TY  - JOUR
AU  - Siegel, Leeann Nicole
AU  - Wiseman, Kara P
AU  - Budenz, Alex
AU  - Prutzman, Yvonne
PY  - 2024
DA  - 2024/5/22
TI  - Identifying Patterns of Smoking Cessation App Feature Use That Predict Successful Quitting: Secondary Analysis of Experimental Data Leveraging Machine Learning
JO  - JMIR AI
SP  - e51756
VL  - 3
KW  - smartphone apps
KW  - machine learning
KW  - artificial intelligence
KW  - smoking cessation
KW  - mHealth
KW  - mobile health
KW  - app
KW  - apps
KW  - applications
KW  - application feature
KW  - features
KW  - smoking
KW  - smoke
KW  - smoker
KW  - smokers
KW  - cessation
KW  - quit
KW  - quitting
KW  - algorithm
KW  - algorithms
KW  - mobile phone
AB  - Background: Leveraging free smartphone apps can help expand the availability and use of evidence-based smoking cessation interventions. However, there is a need for additional research investigating how the use of different features within such apps impacts their effectiveness. Objective: We used observational data collected from an experiment of a publicly available smoking cessation app to develop supervised machine learning (SML) algorithms intended to distinguish the app features that promote successful smoking cessation. We then assessed the extent to which patterns of app feature use accounted for variance in cessation that could not be explained by other known predictors of cessation (eg, tobacco use behaviors). Methods: Data came from an experiment (ClinicalTrials.gov NCT04623736) testing the impacts of incentivizing ecological momentary assessments within the National Cancer Institute’s quitSTART app. Participants’ (N=133) app activity, including every action they took within the app and its corresponding time stamp, was recorded. Demographic and baseline tobacco use characteristics were measured at the start of the experiment, and short-term smoking cessation (7-day point prevalence abstinence) was measured at 4 weeks after baseline. Logistic regression SML modeling was used to estimate participants’ probability of cessation from 28 variables reflecting participants’ use of different app features, assigned experimental conditions, and phone type (iPhone [Apple Inc] or Android [Google]). The SML model was first fit in a training set (n=100) and then its accuracy was assessed in a held-aside test set (n=33). Within the test set, a likelihood ratio test (n=30) assessed whether adding individuals’ SML-predicted probabilities of cessation to a logistic regression model that included demographic and tobacco use (eg, polyuse) variables explained additional variance in 4-week cessation. Results: The SML model’s sensitivity (0.67) and specificity (0.67) in the held-aside test set indicated that individuals’ patterns of using different app features predicted cessation with reasonable accuracy. The likelihood ratio test showed that the logistic regression, which included the SML model–predicted probabilities, was statistically equivalent to the model that only included the demographic and tobacco use variables (P=.16). Conclusions: Harnessing user data through SML could help determine the features of smoking cessation apps that are most useful. This methodological approach could be applied in future research focusing on smoking cessation app features to inform the development and improvement of smoking cessation apps. Trial Registration: ClinicalTrials.gov NCT04623736; https://clinicaltrials.gov/study/NCT04623736 
SN  - 2817-1705
UR  - https://ai.jmir.org/2024/1/e51756
UR  - https://doi.org/10.2196/51756
DO  - 10.2196/51756
ID  - info:doi/10.2196/51756
ER  -