Developing a Machine Learning Model for Personalized, Predictor-Centric, Adaptive Intervention for Vaping Cessation in Young People: Secondary Data Analysis of Smartphone App Data.

Anasua Kundu, Peter Selby, Daniel Felsky, Theo J Moraes, Lynn Planinac, Michael Chaiton

Abstract read

In one paragraph

Article in International journal of environmental research and public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Anasua KunduInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.ORCID 0000-0003-0051-5816

Peter SelbyInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.ORCID 0000-0001-5401-2996

Daniel FelskyInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.

Theo J MoraesInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.ORCID 0000-0001-9968-6601

Lynn PlaninacInstitute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON M5T 3M6, Canada.

Michael ChaitonInstitute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada.ORCID 0000-0002-9589-2122

Funding

Artificial Intelligence for Public Health (AI4PH) scholarship N/AHealth Canada's Substance Use and Addiction Program 1920-HQ-00073

6 · The paper itself

Abstract

Although increasing numbers of young people are trying to quit e-cigarettes, personalized tools to support vaping cessation remain limited. We aimed to build a machine learning model to predict individual probability of short-term relapses and identify person-specific barriers to successful cessation. Data were taken from the "Stop Vaping Challenge" smartphone app. We included past 30-day e-cigarette users aged 15-35 years (

Indexed as

Machine LearningMobile ApplicationsSmartphoneSmoking CessationVapingAdolescentAdultFemaleHumansMalePrediction AlgorithmsPredictive Learning ModelsYoung Adultbehavior therapye-cigarettesmachine learningpredictionvaping cessation

Identifiers

PMID42074466

DOI10.3390/ijerph23040527

PMCPMC13116711

What OpenQuestion holds

Textmetadata

LicenceCC BY

Read underepoch 390

Registered trials

None linked

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.