Trial reportEuropean addiction research2023
Predicting the Users' Level of Engagement with a Smartphone Application for Smoking Cessation: Randomized Trial and Machine Learning Analysis.
Trial report in European addiction research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Willingness to use video-game or gamified application-based interventions to improve or strengthen emotional regulation, mental health, and mental well-being.Internet interventions · 2026Article
- Harnessing machine learning in contemporary tobacco research.Toxicology reports · 2025Review
- Predictors of compulsive cyberporn use: A machine learning analysis.Addictive behaviors reports · 2024Article
- Prediction of the acceptance of telemedicine among rheumatic patients: a machine learning-powered secondary analysis of German survey data.Rheumatology international · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionStudies of the users' engagement with smoking cessation application (apps) can help understand how these apps are used by smokers, in order to improve their reach and efficacy.
objectiveThe present study aimed at identifying the best predictors of the users' level of engagement with a smartphone app for smoking cessation and at examining the relationships between predictors and outcomes related to the users' level of engagement with the app.
methodsA secondary analysis of data from a randomized trial testing the efficacy of the Stop-Tabac smartphone app was used. The experimental group used the "full" app and the control group used a "dressed down" app. The study included a baseline and 1-month and 6-month follow-up questionnaires. A total of 5,293 participants answered at least the baseline questionnaires; however, in the current study, only the 1,861 participants who answered at least the baseline and the 1-month follow-up questionnaire were included. Predictors were measured at baseline and after 1 month and outcomes after 6 months. Data were analyzed using machine learning algorithms.
resultsThe best predictors of the outcomes were, in decreasing order of importance, intention to stop smoking, dependence level, perceived helpfulness of the app, having quit smoking after 1 month, self-reported usage of the app after 1 month, belonging to the experimental group (vs. control group), age, and years of smoking. Most of these predictors were also significantly associated with the participants' level of engagement with the app.
conclusionsThis information can be used to further target the app to specific groups of users, to develop strategies to enroll more smokers, and to better adapt the app's content to the users' needs.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.