories [ 38 , 39 ]. The proportion of early dropouts was 57.06% (610/1069) for iCanQuit, 65.32% (695/1064) for QuitGuide, 55% (682/1240) for WebQuit, and 49.27% (645/1309) for Smokefree.…”

“…Recently, we published 2 studies identifying a type of early dropout from digital interventions that is highly predictive of treatment outcomes [ 24 , 25 ]. Specifically, we examined functional clustering longitudinal patterns of intervention use in a randomized trial (N=2415) that compared 2 conceptually distinct smartphone apps for smoking cessation (ie, iCanQuit and the National Cancer Institute [NCI]’s QuitGuide) and in a trial (N=2637) that compared 2 conceptually distinct websites for smoking cessation (WebQuit.org and Smokefree.gov).…”

“…For example, our iCanQuit smartphone app for smoking cessation based on acceptance and commitment therapy (ACT) had a 23% cessation rate among 1-week users compared with a 30% cessation rate for 4-week users (odds ratio 1.50, 95% CI 1.05-2.14; P =.03). Such differences in cessation rates are substantial, especially when considering that, across all 4 interventions, 49%-65% of all users were 1-week users and indeed were the largest log-in trajectory group [ 24 , 25 ]. The ability to prospectively predict who will become a 1-week user could be highly valuable as it would provide the opportunity to intervene early with an augmented treatment.…”

The Impact of Digital Health on Smoking Cessation

“…However, there are certain characteristics that either predict the time when an app will be used or predict that an app will not be used. The act of smoking up to one-half pack per day, the act of smoking the first cigarette within 5 minutes after waking, a higher mean acceptance of internal physical sensations, female sex, minority race (people of color), Hispanic ethnicity, and a history of smoking for 10 or more years are related to longer periods of app use [ 43 ].…”