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Discretionary Corrigenda (60)

Published on in Vol 14 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/79873, first published .
Correction: Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Correction: Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Correction: Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial

Authors of this article:

Ariana Kamberi1 Author Orcid Image ;   Benjamin Weitz1 Author Orcid Image ;   Julie Flahive2 Author Orcid Image ;   Kavitha Balakrishnan1 Author Orcid Image ;   Julianna Eve3 Author Orcid Image ;   Reem Najjar1 Author Orcid Image ;   Tara Liaghat4 Author Orcid Image ;   Daniel Ford4 Author Orcid Image ;   Peter Lindenauer3 Author Orcid Image ;   Sharina Person2 Author Orcid Image ;   Thomas K Houston5 Author Orcid Image ;   Megan E Gauvey-Kern4 Author Orcid Image ;   Jackie Lobien4 Author Orcid Image ;   Rajani S Sadasivam1 Author Orcid Image
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    Corrigenda and Addenda

    1Division of Health Informatics and Implementation Science, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States

    2Division of Biostatistics and Health Services Research, Department of Population and Quantitative Health Sciences, UMass Chan Medical School, Worcester, MA, United States

    3Department of Healthcare Delivery and Population Sciences, University of Massachusetts Chan Medical School-Baystate, Springfield, MA, United States

    4Institute for Clinical and Translational Research, School of Medicine, Johns Hopkins University, Baltimore, MD, United States

    5Department of Internal Medicine, Wake Forest University, Winston-Salem, NC, United States

    Corresponding Author:

    Ariana Kamberi, MBA

    Division of Health Informatics and Implementation Science

    Department of Population and Quantitative Health Sciences

    UMass Chan Medical School

    55 Lake Ave North

    Worcester, MA, 01655

    United States

    Phone: 1 774 317 1539

    Email: Ariana.Kamberi@umassmed.edu



    In “Testing a Machine Learning–Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial,” [1] the authors made one addition:

    Kavitha Balakrishnan, BS

    was added as an author to the study protocol. Neither the ‘Acknowledgments’ nor ‘Conflicts of Interest’ needed to be amended.

    The correction will appear in the online version of the paper on the JMIR Publications website, together with the publication of this correction notice. Because this was made after submission to PubMed, PubMed Central, and other full-text repositories, the corrected article has also been resubmitted to those repositories.

    1. Kamberi A, Weitz B, Flahive J, Eve J, Najjar R, Liaghat T, et al. Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial. JMIR Res Protoc. Apr 16, 2025;14:e63693. [FREE Full text] [CrossRef] [Medline]

    This is a non–peer-reviewed article. submitted 22.Jul.2025; accepted 13.Nov.2025; published 09.Dec.2025.

    Copyright

    ©Ariana Kamberi, Benjamin Weitz, Julie Flahive, Kavitha Balakrishnan, Julianna Eve, Reem Najjar, Tara Liaghat, Daniel Ford, Peter Lindenauer, Sharina Person, Thomas K Houston, Megan E Gauvey-Kern, Jackie Lobien, Rajani S Sadasivam. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.Dec.2025.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.